The Pennsylvania State University

The Graduate School

College of the Liberal Arts

DEVELOPMENTAL CHANGES IN RESTING-STATE FUNCTIONAL

CONNECTIVITY IN BORDERLINE PERSONALITY DISORDER: A NETWORK

ANALYSIS APPROACH

A Thesis in

Psychology

by

Nathan T. Hall

© 2019 Nathan T. Hall

Submitted in Partial Fulfillment

of the Requirements

for the Degree of

Master of Science

August 2019

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The thesis of Nathan T. Hall was reviewed and approved* by the following:

Michael N. Hallquist

Assistant Professor of Psychology

Thesis Adviser

Frank G. Hillary

Associate Professor of Psychology

Nancy A. Dennis

Associate Professor of Psychology

Melvin M. Mark

Professor of Psychology

Head of the Psychology Department

*Signatures are on file in the Graduate School.

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ABSTRACT

Borderline personality disorder (BPD) is a clinical syndrome that typically emerges

during adolescence, a period of time when sensitivity to social cues is heightened across the

population. The current study of 82 adolescents and young adults (ages 13-30) with BPD

symptoms and age and sex matched healthy controls investigated developmental differences in

functional connectivity (FC) during this developmental period using resting-state fMRI. We

utilized a graph theory approach, computing FC between nodes of a 421 custom-built cortico-

striatal parcellation. Results suggest that across development, two nodes in the canonical salience

network in the right dorsal anterior insula (daINS) and right temporoparietal junction (TPJ) were

robustly altered in their global RSFC to nearly all intrinsic networks in adolescents with BPD

symptoms. Interestingly, while grouped in the same network, the TPJ was hypoconnected while

the daINS was hyperconnected to functionally distinct intrinsic networks. Post-hoc analyses

indicated a strong pattern of hyperconnectivity between the daINS and multiple regions in the

canonical dorsal attention network (DAN), which dynamically interacts with salience network to

control goal-directed action. Mediation analyses indicated that emotional instability fully

mediated the association between connectivity in the daINS and BPD symptoms. Results suggest

an enmeshment of distinct attentional networks in BPD, with a shift towards favoring the

salience network. Further, results indicated that across development, the ventral striatum, a

region that has been extensively implicated in reward learning and motivation, showed age-

related decreases in FC to regions of the mPFC and ACC in the BPD, whereas in the control

group, FC values increased which indicates that impaired developmental changes in value-based

decision-making processes may characterize the development of the disorder in adolescence.

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TABLE OF CONTENTS

List of Tables…………………………………………………………………………………….vi

List of Figures……………………………………………………………………………….......vii

Acknowledgements……………………………………………………………………………..viii

Chapter 1. INTRODUCTION.…………………………………………………………………….1

Evidence of the neural etiology of BPD: fronto-limbic abnormalities………...……………2

Evidence of the neural etiology of BPD: social-cognitive and default mode abnormalities..5

Development of intrinsic network structure in adolescence………………………………...7

The current study…….…….…….…….…….…….…….…….…….…….…….…….……9

Chapter 2. METHODS…………………………………………………………………………...11

Participants……………………………………………………….………...………….......11

Procedure………………………………………………………………………………......11

MR data acquisition and removal of high-motion subjects……….………………....12

RS-fMRI preprocessing procedures…...…….…….…….…….…….…….…….…….…..13

Analytic approach………………………………………………………………………….14

Nodal parcellation…………………………………………………………………...14

Pre-whitening and adjacency matrix generation…………………………………….16

Graph construction and module assignment………………………………………...18

Graph metrics………………………………………………………………………..20

Resting-state activity: amplitude of low frequency fluctuations (ALFF)…………...22

Confirmatory analyses amongst a priori nodes……………………………………...23

Logistic ridge regression analyses…………………………………………………...24

Post-hoc analysis: effective connectivity……………………………………………26

Exploratory whole-brain analysis……………………………………………………27

Post-hoc analyses: symptom measures……………………………………………...28

Post-hoc analyses: visual depiction of edges of interest…………………………….29

Chapter 3. RESULTS……………………………………………………………………………30

Global analyses……………………………………………………………………………30

A priori analysis: fronto-limbic nodes…………………………………………………….30

A priori analysis: social/DMN nodes……………………………………………………...32

Whole-brain nodal centrality analyses…………………………………………………….32

Group differences……………………………………………………………………33

Age-related effects…………………………………………………………………..34

Chapter 4. DISCUSSION………………………………………………………………………..36

The role of the salience network in adolescent BPD…………………………………….37

Fronto-limbic hypothesis: the role of the ventral striatum……………………………….42

A note about the default mode network in the present study…………………………….45

Strengths and limitations…………………………………………………………………46

Conclusion……………………………………………………………………………….47

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References………………………………………………………………………………………..49

Appendix A: Tables……………………………………………………………………………...70

Appendix B: Figures……………………………………………………………………………..59

Appendix C: Supplemental Tables………………………………………………………………86

Appendix D: Supplemental Figures……………………………………………………………...94

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List of Tables

Table 1: Sample Characteristics

Table 2: Results of the edge-wise a priori analysis

Table 3: Global Graph Metrics

Table 4: Whole-brain nodal centrality results

Table 5: Whole-brain nodal ALFF results

Table 6: Significant effects mediated through self-report scales

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List of Figures

Figure 1: Summary of group FC and strength distributions.

Figure 2: Finalized 421 node parcellation of the cortex, thalamus, and striatum.

Figure 3: Results from the usual suspects analyses.

Figure 4: dACC and VS directed connectivity results

Figure 5. Connectivity of the daINS.

Figure 6. Connectivity of the TPJ.

Figure 7. Connectivity of the VS.

Figure 8. Representative sampling of nodes that were significantly different between groups

regardless of age in whole-brain analyses.

Figure 9. Representative sampling of nodes that showed significant group x age interactions in

whole-brain analyses.

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Acknowledgements

This manuscript does not reflect the view of the National Institute of Mental Health or the

United States Government. This project was supported by the Mentored Research Scientist

Career Development Award (K01 MH097091, PI: Hallquist). I would like to thank the members

of my committee, Drs. Michael Hallquist, Frank Hillary, and Nancy Dennis for their time and

effort in providing thoughtful and respectful feedback on this project. I particular, I would like to

thank my primary academic mentor Michael Hallquist for his guidance and his steadfast

commitment to not only my academic training, but to my personal growth. I feel beyond lucky to

have you as a personal and professional mentor throughout my training. I can think of few

people in my life who have garnered as much of my respect as you have in my time as your

student.

The development of this project has not been a solitary effort but reflects the true value of

working with a number of highly gifted individuals. With that being said, I would like to thank

Dr. Hallquist’s team of researchers in Pittsburgh who provided him the personnel, equipment,

and support to collect this data during his time at the University of Pittsburgh. In particular,

Rajpreet Chahal, Dr. Hallquist’s former research coordinator, played a central role in organizing

the study and in data collection efforts. I’ve also been lucky enough to work with a wonderful

group of lab managers, programmers, and research assistants in the Developmental Personality

Neuroscience Lab at Penn State. In particular, Aleece Churney was crucially important in

leading early quality assurance efforts during my first year at Penn State. After Aleece’s

departure our lab was blessed when Melanie Glatz stepped in to fill Aleece’s role and over the

past year I have had the extreme pleasure of getting to know her both at work as an aspiring

clinical psychologist, and outside of work as a lover of life. Thank you for your friendship and

support throughout this process Mel. Finally, and perhaps most importantly, my lab mate and

close friend Alison Schreiber has never been anything but a steady source of emotional support

and encouragement throughout my time at Penn State. Thank you Ally-gash for always

challenging me intellectually, but supporting me personally.

I would also like to thank my past mentors at SUNY Binghamton for their instrumental

roles in sparking my interests in research in psychopathology and neuroscience: Steven Jay

Lynn, Mark Lenzenweger, and Brandon Gibb thank you for supporting me in my early years as

an undergraduate and post-graduate. I am lucky to have a supportive group of friends who have

been with me through the entire development of this project. Thank you, Lia, Chloe, Erin,

Natalia, Ben, Zach, Mel, and Alison, for being there for me throughout this process. Finally, I

would like to thank my parents Gerry Hall and Laura Zajchowski as well as my younger brother

Zach for their unwavering support from the very beginning and for working tirelessly to provide

me with the resources to succeed as a graduate student. Any success I have happened upon in life

belongs to you

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Chapter 1: Introduction

Borderline Personality Disorder (BPD) is a serious psychiatric disorder occurring in

approximately 1-2% of the general population and comprising approximately 14% of the

inpatient population (Lenzenweger, Lane, Loranger, & Kessler, 2007; Modestin, Abrecht,

Tschaggelar, & Hoffmann, 1983). BPD is typified by affective volatility on rapid timescales

(often minutes to hours), chronic suicidality and self-harm, identity disturbance, intense and

tumultuous interpersonal relationships often characterized by vacillating between loving and

hating others, and a persistent pattern of impulsive behavior(American Psychiatric Association,

2013). Leading theories contend that borderline symptoms arise from interactions among

genetic, environmental, and psychosocial factors with the onset of symptoms typically occurring

in adolescence (APA, 2013; Crowell, Beauchaine, & Linehan, 2009). The claim that BPD

emerges in adolescence is supported by studies documenting that individuals with BPD often

first enter treatment at age 18 (Zanarini, Frankenburg, Khera, & Bleichmar, 2001), although

symptoms of BPD are likely present at an earlier age (Cohen, 2008; Zelkowitz et al., 2007). In

fact, Zanarini, et. al., (2006)found that approximately 30% of patients with BPD reported self-

harming for the first time before age 12 with another 30% beginning between the ages of 13 and

17, lending support to the idea that some borderline symptoms in adolescence may exhibit some

degree of homotypic continuity with BPD as it is currently conceptualized in adults.

Not only do symptoms of BPD typically emerge in adolescence, but research further

supports the notion that the symptoms of BPD in adolescence often tend to be more acute, as

adolescence is associated with mean-level increases in impulsive behavior, emotionality, and

sensitivity to social cues in the general population (Pfeifer et al., 2011; Steinberg et al., 2018). In

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particular, self-harming and suicidal behaviors in addition to affective instability and impulsivity

sharply increase in the adolescent years for individuals with BPD (Kaess, Brunner, & Chanen,

2014). However, despite the well-documented severity of borderline pathology during this

crucial period of development, little is known about functional brain changes in adolescents with

BPD, representing an important gap in the literature. Further, the current study draws on

observations that the brain is a hierarchically organized network with specific modules, or

intrinsic networks, whose patterns of within and between-network connectivity help to organize

human behavior across disparate cognitive, socioemotional, and sensorimotor domains (Laird et

al., 2011). As we detail below, cognitive neuroscience studies of typically developing samples

suggest that the brain undergoes important changes in functional connectivity (FC) in

adolescence, making connectivity a clear target for investigation in this clinical population.

As little is known on FC differences in adolescents with BPD we elected to focus first on

two primary lines of thinking that run through the neuroimaging literature in adults with BPD. In

particular, fronto-limbic and socio-cognitive/default mode network alterations largely summarize

the dominant thinking about neural systems that underlie the etiological “core” of borderline

pathology, yet have been limited to studies of adults with BPD. We here provide a brief

summary of these threads and describe how taken together with evidence from developmental

neuroscience they make a case for the importance of studying the adolescent transition in

individuals with BPD.

Evidence of the neural etiology of BPD: fronto-limbic abnormalities

To date, studies of the neural correlates of BPD have been primarily informed by functional

neuroimaging studies in adults. Broadly, these studies have identified abnormalities in circuits

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underlying emotion and social cognition. The first and perhaps oldest and most well-established

thread running through the BPD neuroimaging literature concerns abnormal fronto-limbic

activation and/or connectivity in task-based studies. The amygdala plays a central role in such

accounts, typically in combination with medial and dorsolateral portions of the prefrontal cortex

in addition to the anterior cingulate cortex (ACC). The amygdala is involved in detecting threat

in the environment and encoding the emotional significance of stimuli (LeDoux, 2007); it also

plays a key role in the generation of the subjective experience of fear. Importantly, the amygdala

does not represent emotional experience in isolation. Rather, both human and nonhuman animal

research suggests that regions in the (particularly medial) prefrontal cortex and ACC

(particularly dorsal and rostral regions) encode key appraisal and regulatory signals that evaluate

and reorient emotional experience respectively (Etkin, Egner, & Kalisch, 2011).

A number of task-based activation studies find that patients with BPD show increased

activation of the amygdala and reduced activation of the prefrontal cortex to a range of emotional

stimuli (Donegan et al., 2003; Herpertz et al., 2001; Kamphausen et al., 2013; Minzenberg, Fan,

New, Tang, & Siever, 2007; Soloff, White, Omari, Ramaseshan, & Diwadkar, 2015). Such

findings have led to the hypothesis that hyperactivity of “emotion-producing” regions such as the

amygdala, in addition to hypoactivity of emotion-regulating regions in the PFC leads to the

emotion regulation difficulties seen in BPD. These findings were recently corroborated in a

meta-analysis which found increased BOLD activation in the left amygdala and decreased

activity in the bilateral dlPFC to negative emotional stimuli in BPD (Schulze, Schmahl, &

Niedtfeld, 2016).

However, results pertaining to hyperactivation of the amygdala have not been borne out in

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all studies. For example, Dudas et. al., (2017) found hypoactivation of the amygdala across

emotional conditions, despite finding hyperconnectivity of the dlPFC and amygdala in their

disgust condition. Complicating matters further, a second meta-analysis of task-based activity

found conflicting results with the hyperactive amygdala account and found hypoactivation in the

right amygdala in response to negative emotion (Ruocco, Amirthavasagam, & Zakzanis, 2013).

However, the conflict between these two meta-analyses has led to productive conversations

about the contrasts being tested in task-based imaging studies as well as heterogeneity (in terms

of symptomology, demographic features, and medication status) within and between studies.

These sometimes-inconsistent activation-based results indicate that emotionally evocative

contexts may lead to differential modulation of the PFC/ACC-amygdala circuit in BPD, which is

thought to play a key role in the experience and regulation of negative emotion. This line of

thinking has begun to receive attention with a number of neuroimaging studies showing

heightened FC between the amygdala and ACC (Cullen et al., 2011; Kamphausen et al., 2013)

and PFC (Dudas et al., 2017) in task-based studies. On the other hand, others have argued for a

“disconnection” between the PFC and the amygdala with BPD patients showing overall

decreases in coupling between the amygdala and the OFC (New et al., 2007). Regardless, both

proponents of the disconnection and the hyperconnectivity perspective agree that not only is

functional activation important but that the relative synchrony of neural activity in these regions

may play an especially important role in describing individual differences in emotionality and

emotion regulation. These studies together support the idea that an imbalance of neural co-

fluctuation amongst frontolimbic structures may interact to produce the intense and unregulated

emotional experiences of individuals with BPD. However, interpretations of fronto-limbic

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connectivity results remain opaque in human neuroimaging applications. For example, animal

studies of direct neural communication between the (particularly medial) PFC and the amygdala

have demonstrated that PFC stimulation inhibits amygdala activity (Quirk, Likhtik, Pelletier, &

Paré, 2003; Rosenkranz, Moore, & Grace, 2003), which further accords with accounts that

resting-state functional connectivity (RSFC) between the amygdala and the mPFC and ACC

have been shown to strengthen with age (Cunningham, Bhattacharyya, & Benes, 2002; Gabard-

Durnam et al., 2014). However, often hyperconnectivity of the amygdala and PFC in fMRI

studies are interpreted as reflecting the influence of a hyperactive amygdala and the need to

recruit regulatory circuits in BPD. This inconsistency suggests that the exact nature of

connectivity between fronto-limbic structures in BPD has not been fully resolved in adults yet

remains a crucial task.

Evidence of the neural etiology of BPD: social-cognitive and default mode abnormalities

In a second series of studies, BPD has been associated with abnormal activation and

connectivity within and between regions involved in social cognitive and self-other

differentiation. These studies add to theoretical and empirical work suggesting that interpersonal

sensitivity (broadly construed) is a phenotype of BPD (Gunderson & Lyons-Ruth, 2008;

Hopwood, Wright, Ansell, & Pincus, 2013; Korn, Rosée, Heekeren, & Roepke, 2016; Roepke,

Vater, Preißler, Heekeren, & Dziobek, 2013). This set of studies tends to separate along studies

that focus on task-based activation of the so-called “social brain network,” and RSFC studies of

the default mode network (DMN; Raichle et al., 2001) in borderline samples.

The social brain network minimally consists of the mPFC, ACC, temporo-parietal junction

(TPJ), posterior superior temporal sulcus (pSTS), and anterior insula (aINS) (Adolphs, 2009;

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Blakemore, 2008). Such regions encode social context and further aid in understanding the

mental states of oneself and others, a term in the clinical literature known as “mentalizing”

(Fonagy & Bateman, 2008). While humans are unrivaled in their ability to infer the mental states

of others, more basic animal work demonstrates that the crucial nodes of the social cognitive

system (primarily mPFC and TPJ) are generally conserved in the macaque (Noonan et al., 2017).

In line with clinical theory, a number of tasks designed to engage social cognitive processes

including making personality evaluations of self and other (Beeney, Hallquist, Ellison, & Levy,

2016), experiencing social rejection (Domsalla et al., 2014; Ruocco et al., 2010), and feeling

empathy for others (Dziobek et al., 2011) all tend to elicit hyperactivation in regions of the social

network in patients with BPD. From these studies among others, regions such as the mPFC, TPJ,

precuneus/posterior cingulate cortex (PCC), and aINS have been identified as key targets for

thinking about social cognitive impairments in BPD.

Importantly, there is a non-trivial amount of overlap between the social brain network and

what resting-state fMRI (RSMRI) studies have identified as the default mode network (DMN;

Raichle et al., 2001; Raichle & Snyder, 2007). The DMN includes the posterior cingulate cortex

(PCC)/ precuneus, mPFC, and bilateral angular gyrus (AG) and anterior temporal pole, which

together exhibit coordinated functional activation while subjects are at rest. In addition, the

DMN shows coordinated deactivation during cognitive task execution, which scales with task

performance, leading some to hypothesize that the DMN may compete with task positive

networks for resources at rest and during task (Kelly, Uddin, Biswal, Castellanos, & Milham,

2008; McKiernan, Kaufman, Kucera-Thompson, & Binder, 2003; Weissman, Roberts, Visscher,

& Woldorff, 2006). The DMN has been linked to self-referential thought, rumination, thinking

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about the past, and planning for the future and may generally be thought of as an “internally-

directed” network (Zabelina & Andrews-Hanna, 2016).

