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SUPPLEMENT

Table of contents:

Study Population

Methods: MRI Examination, Data Analysis, Details of Statistics

Group ICA results and Supplemental Figure 1

Concurrent Findings from the ICA and Supplemental Figure 2

References for Supplement

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Study Population

Thirty-four full-term healthy infants from uncomplicated pregnancies were included in this study.

Informed consents were obtained from the pregnant mothers. Subjects were prospectively

enrolled from the research population of an ongoing longitudinal study of pregnant women

(ClinicalTrials.gov ID: NCT01131117). Inclusion criteria for the pregnant women were: pre-

pregnancy self-reported BMI 18.5-24.9 (normal-weight) or 30-35 (obese); second parity,

singleton pregnancy; ≥ 21 years of age; conceived without assisted fertility treatments.

Exclusion criteria were: preexisting medical conditions; medical complications during pregnancy;

medications during pregnancy known to influence fetal growth; smoking or alcohol drinking. All

enrolled women had their body composition assessed using air displacement plethysmography

(Bodpod, Cosmed, Chicago, IL) and BMI measured within the first 10 weeks of gestation during

their first research visit. Maternal IQ was assessed using the Wechsler Abbreviated Scale of

Intelligence (WASI, Pearson, San Antonio, TX). Total gestational weight gain was measured at

36 weeks of gestation. Birth weight and length of the infants were retrieved from medical

records and head circumference was measured at age 2 weeks. Only infants born full-term (≥

37 weeks of gestation), with size at birth appropriate for gestational age (AGA), and without

medical conditions known to influence growth and development were included in the MRI study.

In total, 44 women (and their infants) were enrolled in the study, 40 infants had a valid structural

MRI scan and completed the RS-fMRI scan, but 5 were later excluded due to excessive motion

during the RS-fMRI (criteria in the data analysis section) and 1 was excluded due to incomplete

clinical data. The remaining 18 infants born to normal-weight mothers and 16 infants born to

obese mothers successfully completed all studies and were included.

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Methods: MRI examination

At 2 weeks of age, MRI examination of the brain was conducted in the Department of Radiology

at the Arkansas Children’s Hospital. Infants were fed 15~30 minutes prior to the scan, swaddled

in warm sheets, and immobilized using a MedVac Infant Immobilizer (CFI Medical Solutions,

Fenton, MI). No sedation was used. A pulse oximeter probe (InVivo Corp, Florida, US) was

placed on a foot to monitor oxygen saturation and heart rate, and mini-muffs and a headset

were placed over the ears to protect the infants from the noise generated during the scan. The

MRI examinations were performed on a 1.5 Tesla Achieva MRI scanner (Philips Healthcare,

Best, the Netherlands) with 60 cm bore size, 33 mT/m gradient amplitude, and 100 mT/m/ms

maximum slew rate. A pediatric 8-channel SENSE head coil was used. A neonatal brain MRI

protocol was used, which included sagittal 3D T1 weighted reconstructed to 3 planes, axial T2

weighted, axial diffusion weighted, and axial susceptibility weighted imaging sequences. This

conventional neonatal MRI protocol was used for the investigators to exclude subjects with

apparent brain abnormalities. In addition, a single-shot gradient echo T2*-weighted EPI

sequence with TR/TE 2400ms/50ms, acquisition voxel size 2X2X4 mm3 and 150 dynamics was

used to acquire the RS- fMRI data. The imaging quality was reviewed on the scanner to exclude

subjects with apparent motion artifacts on the structural MRI using clinical standards.

Data analyses

The 3D T1 structural images and the gradient echo EPI data were exported to a workstation

with FSL 5.0 (FMRIB Software Library, created by the Functional MRI of the Brain Analysis

Group, University of Oxford, UK) installed on a VMware Linux virtual machine (VMware, Inc.,

Palo Alto, CA USA) for independent component analyses (ICA) of the RS- fMRI data. The

Multivariate Exploratory Linear Optimized Decomposition into Independent Components

(MELODIC) toolbox and associated functions in FSL were used. Specifically, the MCFLIRT

function was used for motion correction of the RS-fMRI data, followed by brain extraction using

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the BET function. Spatial smoothing with a 5mm FWHM was then applied, and high pass

temporal filtering was used to remove low frequency drifting artifacts. After these preprocessing

steps, the RS-fMRI data for each subject were registered to the 3D T1 structural images for the

subject using the FLIRT function and consequently normalized to the structural images for a

most representative subject (with the least amount of total imaging warping, which was found to

be from the normal-weight group, with no group differences in deformation relative to this

subject), which served as a customized standard space (instead of the conventional MNI152

standard space) for our neonatal data. Resampling resolution was set at 3mm. Multi-session

temporal concatenation option in MELODIC with automatic dimensionality estimation was used

to compute the independent components at a group level for all infants with the default

threshold of 0.5 (i.e., the voxel-wise probability of activation being greater than background

noise). The registration summary and the estimated head rotation/translation in the MELODIC

output summary were reviewed to ensure no mis-registration and no excessive motion artifacts

were present before proceeding to the next steps. Five subjects were excluded because of

excessive motion defined as maximum translation on any plane >1.5 mm or maximum rotation

in any direction >5º. T1 maps were of sufficient quality for registration in all of the subjects used

in the final analysis.