To date, there have been relatively few RS studies in the BPD literature (Das, Calhoun, &

Malhi, 2014; Doll et al., 2013; Krause-Utz et al., 2014; Salvador et al., 2016; Sarkheil, Ibrahim,

Schneider, Mathiak, & Klasen, 2019; Wolf et al., 2011; Xu et al., 2016). However, the extant

literature shows initial signs of convergence around increased levels of intrinsic activity in the

DMN (Salvador et al., 2016; Wolf et al., 2011) and decreases in activity of frontal executive

control networks in BPD (Das et al., 2014; Doll et al., 2013; Wolf et al., 2011). One recent meta-

analysis of resting-state studies in BPD included four RS-fMRI studies and found increased

levels of activity in midline regions including the ACC and mPFC and reduced activity in the

right middle temporal cortex compared to controls (Visintin, De Panfilis, et al., 2016). However,

the studies reviewed in Visintin et al. (2016) still focused on functional activation (i.e. the

magnitude of BOLD fluctuations in a network at rest) and further utilized independent

component analysis (ICA) to detect network structure. While useful, this approach may miss

subtle differences in connectivity patterns across functionally separable brain regions, perhaps

within the same intrinsic network. Analytic approaches from graph theory provide finer levels of

detail concerning the unique roles of individual brain regions within the broader context of a

whole brain network (Bullmore & Sporns, 2009).

Development of intrinsic network structure in adolescence

While little is known about developmental differences in FC in adolescents with BPD, the

field of developmental cognitive neuroscience can provide insight into normative changes in

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functional connectivity occurring during adolescence. The idea that networks are separable but

interacting is typically talked about in network neuroscience (Bassett & Sporns, 2017) as

network segregation and integration, respectively. One important finding in studies of brain

development during adolescence is that FC patterns transition from primarily local connectivity

(i.e. between regions that are close in Euclidean distance) to distributed connectivity (i.e.

strengthening of long-range connections that is perhaps explained by the myelination of axons

supporting communication between spatially distant regions; Fair et al., 2007). Thus, in

developmental neuroscience, a foundational line of research suggests that across adolescent

development, intrinsic networks undergo a process of segregation (separation of networks into

functionally distinct modules) and integration (cross-module communication).

More specifically, while the basic architecture of the brain is well-established by

adolescence (Hwang, Hallquist, & Luna, 2013), fine-grained tuning of long-range connections is

thought to support the strengthening of within-network connections (Fair et al., 2007). However,

network integration also increases over time in adolescence, contributing to the ability to recruit

a variety of cognitive processes flexibly, thereby supporting developmental improvements in

cognitive control (Luna, Marek, Larsen, Tervo-Clemmens, & Chahal, 2015). In particular, there

is evidence that both segregation and integration of the salience/cingulo-opercular network,

generally charged with maintaining attention to goals (i.e. task-set maintenance), is a key

neurodevelopmental change in adolescence that supports developmental improvements in task

performance (Fair et al., 2007; Hallquist, Geier, & Luna, 2018; Marek, Hwang, Foran, Hallquist,

& Luna, 2015).

The salience network is centrally comprised of portions of the ACC, frontal

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operculum/anterior insula, inferior frontal gyrus (IFG) and TPJ, which shows a high degree of

overlap with social brain regions (Rosen et al., 2018). This is consistent with findings that the

adolescent brain is particularly sensitive to social information, including increased sensitivity to

social context (e.g. peer influence; Chein, Albert, O’Brien, Uckert, & Steinberg, 2011).

Likewise, many crucial regions involved in social cognitive processing undergo drastic changes

during adolescence, including the mPFC, anterior insula, TPJ, ACC, and pSTS (Blakemore,

2008; Blakemore & Mills, 2014), all of which have been associated with BPD in adults.

The current study

Despite evidence from developmental neuroscience that adolescence is a period of

segregation and integration of distant brain regions in addition to a period of social cognitive

development, little is known about how adolescents with emerging borderline symptoms may or

may not conform to this normative process. The central goal of the current study is to leverage

insights from developmental neuroscience to help contextualize the existing evidence for

neurobiological abnormalities in adults with BPD. With these goals in mind, an analytic

approach rooted in graph theory provides the opportunity to examine where in the brain and at

what level of analysis adolescents with BPD may exhibit neurodevelopmental differences. Graph

theory is a branch of discrete mathematics that considers pairwise relationships (links/edges)

between elements (nodes) of a complex network (i.e. a graph) to be the atomic unit of analysis.

Graph theoretical analyses often aim to identify nodes and edges that play a “central” role in the

topology of the graph based on a number of metrics, each designed to capture a different feature

of the graph’s structure. Graph theory is often employed in conjunction with RS studies, as these

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studies do not have a “ground truth” signal or set of inputs by which to constrain analyses, yet

carry a number of advantages over task-based connectivity studies including ease of acquisition,

high signal-to-noise ratio, and good test-retest reliability (Cao et al., 2014; Fox & Greicius,

2010).

In the current study, our goal was to address the broader need to 1) test the possibility of

altered resting-state connectivity in a sample of adolescents with BPD symptoms rather than base

neurobiological thinking on BPD primarily on activation-based studies, 2) examine aberrant

patterns of age-related differences in intrinsic networks in adolescence and early adulthood, and

3) investigate how connectivity findings can be complimented by analyses metabolic demands

during rest via the amplitude of low-frequency fluctuations (ALFF; Zang et al., 2007). In order to

investigate how intrinsic networks may display differential developmental connectivity patterns

in individuals with BPD, we used graph theory analyses of resting-state fMRI data as one of the

first explorations into uncovering the developmental pathogenesis of borderline pathology. As

we detail below, two primary prongs of the study are to first perform confirmatory analyses on a

priori subsets of nodes previously identified in the BPD literature and then follow up these

analyses with a more exploratory whole-brain approach that places fronto-limbic and socio-

cognitive systems within a more distributed brain network.

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Chapter 2: Methods

Participants

Our sample consisted of 46 adolescents and young adults with BPD symptoms recruited

from community and outpatient settings, as well as 44 sex- and age-matched healthy controls.

The average age was 20.53 years (range 13-30 years); 59 participants were female and 31 were

male. Six participants were excluded due to excessive head motion during the MRI scan (n BPD

= 4; criteria described below), resulting in a trimmed sample of 84 participants. We removed one

additional subject from the BPD group whose data did not pass serial residual correlation checks

after pre-whitening our data (described below). We further removed one participant from the

BPD group who passed our head motion criteria but whose functional connectivity matrix was

remarkably different from the group average. This check was based on the Mahalanobis distance

of every subject’s adjacency matrix compared to the group average (fig S1). A complete

demographic characterization of the final sample for the analysis can be found in Table 1.

Procedure

Participants were interviewed using the Structured Clinical Interview for DSM IV (SCID-IV;

First, Spitzer, Gibbon, & Williams, 2002)) to screen for clinical disorders and the Structured

Interview for DSM-IV Personality (SIDP; Pfohl, Blum, & Zimmermann, 1997) to screen for

personality pathology by a trained research assistant and supervised by the senior author.

Diagnostic interviews were completed in a separate visit prior to the MRI scan. Participants in

the BPD group met diagnostic criteria for three or more of the DSM-IV-TR BPD symptoms, an

empirically derived threshold for identifying clinically significant symptoms (Clifton & Pilkonis,

2007). Control participants had no history of psychiatric or substance abuse disorders, nor did

12

they have a history of head injury or neurological disease. Exclusionary criteria for the BPD

group included having a first-degree relative diagnosed with Bipolar I disorder or any psychotic

disorder.

Before the RS-fMRI session, participants completed a battery of self-report questionnaires.

The current report focuses on the Borderline Personality Questionnaire (BPQ, Poreh et al.,

2006). The BPQ contains a wide-range of borderline-related dimensions of dysfunction including

impulsivity, affective instability, fears of abandonment, intense and stormy relationships, self-

image difficulties, suicidality and self-harming behavior, emptiness, intense anger, and quasi-

psychotic states. The BPQ showed excellent internal consistency in our sample at baseline ( =

0.97, mean subscale = 0.86).

MR data acquisition and removal of high-motion subjects. Data were acquired using a

32-channel Siemens 3T Tim Trio at the University of Pittsburgh Medical Center Magnetic

Resonance Research Center. We collected five minutes of resting-state fMRI data at the end of

an experimental protocol using a simultaneous multi-slice echo-planar sequence sensitive to

BOLD contrast (T2*, TR = 1.0s, TE = 30ms, FoV = 220 mm, flip angle = 55, voxel size =

2.3mm isotropic, 5x multiband acceleration) while subjects were asked to close their eyes and

relax, but not fall asleep. Participants completed a self-report questionnaire at the end of the

protocol to determine if they fell asleep during the resting-state scan or had problems with

alertness. No subjects were excluded for sleepiness.

For all participants, we calculated volume-to-volume framewise displacement (FD; Power,

Barnes, Snyder, Schlaggar, & Petersen, 2012). We excluded subjects with FD > 0.5mm in at

13

least 20% of the volumes, or any FD > 10mm. This led to the removal of six participants, four of

whom were in the BPD group.

RS-fMRI preprocessing procedures

RS-fMRI preprocessing was conducted within FSL (Smith et al., 2004), NiPy (Millman &

Brett, 2007), and AFNI (Cox, 1996). Structural scans were registered to the MNI152 template

(Fonov, Evans, McKinstry, Almli, & Collins, 2009) using affine and nonlinear transformations

conducted in FSL. Functional image preprocessing included simultaneous 4-D interpolation of

motion and slice-timing correction (Roche, 2011), brain extraction, alignment of subject’s

functional images to their anatomical scan using a boundary-based registration algorithm (Greve

& Fischl, 2009), a one-step nonlinear warp to MNI152 space that concatenated functional-to

structural, structural-to-MNI152 and fieldmap unwarping transformations.

We then used ICA-AROMA (Pruim, Mennes, van Rooij, et al., 2015) to remove motion-

related artifacts. Although our connectivity analyses were conducted on unsmoothed data (see

below), ICA-AROMA was conducted on data that was spatially smoothed with a 5mm FWHM

gaussian kernel (FSL susan), consistent with recommended guidelines. Specifically, Pruim and

colleagues (2015) note that spatial smoothing increases SNR in BOLD data, allowing for an

increased ability to detect structured artifacts that should be removed from the signal (such as

components related to subject movement). AROMA’s automated component selection approach

has recently been shown to be superior to other competing procedures in removing motion

artefacts while preserving the signal of interest, and it largely eliminates distance-dependent

motion-FC correlation effects (QC-FC correlation; Ciric et al., 2017; Pruim, Mennes, Buitelaar,

14

& Beckmann, 2015).

Based on the results of ICA-AROMA, we regressed motion-related components out of the

unsmoothed data using fsl regfilt. This approach performs the noise removal by running a

regression on the full ICA mixing matrix generated by AROMA (i.e. “nonaggressive” noise

removal) in addition to mean WM and CSF regressors, thus removing only the variance specific

to noise components that does not overlap with other components that likely reflect neural signal.

We decided to analyze unsmoothed data based on a recent report that spatial smoothing across a

range of FWHM values non-uniformly increases the correlation of ROI time series, which

spuriously increases estimates of network segregation at the cost of losing information about

functional network integration (Alakörkkö, Saarimäki, Glerean, Saramäki, & Korhonen, 2017).

Analytic approach

Nodal parcellation. After the data were preprocessed, we parceled voxels into ROIs (i.e.

nodes) based on a custom-built 422 cortico-striatal parcellation. Our parcellation was based on

the 400 node cortical parcellation derived recently by Schaefer et al. (2018). The study authors

used RS-fMRI data from 1489 participants and were able to demonstrate that the parcellation

(which they denote gwMRF, to denote the gradient-weighted Markov Random Field approach

used to derive the parcellation) provided a more homogenous partition of whole-brain neural

activity compared with four leading cortical parcellations and was shown to agree well with

cortical boundaries. The gwMRF parcellation also accords well with the previously defined

seven-network structure from Yeo et al. (2011), which promotes comparability of network-level

results between studies.

15

However, given that cortico-striato-thalamic loops are thought to play a central role in the

pathophysiology of neuropsychiatric disorders (Maia & Frank, 2011; Tekin & Cummings, 2002),

we sought to include subcortical regions in our final parcellation (as suggested in Hallquist &

Hillary, 2018). Thus we included a detailed parcellation of the human striatum (Choi, Yeo, &

Buckner, 2012), which aligns with the Yeo et al (2011) network structure, a thalamic parcellation

derived from diffusion weighted imaging (Behrens et al., 2003), and two bilateral subnuclei of

the amygdala (the basolateral amygdala [BLA], and centro-medial nucleus [CeM]) from the

Harvard-Oxford subcortical atlas in FSL.

After creating our combined parcellation we calculated subject-level masks that reflected

the proportion of voxels in each ROI that contained unreliable signal, as indicated by voxelwise

standard deviation equal to zero and/or all values equal to zero. Visual inspection of the subject-

level masks indicated that problematic voxels were located predominantly in inferior temporal

regions and to a lesser extent, orbitofrontal regions, reflecting signal loss due to susceptibility.

We then merged all binary masks into a group-level mask with voxel-wise values equal to the

proportion of subjects with reliable signal in the voxel. In order to ensure that ROI time series

reflected the same voxels across participants, we removed all voxels from the parcellation in

which less than 95% of subjects had reliable signal. This procedure removed 600 voxels —

approximately .7% of the total voxels — from our parcellation. We then calculated the

proportion of voxels removed from each node in the parcellation and removed nodes from the

final parcellation if 50% or more voxels in a node were dropped in order to ensure robust and

homogenous signal estimation. This led us to drop one ROI located near the left inferior

16

temporal pole for a final parcellation consisting of 421 nodes. A visual depiction of the finalized

parcellation is provided in figure 2.

Pre-whitening and adjacency matrix generation. After performing motion removal

procedures and constructing our combined parcellation, we pre-whitened our nodal time series

prior to calculating functional connectivity based on cross-correlation. This decision was based

on the concern that failing to remove autoregressive components of fMRI time-series violates a

key assumption of the general linear model (specifically, residuals must be i.i.d. and normally

distributed; Bright, Tench, & Murphy, 2017). Furthermore, estimates of cross-correlation can be

misestimated when time series have similar autoregressive properties that do not reflect true

interregional connectivity. To overcome this concern, auto-regressive models such as Auto-

Regressive Moving Average (ARMA(p,q); Box & Jenkins, 1990) models have received

increasing attention in the fMRI literature in recent years, where high amounts of serial

correlation are inherent in the data structure (Arbabshirani et al., 2014; Christova, Lewis, Jerde,

Lynch, & Georgopoulos, 2011; Woolrich, Ripley, Brady, & Smith, 2001). Prior to running

ARMA models, we computed aggregated nodal time series by taking the average of all time

series for voxels included in a given node, excluding those voxels that were missing (primarily

from truncation described above) or had no variance.

ARMA models represent the temporal dependence of observations in a time series,

allowing one to remove the autoregressive components of the signal to achieve a “white” error

time series. ARMA models were fit to each average nodal time series using the Arima function

included in the forecast package in R (Hyndman et al., 2019) such that

17

𝜂𝑡 = 𝑐 + 𝜀𝑡 + ∑ φ𝑖𝜂𝑡−𝑖

𝑝

𝑖=1

+ ∑ θ𝑖𝜀𝑡−𝑖

𝑞

𝑖=1

where φ𝑖 … φ𝑝 denote freely estimated AR coefficients that quantify the degree of

autocorrelation between the current realization (𝜂𝑡) and previous realizations, and θ𝑖 … θ𝑝

denote freely estimated MA coefficients that quantify the degree of dependence of the current

innovation (𝜀𝑡) on prior innovations. The residual error term, also called the innovation term in

an AR model, is assumed to be normally distributed (i.e. “white”) noise

𝜀𝑡~ 𝑖𝑖𝑑 𝑁(0, 𝜎2).

The unique coefficients of a given ARMA model for a node and subject act as a filter on

the time series that whitens the residuals. However, the important quantity of interest in a graph

theoretical analysis is the cross-correlation between every pair of nodal time series within a given

subject, which are then used as cells in the adjacency matrix. Thus, to compute functional

connectivity between regions, we stored the ARMA coefficients (sometimes referred to as a

transfer function) for one node and used these to filter both time series for a given subject. We

used the Pearson product-moment correlation coefficient to quantify the functional connectivity

between nodal time series that had been passed through the same ARMA coefficients:

𝑟𝜙(𝑦),𝜙(𝑥) = 1

𝑇 − 1 ∑

𝜙(𝑦𝑡) − 𝜙(𝑦)̅̅ ̅̅ ̅̅ ̅

𝑠𝜙(𝑦)

𝑇

𝑡=1

⋅𝜙(𝑥𝑡) − 𝜙(𝑥)̅̅ ̅̅ ̅̅ ̅

𝑠𝜙(𝑥)

where 𝜙(𝑦) and 𝜙(𝑥) denote the time series for two nodes that have both been filtered by the

fitted ARMA coefficients for y.

We fit a series of increasing complex ARMA models until the number of subject-wide

18

“non-white” residuals fell below 5% of voxels for all subjects (i.e., the false positive rate on the

test). Time series were deemed “non-white” on the basis of the Breusch-Godfrey test, which

tested null hypotheses of serial correlation our nodal time series, which was computed up to six

lags prior to the current realization (i.e., six seconds in the past). Through this procedure we

retained our results from an ARMA(4,2) model as the edges of subject-level graphs for further

analysis.

Graph construction and module assignment. After estimating nodal functional

connectivity matrices for each subject, we converted these adjacency matrices to graphs using

the igraph and brainGraph R packages (Csárdi & Nepusz, 2006; Watson, 2017) in order to

estimate all network metrics (e.g., strength centrality). After the construction of undirected

weighted graphs, it is common to threshold graphs by setting some edges to zero that reflect

unreliable or uncommon connections in the sample. Based on recent reports that proportional

thresholding (PT) can lead to spurious results in group comparison studies (Hallquist & Hillary,

2018; van den Heuvel et al., 2017), we instead used a moderate consensus threshold to eliminate

spurious edges in our graphs (de Reus & van den Heuvel, 2013). Specifically, we removed edges

from all subjects that did not have a weight of r = .1 or higher in 25% or more of subjects. This

decision was guided by three factors. First, cross-correlations among pre-whitened time series

are lower than correlations without pre-whitening. Second, many nodes in the cortico-limbic

network, which is important in BPD, had relatively weak functional connectivity values

compared to other regions (fig S1); thus, we wanted to define a low threshold that would retain

many cortico-limbic edges. Third, the central goal of thresholding was to remove only the highly

unreliable edges prior to conducting weighted graph analyses. Prior to thresholding, we removed

19

all negative edges from our adjacency matrices because the role of anti-correlated nodal time-

series in rsfMRI data is not well understood and many graph metrics are not well-suited to

handle negative edges (Hallquist & Hillary, 2018; Rubinov & Sporns, 2011). Moreover, negative

edges were uncommon (across subjects, M = 611.34, SD = 164.40), and the negative values were

substantially weaker than the strongly positive FC distribution.

In order to compare levels of connectivity within and between intrinsic networks of

interest, we assigned nodes to one of the seven networks from Yeo et al. (2011) and reported in

(Schaefer et al., 2018). We note here that we use the term network to the describe those sets of

nodes that share a high degree of connectivity amongst themselves (rather than the “whole-brain

network” which comprises all brain regions), which are synonymous with modules in graph

theory. The seven networks correspond to default mode (DMN), fronto-parietal (FPN),

salience/ventral attention (Sal), dorsal attention (DAN), sommato-motor (SomMot), visual (Vis),

and cortico-limbic networks (Limbic). Despite the fact that others (e.g. Betzel, Gu, Medaglia,

Pasqualetti, & Bassett, 2016) have grouped subcortical nodes in separate modules, reflecting

more of a “bottom-up” vs “top-down” focus, we sought to capture the looping nature of cortical-

striato-thalamic connections as being centrally important in our conceptualization of intrinsic

networks (Alexander & Crutcher, 1990). With this in mind, striatal nodes were assigned to their

corresponding cortical networks according to (Choi et al., 2012). The eight subdivisions of the

thalamus were assigned to bilateral fronto-parietal, somato-motor, dorsal attention, and default

networks based on prominent white matter projections reported in Behrens et al., (2003). All

amygdala ROIs (4 in total) were assigned to the cortico-limbic network. In order to check if

within-network FC was greater between-network FC, we visually inspected within and between

20

network edge distributions and confirmed that this was the case (fig S2), lending support to the

validity of our network partition.

Graph metrics. At the global level, we inspected the overall FC and strength centrality

distributions as global indicators of the overall FC in our BPD and control participants. We

further examined several global graph metrics in order to examine the possibility of global

differences between groups that would qualify more focused nodal analyses. In particular, we

calculated weighted modularity, characteristic path length, transitivity, global efficiency, and the

weighted diameter of individual graphs (Rubinov & Sporns, 2011).

At the nodal level, we calculated strength centrality, which is the sum of all edges

incident to a node of interest:

𝑘𝑖𝑤 = ∑ 𝑤𝑖,𝑗

𝑗 ∈ 𝐺

such that 𝑘𝑖𝑤 is the weighted degree (strength) centrality for node i, and 𝑤𝑖,𝑗 is the weight of the

connection between node i and each node j for all nodes in the graph (G). In order to test

network-to-network connectivity, our analyses focused on connections within and between the

seven networks of interest. For each node, we partitioned strength centrality into seven network-

specific estimates (one per network, which we refer to as net-strengths):

𝑘𝑖,𝑁𝑤 = ∑ 𝑤𝑖,𝑗

𝑗 ∈ 𝑁

21

Here, N corresponds to the set of all nodes in one of seven networks. Since the seven networks

each contain a different number of nodes, we normalized the net-strength metrics 𝑘𝑖,𝑁𝑤 so as not

to award larger networks with higher estimates:

𝑧𝑖,𝑁𝑤 =

𝑘𝑖,𝑁𝑤 − 𝑘 𝑁

𝑤̅̅ ̅̅ ̅

𝜎𝑘 𝑁𝑤

.

Thus, if node 𝑖 is in module N, then 𝑧𝑖,𝑁𝑤 corresponds to node 𝑖’s within-module degree z-score

(Guimerà & Amaral, 2005). Otherwise, for 𝑖 ∉ 𝑁, 𝑧𝑖,𝑁𝑤 corresponds to the normalized inter-

module connectivity between node i and all nodes in 𝑁. We further calculated the betweenness

centrality of every node, corresponding to the number of shortest paths that run through a node.

In the weighted variant, we summed edge weights to compute shortest paths rather than counting

edges:

𝑏𝑖 = 1

(𝑛 − 1)(𝑛 − 2)∑

𝜌ℎ𝑗(𝑖)

𝜌ℎ𝑗ℎ,𝑗 ∈ 𝐺ℎ≠𝑗,ℎ≠𝑖,𝑗≠𝑖

where 𝜌ℎ𝑗(𝑖) are the number of shortest paths between nodes h and j that run through node i and

𝜌ℎ𝑗 is the number of shortest paths between h and j. Given that betweenness centrality follows a

power-law distribution, we log-transformed estimates prior to analysis. In addition, for

betweenness, we Winsorized outliers by replacing observations that were outside of the 2.5%

and 97.5% quantiles of the log-betweenness distribution with the values associated with the 2.5%

and 97.5% quantiles, respectively. No Winsorizing procedures were required for any of the net-

strength scores.

22

Resting-state activity: amplitude of low frequency fluctuations (ALFF). While the

primary goals of our study were to examine group and age-related differences in RSFC, we were

interested in the degree of convergence and/or divergence between connectivity-based measures

and the level of low frequency fluctuations between groups. This is quantified in RS data as the

amplitude of low frequency fluctuations (ALFF; Zang et al., 2007) and is generally considered to

be akin to be the resting-state analog of “activation” task fMRI data and has been shown to

correspond to metabolic demands in RS-fMRI(Tomasi, Wang, & Volkow, 2013). ALFF is

calculated by computing the power spectrum of low frequencies (typically, .01 – .1 Hz; Biswal,

Yetkin, Haughton, & Hyde, 1995)

𝐴𝐿𝐹𝐹 = 1

𝐿∑ 𝑃𝑜𝑤𝑒𝑟

0.1𝐻𝑧

0.01𝐻𝑧

where the power of a given frequency is obtained via a Fast Fourier Transformation and L

denotes the number of Fourier coefficients, thus averaging the summed amplitudes across the

selected frequency range (Zang et al., 2007). However, ALFF suffers from being susceptible to

physiological noise, which led to the subsequent development and validation of a normalized

variant known as fractional ALFF (fALFF)

𝑓𝐴𝐿𝐹𝐹 =

1𝐿𝑙𝑜𝑤

∑ 𝑃𝑜𝑤𝑒𝑟0.1𝐻𝑧0.01𝐻𝑧

1𝐿𝑎𝑙𝑙

∑ 𝑃𝑜𝑤𝑒𝑟.25𝐻𝑧0 𝐻𝑧

⁄

such that the denominator includes RS fluctuations across the entire frequency range. Both ALFF

and fALFF are thought to reflect different aspects of neural processing and have been

demonstrated to be reliable over time (Zuo et al., 2010). ALFF and fALFF were calculated on

23

the preprocessed timeseries before pre-whitening (as this procedure necessarily affects the

spectral properties of a time series) was applied. These metrics were computed using the alffmap

function in the ANTSR package in R (Avants, 2018).

Confirmatory analyses amongst a priori nodes. In order to test the robustness of the

effects described in the adult literature in our adolescent sample, we ran two focused analyses on

the “usual suspects” from BPD research in adults. An edge-by-edge analysis of our adjacency

matrices would require 88,410 tests (unique elements of the lower triangle of our adjacency

matrices), making for a massive multiple comparisons problem. Thus, we selected a subset of

nodes that are of particular interest to fronto-limbic and social/DMN accounts of BPD for

focused tests on connection strengths between regions that have been previously identified1. In

the first a priori analysis, we selected 19 front-limbic nodes of interest from the 421-node

parcellation and examined group and group x age differences in edge values that connected these

nodes. More specifically, the fronto-limbic nodes were bilateral mPFC, ACC, orbitofrontal

cortex (OFC), and amygdala (bilateral BLA and CeM), as well as the bilateral ventral striatum

(VS), as this region plays a central role in reward processing and motivation and has been

implicated in BPD (Sarkheil et al., 2019; Silbersweig et al., 2007). In a second confirmatory

analysis we selected 25 nodes representing “social brain” and DMN hubs. These were chosen

based on a review of the literature, especially the Visintin et. al. (2016) meta-analysis. We

included the same mPFC and ACC nodes in the fronto-limbic and social/DMN analyses, as these

1 Since the usual suspects analysis was designed to be a targeted analysis and our focus in on

connectivity here, we only tested ALFF and fALFF in whole-brain exploratory analyses

(described below)

24

regions are crucially involved in both emotion regulation and social cognition. In addition, in the

social/DMN usual suspects analysis we included the TPJ, Precuneus, aINS and pSTS, as well as

mPFC and ACC. Detailed information on the nodes selected can be found in Table S1.

Before conducting formal analyses, we aimed to determine if age-related changes in an

edge’s FC value conformed better to linear, inverse linear (asymptotic), or quadratic (u-shaped)

functions of age. We first fit a series of regression models predicting edge FC as a function of

subjects’ mean FC values and interacting group and age variables. In order to test for different

shapes of age-related effects we fit three regression models for each edge (i.e. every combination

of nodes in the selected set of usual suspects), each corresponding to linear, inverse linear, and

quadratic age effects (quadratic models also included a linear component). We ran this procedure

for every edge and estimated linear, inverse linear, and quadratic age effects. We assumed a

linear age effect to be the default but retained quadratic or inverse age effects for further analyses

if a Vuong likelihood ratio test rejected the null hypothesis (p < .05) that the two models are

equally close to the data generating process (Vuong, 1989).

Logistic ridge regression analyses. The primary aim of our a priori analyses was to

examine which edges best described age and BPD-related differences in functional network

organization. Given the large number of nodes, however, we sought to avoid running multiple

separate models given that this mass univariate approach would have a high risk of false positive

findings. Moreover, univariate analyses of edges would not provide insight into which subset of

edges jointly discriminate network differences as a function of BPD and age. With this in mind,

we elected to run two logistic ridge regression analyses — one for each set of usual suspects —

in which we predicted group status (BPD vs. HC) as a function of edge FC values for all edges in

25

the joint set for the analysis in question, as well as age-by-FC interactions from the age models

selected above. Thus, the results we interpret are based on parameter estimates that jointly best

discriminated between adolescents in the BPD and HC groups. By including edge x age

interactions, the models were also able to identify nodes whose pattern of age-related change

differed by group. For example, a model predicting group status from edges in the fronto-limbic

comparison was specified as

𝑙𝑜𝑔𝑖𝑡(𝐵𝑃𝐷) = 𝛽0 + 𝛽1𝐴𝑔𝑒 + +𝛽2𝐴𝑔𝑒2 + 𝛽3𝐹𝐶𝑚𝑒𝑎𝑛 + 𝛽4𝐹𝐶173−401 + 𝛽5𝐹𝐶173−401 × 𝐴𝑔𝑒

+ ⋯ + 𝛽𝑝−2𝐹𝐶404−420 + 𝛽𝑝−1𝐹𝐶404−420 × 𝐴𝑔𝑒 + 𝛽𝑝𝐹𝐶404−420 × 𝐴𝑔𝑒2 + 𝑒

where p is equal to the number of parameters in the model and 𝐹𝐶404−420 denotes the FC value

between node 404 (R CeM) and node 420 (R VS). In this example, the Age2 term for 𝐹𝐶404−420

denotes that a quadratic age model fit better than linear or inverse variants according to the

Vuong test, whereas for 𝐹𝐶173−401 the linear model fit the best. Nodes that were fit best by a

quadratic model included both linear and quadratic age terms (as in the example above) in order

to partition variance to the linear and quadratic components appropriately.

We elected to use a regularized regression approach to overcome p ≫ n problems in

which the number of estimated parameters is greater than the number of observations (Hastie,

Tibshirani, & Friedman, 2016). Ridge regression shrinks model coefficients towards zero by

penalizing the summed parameter estimates. This is achieved by augmenting the standard OLS

loss function with an L2 penalty such that

𝐿𝑟𝑖𝑑𝑔𝑒(�̂�) = ∑(𝑦𝑖 − 𝑥𝑖′�̂�)2

𝑛

𝑖=1

+ ∑ �̂�𝑗2

𝑚

𝑗=1

26

where is a penalty parameter corresponding to the level of shrinkage on the standard OLS

regression parameter estimate. The penalty parameter for our ridge regression was chosen using

an automated selection algorithm implemented in the R ridge package. The auto-selected

parameters for each analysis (including subsequent whole-brain connectivity and ALFF/fALFF

analyses) are displayed in Table S2. We elected to retain parameters as significant if their ridge

p-value was < .005. We note that in such an analysis, there is no inherent need to correct for

multiple comparisons since all edges are tested simultaneously. However, given the large number

of potential contributing parameters in the fitted models we chose to retain a subset of results that

were the most potent in distinguishing our groups and thus elected p < .005 as a more stringent

test of significance.

Post-hoc analysis: effective connectivity. After examining the results of the usual

suspects analyses, we sought to identify if any of the functional connectivity results in our edge-

level analyses could be explained by the directed influence of one node on another. In order to

assess group-level differences in effective connectivity we retained the nodes from the usual

suspects analysis that showed evidence of significant group differences in logistic ridge

regression analyses. We then estimated directed connectivity between these nodes using the

confirmatory subgrouping Group Iterative Multiple Model Estimation algorithm (Henry et al.,

2019). CS-GIMME is a recently validated extension of the GIMME search algorithm (Gates &

Molenaar, 2012), which has been demonstrated to reliably detect the presence and direction of

edges in fMRI data both at the individual and the group level. GIMME estimates both lagged and

contemporaneous relationships between nodal time series (and within time series, corresponding

to AR processes) and benefits from relaxing the assumption that all individuals must be fit to the

27

same model, yet utilizes regularities in individual subjects’ path estimates to derive group-level

edges that are estimated for every subject in the sample. Likewise, Henry et al., (2019) recently

extended GIMME to also search for edges that uniquely exist in one a priori subgroup (i.e. a

clinical disorder) but not for another, thus allowing for the investigation of subgroup differences

between edges that are estimated for all subjects (group-level edges) in the sample but also for

the existence of edges that are unique to an a priori subgroup. We fit two CS-GIMME models

(one for fronto-limbic, one for S/DMN nodes) to the preprocessed (before pre-whitening) time

series from each of the identified nodes using the gimme R packages (Gates & Molenaar, 2012).

Exploratory whole-brain analysis. A second goal of our analysis was to examine

fronto-limbic and social/DMN differences in BPD within the context of a whole-brain graph,

rather than in selected regions. We first tested differences in global graph properties by

regressing edge/FC values on age, group status, and an age x group interaction. In preliminary

analyses, we found that the borderline group had significantly lower overall FC, which prompted

us to include each subjects’ mean FC value as a covariate in subsequent analyses (fig 1).

Including all subjects’ mean FD as a covariate in the analysis did not significantly alter these

results or results from other analyses. To investigate group differences in the global graph

metrics calculated above we ran five linear regressions predicting global graph metrics (one for

each metric) by group status and age as well as the interaction of age and group status to test null

hypotheses of no difference in global graph metrics of interest. Given the overall lower mean FC

in the BPD group, mean FC was included as a covariate of no interest in all analyses, including

the usual suspects analyses described above.

28

We then tested the contribution of nodal centrality metrics (strength, net-strengths,

betweenness) and RS “activation” (ALFF, fALFF) to group differences using the same logistic

ridge regression approach described above. As in the usual suspects analysis, using a Vuong test

we estimated the best-fitting function of age for each node, which was then entered into the

whole-brain nodal regression. In this situation, however, we included all 421 nodes and their age

interactions as parameters in 11 separate logistic ridge regression models (nine centrality metrics

and two ALFF measures) with group status as the dependent variable. We again interpreted

results if their ridge p-value was < .005.

Post-hoc analyses: symptom measures. We were interested further in connectivity

patterns as they relate to dimensional variation in BPQ subscales described above. Since our

initial analyses focused on group discriminability based off of neural measures, we decided to

focus on the mediational effects of BPQ subscale measures as a test of group differences being

explained by variation in particular dimensions of borderline pathology. While the mediations

we tested are likely best fit within a structural equation modelling (SEM) framework, our sample

size was not large enough to fit an SEM mediation model. Thus, we fit a single factor

confirmatory factor analysis (CFA) to the BPQ subscales and retained the factor scores for

further analysis. This approach is more advisable than summing or averaging self-report scales,

as fitting a CFA will remove measurement error and thus provide a single estimate for a given

subject’s standing on a latent factor explaining covariation in item responding. We performed a

similar analysis on the results from our graph analysis, given that some nodes showed significant

differences across a number of mod-strength metrics. When this was the case all results were in

the same direction, thus lending support to the idea of a shared signal for some nodes (i.e. we did

29

not observe any group differences suggesting that a node had higher connectivity to one module

and lower connectivity to a different module in controls or BPD participants). If nodes had three

or more significant results across our nodal centrality analyses we attempted to fit a single-factor

CFA to the identified centrality metrics as indicators of a latent variable corresponding to the

connectivity of a given node. This procedure was designed to reduce the number of mediation

tests. Models were specified such that the mediating influence of one of the self-report measures

documented above (mediating variable) explains the association between group status

(independent variable) and a neural metric of interest (dependent variables taken from Tables 4

and 5 could be nodal centrality, or ALFF, or a factor score if multiple centrality metrics were

significant in the ridge analyses). Mediation analyses were conducted in the mediation package

in R (Tingley, Yamamoto, Hirose, Keele, & Imai, 2019) .

Post-hoc analyses: visual depiction of edges of interest. We were further interested in

learning about the edges that contributed to our nodal results. In particular, given that our mod-

strength metrics reflect the sum of edge weights between a given node and a network, post hoc

analyses offer the potential to uncover the edges that differ most as a function of group. We

investigated “affected” nodes (which were identified via the p-values from ridge regression) by

running post-hoc t-tests of group differences for each edge of an affected nodes. When

discussing nodal results, post hoc edge analyses served as an informal guide for identifying the

most salient bivariate group differences at the edge level that may underlie the nodal results led

to a consistent pattern of connectivity (for example to nearby nodes within the same network).

By contrast, betweenness centrality is based on path length. Thus, specific connections to and

30

from nodes are less important in yielding significant results, so only nodes that differed in

strength were further interrogated.

Results

Global analyses

At the global level, the BPD and HC groups differed in overall FC. Student’s t-tests

between the control and borderline group indicated that the control group has greater edge

weights on average (t = 5.46, p < .0001) and greater strength centrality (t = 15.18, p < .0001)

(fig1a). Further our groups differed in age-related changes in FC, which were fit best by a

quadratic age model. Results from a linear regression revealed a significant quadratic age x

group interaction (t = -17.96, p < .0001) such that the control group showed evidence of

quadratic decreases in FC over the adolescent-early adult window we sampled where the BPD

group showed evidence of lower overall FC during adolescence with an attenuated slope

compared to controls (fig1b). We ran this analysis again, also including the mean FD for each

subject as a covariate of no interest and saw no differences in the results, ruling out the

possibility that these results were due to excessive head motion in the BPD group. Linear

models predicting global graph metrics of interest showed no significant group or group x age

effects with mean FC included as a covariate in each model (Table 2).

a priori analysis: fronto-limbic nodes

In our fronto-limbic usual suspects analysis, we first identified and dropped any edges

that were eliminated as a result of consensus thresholding. Of the 171 identified edges we

proposed to test, 19 were zeroed-out on this basis. In the social/DMN usual suspects analysis, 14

31

edges were dropped on this basis, leading to an overall 311 edges tested in the social/DMN

analysis and 152 edges in the fronto-limbic analysis. Results are displayed in Table 2 and

visualized in Figure 3.