The independent components computed by MELODIC were visually inspected to label

functional connectivity networks. Anatomical locations of known networks in neonates based on

published reports were used to identify all meaningful components1-3. Furthermore, components

with activation predominantly in the peripheral regions of the brain, in the ventricles, near major

blood vessels such as the Circle of Willis, surrounded by ring-shape deactivation, or in spotty

patterns were discarded, and components with activation predominantly in grey matter with 50%

of power spectrum below 0.1 Hz were considered valid functional connectivity networks4. In

addition to the group ICA analysis for all infants, group ICA for all infants born to normal-weight

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mothers and for all infants born to obese mothers were run separately and components were

identified respectively.

Dual regression and Randomise tools in FSL were used for voxel-wise comparisons of

functional connectivity in the prefrontal lobe network identified by ICA between infants born to

normal-weight or obese mothers. Specifically, the group spatial maps (for all infants) obtained

from MELODIC were regressed into each subject’s 4D space-time data set to generate a set of

time courses, which were subsequently regressed to generate subjects-specific spatial maps5.

Group differences were tested using these maps by Randomise permutation testing at voxel-

wise level. The z-score maps for each subject were also exported to MATLAB for further ROI

analyses including group comparison and correlation with maternal body composition.

Details of Statistics

For the comparison of demographic parameters between normal-weight and obese mothers and

between their infants, Fisher’s exact tests (for variables measured by counts) or Wilcoxon rank-

sum tests (for other numerical parameters/variables) were used to determine if there were

significant differences (P<0.05) between groups. For the voxel-wise comparison of functional

connectivity between groups, randomization with the threshold-Free Cluster Enhancement

(TFCE) option in FSL with 5000 permutations were used. P<0.05 after multiple comparison

correction across voxels was regarded as significant. To control for potential confounders, a

number of variables were added into the randomization of FSL as covariates, including IQ and

gestational weight gain for the mothers and postmenstrual age (defined as gestational age plus

postnatal age at MRI), gender, birth weight, birth length, neonatal diet (breastfeeding or not),

and head circumference at age 2 weeks for the infants. Some of these variables are known to

have significant effects on brain development in newborn infants, such as postmenstrual age,

others are variables that we have acquired data and that may potentially have effects or interact

with brain development but have yet been demonstrated in newborn infants. In addition, the

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mean translation and rotation of raw images during the RS-fMRI experiment for each subject

were also included as covariates. Average functional connectivity (z-scores) for each subject in

clusters which showed voxel-wise differences were compared between groups using general

linear model analyses and were correlated with maternal fat mass percentage using partial

Pearson Correlation tests controlling for the covariates.

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Group ICA Results and Supplemental Figure 1

Supplemental Figure 1: The group ICA analysis of the RS-fMRI data for all infants by

MELODIC generated 25 components, of which 15 were determined to be artefacts or unreliable

based on considerations specified in the previous section (e.g., motion-related, CSF pulsation

artefact, close to Circle of Willis, in brainstem, or in regions not known to be involved in

functional connectivity networks). The remaining 10 components were determined to be

meaningful functional connectivity networks at resting-state and are presented in the figure

below: A) primary motor; B) primary visual; C) visual association; D) auditory-right; E) auditory-

left; F) basal ganglia; G) cerebellum; H) somatosensory/posterior insula; I) prefrontal lobe; and

J) default mode network (DMN).

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Recent studies have showed that resting state functional connectivity can be reliably measured

in newborn infants by seed correlation or independent component analyses6-9. The functional

connectivity networks identified in our study of 2-week-old term infants are consistent with these

literature findings, while differ from findings in adults. In general, short distance connectivity

appeared to be stronger (more neighboring regions were recruited) while long distance

connectivity appeared to be weaker (fewer distant regions were recruited) for the infants in our

study, agreeing with studies on the development of functional connectivity in children based on

graph theory10. One example of the stronger short distance connectivity is the

somatosensory/posterior insula component (Supplemental Figure 1H). This component

involved not only the somatosensory cortex but also the neighboring posterior insula and

operculum and part of the mid cingulate cortex. This may be a reflection of integration of the

somatosensory network with the posterior insula network, as these regions showed functional

connectivity to the posterior insula in neonates11, and confirms reduced specificity in aspects of

the functional connectivity of newborn infants compared to adults12. An example for the weaker

longer distance connectivity is the DMN component (Supplemental Figure 1J). It involved

posterior regions including the posterior cingulate (PCC) and precuneus and relatively weak

connectivity to the anterior region in the prefrontal lobe including the medial prefrontal cortex