While results from the fronto-limbic analyses point to a robust pattern of age-varying

differences in connectivity in fronto-limbic edges, we found evidence of decreased FC in the left

CeM of the amygdala. In particular, the BPD group had lower connectivity between the left CeM

and two contiguous nodes in the right dmPFC across development.

For fronto-limbic edges we found a series of group x age interactions between the

bilateral VS and regions of the mPFC and ACC. In particular, in the right VS young adolescents

in the BPD group had higher FC compared to HC subjects with the left subgenual ACC

(sgACC), ventromedial PFC (vmPFC), and rostromedial PFC (rmPFC). Across the adolescent

period FC decreased in these connections in the BPD group, whereas HC subjects showed age-

related increases in the same connections (fig 3b, 3d). Further visual inspection of these nodes

revealed that three were spatially contiguous roughly corresponding to BAs 25, 12, and 10. In the

left VS, we found quadratic group x age interactions in edges to the left dACC and right rACC.

In these two edges, HC subjects showed a strong u-shaped trajectory, with edge values being

highest in early adolescence and in late 20’s. In the BPD group, however, the edge connecting

the VS and dACC shows much less age-related change and specifically does not rise in the mid

20’s as it does in HC subjects (fig 3c, 3e). Results from the CS-GIMME analyses revealed an

edge estimated at the subgroup level in all controls from the left dACC (node 177) to the left VS

(node 415), yet this was only estimated for one individual in the BPD group. Further

32

investigation revealed that in HC subjects path estimates for this directed edge from the dACC to

the VS showed age-related increases (fig 4).

a priori analysis: social/DMN nodes

In contrast to the fronto-limbic analysis, in the social/DMN a priori analysis we found

pattern of hypoconnectivity in the BPD group compared to the control group regardless of age.

In this analysis, three main findings emerged. First, the most highly affected node was a region

in the right TPJ located near the posterior angular gyrus (AG). This TPJ region exhibited

significantly lower FC to the left dmPFC, posterior middle temporal sulcus (pMTS), rACC, and

TPJ/AG which was not moderated by age. Second, we found lower values for an edge

connecting the left rACC and left precuneus in the BPD group. Third, in the BPD group we

found higher FC values for an edge connecting the right dorsal and ventral anterior insula.

Whole-brain nodal centrality analyses

We retained all nodal results with a p value < 0.005 for interpretation, which led to the

identification of 38 affected nodes (Table 4), which were distributed across several networks (9

DMN and Vis, 5 FPN and SomMot, 4 Sal and Limbic, and 2 DAN, table S3). There was also a

mix of hyper- (31) and hypoconnectivity (24) in the borderline group compared to controls

(detailed in Table S2). A further breakdown of the significant effects by network, metric and

whether or not the result indicated higher or lower connectivity for the borderline group can be

found in Table S3. These analyses uncovered a number of effects that distinguished participants

with BPD from the HC group regardless of age in addition to a number of findings that showed

33

differences in age-related effects that distinguished the groups. Results from whole-brain

analyses are depicted in Figures 8 and 9.

Group differences. In the Salience network we identified a pattern of hyper-connectivity

in the bilateral dorsal mid/anterior insula (daINS) such that the left daINS node identified (node

102) showed significantly higher overall strength of connections with the DMN. The right daINS

(node 307) showed a robust pattern of hyperconnectivity in borderline adolescents with all

intrinsic networks besides the limbic regardless of age. Further, post-hoc edge analyses revealed

that the edges of this daINS node that were the most hyperconnected in the BPD group projected

to an anatomically contiguous cluster of nodes in the bilateral parietal lobe that were grouped in

the DAN (fig 5b). Results from ALFF analyses indicated that this same node in the daINS had

higher ALFF in the BPD group regardless of age. We further found that the association between

group status and the strength of this node (which we estimated by extracting factor scores from a

single factor CFA, see Method) was fully mediated by heightened levels of affective instability

(fig 5c). We also found a robust pattern of general hypoconnectivity in strength centrality in

addition to a number of mod-strengths in the right TPJ in the BPD group across the

developmental window (fig 6).

In the DAN one node in the right middle occipital gyrus had higher betweenness

centrality across development in the BPD group. However, results from whole-brain ALFF

analyses indicated a robust pattern of lower ALFF across the bilateral inferior and superior

parietal lobules. This finding was paralleled by multiple nodes in the FPN that showed

significantly lower ALFF in the BPD group.

34

Within the DMN we found mixed evidence for hyper vs hypo connectivity in nodes

across the network. In one node in the left superior frontal gyrus we found hyperconnectivity to

salience and FPN networks in the BPD group across age. Additionally, two spatially contiguous

nodes in the left AG (164, 161) had higher betweenness centrality in the BPD group. Further in

the DMN the only node that aided the ridge regression in distinguishing the BPD and HC group

in the whole-brain ALFF analysis was the left sgACC (169) which was hyperactive in the BPD

group regardless of age. Contrary to our expectations, a number of nodes in the DMN were also

hypoconnected. In fact, we found that hypoconnectivity between the left rACC and the

somatomotor network was fully mediated by dimensional scores on the BPQ affective instability

subscale. We further found that one node in the left precuneus (200) was hypoconnected to the

limbic network in the BPD group.

Finally, similarly to the DMN, we found a mixed pattern of hyper and hypo connectivity

in the visual network. However, it is notable that we found evidence of one node in the left

middle occipital gyrus that was robustly hyperconnected to multiple network in the BPD group.

Age-related effects. In our whole-brain ridge regression analyses, we identified two

primary leads in terms of age-related effects that distinguished the BPD group from the HC

group. First, we found a set of age-related effects that corresponded to connectivity between the

DAN and salience network. In particular, we found that a node in the right putamen (419),

showed evidence of a group x age interaction, such that connectivity to the DAN increased in the

BPD group, where in the HC group connectivity to the DAN decreased. Further we found a

quadratic group x age interaction in the right putamen, such that during adolescence both groups

showed decreases in betweenness centrality when around age 23, the BPD group showed

35

evidence of an accelerated rate of age-related increases in betweenness compared to controls.

Interestingly, in a DAN node located in the left superior parietal lobule (SPL, 82), we found a

very similar quadratic age-related effect where connectivity to the salience network in this node

was highest in early adolescence and towards the end of early adulthood in the BPD group,

where the control group showed evidence of a gradual increase in connectivity between the SPL

and the salience network. Related to our finding that ALFF in the DAN was lower in the BPD

group regardless of age, we further found evidence that ALFF in a node located in the right

precuneus (288) decreased in ALFF in the BPD group, where the HC group showed evidence of

age-related increases in ALFF. We further found a quadratic age x group interaction in the right

TPJ (295, fig 6) and two spatially contiguous nodes in the pMTS and supramarginal gyrus

(SMG) showing nearly identical quadratic age x group interactions. In these interactions young

adolescents with BPD were significantly lower in ALFF compared to their control counterparts

and increased in ALFF until approximately age 20, after which ALFF values decreased. In the

HC group we observed the opposite pattern such that in early adolescents showed decreases in

ALFF until approximately age 20, at which point ALFF increased.

Second, we found a robust pattern of decreases in net-strength scores to the DMN, FPN,

and Limbic networks in three limbic nodes. In particular in the left temporal pole (125) the HC

group showed evidence of developmental increases in within-network strength, whereas the BPD

group, showed evidence of a slight age-related decrease. We found a similar age x group

interaction such that connectivity between the DMN and the right VS (415, fig 7b) was higher in

young adolescents with BPD symptoms compared with the HC group, yet in older BPD

participants, VS-DMN connectivity was lower than in older HC participants. Mediation analyses

36

indicated that the association of BPD and connectivity between the right VS and the DMN was

fully mediated by the negative self-image BPQ scale (fig 7c). We also found a quadratic age x

group interaction in connectivity between the left VS (415) and the FPN, such that controls

showed a u-shaped pattern of connectivity, which started higher in early adolescence slightly

decreased until approximately age 20, and then showed an accelerated increase whereas BPD

participants showed a decline in VS-FPN connectivity in early adulthood.

Discussion

While the pervasive symptoms of BPD are generally thought to become first apparent

during early adolescence (Zanarini et al., 2006), this is the first study to our knowledge to

directly investigate functional whole-brain connectivity in a sample of borderline adolescents.

With this in mind, we used RSFC MRI coupled with graph theoretic analyses in order to

examine group-level differences in FC between a group of adolescents and young adults

diagnosed with clinically heightened borderline symptoms with an age and sex matched cohort

of healthy control subjects. Another goal of the current study was to examine age-related

differences in connectivity between our BPD and HC group. A final goal of the current study

was to look at how the connectivity findings from our first set of analyses might be augmented

by an analysis of ALFF, as only one study to our knowledge has applied ALFF analysis in

individuals with BPD (Salvador et al., 2016). We sought to employ an approach that first

examines connectivity differences amongst previously identified circuits and then tested the

importance of connectivity in these regions within the context of a whole brain network. Our

results indicate the importance of the salience network in the pathogenesis of BPD and further

point to age-related differences in connectivity between the salience network and the DAN

37

compared to controls. A second dominant signal in our results is that age-related differences in

connectivity in the VS, rather than the amygdala were better able to discriminate participants in

the BPD group from their HC counterparts.

The role of the salience network in adolescent BPD

The most robust finding in our data was that a region in the right dorsal anterior insula

showed evidence of heightened strength centrality amongst nearly every resting state network

included in our parcellation in addition to significantly higher ALFF across development. In the

control group this node showed age-related increases in centrality across the development, which

is consistent with prior reports that control networks and notably salience network show an age-

related pattern of integration with other RSNs (fig 5d, Betzel et al., 2014; Fair et al., 2007; Marek

et al., 2015; Power, Fair, Schlaggar, & Petersen, 2010). However, in our data the right daINS

was higher in centrality in young adolescents with BPD and this heightened pattern of

connectivity remained high across development. Further, heightened levels of affective

instability fully mediated the relationship between inclusion in the BPD group and connectivity

in this region (as measured by the factor scores extracted from a simple CFA). Affective

instability is considered by many to constitute one core dimension of borderline pathology

(Crowell et al., 2009) and is typified by rapid vacillations between mood states, typically in

response to perceived interpersonal slights.

This finding may speak to the insula’s role as a central node in assigning salience to

relevant stimuli and mediating attentional shifts in accordance to task-related goals (Menon &

Uddin, 2010; Uddin, Nomi, Hébert-Seropian, Ghaziri, & Boucher, 2017). In particular, the right

38

insula, is involved in cognitive-emotional processing, with some parcellations of the insula

categorizing the anterior insula into dorsal and ventral components corresponding to more

cognitive and emotional processing respectively, with the posterior insula playing a key role in

interoceptive awareness (Chang, Yarkoni, Khaw, & Sanfey, 2013). Further, the insula has been

hypothesized to mediate dynamic switches from more internally-directed processing (such as

processing conducted by the DMN) and processing that is mediated by external or cognitively

demanding tasks (such as those conducted by the FPN and DAN, Menon & Uddin, 2010). Our

results accord well with extant evidence showing that the insula is a key connector hub in the

brain that is responsible for a high degree of output to the rest of the brain and shows evidence of

early activation post-stimulus in order to signal the need for an attentional shift (Sridharan,

Levitin, & Menon, 2008). Our results in this region of the dorsal anterior insula speculatively

imply that young adolescents with BPD may experience hyper-frequent switching between tasks

or “brain states”, which may be associated with the intense and frequent emotional changes

experienced by individuals with BPD. In fact, it has been hypothesized that the insula plays a key

role in producing interoceptive predictions on the body’s internal state, consistent with

computational theories of predictive coding that occur in the VS for reward value (Barrett &

Simmons, 2015). In conjunction with theories that suggest that psychosomatic markers

underlying emotional processing may have a bioregulatory basis (Bechara & Damasio, 2005), it

may be the case that adolescents with BPD receive a high throughput of such somatic markers

that lead to a constant need to shift goals. Another possibility is that such somatic markers are

similar in intensity and frequency in adolescents with BPD, yet these adolescents may be more

39

sensitive to their internal state with a lower threshold required to signal the need to transition

goals, although future studies are strongly recommended to directly test these hypotheses.

Further, our analyses indicate that perhaps increased integration between the DAN and

the right daINS plays an important role in the development of BPD. In particular, edge analyses

implicate the right daINS and its connectivity to multiple nodes in the left Parietal lobe (see fig

4) as being key edges that differentiate our groups. Past resting-state investigations of integration

and segregation of the dorsal and ventral attention networks indicates that a key developmental

task of adolescence is the segregation of these two networks (Fair et al., 2007), which individuals

with BPD seem to show less evidence of. Extant research suggests that these two networks

dynamically interact to control both tonic and transient aspects of attention, with the salience

network being generally implicated in maintaining tonic levels of attention and the dorsal

attention network mediating transient shifts in attention which bias the visual network towards

task-relevant stimuli (Corbetta & Shulman, 2002; Vossel, Geng, & Fink, 2014). The hypothesis

that borderline adolescents show a lack of segregation in these networks is further corroborated

by our finding that a node in the right putamen, which was identified in (Choi et al., 2012) as

having high FC with the salience network showed developmental increases in early adulthood in

betweenness centrality in the BPD group in addition to increased connectivity with DAN. This is

doubly implicates an overall age-related increase in the number of shortest paths that go through

this node in the BPD group (indicative of increasing reliance on salience-related processing over

development in borderline adolescents) and an increasing enmeshment of salience and DAN

BOLD fluctuations over development respectively. Further, animal models of decision making

indicate that the putamen/dorsolateral striatum is thought to be involved in the development of

40

rigid and habitual responding, with portions of the dorsomedial striatum being involved in

flexible, goal-based decision making (Balleine, Delgado, & Hikosaka, 2007). This very

speculatively suggests that the development of BPD in adolescents may involve a transition from

goal-directed to habitual/rigid decision making.

While nodal centrality results suggested heightened connectivity amongst nodes in DAN,

results from ALFF analyses indicated that nodes in the bilateral parietal lobes (included in DAN)

show decreases in ALFF. While ALFF is typically thought to relate to metabolic consumption

(Tomasi et al., 2013), one untested hypothesis to our knowledge in the more basic neuroscience

literature is that ALFF from RS-fMRI may reflect the summed inputs to a region (i.e. the

integration of EPSPs and IPSPs) and the ability of these summed inputs to generate coordinated

oscillations, which correspond to the low-frequency fluctuations of interest in RS analyses. If

true, it could be that high levels of input from the daINS (perhaps indicated by higher ALFF in

daINS) knocks DAN nodes in the parietal lobes out of their standard oscillatory dynamics in

adolescents with BPD, although this claim is obviously speculative. This same pattern of robust

hypo-activation in the BPD group was seen in the FPN, which accords well with a previous

study that documented a shift in intrinsic connectivity from the central executive network (CEN,

which we call FPN) to the salience network in BPD (Doll et al., 2013). Such an account speaks

to one hypothesis that that salience network competes with and can potentially override higher

order networks involved in cognitive control and the voluntary deployment of attentional

resources (Corbetta & Shulman, 2002).

Another set of key findings that implicates the salience network in the neural basis of

BPD is that regions in the social brain network and DMN showed hypoconnectivity in the BPD

41

group irrespective of age. In particular, we found strong evidence of hypoconnectivity in the

right TPJ in our a priori analyses, which was corroborated in the whole-brain analysis. The right

TPJ has been implicated in social processing and particularly signaling the presence of social

agents and influences decision making via the establishment of a social context (Carter &

Huettel, 2013). The right TPJ has also been implicated in theory of mind, which is known to be

disrupted in borderline adolescents (Sharp et al., 2013). In fact, the TPJ is positioned in an

anatomically advantageous position to integrate signals from multiple seemingly disparate

cognitive domains. With this in mind, some have argued that the TPJ serves as a key node in

signaling contextual updates (Geng & Vossel, 2013). The contextual updating account suggests

that TPJ activity is not directly influenced by bottom-up salient sensory stimuli, yet acts as a

“circuit break” that allows for the reorienting of attention to information that is unexpected but

relevant (Corbetta & Shulman, 2002; Geng & Vossel, 2013). These findings align with task-

based studies that implicate the TPJ in social and self-other processing in BPD (Beeney et al.,

2016). Interestingly, in our data we found a widespread pattern of hypoconnectivity in our BPD

group in the right TPJ at rest across development yet aligns with studies finding

hypoconnectivity of the TPJ in individuals with BPD (Haas & Miller, 2015; O’Neill et al., 2015).

This effect was slightly complicated by a quadratic age-related effect of ALFF in the TPJ, such

that adolescents with BPD showed an inverted u-shaped trajectory of activity in the TPJ (fig 6c).

This suggests that over the adolescence, subjects with BPD tended to increase to HC levels of

TPJ ALFF yet experienced an inflection point such that by the end of adolescence TPJ ALFF

levels at rest returned to pre-adolescent levels. Interestingly, in BPD participants, results from

whole-brain centrality analyses suggest that this even this temporary increase in ALFF in the

42

adolescent years did not increase this region’s level of shared neural synchrony across the brain

and may reflect a dampened ability to influence the function of the brain as a whole in BPD.

The differential hyper vs hypo connectivity in the TPJ and daINS further underscores the

ability of graph analysis to uncover differences in connectivity between regions within the same

network. These findings taken together speculatively suggest that a core discrepancy in

adolescents with BPD is an over-reliance on the daINS, which as explained above is associated

with task set switching on very fast time scales (in Sridharan et al., 2008 onset latencies for the

insula were around 0.5 seconds post-stimulus). This effect might be mirrored by an under-

reliance on the TPJ, which is located in the same network but is typically thought of as being

crucially important in integrating new information into the representation of context, especially

of social agents. Another way of thinking about this hypothesis is that adolescents and young

adults with BPD may rely more on the more reflexive, fast-unfolding activity of the insula,

which was recently shown to play a key role in social approach behaviors in mice (Rogers-Carter

et al., 2018). On the other hand, impaired higher order mentalizing capacities, may rely on

connectivity in the TPJ, which is weakly connected in borderline adolescents.

Fronto-limbic hypothesis: the role of the ventral striatum

The second dominant signal points to the role of the VS in the pathophysiology of BPD.

Importantly, we found evidence across our a priori analysis and subsequent whole-brain analysis

of age-related decreases in coupling between the bilateral VS and portions of the mPFC and the

ACC in borderline adolescents over development, a pattern of development that was directly

opposite to HC subjects. The VS has been extensively studied in nonhuman animals and plays a

43

central role in both learning and motivation and is one of the primary targets of dopaminergic

projections from the VTA that are thought to underlie motivated reward-related behavior

(Cardinal, Parkinson, Hall, & Everitt, 2002; Russo & Nestler, 2013). Further, the VS receives

direct projections from the ACC and together these regions are thought to form a circuit that play

a central role in autoshaping paradigms, where a Pavlovian CS can induce approach behaviors

regardless of the approach behavior being beneficial for producing the desired outcome

(Parkinson, Willoughby, Robbins, & Everitt, 2000). Further, results from lesion studies suggest

that one role of the ACC (as it interacts with the VS) is to disambiguate the value of multiple

conditioned stimuli, preventing generalization between stimuli, which gains behavioral

expression through the VS (Cardinal et al., 2002). Further, the CS-GIMME results accord well

with thinking that the ACC acts directly upon the VS, rather than the other way around, yet in the

BPD group this edge was only estimated for two individuals, whereas the same edge was

estimated for all subjects in the HC group to describe the connectivity pattern. Further, the

directed influence of this dACC node increased in healthy controls over the adolescent period

(fig 4). This suggests that perhaps one key neuro-developmental difference in adolescents and

young adults with BPD is the absence of regulatory signals from the mPFC and ACC onto the

VS in BPD that shape value-based decision processes over development.

When considered jointly with the rest of the brain in our whole-brain nodal centrality

analyses, the VS showed evidence of age-related changes in connectivity to the DMN and FPN,

suggestive of their importance in the overall structure of the functional connectome in

discriminating groups. Further, connectivity of the VS to DMN nodes fully mediated the

relationship between BPD group membership and problems with negative self-image in the BPD

44

group, which measures generally feeling inadequate and unlikeable (fig 7c). This VS-DMN

finding compounds with our finding of hyper-activation in the sgACC irrespective of age in our

ALFF analyses, which is a crucial region involved in the expression and experience of emotion

(Drevets, Savitz, & Trimble, 2008; Greicius et al., 2007). This sgACC node was further involved

in an age x group interaction with the VS (fig 3d), suggesting that hypermetabolism in the

sgACC in concert with age-related decreases in connectivity with the VS could play a role in the

developmental progression of borderline pathology in adolescents, although the role of the

sgACC has been well documented in other psychiatric disorders such as major depression (Price

& Drevets, 2010). However, in our data this signal may reflect a more general “negative

baseline” of affect reported by individuals with BPD (Kuo & Linehan, 2009), which may not

change with learning.

It is worth further mention that in our fronto-limbic a priori analysis, in contrast to our

expectations, we did not find age-related effects in connectivity in the amygdala that

discriminated the BPD group from the HC group. Further we did not find evidence for either

hyper or hypo activation of either basolateral or central amygdala at rest in the BPD group in

ALFF analyses, which was found in a previous report on adults with BPD (Salvador et al., 2016).

However, we did find evidence that across development, the left CeM had lower levels of FC

with the dmPFC and rmPFC in BPD. This is consistent with a view that the phylogenetically

older CeM plays a key role in acquired fear conditioning and the mPFC has been shown to

encode memory for extinguished fear conditioning and acts to gate the output of central

amygdalar neurons to the brainstem (Milad & Quirk, 2002; Morgan, Romanski, & LeDoux,

1993; Quirk et al., 2003). However, it is worth noting that this finding was abolished when

45

included in the whole-brain network analysis, while findings in the VS survived being

contextualized within a whole-brain network.

This speculatively implies that conditioned Pavlovian responses in the CeM may not be

adequately controlled subsequent to extinction in young adults and adolescents with BPD

regardless of age. This finding in conjunction with our age-related findings in the VS provides an

early failure to falsify a recent proposal from our group that BPD is typified by alterations in

circuits that support adaptive decision-making (Hallquist, Hall, Schreiber, & Dombrovski, 2018),

while more positive mechanistic evidence is still lacking.

A note about the default mode network in the present study

Similarly, while ICA-based RS-fMRI studies have focused on hyper-connectivity of the

DMN in BPD our analytic approach was sensitive to subtle differences in individual nodes

within the DMN rather than broad hyperactivity of this network. Our results suggest a more

nuanced story with regard to the DMN, as we found a mix of hyper vs hypo connected nodes in

the DMN suggesting the hyperconnectivity in the DMN, especially in the angular gyrus, which

was higher in betweenness centrality across the development of BPD plays a key role in how

information is transferred amongst distributed brain regions in a whole brain graph. However,

hypoconnectivity, particularly of the ACC and Precuneus/PCC was also associated with

borderline symptoms, which calls into question the idea that within-network communication

amongst DMN nodes has a direct mapping onto a homogenous pattern of inter-module

connectivity across DMN nodes. Further, while more a robust pattern of results in our ALFF

analyses emerged amongst a variety of nodes in the FPN and DAN, we found no such pattern

46

amongst DMN nodes. It will become increasingly important in future research to continue to

utilize methods that allow for more global network-level effects as well as effects that may be

unique to particular components of a network. Thus, our utilization of graph theoretical analyses

in conjunction with a well-validated detailed brain parcellation provides a clear methodological

advantage to much of the resting-state literature in BPD.

Strengths and limitations

Our study has several strengths and limitations. Strengths include the use of a

comparatively large sample size (n BPD =40) that allows for finer-grained detailed analyses of

the network structure of individuals with BPD in addition to providing enough information to

estimate non-linear effects, which yielded a number of more specific novel findings (i.e.

increased DAN-Sal connectivity may play a key role in the etiology of BPD). In addition, as

compared to the extant resting state literature in BPD, our utilization of a graph theoretic

approach allowed for us to test a number of key hypotheses about the network structure of BPD

from global measures like graph-level transitivity down to connections between specific nodes,

such as between the rACC and Precuneus. Further this is the first RSFC MRI study of

adolescents with BPD symptoms that have been sampled to cover the developmental transition to

early adulthood, which has been documented as a key period of vulnerability to developing BPD.

Our study also has a number of limitations. First, while much work in developmental

psychopathology is cast in terms of developmental trajectories, our study is ultimately cross-

sectional nature, making stronger causal claims difficult to fully support (i.e. in cross-sectional

graph-theoretical studies a chicken vs egg problem arises when wondering if connectivity in the

47

salience network comes first thus causing disruptions in FPN and DAN). This speaks to the need

for longitudinal studies following at-risk adolescents as they develop. Such studies could

augment our results with studies of within-person change across this developmental period,

which may allow for a finer-grained dissection of potential equifinal and multifinal trajectories

of brain development that culminate in the development of personality pathology. A second

limitation may be the other side of the coin with respect to our analytic procedure. One problem

with finer-grained analyses is that the amount of output generated by such approaches relies

heavily on the role of the scientist in digesting results. As such, we encourage those interested to

consider whether further digestion of specific results not included in this discussion may be

warranted. While this may inject some subjectivity into the interpretation process, we

acknowledge that this is inherent in the scientific enterprise across neuroimaging studies and is

not unique to the current study. A final limitation is that in our study is that in addition to self-

reported symptoms differences, future studies should look to examine the association of resting

state connectivity as playing a key role in laboratory tasks that tap more specific cognitive

processes. In other words, conetextualizing these findings in how they predict behavior will be

crucially important moving forward.

Conclusion

These limitations notwithstanding, we reported findings of the first developmental study

of resting-state functional connectivity and found that the developmental transition from early

adolescence to young adulthood is unsurprisingly more complicated than early accounts of either

amygdala hyperactivity (Ruocco, Amirthavasagam, & Zakzanis, 2012) or hyperactivation and

hyperconnectivity of the DMN (Visintin, Panfilis, et al., 2016; Whitfield-Gabrieli & Ford, 2012).

48

Our results speak to a central role of abnormal hyper and hypoconnectivity of two key nodes in

the salience network, the right daINS and TPJ in the etiology of BPD in a sample of young adults

and adolescents. We further posited a hypothesis that developmental increases in a reliance on

salience-related circuitry in the striatum and increased enmeshment of attentional intrinsic

networks may play a role in affective instability in adolescents with BPD via frequent transitions

in attention, which is mediated through the insula. We additionally document for the first time a

developmental reversal of resting state connectivity strength between subcortical regions

involved in value-based decision making and the medial prefrontal regions that regulate the

behavioral expression of motivated behaviors. Future work should aim to integrate such findings

with data on task-evoked behavior and should aim to replicate and expand on central findings

through additional developmental neuroimaging studies in this vulnerable population.

49

References

Adolphs, R. (2009). The Social Brain: Neural Basis of Social Knowledge. Annual Review of

Psychology, 60(1), 693–716. https://doi.org/10.1146/annurev.psych.60.110707.163514

Alakörkkö, T., Saarimäki, H., Glerean, E., Saramäki, J., & Korhonen, O. (2017). Effects of

spatial smoothing on functional brain networks. European Journal of Neuroscience,

46(9), 2471–2480. https://doi.org/10.1111/ejn.13717

Alexander, G. E., & Crutcher, M. D. (1990). Functional architecture of basal ganglia circuits:

neural substrates of parallel processing. Trends in Neurosciences, 13(7), 266–271.

https://doi.org/10.1016/0166-2236(90)90107-L

American Psychiatric Association. (2013). Diagnostic and Statistical Manual of Mental

Disorders (DSM-5®). American Psychiatric Pub.

Arbabshirani, M. R., Damaraju, E., Phlypo, R., Plis, S., Allen, E., Ma, S., … Calhoun, V. D.

(2014). Impact of autocorrelation on functional connectivity. NeuroImage, 102, 294–308.

https://doi.org/10.1016/j.neuroimage.2014.07.045

Avants, B. B. (2018). ANTsR: ANTs in R: Quantification Tools for Biomedical Images (Version

0.4.5). Retrieved from https://neuroconductor.org/package/ANTsR

Balleine, B. W., Delgado, M. R., & Hikosaka, O. (2007). The Role of the Dorsal Striatum in

Reward and Decision-Making. Journal of Neuroscience, 27(31), 8161–8165.

https://doi.org/10.1523/JNEUROSCI.1554-07.2007

Barrett, L. F., & Simmons, W. K. (2015). Interoceptive predictions in the brain. Nature Reviews

Neuroscience, 16(7), 419–429. https://doi.org/10.1038/nrn3950

50

Bassett, D. S., & Sporns, O. (2017). Network neuroscience. Nature Neuroscience, 20(3), 353–

364. https://doi.org/10.1038/nn.4502

Bechara, A., & Damasio, A. R. (2005). The somatic marker hypothesis: A neural theory of

economic decision. Games and Economic Behavior, 52(2), 336–372.

https://doi.org/10.1016/j.geb.2004.06.010

Beeney, J. E., Hallquist, M. N., Ellison, W. D., & Levy, K. N. (2016). Self–other disturbance in

borderline personality disorder: Neural, self-report, and performance-based evidence.

Personality Disorders: Theory, Research, and Treatment, 7(1), 28–39.

https://doi.org/10.1037/per0000127

Behrens, T. E. J., Johansen-Berg, H., Woolrich, M. W., Smith, S. M., Wheeler-Kingshott, C. a.

M., Boulby, P. A., … Matthews, P. M. (2003). Non-invasive mapping of connections

between human thalamus and cortex using diffusion imaging. Nature Neuroscience, 6(7),

750–757. https://doi.org/10.1038/nn1075

Betzel, R. F., Byrge, L., He, Y., Goñi, J., Zuo, X.-N., & Sporns, O. (2014). Changes in structural

and functional connectivity among resting-state networks across the human lifespan.

NeuroImage, 102, 345–357. https://doi.org/10.1016/j.neuroimage.2014.07.067

Betzel, R. F., Gu, S., Medaglia, J. D., Pasqualetti, F., & Bassett, D. S. (2016). Optimally

controlling the human connectome: the role of network topology. Scientific Reports, 6,

30770. https://doi.org/10.1038/srep30770

Biswal, B. B., Yetkin, F. Z., Haughton, V. M., & Hyde, J. S. (1995). Functional connectivity in

the motor cortex of resting human brain using echo-planar MRI. Magnetic Resonance in

Medicine, 34(4), 537–541. https://doi.org/10.1002/mrm.1910340409

51

Blakemore, S.-J. (2008). The social brain in adolescence. Nature Reviews Neuroscience, 9(4),

267–277. https://doi.org/10.1038/nrn2353

Blakemore, S.-J., & Mills, K. L. (2014). Is adolescence a sensitive period for sociocultural

processing? Annual Review of Psychology, 65, 187–207. https://doi.org/10.1146/annurev-

psych-010213-115202

Box, G. E. P., & Jenkins, G. (1990). Time Series Analysis, Forecasting and Control. San

Francisco, CA, USA: Holden-Day, Inc.

Bright, M. G., Tench, C. R., & Murphy, K. (2017). Potential pitfalls when denoising resting state

fMRI data using nuisance regression. Neuroimage, 154, 159–168.

https://doi.org/10.1016/j.neuroimage.2016.12.027

Bullmore, E., & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of

structural and functional systems. Nature Reviews Neuroscience, 10(3), 186–198.

https://doi.org/10.1038/nrn2575

Burgess, P. W. (1997). Theory and methodology in executive function research. In P. Rabbitt

(Ed.), Theory and Methodology of Frontal and Executive Function (p. pp.81-116).

Retrieved from http://www.psypress.com/9780863774850

Cao, H., Plichta, M. M., Schäfer, A., Haddad, L., Grimm, O., Schneider, M., … Tost, H. (2014).

Test–retest reliability of fMRI-based graph theoretical properties during working

memory, emotion processing, and resting state. NeuroImage, 84, 888–900.

https://doi.org/10.1016/j.neuroimage.2013.09.013

52

Cardinal, R. N., Parkinson, J. A., Hall, J., & Everitt, B. J. (2002). Emotion and motivation: the

role of the amygdala, ventral striatum, and prefrontal cortex. Neuroscience &

Biobehavioral Reviews, 26(3), 321–352. https://doi.org/10.1016/S0149-7634(02)00007-6

Carter, R. M., & Huettel, S. A. (2013). A Nexus Model of the Temporal-Parietal Junction.

Trends in Cognitive Sciences, 17(7), 328–336. https://doi.org/10.1016/j.tics.2013.05.007

Chang, L. J., Yarkoni, T., Khaw, M. W., & Sanfey, A. G. (2013). Decoding the Role of the

Insula in Human Cognition: Functional Parcellation and Large-Scale Reverse Inference.

Cerebral Cortex, 23(3), 739–749. https://doi.org/10.1093/cercor/bhs065

Chein, J., Albert, D., O’Brien, L., Uckert, K., & Steinberg, L. (2011). Peers increase adolescent

risk taking by enhancing activity in the brain’s reward circuitry. Developmental Science,

14(2), F1-10. https://doi.org/10.1111/j.1467-7687.2010.01035.x

Choi, E. Y., Yeo, B. T. T., & Buckner, R. L. (2012). The organization of the human striatum

estimated by intrinsic functional connectivity. Journal of Neurophysiology, 108(8), 2242–

2263. https://doi.org/10.1152/jn.00270.2012

Christova, P., Lewis, S. M., Jerde, T. A., Lynch, J. K., & Georgopoulos, A. P. (2011). True

associations between resting fMRI time series based on innovations. Journal of Neural

Engineering, 8(4), 046025. https://doi.org/10.1088/1741-2560/8/4/046025

Ciric, R., Wolf, D. H., Power, J. D., Roalf, D. R., Baum, G. L., Ruparel, K., … Satterthwaite, T.

D. (2017). Benchmarking of participant-level confound regression strategies for the

control of motion artifact in studies of functional connectivity. NeuroImage,

154(Supplement C), 174–187. https://doi.org/10.1016/j.neuroimage.2017.03.020

53

Clifton, A., & Pilkonis, P. A. (2007). Evidence for a single latent class of Diagnostic and

Statistical Manual of Mental Disorders borderline personality pathology. Comprehensive

Psychiatry, 48(1), 70–78. https://doi.org/10.1016/j.comppsych.2006.07.002

Cohen, P. (2008). Child Development and Personality Disorder. Psychiatric Clinics of North

America, 31(3), 477–493. https://doi.org/10.1016/j.psc.2008.03.005

Corbetta, M., & Shulman, G. L. (2002). Control of goal-directed and stimulus-driven attention in

the brain. Nature Reviews. Neuroscience, 3(3), 201–215. https://doi.org/10.1038/nrn755

Cox, R. W. (1996). AFNI: Software for Analysis and Visualization of Functional Magnetic

Resonance Neuroimages. Computers and Biomedical Research, 3(29), 162–173.

https://doi.org/10.1006/cbmr.1996.0014

Crowell, S. E., Beauchaine, T. P., & Linehan, M. M. (2009). A biosocial developmental model

of borderline personality: Elaborating and extending Linehan’s theory. Psychological

Bulletin, 135(3), 495–510. https://doi.org/10.1037/a0015616

Csárdi, G., & Nepusz, T. (2006). The igraph software package for complex network research.

InterJournal, 1695.

Cunningham, M. G., Bhattacharyya, S., & Benes, F. M. (2002). Amygdalo-cortical sprouting

continues into early adulthood: Implications for the development of normal and abnormal

function during adolescence. The Journal of Comparative Neurology, 453(2), 116–130.

https://doi.org/10.1002/cne.10376

Das, P., Calhoun, V., & Malhi, G. S. (2014). Bipolar and borderline patients display differential

patterns of functional connectivity among resting state networks. NeuroImage, 98, 73–81.

https://doi.org/10.1016/j.neuroimage.2014.04.062

54

de Reus, M. A., & van den Heuvel, M. P. (2013). Estimating false positives and negatives in

brain networks. NeuroImage, 70, 402–409.

https://doi.org/10.1016/j.neuroimage.2012.12.066

Doll, A., Sorg, C., Manoliu, A., Meng, C., Wöller, A., Förstl, H., … Riedl, V. (2013). Shifted

intrinsic connectivity of central executive and salience network in borderline personality

disorder. Frontiers in Human Neuroscience, 7.

https://doi.org/10.3389/fnhum.2013.00727

Domsalla, M., Koppe, G., Niedtfeld, I., Vollstädt-Klein, S., Schmahl, C., Bohus, M., & Lis, S.

(2014). Cerebral processing of social rejection in patients with borderline personality

disorder. Social Cognitive and Affective Neuroscience, 9(11), 1789–1797.

https://doi.org/10.1093/scan/nst176

Drevets, W. C., Savitz, J., & Trimble, M. (2008). The Subgenual Anterior Cingulate Cortex in

Mood Disorders. CNS Spectrums, 13(8), 663–681.