(mPFC). Additional regions that are part of the DMN in adults and are relatively distant such as

the lateral temporal cortex and hippocampus were not involved. Consistent with the literature,

weaker longer distance connectivity may be a reflection of maturation stage of functional

connectivity at young infancy. For example, a primitive but incomplete DMN with the PCC and

mPFC serving as main hubs was observed in 2-week-old term infants and connectivity to other

regions of DMN not fully developed until at age 2 years8.

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Concurrent Findings from the ICA and Supplemental Figure 2

While the prefrontal lobe network was the main interest of this study, we also performed dual

regression analyses on the other 9 meaningful components. The somatosensory/posterior

insula component also showed 3 clusters for which infants born to obese mothers had lower z-

scores (P<0.05, corrected for the voxel-wise comparison and adjusted for all covariates)

compared to infants born to normal-weight mothers (Supplemental Figure 2). No other

voxels/clusters in any other components showed significant differences (P<0.05) in functional

connectivity between the two groups.

Supplemental Figure 2: Top: Average functional connectivity maps for the

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somatosensory/posterior insula network for 2-week-old infants born to normal-weight (N=18) vs.

obese (N=16) mothers (obtained from respective group ICA analyses). Voxel-wise dual-

regression analysis based on combined group ICA showed three small clusters (arrows) with

significantly lower functional connectivity in the obese than normal-weight groups (P<0.05,

corrected for multiple comparisons), suggesting weaker recruitment into the network. Bottom:

The average z-scores in the clusters were significantly higher for infants born to normal-weight

vs. obese mothers.

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References for Supplement

1. Fransson P, Aden U, Blennow M, Lagercrantz H. The Functional Architecture of the Infant Brain as Revealed by Resting-State fMRI. Cereb. Cortex 2011; 21(1): 145-154.

2. Smyser CD, Snyder AZ, Neil JJ. Functional connectivity MRI in infants: Exploration of the functional organization of the developing brain. Neuroimage 2011; 56(3): 1437-1452.

3. Fransson P, Skiold B, Horsch S, Nordell A, Blennow M, Lagercrantz H et al. Resting-state networks in the infant brain. Proc. Natl. Acad. Sci. U. S. A. 2007; 104(39): 15531-15536.

4. Kelly RE, Alexopoulos GS, Wang ZS, Gunning FM, Murphy CF, Morimoto SS et al. Visual inspection of independent components: Defining a procedure for artifact removal from fMRI data. Journal of Neuroscience Methods 2010; 189(2): 233-245.

5. Beckmann CF, Mackay CE, Filippini N, Smith SM. Group comparison of resting-state FMRI data using multi-subject ICA and dual regression. OHBM 2009.

6. Doria V, Beckmann CF, Arichi T, Merchant N, Groppo M, Turkheimer FE et al. Emergence of resting state networks in the preterm human brain. Proc. Natl. Acad. Sci. U. S. A. 2010; 107(46): 20015-20020.

7. Fransson P, Skiold B, Engstrom M, Hallberg B, Mosskin M, Aden U et al. Spontaneous Brain Activity in the Newborn Brain During Natural Sleep-An fMRI Study in Infants Born at Full Term. Pediatric Research 2009; 66(3): 301-305.

8. Gao W, Zhu HT, Giovanello KS, Smith JK, Shen DG, Gilmore JH et al. Evidence on the emergence of the brain's default network from 2-week-old to 2-year-old healthy pediatric subjects. Proc. Natl. Acad. Sci. U. S. A. 2009; 106(16): 6790-6795.

9. Smyser CD, Inder TE, Shimony JS, Hill JE, Degnan AJ, Snyder AZ et al. Longitudinal Analysis of Neural Network Development in Preterm Infants. Cereb. Cortex 2010; 20(12): 2852-2862.

10. Power JD, Fair DA, Schlaggar BL, Petersen SE. The Development of Human Functional Brain Networks. Neuron 2010; 67(5): 735-748.

11. Alcauter S, Lin W, Smith JK, Gilmore JH, Gao W. Consistent Anterior-Posterior Segregation of the Insula During the First 2 Years of Life. Cereb. Cortex 2015; 25(5): 1176-1187.

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12. Wylie KP, Rojas DC, Ross RG, Hunter SK, Maharajh K, Cornier MA et al. Reduced brain resting-state network specificity in infants compared with adults. Neuropsychiatr. Dis. Treat. 2014; 10: 1349-1359.

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