Dziobek, I., Preißler, S., Grozdanovic, Z., Heuser, I., Heekeren, H. R., & Roepke, S. (2011).

Neuronal correlates of altered empathy and social cognition in borderline personality

disorder. NeuroImage, 57(2), 539–548. https://doi.org/10.1016/j.neuroimage.2011.05.005

Fair, D. A., Cohen, A. L., Power, J. D., Dosenbach, N. U. F., Church, J. A., Miezin, F. M., …

Petersen, S. E. (2009). Functional Brain Networks Develop from a “Local to Distributed”

Organization. PLoS Computational Biology, 5(5).

https://doi.org/10.1371/journal.pcbi.1000381

Fair, D. A., Dosenbach, N. U. F., Church, J. A., Cohen, A. L., Brahmbhatt, S., Miezin, F. M., …

Schlaggar, B. L. (2007). Development of distinct control networks through segregation

55

and integration. Proceedings of the National Academy of Sciences, 104(33), 13507–

13512. https://doi.org/10.1073/pnas.0705843104

First, M. B., Spitzer, R. L., Gibbon, M., & Williams, J. B. W. (2002, November). Structured

Clinical Interview for DSM-IV-TR Axis I Disorders. New York:Biometrics Research,

New York State Psychiatric Institute.

Fonagy, P., & Bateman, A. (2008). The development of borderline personality disorder--a

mentalizing model. Journal of Personality Disorders, 22(1), 4–21.

https://doi.org/10.1521/pedi.2008.22.1.4

Fonov, V. S., Evans, A. C., McKinstry, R. C., Almli, C. R., & Collins, D. L. (2009). Unbiased

nonlinear average age-appropriate brain templates from birth to adulthood. NeuroImage,

Supplement 1(47), S102. https://doi.org/10.1016/S1053-8119(09)70884-5

Fox, M. D., & Greicius, M. (2010). Clinical applications of resting state functional connectivity.

Frontiers in Systems Neuroscience, 4. https://doi.org/10.3389/fnsys.2010.00019

Gabard-Durnam, L. J., Flannery, J., Goff, B., Gee, D. G., Humphreys, K. L., Telzer, E., …

Tottenham, N. (2014). The development of human amygdala functional connectivity at

rest from 4 to 23 years: a cross-sectional study. NeuroImage, 95, 193–207.

https://doi.org/10.1016/j.neuroimage.2014.03.038

Gates, K. M., & Molenaar, P. C. M. (2012). Group search algorithm recovers effective

connectivity maps for individuals in homogeneous and heterogeneous samples.

NeuroImage, 63(1), 310–319. https://doi.org/10.1016/j.neuroimage.2012.06.026

56

Geng, J. J., & Vossel, S. (2013). Re-evaluating the role of TPJ in attentional control: Contextual

updating? Neuroscience & Biobehavioral Reviews, 37(10, Part 2), 2608–2620.

https://doi.org/10.1016/j.neubiorev.2013.08.010

Greicius, M. D., Flores, B. H., Menon, V., Glover, G. H., Solvason, H. B., Kenna, H., …

Schatzberg, A. F. (2007). Resting-state functional connectivity in major depression:

abnormally increased contributions from subgenual cingulate cortex and thalamus.

Biological Psychiatry, 62(5), 429–437.

Greve, D. N., & Fischl, B. (2009). Accurate and robust brain image alignment using boundary-

based registration., Accurate and Robust Brain Image Alignment using Boundary-based

Registration. NeuroImage, NeuroImage, 48, 48(1, 1), 63, 63–72.

https://doi.org/10.1016/j.neuroimage.2009.06.060, 10.1016/j.neuroimage.2009.06.060

Guimerà, R., & Amaral, L. A. N. (2005). Cartography of complex networks: modules and

universal roles. Journal of Statistical Mechanics: Theory and Experiment, 2005(02),

P02001. https://doi.org/10.1088/1742-5468/2005/02/P02001

Gunderson, J. G., & Lyons-Ruth, K. (2008). BPD’s Interpersonal Hypersensitivity Phenotype: A

Gene-Environment-Developmental Model. Journal of Personality Disorders, 22(1), 22–

41. https://doi.org/10.1521/pedi.2008.22.1.22

Haas, B. W., & Miller, J. D. (2015). Borderline personality traits and brain activity during

emotional perspective taking. Personality Disorders: Theory, Research, and Treatment,

6(4), 315–320. http://dx.doi.org/10.1037/per0000130

57

Hallquist, M. N., Geier, C. F., & Luna, B. (2018). Incentives facilitate developmental

improvement in inhibitory control by modulating control-related networks. NeuroImage,

172, 369–380. https://doi.org/10.1016/j.neuroimage.2018.01.045

Hallquist, M. N., Hall, N. T., Schreiber, A. M., & Dombrovski, A. Y. (2018). Interpersonal

dysfunction in borderline personality: a decision neuroscience perspective. Current

Opinion in Psychology, 21, 94–104. https://doi.org/10.1016/j.copsyc.2017.09.011

Hallquist, M. N., & Hillary, F. G. (2018). Graph theory approaches to functional network

organization in brain disorders: A critique for a brave new small-world. Network

Neuroscience, 1–26. https://doi.org/10.1162/netn_a_00054

Hastie, T., Tibshirani, R., & Friedman, J. (2016). The Elements of Statistical Learning: Data

Mining, Inference, and Prediction, Second Edition (2nd edition). New York, NY:

Springer.

Henry, T. R., Feczko, E., Cordova, M., Earl, E., Williams, S., Nigg, J. T., … Gates, K. M.

(2019). Comparing directed functional connectivity between groups with confirmatory

subgrouping GIMME. NeuroImage, 188, 642–653.

https://doi.org/10.1016/j.neuroimage.2018.12.040

Hopwood, C. J., Wright, A. G. C., Ansell, E. B., & Pincus, A. L. (2013). The interpersonal core

of personality pathology. Journal of Personality Disorders, 27(3), 270–295.

https://doi.org/10.1521/pedi.2013.27.3.270

Hwang, K., Hallquist, M. N., & Luna, B. (2013). The Development of Hub Architecture in the

Human Functional Brain Network. Cerebral Cortex, 23(10), 2380–2393.

https://doi.org/10.1093/cercor/bhs227

58

Hyndman, R., Athanasopoulos, G., Bergmeir, C., Caceres, G., Chhay, L., O’Hara-Wild, M., …

Zhou, Z. (2019). forecast: Forecasting Functions for Time Series and Linear Models

(Version 8.5). Retrieved from https://CRAN.R-project.org/package=forecast

Kelly, A. M. C., Uddin, L. Q., Biswal, B. B., Castellanos, F. X., & Milham, M. P. (2008).

Competition between functional brain networks mediates behavioral variability.

NeuroImage, 39(1), 527–537. https://doi.org/10.1016/j.neuroimage.2007.08.008

Korn, C. W., Rosée, L. L., Heekeren, H. R., & Roepke, S. (2016). Social feedback processing in

borderline personality disorder. Psychological Medicine, 46(3), 575–587.

https://doi.org/10.1017/S003329171500207X

Krause-Utz, A., Veer, I. M., Rombouts, S. a. R. B., Bohus, M., Schmahl, C., & Elzinga, B. M.

(2014). Amygdala and anterior cingulate resting-state functional connectivity in

borderline personality disorder patients with a history of interpersonal trauma.

Psychological Medicine, 44(13), 2889–2901.

https://doi.org/10.1017/S0033291714000324

Kuo, J. R., & Linehan, M. M. (2009). Disentangling emotion processes in borderline personality

disorder: physiological and self-reported assessment of biological vulnerability, baseline

intensity, and reactivity to emotionally evocative stimuli. Journal of Abnormal

Psychology, 118(3), 531–544. https://doi.org/10.1037/a0016392

Laird, A. R., Fox, P. M., Eickhoff, S. B., Turner, J. A., Ray, K. L., McKay, D. R., … Fox, P. T.

(2011). Behavioral Interpretations of Intrinsic Connectivity Networks. Journal of

Cognitive Neuroscience, 23(12), 4022–4037. https://doi.org/10.1162/jocn_a_00077

59

Lenzenweger, M. F., Lane, M. C., Loranger, A. W., & Kessler, R. C. (2007). DSM-IV

Personality Disorders in the National Comorbidity Survey Replication. Biological

Psychiatry, 6(62), 553–564. https://doi.org/10.1016/j.biopsych.2006.09.019

Luna, B., Marek, S., Larsen, B., Tervo-Clemmens, B., & Chahal, R. (2015). An Integrative

Model of the Maturation of Cognitive Control. Annual Review of Neuroscience, 38(1),

151–170. https://doi.org/10.1146/annurev-neuro-071714-034054

Maia, T. V., & Frank, M. J. (2011). From reinforcement learning models to psychiatric and

neurological disorders. Nature Neuroscience, 14(2), 154. https://doi.org/10.1038/nn.2723

Marek, S., Hwang, K., Foran, W., Hallquist, M. N., & Luna, B. (2015). The Contribution of

Network Organization and Integration to the Development of Cognitive Control. PLOS

Biology, 13(12), e1002328. https://doi.org/10.1371/journal.pbio.1002328

McKiernan, K. A., Kaufman, J. N., Kucera-Thompson, J., & Binder, J. R. (2003). A Parametric

Manipulation of Factors Affecting Task-induced Deactivation in Functional

Neuroimaging. Journal of Cognitive Neuroscience, 15(3), 394–408.

https://doi.org/10.1162/089892903321593117

Menon, V., & Uddin, L. Q. (2010). Saliency, switching, attention and control: a network model

of insula function. Brain Structure and Function, 214(5–6), 655–667.

https://doi.org/10.1007/s00429-010-0262-0

Milad, M. R., & Quirk, G. J. (2002). Neurons in medial prefrontal cortex signal memory for fear

extinction. Nature, 420(6911), 70. https://doi.org/10.1038/nature01138

60

Millman, K. J., & Brett, M. (2007). Analysis of Functional Magnetic Resonance Imaging in

Python. Computing in Science Engineering, 9(3), 52–55.

https://doi.org/10.1109/MCSE.2007.46

Modestin, J., Abrecht, I., Tschaggelar, W., & Hoffmann, H. (1983). Diagnosing borderline. A

contribution to the question of its conceptual validity. Archiv Fur Psychiatrie Und

Nervenkrankheiten, 233(5), 359–370.

Morgan, M. A., Romanski, L. M., & LeDoux, J. E. (1993). Extinction of emotional learning:

Contribution of medial prefrontal cortex. Neuroscience Letters, 163(1), 109–113.

https://doi.org/10.1016/0304-3940(93)90241-C

New, A. S., Hazlett, E. A., Buchsbaum, M. S., Goodman, M., Mitelman, S. A., Newmark, R., …

Siever, L. J. (2007). Amygdala–Prefrontal Disconnection in Borderline Personality

Disorder. Neuropsychopharmacology, 32(7), 1629–1640.

https://doi.org/10.1038/sj.npp.1301283

Noonan, M. P., Mars, R. B., Neubert, F. X., Ahmed, B., Smith, J., Krug, K., & Sallet, J. (2017).

Chapter 15 - Organization of the Social Brain in Macaques and Humans. In J.-C. Dreher

& L. Tremblay (Eds.), Decision Neuroscience (pp. 189–198).

https://doi.org/10.1016/B978-0-12-805308-9.00015-4

O’Neill, A., D’Souza, A., Samson, A. C., Carballedo, A., Kerskens, C., & Frodl, T. (2015).

Dysregulation between emotion and theory of mind networks in borderline personality

disorder. Psychiatry Research: Neuroimaging, 231(1), 25–32.

https://doi.org/10.1016/j.pscychresns.2014.11.002

61

Parkinson, J. A., Willoughby, P. J., Robbins, T. W., & Everitt, B. J. (2000). Disconnection of the

anterior cingulate cortex and nucleus accumbens core impairs Pavlovian approach

behavior: further evidence for limbic cortical-ventral striatopallidal systems. Behavioral

Neuroscience, 114(1), 42–63.

Pfeifer, J. H., Masten, C. L., Moore, W. E., Oswald, T. M., Mazziotta, J. C., Iacoboni, M., &

Dapretto, M. (2011). Entering adolescence: resistance to peer influence, risky behavior,

and neural changes in emotion reactivity. Neuron, 69(5), 1029–1036.

https://doi.org/10.1016/j.neuron.2011.02.019

Pfohl, B., Blum, N. S., & Zimmermann, M. (1997). Structured interview for DSM-IV

personality: SIDP-IV. Washington, DC: American Psychiatric Press.

Poreh, A. M., Rawlings, D., Claridge, G., Freeman, J. L., Faulkner, C., & Shelton, C. (2006).

The BPQ: A Scale for the Assessment of Borderline Personality Based on DSM-IV

Criteria. Journal of Personality Disorders, 20(3), 247–260.

https://doi.org/10.1521/pedi.2006.20.3.247

Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012). Spurious

but systematic correlations in functional connectivity MRI networks arise from subject

motion. NeuroImage, 59(3), 2142–2154.

https://doi.org/10.1016/j.neuroimage.2011.10.018

Power, J. D., Fair, D. A., Schlaggar, B. L., & Petersen, S. E. (2010). The development of Human

Functional Brain Networks. Neuron, 67(5), 735–748.

https://doi.org/10.1016/j.neuron.2010.08.017

62

Price, J. L., & Drevets, W. C. (2010). Neurocircuitry of mood disorders.

Neuropsychopharmacology, 35(1), 192–216.

Pruim, R. H. R., Mennes, M., Buitelaar, J. K., & Beckmann, C. F. (2015). Evaluation of ICA-

AROMA and alternative strategies for motion artifact removal in resting state fMRI.

NeuroImage, 112, 278–287. https://doi.org/10.1016/j.neuroimage.2015.02.063

Pruim, R. H. R., Mennes, M., van Rooij, D., Llera, A., Buitelaar, J. K., & Beckmann, C. F.

(2015). ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from

fMRI data. NeuroImage, 112, 267–277.

https://doi.org/10.1016/j.neuroimage.2015.02.064

Quirk, G. J., Likhtik, E., Pelletier, J. G., & Paré, D. (2003). Stimulation of Medial Prefrontal

Cortex Decreases the Responsiveness of Central Amygdala Output Neurons. Journal of

Neuroscience, 23(25), 8800–8807. https://doi.org/10.1523/JNEUROSCI.23-25-

08800.2003

Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G.

L. (2001). A default mode of brain function. Proceedings of the National Academy of

Sciences, 98(2), 676–682. https://doi.org/10.1073/pnas.98.2.676

Raichle, M. E., & Snyder, A. Z. (2007). A default mode of brain function: A brief history of an

evolving idea. NeuroImage, 37(4), 1083–1090.

https://doi.org/10.1016/j.neuroimage.2007.02.041

Roche, A. (2011). A Four-Dimensional Registration Algorithm With Application to Joint

Correction of Motion and Slice Timing in fMRI. IEEE Transactions on Medical Imaging,

30(8), 1546–1554. https://doi.org/10.1109/TMI.2011.2131152

63

Roepke, S., Vater, A., Preißler, S., Heekeren, H. R., & Dziobek, I. (2013). Social cognition in

borderline personality disorder. Frontiers in Neuroscience, 6, 195.

https://doi.org/10.3389/fnins.2012.00195

Rogers-Carter, M. M., Varela, J. A., Gribbons, K. B., Pierce, A. F., McGoey, M. T., Ritchey, M.,

& Christianson, J. P. (2018). Insular cortex mediates approach and avoidance responses

to social affective stimuli. Nature Neuroscience, 21(3), 404.

https://doi.org/10.1038/s41593-018-0071-y

Rosen, M. L., Sheridan, M. A., Sambrook, K. A., Dennison, M. J., Jenness, J. L., Askren, M. K.,

… McLaughlin, K. A. (2018). Salience network response to changes in emotional

expressions of others is heightened during early adolescence: relevance for social

functioning. Developmental Science, 21(3), e12571. https://doi.org/10.1111/desc.12571

Rosenkranz, J. A., Moore, H., & Grace, A. A. (2003). The Prefrontal Cortex Regulates Lateral

Amygdala Neuronal Plasticity and Responses to Previously Conditioned Stimuli. Journal

of Neuroscience, 23(35), 11054–11064. https://doi.org/10.1523/JNEUROSCI.23-35-

11054.2003

Rubinov, M., & Sporns, O. (2011). Weight-conserving characterization of complex functional

brain networks. NeuroImage, 56(4), 2068–2079.

https://doi.org/10.1016/j.neuroimage.2011.03.069

Ruocco, A. C., Amirthavasagam, S., & Zakzanis, K. K. (2012). Amygdala and hippocampal

volume reductions as candidate endophenotypes for borderline personality disorder: A

meta-analysis of magnetic resonance imaging studies. Psychiatry Research:

Neuroimaging, 201(3), 245–252. https://doi.org/10.1016/j.pscychresns.2012.02.012

64

Ruocco, A. C., Medaglia, J. D., Tinker, J. R., Ayaz, H., Forman, E. M., Newman, C. F., …

Chute, D. L. (2010). Medial prefrontal cortex hyperactivation during social exclusion in

borderline personality disorder. Psychiatry Research: Neuroimaging, 181(3), 233–236.

https://doi.org/10.1016/j.pscychresns.2009.12.001

Russo, S. J., & Nestler, E. J. (2013). The brain reward circuitry in mood disorders. Nature

Reviews Neuroscience, 14(9), 609–625. https://doi.org/10.1038/nrn3381

Salvador, R., Vega, D., Pascual, J. C., Marco, J., Canales-Rodríguez, E. J., Aguilar, S., …

Pomarol-Clotet, E. (2016). Converging Medial Frontal Resting State and Diffusion-Based

Abnormalities in Borderline Personality Disorder. Biological Psychiatry, 79(2), 107–116.

https://doi.org/10.1016/j.biopsych.2014.08.026

Sarkheil, P., Ibrahim, C. N., Schneider, F., Mathiak, K., & Klasen, M. (2019). Aberrant

functional connectivity profiles of brain regions associated with salience and reward

processing in female patients with borderline personality disorder. Brain Imaging and

Behavior. https://doi.org/10.1007/s11682-019-00065-z

Schaefer, A., Kong, R., Gordon, E. M., Laumann, T. O., Zuo, X.-N., Holmes, A. J., … Yeo, B.

T. T. (2018). Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic

Functional Connectivity MRI. Cerebral Cortex, 28(9), 3095–3114.

https://doi.org/10.1093/cercor/bhx179

Schulze, L., Schmahl, C., & Niedtfeld, I. (2016). Neural Correlates of Disturbed Emotion

Processing in Borderline Personality Disorder: A Multimodal Meta-Analysis. Biological

Psychiatry, 79(2), 97–106. https://doi.org/10.1016/j.biopsych.2015.03.027

65

Sharp, C., Ha, C., Carbone, C., Kim, S., Perry, K., Williams, L., & Fonagy, P. (2013).

Hypermentalizing in adolescent inpatients: treatment effects and association with

borderline traits. Journal of Personality Disorders, 27(1), 3–18.

https://doi.org/10.1521/pedi.2013.27.1.3

Silbersweig, D., Clarkin, J. F., Goldstein, M., Kernberg, O. F., Tuescher, O., Levy, K. N., …

Stern, E. (2007). Failure of Frontolimbic Inhibitory Function in the Context of Negative

Emotion in Borderline Personality Disorder. American Journal of Psychiatry, 164(12),

1832–1841. https://doi.org/10.1176/appi.ajp.2007.06010126

Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-

Berg, H., … Matthews, P. M. (2004). Advances in functional and structural MR image

analysis and implementation as FSL. NeuroImage, Supplement 1(23), S208–S219.

https://doi.org/10.1016/j.neuroimage.2004.07.051

Sridharan, D., Levitin, D. J., & Menon, V. (2008). A critical role for the right fronto-insular

cortex in switching between central-executive and default-mode networks. Proceedings

of the National Academy of Sciences, 105(34), 12569–12574.

https://doi.org/10.1073/pnas.0800005105

Steinberg, L., Icenogle, G., Shulman, E. P., Breiner, K., Chein, J., Bacchini, D., … Takash, H.

M. S. (2018). Around the world, adolescence is a time of heightened sensation seeking

and immature self-regulation. Developmental Science, 21(2), e12532.

https://doi.org/10.1111/desc.12532

66

Tekin, S., & Cummings, J. L. (2002). Frontal–subcortical neuronal circuits and clinical

neuropsychiatry: An update. Journal of Psychosomatic Research, 53(2), 647–654.

https://doi.org/10.1016/S0022-3999(02)00428-2

Tingley, D., Yamamoto, T., Hirose, K., Keele, L., & Imai, K. (2019). mediation: Causal

Mediation Analysis (Version 4.4.7). Retrieved from https://CRAN.R-

project.org/package=mediation

Tomasi, D., Wang, G.-J., & Volkow, N. D. (2013). Energetic cost of brain functional

connectivity. Proceedings of the National Academy of Sciences, 110(33), 13642–13647.

https://doi.org/10.1073/pnas.1303346110

Uddin, L. Q., Nomi, J. S., Hébert-Seropian, B., Ghaziri, J., & Boucher, O. (2017). Structure and

Function of the Human Insula. Journal of Clinical Neurophysiology, 34(4), 300.

https://doi.org/10.1097/WNP.0000000000000377

van den Heuvel, M. P., de Lange, S. C., Zalesky, A., Seguin, C., Yeo, B. T. T., & Schmidt, R.

(2017). Proportional thresholding in resting-state fMRI functional connectivity networks

and consequences for patient-control connectome studies: Issues and recommendations.

NeuroImage, 152, 437–449. https://doi.org/10.1016/j.neuroimage.2017.02.005

Visintin, E., De Panfilis, C., Amore, M., Balestrieri, M., Wolf, R. C., & Sambataro, F. (2016).

Mapping the brain correlates of borderline personality disorder: A functional

neuroimaging meta-analysis of resting state studies. Journal of Affective Disorders, 204,

262–269. https://doi.org/10.1016/j.jad.2016.07.025

Visintin, E., Panfilis, C. D., Amore, M., Balestrieri, M., Wolf, R. C., & Sambataro, F. (2016).

Mapping the brain correlates of borderline personality disorder: A functional

67

neuroimaging meta-analysis of resting state studies. Journal of Affective Disorders, 204,

262–269. https://doi.org/10.1016/j.jad.2016.07.025

Vossel, S., Geng, J. J., & Fink, G. R. (2014). Dorsal and Ventral Attention Systems: Distinct

Neural Circuits but Collaborative Roles. The Neuroscientist, 20(2), 150–159.

https://doi.org/10.1177/1073858413494269

Vuong, Q. H. (1989). Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses.

Econometrica, 57(2), 307–333. https://doi.org/10.2307/1912557

Watson, C. G. (2017). brainGraph: Graph Theory Analysis of Brain MRI Data (Version 1.0.0).

Retrieved from https://cran.r-project.org/web/packages/brainGraph/index.html

Weissman, D. H., Roberts, K. C., Visscher, K. M., & Woldorff, M. G. (2006). The neural bases

of momentary lapses in attention. Nature Neuroscience, 9(7), 971.

https://doi.org/10.1038/nn1727

Whitfield-Gabrieli, S., & Ford, J. M. (2012). Default mode network activity and connectivity in

psychopathology. Annual Review of Clinical Psychology, 8(1), 49–76.

https://doi.org/10.1146/annurev-clinpsy-032511-143049

Wolf, R. C., Sambataro, F., Vasic, N., Schmid, M., Thomann, P. A., Bienentreu, S. D., & Wolf,

N. D. (2011). Aberrant connectivity of resting-state networks in borderline personality

disorder. Journal of Psychiatry & Neuroscience: JPN, 36(6), 402–411.

https://doi.org/10.1503/jpn.100150

Woolrich, M. W., Ripley, B. D., Brady, M., & Smith, S. M. (2001). Temporal Autocorrelation in

Univariate Linear Modeling of FMRI Data. NeuroImage, 14(6), 1370–1386.

https://doi.org/10.1006/nimg.2001.0931

68

Xu, T., Cullen, K. R., Mueller, B., Schreiner, M. W., Lim, K. O., Schulz, S. C., & Parhi, K. K.

(2016). Network analysis of functional brain connectivity in borderline personality

disorder using resting-state fMRI. NeuroImage : Clinical, 11, 302–315.

https://doi.org/10.1016/j.nicl.2016.02.006

Yeo, B. T. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., …

Buckner, R. L. (2011). The organization of the human cerebral cortex estimated by

intrinsic functional connectivity. Journal of Neurophysiology, 106(3), 1125–1165.

https://doi.org/10.1152/jn.00338.2011

Zabelina, D. L., & Andrews-Hanna, J. R. (2016). Dynamic network interactions supporting

internally-oriented cognition. Current Opinion in Neurobiology, 40, 86–93.

https://doi.org/10.1016/j.conb.2016.06.014

Zanarini, M. C., Frankenburg, F. R., Khera, G. S., & Bleichmar, J. (2001). Treatment histories of

borderline inpatients. Comprehensive Psychiatry, 42(2), 144–150.

https://doi.org/10.1053/comp.2001.19749

Zanarini, M. C., Frankenburg, F. R., Ridolfi, M. E., Jager-Hyman, S., & al, et. (2006). Reported

Childhood Onset of Self-Mutilation Among Borderline Patients. Journal of Personality

Disorders, 20(1), 9–15.

Zang, Y. F., He, Y., Zhu, C. Z., Cao, Q. J., Sui, M. Q., Liang, M., … Wang, Y. F. (2007).

Altered baseline brain activity in children with ADHD revealed by resting-state

functional MRI. Brain & Development, 29(2), 83–91.

https://doi.org/10.1016/j.braindev.2006.07.002

69

Zelkowitz, P., Paris, J., Guzder, J., Feldman, R., Roy, C., & Rosval, L. (2007). A Five-Year

Follow-Up of Patients With Borderline Pathology of Childhood. Journal of Personality

Disorders, 21(6), 664–674. https://doi.org/10.1521/pedi.2007.21.6.664

Zuo, X.-N., Di Martino, A., Kelly, C., Shehzad, Z. E., Gee, D. G., Klein, D. F., … Milham, M. P.

(2010). The Oscillating Brain: Complex and Reliable. NeuroImage, 49(2), 1432–1445.

https://doi.org/10.1016/j.neuroimage.2009.09.037

70

Appendix A

Table 1.

Sample Characteristics

Characteristic BPD (n = 40) HC (n = 42)

Age (SD) 20.84 (4.42) 20.61 (4.16)

Ethnicity

Hispanic or Latino 3 2

Not Hispanic or Latino 36 40

Not Provided/Missing 1 0

Race

Caucasian 31 30

African American 3 7

Asian American 2 1

Bi/Multiracial 2 4

Not Provided/Missing 2 0

Average Annual Income

< $5,000-$19,999 10 11

$20,000-$34,9999 9 7

$35,000 - $59,999 8 5

$60,000 - $99,999 5 6

$100,000 + 3 10

Not Provided/Missing 5 3

Sexuality

Heterosexual 28 40

Gay/Lesbian 1 1

Bisexual 8 0

Other 1 1

Not Provided/Missing 2 0

71

Table 2.

Results of the edge-wise a priori analysis

Edge (labels) Effect Est.(S.E.) t-score

R VS – L sgACC (420-169) Group x Age -0.056(0.015) -3.79**

R VS – L vmPFC (420-168) Group x Age -0.048(0.014) -3.35**

R VS – L rmPFC (420-173) Group x Age -0.044(0.015) -2.97*

L VS – L dACC (415-177) Group x Age2 -0.047(0.015) -3.15*

L VS – R rACC (415-381) Group x Age2 -0.041(0.014) -2.96*

L CeM – R dmPFC (403-385) Group -0.045(0.015) -2.95*

L CeM – R rmPFC (403-382) Group -0.043(0.015) -2.82*

L rACC – L Precuneus (174-200) Group -0.009(0.002) -4.04***

R TPJ – L dmPFC (295-178) Group -0.007(0.002) -3.19*

R TPJ – L pMTS (295-158) Group -0.007(0.002) -3.18*

R TPJ – L rACC (295-174) Group -0.007(0.002) -3.16*

R TPJ – L TPJ/AG (295-163) Group -0.006(0.002) -2.94*

R daINS – R vaINS (307-302) Group 0.006(0.002) 2.83*

Note: Results are displayed with fronto-limbic results in the top section and

social/DMN results in the bottom section and are sorted from highest absolute t-

score, while grouping edges that come from the same node (***p < .0001, **p

<.001, *p<.005).

72

Table 3.

Global Graph Metrics

Metric BPD HC Group Group x Age

Modularity 0.038(0.018) 0.035 (0.016) 0.30(0.77) -0.33(0.74)

Transitivity 0.928(0.033) 0.938(0.031) -1.57(0.12) 1.42(0.16)

Global efficiency 0.240(0.041) 0.248(0.040) -1.33(0.19) -0.98 (0.33)

Characteristic path

length

1.09(0.04) 1.10(0.04) 1.23(0.22) -1.12(0.27)

Diameter 16.77(3.12) 15.71 (3.24) -0.78(0.43) 0.43(0.67)

Note. Group and group x age effects for regression models predicting global

graph metrics. The first two columns give the mean and sd of the graph metric

per group, while the second two columns denote the respective t-scores and p-

values for main effects of group and the interaction of group and age in

predicting the global metric of interest. All models included the subject’s mean

FC as a covariate of no interest given group differences in overall strength of

connections displayed in Fig 1. Developmental trajectories are displayed in

figure S1.

73

Table 4.

Whole-brain nodal centrality results

Node (label) Module Metric Effect Est.(S.E.) t-score

R daINS (Roi307) Sal Vis_z Group 0.357(0.096) 3.70**

SomMot_z Group 0.149(0.038) 3.93***

DAN_z Group 0.016(0.003) 4.55***

Sal_z Group 0.784(0.234) 3.35**

FPN_z Group 0.241(0.078) 3.11*

DMN_z Group 0.165(0.050) 3.58**

R TPJ (Roi295) Sal Strength Group -0.003(0.001) -2.99*

Vis_z Group -0.288(0.093) -3.11*

DAN_z Group -0.011(0.003) -3.30**

FPN_z Group -0.272(0.073) -3.71**

DMN_z Group -0.143(0.043) -3.31**

R Putamen (Roi419) Sal DAN_z Group x Age 0.010(0.003) 2.87*

Betweenness Group x Age2 0.0002(0.000) 2.82*

L daINS (Roi102) Sal DMN_z Group 0.121(0.042) 2.89*

L SPL (Roi82) DAN Sal_z Group x Age2 0.560(0.179) 3.12*

R Mid Occipital Gyrus (Roi274) DAN Betweenness Group 0.0002(0.000) 3.14*

R VS (Roi420) Limbic DMN_z Group x Age -0.108(0.038) -2.84*

L VS (Roi415) Limbic FPN_z Group x Age2 -0.168(0.057) -2.96*

L Temporal Pole (Roi125) Limbic Limbic_z Group x Age -0.141(0.038) -3.75**

R IPL (Roi336) FPN DAN_z Group 0.010(0.003) 2.90*

L Precuneus (Roi144) FPN Limbic_z Group x Age -0.107(0.036) -2.93*

R IPL (Roi333) FPN FPN_z Group x Age 0.201(0.061) 3.44**

R Mid Frontal Gyrus (Roi352) FPN DMN_z Group x Age 0.112(0.037) 3.00*

L IPL (Roi127) FPN DMN_z Group x Age -0.106(0.038) -2.81*

L Sup Frontal Gyrus (Roi186) DMN Sal_z Group 0.707(0.231) 3.06*

FPN_z Group 0.218(0.077) 2.84*

L Precuneus (Roi200) DMN Limbic_z Group -0.113(0.037) -3.03*

FPN_z Group x Age -0.191(0.061) -3.14*

L rACC (Roi174) DMN SomMot_z Group -0.121(0.038) -3.20*

L MTG (Roi157) DMN Vis_z Group -0.288(0.100) -2.88*

R Mid OFC (Roi376) DMN Vis_z Group 0.279(0.099) 2.83*

R Thalamus (Roi412) DMN FPN_z Group x Age2 -0.165(0.058) -2.83*

R PCC (Roi398) DMN DMN_z Group x Age -0.118(0.041) -2.89*

L AG (Roi164) DMN Betweenness Group 0.0002(0.000) 2.88*

L Mid Occipital Gyrus (Roi161) DMN Betweenness Group 0.0002(0.000) 3.06*

R Postcentral Gyrus (Roi253) SomMot Vis_z Group -0.286(0.101) -2.84*

74

R SMA (Roi254) SomMot Limbic_z Group x Age -0.124(0.038) -3.28*

L Postcentral Gyrus (Roi43) SomMot Limbic_z Group x Age2 -0.098(0.034) -2.89*

R pSTG (Roi237) SomMot Betweenness Group x Age 0.0002(0.000) 3.43**

L Postcentral Gyrus (Roi60) SomMot Betweenness Group 0.0002(0.000) 2.81*

L Mid Occipital Gyrus (Roi14) Vis Vis_z Group 0.348(0.100) 3.48**

SomMot_z Group 0.142(0.039) 3.68**

DAN_z Group 0.012(0.003) 3.54**

Sal_z Group 0.670(0.232) 2.89*

FPN_z Group 0.258(0.078) 3.31**

DMN_z Group 0.177(0.043) 4.04***

R Cuneus (Roi229) Vis SomMot_z Group 0.117(0.037) 3.14*

R Lingual Gyrus (Roi207) Vis DAN_z Group -0.100(0.003) -2.84*

R Fusiform Gyrus (Roi201) Vis DAN_z Group -0.011(0.003) -3.06*

R Lingual Gyrus (Roi206) Vis Limbic_z Group x Age2 -0.094(0.032) -2.93*

R ITG (Roi209) Vis Limbic_z Group -0.122(0.039) -3.09*

L Inf Occipital Gyrus (Roi12) Vis DMN_z Group 0.145(0.044) 3.29*

R Cuneus (Roi224) Vis Betweenness Group 0.0002(0.000) 2.92*

R Lingual Gyrus (Roi205) Vis Betweenness Group x Age -0.0002(0.000) -2.81*

Note. Nodal centrality results from the logistic regression that fell below .005 (***p < .0001, **p <.001, *p<.005).

To aide in visual inspection, we grouped our results into sections. From top to bottom, sections separated by thick

bars represent nodes within a given module. Node labels in the first column denote labels taken from Schaefer et.

al., (2017) for comparability, while node numbers above 400 were subcortical nodes added from various

parcellations (see Method). All metrics with a “_z” suffix refer to one of seven mod-strength scores (see graph

metrics for more details).

75

Table 5.

Whole-brain nodal ALFF results

Node (label) Network Metric Effect Est.(S.E.) t-score

R daINS (Roi307) Sal ALFF Group 0.023(0.006) 3.763**

L anterior temporal gyrus (Roi97) Sal ALFF Group 0.018(0.006) 2.86*

L daINS (Roi103) Sal ALFF Group 0.020(0.006) 3.13*

L dACC (Roi107) Sal ALFF Group 0.022(0.006) 3.62**

R TPJ (Roi295) Sal fALFF Group x Age2 -0.065(0.018) -3.55**

Sal ALFF Group x Age2 -0.021(0.006) -3.35**

R pMTS (Roi294) Sal ALFF Group x Age2 -0.019(0.006) -3.03*

R SMG (Roi296) Sal ALFF Group x Age2 -0.019(0.006) -3.01*

R Precuneus (Roi288) DAN fALFF Group -0.049(0.017) -2.97*

ALFF Group -0.022(0.007) -3.37**

Group x Age -0.021(0.007) -3.23*

R Parieto-occip sulcus (Roi277) DAN ALFF Group -0.022(0.006) -3.43**

R SPL (Roi280) DAN ALFF Group -0.019(0.006) -3.03*

R SPL (Roi282) DAN ALFF Group -0.020(0.007) -3.09*

L IPL (Roi73) DAN fALFF Group -0.049(0.014) -3.40**

ALFF Group -0.020(0.006) -3.31**

L IPL (Roi76) DAN fALFF Group -0.044(0.015) -3.00*

ALFF Group -0.019(0.006) -3.12**

L SPL (Roi81) DAN fALFF Group -0.048(0.016) -3.05*

ALFF Group -0.022(0.007) -3.37**

L mOFC (Roi116) Limbic ALFF Group x Age -0.019(0.007) -2.81*

R Inf temporal gyrus (Roi328) Limbic ALFF Group 0.022(0.007) -3.26*

L vlOFC (Roi135) FPN fALFF Group -0.056(0.018) -3.08*

L IFG (Roi136) FPN ALFF Group x Age2 -0.017(0.006) -2.96*

L dlPFC (Roi137) FPN fALFF Group -0.058(0.017) -3.35**

L Inf precentral gyrus (Roi141) FPN fALFF Group -0.056(0.017) -3.24*

R Sup occipital gyrus (Roi334) FPN ALFF Group x Age -0.022(0.006) -3.52*

R vlOFC (Roi342) FPN fALFF Group x Age2 -0.056(0.018) -3.06**

L sgACC (Roi169) DMN ALFF Group 0.022(0.007) 3.39**

L Thalamus (Roi406) SomMot ALFF Group 0.019(0.006) 3.27*

L Fusiform gyrus (Roi2) Vis ALFF Group x Age -0.019(0.006) -3.09*

L IPL (Roi31) Vis ALFF Group -0.018(0.006) -2.97*

Note. Nodal ALFF results from the logistic regression that fell below .005 (***p < .0001, **p <.001,

*p<.005). To aide in visual inspection, we grouped our results into sections. From top to bottom,

sections separated by thick bars represent nodes within a given module

76

Table 6.

Significant effects mediated through self-report scales

Node (label) Network Outcome Self-report measure Effect Est.[95%CI] p-value

L rACC (Roi174) DMN SomMot_z BPQ-instability ACME -0.93[-1.81,-0.29] < 0.01

ADE 0.233[-0.49,1.047] 0.55

Total -0.70[0.43,3.21] < 0.01

R VS (Roi420) Limbic DMN_z BPQ-self-image ACME -0.51[-0.95,-0.07] 0.03

ADE 0.23[-0.40, 0.80] 0.46

Total -0.28[-0.72,0.18] 0.22

R daINS (Roi307) Sal Strength factor BPQ-instability ACME 0.92[0.04, 1.68] 0.04

ADE 0.05[-0.69, 0.96] 0.91

Total 0.97[0.57, 1.35] <0.01

Note. In all cases, full mediation is signified by a significant ACME (average causal mediation effect) and a

nonsignificant ADE (average direct effect). In this case the strength factor for node 307 denotes the factor

score of a single factor CFA with Vis_z, SomMot_z, DAN_z, Sal_z, FPN_z, and DMN_z as indicators.

77

Appendix B

Figure 1. Summary of group FC and strength distribution. (A) Group differences in overall FC

(functional connectivity/edge weight) for every edge and every subject in addition to nodal

strength centrality distribution. These distributions reflect the FC distribution after applying a

mild consensus threshold. Student’s t-tests between the control and borderline group indicated

that the control group has greater edge weights on average (t = 5.46, p < .0001) and greater

strength centrality (t = 15.18, p < .0001). (B) Age-related changes in FC fit by a quadratic age

model. Results from a linear regression revealed a significant quadratic age x group interaction (t

= -17.96, p < .0001). For visualization purposes, points indicate the mean FC value per subject

along the y-axis

FC strength

0.0 0.2 0.4 0.6 0 50 100 150 200

0

500

1000

1500

0

5000

10000

15000

20000

metric

cou

nt

A

40

60

80

100

120

140

15 20 25 30

Age (years)

FC

Group

Control

BPD

B

78

Figure 2. Finalized 421 node parcellation of the cortex, thalamus, and striatum from two

representative slices. Notice the truncation in inferior temporal regions described in the Method

section. We also included bilateral CeM and BLA, which are not depicted here.

79

Figure 3. Results from the usual suspects analyses. Results in the top panel (a-c) are plotted

using the BrainNet Viewer (Xia, Wang, & He, 2013). In the top panel, nodes are labeled in

accordance with the modular structure from (Yeo et al., 2011), with magenta representing

salience network, cream representing the limbic network, and red representing DMN regions.

Magenta edges are results from the social/DMN usual suspects analyses since they were highly

overlapping with salience regions and red edges are significant results from the fronto-limbic

usual suspects analysis due to their concentration on medial prefrontal regions. Top panel

figures are displayed on the medial surface from the right (a) and left (c) in addition to from the

front (b, left = right). The bottom panel depicts age x group interactions between the bilateral VS

and portions of the ACC.

TPJdaINS

Precun

dACC

CeM

pMTS

TPJ/AG

a b c

d e

VSVS

sgACC

0.0

0.1

0.2

0.3

0.4

15 20 25 30

L VS–L dACC (415-177)

Ed

ge e

stim

ate

Age

0.0

0.1

0.2

0.3

0.4

15 20 25 30

Ed

ge e

stim

ate

Age

R VS–L sgACC (420-169)

Group

Control

BPD

80

Figure 4. dACC and VS directed connectivity results. Results from CS-GIMME revealed a

directed edge was present from the left dACC to the left VS and that this edge increased with

age. Since GIMME allows individual paths to be estimated even in the absence of a group-level

edge, two participants from the BPD group are included in the scatterplot.

0.0

0.1

0.2

0.3

0.4

15 20 25 30

dA

CC

V

S

beta

est

imate

Age

Group

Control

BPD

x = -3

y = 10

81

Figure 5. Connectivity of the daINS. (A) The right daINS in MNI 152 space. (B) Edge width and

color correspond to the size of t statistic in a between groups comparison. Thus, redder and

thicker edges denote stronger group differences. (C) Affective instability fully mediated the

association between group membership and connectivity of the daINS.

a ective

daINSBPD

instability

strength

0.890* 0.432*

0.111

x =39a b

ca ective

daINS

− 2

− 1

0

1

15 2 0 2 5 30

DA

N_z

Age

Group

Control

BPD

d

82

Figure 6. Connectivity of the TPJ. (A) The right TPJ (Roi295) plotted in MNI 152 space in

addition to group differences in strength centrality (B) and group x age interaction of ALFF (C).

83

Figure 7. Connectivity of the VS. (A) The right VS in MNI 152 space. (B) Age x group

interaction of VS-DMN connectivity (C) Negative self-image fully mediated the association

between group membership and connectivity of the VS and DMN.

− 1

0

1

2

15 20 2 5 30

DM

N_z

Age

BPD

self-image

VS-DMN

0.924* 0.418*

0.204

b

cnegative

Group

Control

BPD

a y =10

84

Figure 8. Representative sampling of nodes that were significantly different between groups

regardless of age in whole-brain analyses. Red panels represent nodes that were hyper-connected

(for centrality analyses) or hyper-active (for ALFF analyses) and blue panels denote hypo-

connected/activated nodes.

85

Figure 9. Representative sampling of nodes that showed significant group x age interactions in

whole-brain analyses. Red panels represent nodes that were hyper-connected (for centrality

analyses) or hyper-active (for ALFF analyses) and blue panels denote hypo-connected/activated

nodes.

L SPL (82)

z = 60

R Precuneus (288)

x = 13

R Mid Frontal Gyrus

(352)

x = 44

L Precuneus (200)

x = -6

0

1

2

15 20 25 30

Betw

eenn

ess

Age

−3

−2

−1

0

1

15 20 25 30

DA

N_z

Age

Group

Control

BPD

R Putamen (419)

y = 9

−1

0

1

15 20 25 30

Sal_

z

Age

2

3

4

5

6

7

15 20 25 30

ALFF

Age

0

1

2

3

15 20 25 30

DM

N_z

Age

− 1

0

1

2

15 20 25 30

FPN

_z

Age

86

Appendix C

Table S1.

Selected lambda for ridge regression

US-FL 0.61

US-S/DMN 109.85

ALFF 32.25

fALFF 10.74

Vis_z 0.001

SomMot_z 6.51

DAN_z 70.02

Sal_z 0.22

Limbic_z 8.28

FPN_z 3.64

DMN_z 7.93

Betweenness 0.003

Strength 1.99

87

Table S2.

MNI center of mass coordinates for a priori analyses

Node x y z Node label Network a priori set

L OFC -12.98 46.50 -21.66 116 Limbic Fronto-limbic

L vmPFC -5.70 54.74 -13.64 168 DMN Fronto-limbic

L sgACC -6.88 34.32 -10.30 169 DMN Fronto-limbic

L mPFC -7.71 58.69 1.60 173 DMN Both

L rACC -7.33 43.64 4.32 174 DMN Both

L rACC -6.45 33.67 21.20 177 DMN Both

L mPFC -5.88 52.76 25.03 178 DMN Both

R OFC 6.26 46.91 -23.98 321 Limbic Fronto-limbic

R dACC 6.37 34.50 26.49 360 FPN Fronto-limbic

R sgACC/vmPFC 3.95 38.90 -12.17 379 DMN Fronto-limbic

R rACC 6.51 41.90 3.48 381 DMN Both

R mPFC 6.07 54.94 9.13 382 DMN Both

R dmPFC 4.90 59.54 26.88 385 DMN Both

L BLA -25.46 -4.66 -21.95 401 Limbic Fronto-limbic

R BLA 25.27 -3.40 -21.83 402 Limbic Fronto-limbic

L CMN -20.13 -6.02 -14.97 403 Limbic Fronto-limbic

R CMN 19.34 -5.04 -15.04 404 Limbic Fronto-limbic

L VS -13.14 11.67 -8.31 415 Limbic Fronto-limbic

R VS 10.58 12.85 -7.88 420 Limbic Fronto-limbic

L vaINS -40.64 2.40 -4.40 98 Sal Social/DMN

L aINS/ IFG -35.12 24.98 -1.21 99 Sal Social/DMN

L daINS -35.42 19.50 7.92 101 Sal Social/DMN

L daINS -38.66 4.78 11.17 102 Sal Social/DMN

L pMTS -54.76 -44.01 5.45 158 DMN Social/DMN

L pSTS -60.98 -49.24 15.83 159 DMN Social/DMN

L TPJ/AG -57.00 -57.50 28.06 163 DMN Social/DMN

L TPJ/AG -43.19 -74.47 42.54 164 DMN Social/DMN

L Precuneus -6.30 -61.18 30.90 195 DMN Social/DMN

L Precuneus -7.54 -52.75 44.37 200 DMN Social/DMN

R TPJ/pSTS 59.12 -48.41 9.77 294 Sal Social/DMN

R TPJ/pSTS 51.54 -43.31 16.94 295 Sal Social/DMN

R vaINS 39.40 6.278 -16.66 302 Sal Social/DMN

R aINS 40.40 9.00 -3.28 303 Sal Social/DMN

R daINS 36.50 22.90 4.06 306 Sal Social/DMN

88

R daINS 37.80 6.76 11.36 307 Sal Social/DMN

R Precuneus 15.30 -63.43 28.64 394 DMN Social/DMN

R Precuneus 4.91 -52.48 24.46 395 DMN Social/DMN

R Precuneus 3.32 -64.13 31.83 396 DMN Social/DMN

89

Table S3.

MNI center of mass coordinates for significant nodes (ALFF and centrality)

Node x y z Node label Network High/low

L Fusiform Gyrus -31.65 -33.25 -17.29 2 Vis low

L Inf Occ Gyrus -26.32 -98.52 -11.38 12 Vis high

L Mid Occ Gyrus -42.89 -88.31 -5.84 14 Vis high

L IPL -24.13 -80.26 44.37 31 Vis low

L Postcentral Gyrus -61.42 -1.54 24.51 43 SomMot low

L Postcentral Gyrus -33.24 -35.87 65.85 60 SomMot high

L IPL -28.41 -72.25 30.88 73 DAN low

L IPL -48.40 -28.83 44.39 76 DAN low

L SPL -15.03 -71.18 59.46 81 DAN low

L SPL -32.51 -59.95 64.39 82 DAN high

L anterior temporal gyrus -46.10 6.00 -16.21 97 Sal high

L daINS -38.66 4.78 11.17 102 Sal high

L daINS -46.76 11.49 1.94 103 Sal high

L OFC -12.98 46.50 -21.66 116 Limbic low

L Temporal Pole -33.93 11.29 -29.12 125 Limbic low

L IPL -31.12 -75.28 42.73 127 FPN low

L vlOFC -44.82 49.03 -8.72 135 FPN low

L IFG -51.51 32.95 9.99 136 FPN low

L dlPFC -42.17 49.35 7.39 137 FPN low

L Inf Precentral Gyrus -41.60 6.63 35.10 141 FPN low

L Precuneus -10.73 -79.30 45.94 144 FPN low

L MTG -64.54 -33.37 6.03 157 DMN low

L Mid Occipital Gyrus -41.44 -81.75 27.92 161 DMN high

L AG -43.19 -74.47 42.54 164 DMN high

L sgACC -6.88 34.32 -10.30 169 DMN high

L rACC -7.33 43.64 4.32 174 DMN low

L Sup Frontal Gyrus -32.30 10.33 59.90 186 DMN high

L Precuneus -7.54 -52.75 44.37 200 DMN low

R Fusiform Gyrus 32.07 -37.39 -22.11 201 Vis low

R Lingual Gyrus 24.77 -54.59 -7.80 205 Vis low

R Lingual Gyrus 21.86 -76.86 -10.18 206 Vis low

R Lingual Gyrus 17.03 -36.01 -12.07 207 Vis low

R ITG 49.27 -65.00 -10.09 209 Vis low

R Cuneus 14.82 -66.30 20.28 224 Vis high

90

R Cuneus 14.09 -88.20 36.86 229 Vis high

R pSTG 60.12 -26.49 12.93 237 SomMot high

R Postcentral Gyrus 47.10 -26.3 58.34 253 SomMot low

R SMA 5.55 -10.19 51.85 254 SomMot low

R Mid Occipital Gyrus 42.08 -78.91 30.29 274 DAN high

R Parieto-occip sulcus 17.17 -80.94 48.68 277 DAN low

R SPL 31.07 -67.03 50.55 280 DAN low

R SPL 20.32 -71.37 51.78 282 DAN low

R Precuneus 13.15 -67.29 64.17 288 DAN low

R pMTS 59.12 -48.41 9.77 294 Sal low

R TPJ 51.54 -43.31 16.94 295 Sal low

R SMG 60.73 -42.86 26.80 296 Sal low

R daINS 37.80 6.76 11.36 307 Sal high

R Inf temporal gyrus 39.11 -14.70 -29.52 328 Limbic low

R IPL 53.47 -42.99 50.50 333 FPN high

R Sup Occipital Gyrus 34.40 -74.21 45.73 334 FPN low

R IPL 44.11 -49.53 47.94 336 FPN high

R vlOFC 40.76 52.44 -9.92 342 FPN low

R Mid Frontal Gyrus 43.49 17.20 45.66 352 FPN high

R Mid OFC 33.44 38.24 -14.09 376 DMN high

R PCC 2.78 -20.38 38.61 398 DMN low

L Thalamus -10.08 -14.09 7.39 406 SomMot high

R Thalamus 9.64 -20.04 10.99 412 DMN low

L VS -13.14 11.67 -8.31 415 Limbic low

R Putamen 24.89 7.51 3.85 419 Sal high

R VS 10.58 12.85 -7.88 420 Limbic low

91

Table S4.

Module-specific dissection of nodal centrality results

Network Metric High Low

Vis (9) Vis_z 1 SomMot_z 2

DAN_z 1 2

Sal_z 1

Limbic_z 2

FPN_z 1

DMN_z 2

Betweenness 1 1

Total 9 5

SomMot (5) Vis_z 1

Limbic_z 2

Betweenness 2

Total 2 3

DAN (2) Sal_z 1

Betweenness 1

Total 2 0

Sal (4) Vis_z 1 1

SomMot_z 1

DAN_z 2 1

Sal_z 1

FPN_z 1 1

DMN_z 2 1

Strength 1

Betweenness 1

Total 9 5

Limbic (4) Limbic_z 1 1

FPN_z 1

DMZ_z 1

Total 1 3

FPN (5) DAN_z 1

Limbic_z 1

FPN_z 1

DMN_z 1 1

92

Total 3 2

DMN (9) Vis_z 1 1

SomMot_z 1

Sal_z 1

Limbic_z 1

FPN_z 1 2

DMN_z 1

Betweenness 2

Strength 1

Total 5 7

93

Table S5.

Module-specific dissection of nodal ALFF results

Network Metric High Low

Vis (2) ALFF 2

Total 0 2

SomMot (1) ALFF 1

Total 1 0

DAN (7) ALFF 7

fALFF 4

Total 0 11

Sal (7) ALFF 4 3

fALFF 1

Total 4 4

Limbic (2) ALFF 1 1

Total 1 1

FPN (6) ALFF 2

fALFF 4

Total 0 6

DMN (1) ALFF 1

Total 1 0

94

Appendix D

Figure S1.

Note. Static pearson correlations of individual adjacency matrices by subject. This was

conducted on the original 84 subjects that passed our excessive motion screen and is simply

meant to demonstrate thinking regarding removal of one subject in the BPD group (who can be

spotted by eye in row/column 20) whose adjacency matrix had an unusually low correlation on

the whole (~.1) with all other subjects in the study and showed divergence measured by the

mahalanobis distance.

95

Figure S2.

Note. Average FC within and between intrinsic networks. Panels represent that we took all edges

from nodes in the corresponding and calculated the average FC value of nodes connecting all

seven networks. As expected, edges going from nodes in an intrinsic network to other nodes

within the network were generally higher than edges going to nodes that were assigned to other

networks.

●●

●

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●

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●●●●●●●●●●●

0.0

0.2

0.4

0.6

Vis SomMot DAN Sal Limbic FPN DMN

FC

(avg

)

Vis

●●●●●●●●●

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●●●●●●●●●●●●●

●●●●●●

0.0

0.1

0.2

0.3

0.4

0.5

Vis SomMot DAN Sal Limbic FPN DMN

FC

(avg

)

SomMot

●●●●●●●●

●●●●●

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●●●

●

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●●●●●●●●●●

●● ●●●●●●●●●●●●●

0.0

0.2

0.4

Vis SomMot DAN Sal Limbic FPN DMN

FC

(avg

)

DAN

●●●●●●●●●●●

●●

●●●●

●●

●●●

●

●●

●●

● ●●●●●

●●●

●

●

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●

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●●●●

●●●●●●●●●

●

●

●●

●●● ●

0.0

0.2

0.4

Vis SomMot DAN Sal Limbic FPN DMN

FC

(avg

)

Sal

●

●●

●

●

●●●●●●●●●●●●●●●

●●

●

●●●

●

●

0.000

0.025

0.050

0.075

Vis SomMot DAN Sal Limbic FPN DMN

FC

(avg

)

Limbic

●

●●●●

●●●●●

●

●●●

●

●●●●●●●●

●

●

●●●●

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●●●●●●●●●●●

●●●●●●●●●●●●

0.0

0.2

0.4

Vis SomMot DAN Sal Limbic FPN DMN

FC

(avg

)

FPN

●

●●●●●●●●●●●●●●●

●●●●●●

●●●●●●●●●●●●●●●●

●

● ●●●●●

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●0.0

0.1

0.2

0.3

0.4

0.5

Vis SomMot DAN Sal Limbic FPN DMN

FC

(avg

)

DMN

96

Figure S3.

Note. Age-related changes in global graph metrics.

modularity transitivity

characteristic path length diameter global efficiency

15 20 25 30 15 20 25 30

15 20 25 30 15 20 25 30 15 20 25 30

0.20

0.25

0.30

0.35

10

15

20

25

0.85

0.90

0.95

1.05

1.10

1.15

1.20

0.02

0.04

0.06

0.08

Age (years)

Glo

ba

l m

etr

ic v

alu

e

Group

Control

BPD