supplemental materials methods

Supplemental Materials

Methods

Patients

We retrospectively studied 34 brain tumor patients (mean age±standard deviation (SD): 46.9±12.7

years, 20 females) who underwent presurgical fMRI language mapping at Brigham and Women’s

Hospital between November 2010 and April 2016. The inclusion criteria were: (1) confirmed

diagnosis of brain tumor according to the 2016 World Health Organization classification of tumors

of the central nervous system; (2) underwent AntGen tb-fMRI and mw-fMRI. Of these patients,

22 also underwent rs-fMRI. All patients were native English speakers, and none were fluent in a

second language. Handedness was determined by the Edinburgh Handedness Inventory (EHI) [1].

For clinical purposes, the language hemispheric dominance was estimated by a neuroradiologist,

based on qualitative assessment of language maps derived from a battery of fMRI tasks, including

AntGen and 1-2 other language tasks (noun categorization, sentence completion, or auditory

naming) [2]. Patients’ language deficits were evaluated presurgically and postsurgically by

neurosurgeons. For tb-fMRI, the data of 4 patients were excluded from analysis due to excessive

in-scanner head motion, for a total of 30 patients. For mw-fMRI, the data of 1 patient were

excluded due to excessive in-scanner head motion, and from 1 other patient due to inability to

identify language components from the output of independent component analysis (ICA), for a

total of 32 patients. For rs-fMRI, the data of 1 patient were excluded from data analysis due to

excessive in-scanner head motion, and from 1 other patient due to inability to identify language

components from the output of ICA. Data from one other patient were removed from rs-fMRI

BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any relianceSupplemental material placed on this supplemental material which has been supplied by the author(s) J Neurol Neurosurg Psychiatry

doi: 10.1136/jnnp-2020-325738–2.:10 2021;J Neurol Neurosurg Psychiatry, et al. Yao S

because the patient was excluded from the tb-fMRI and mw-fMRI, for a total of 18 patients. Of

note, the data excluded due to ICA failure was from the same patient in mw-fMRI and rs-fMRI.

All research procedures in this study were approved by the Mass General Brigham

Institutional Review Board. The study protocol was fully explained to the patients prior to data

acquisition, and all patients provided written informed consent.

MRI acquisition

MRI was acquired on a 3.0 Tesla GE scanner (Signa system, General Electric, Milwaukee, WI,

USA) with an 8-channel head coil, or a 3.0 Tesla Siemens scanner (Verio, Trio or Skyra system,

Siemens, Munich, Germany) with a 20-channel head coil. Patients were placed supine with their

head immobilized by a custom-fit cushion to minimize motion.

Blood oxygen level-dependent (BOLD) fMRI was acquired using a single-shot gradient-

echo echo-planar imaging (EPI) sequence with repetition time (TR)/echo time (TE)=2000/30ms,

flip angle (FA)=85°, matrix=64´64, field of view (FOV)=220´220mm, voxel

size=3.44´3.44´(4.0 or 5.0) mm3, 24 or 33 slices, in an ascending interleaved order. High-

resolution Gadolinium contrast-enhanced T1/T2 images were acquired in a separate session within

approximately one week of the fMRI session, using the 3D fast spoiled gradient-recalled (SPGR)

sequence (TR/TE=8.8/4.8ms, FA=20°, matrix=256´256, FOV=256´256mm, voxel

size=1´1´1mm3, 176 slices) or magnetization-prepared rapid gradient-echo (MPRAGE) sequence

(TR/TE=1790/3.62ms, FA=15°, matrix=256´256, FOV=256´256mm, voxel size=1´1´1mm3,

176 slices).



Behavioral paradigms

The fMRI scanning protocol varied slightly between patients due to individual clinical

requirements, patient tolerance, and constraints on scanner availability. The order of fMRI runs

was fixed as follows: tb-fMRI, rs-fMRI, and mw-fMRI.

Blocked design AntGen tb-fMRI: Patients were instructed to silently think of antonyms to

words presented for 2 sec with 2 sec interstimulus-intervals in blocks of five words. Eight 20 sec

task blocks were interleaved with eight 20 sec control blocks in which patients were instructed to

look at a crosshair in the center of the screen, for a total duration of 5 min. Prior to scanning, all

patients practiced the task with words not used during scanning. All the patients except one were

able to perform the antonym generation task. For this patient, the task was modified to a simple

word reading task.

Mw-fMRI: The movie stimulus consisted of a 7-min excerpt from the family film “The

Parent Trap” (directed by Nancy Meyers, produced by The Meyers/Shyer Company and Walt

Disney Pictures, 1998), as in our previous study [3]. The clip comprised 7 segments of spoken

dialogue (segment duration range: 5-119 sec, total length: 5 min 54 sec), interleaved with 6

segments of indoor or outdoor scenes with no dialogue (segment duration range: 4-25 sec, total

length: 1 min 6 sec). Among the 34 patients, 18 watched the entire 7-min clip, 15 watched the first

5 min, and 1 watched the first 4 min. Patients were instructed to watch the movie, and that they

would be asked a few comprehension questions after the scan. All patients confirmed ability to

hear and comprehend the spoken instructions, and movie spoken narrative.

Rs-fMRI: Patients were instructed to keep their eyes closed and relax without thinking

about anything specific. The scan duration varied between 7 min 10 sec (7 patients), 7 min 4 sec

(1 patient), 5 min (11 patients), and 4 min (3 patients).



The tasks and movie clip were presented using E-Prime software (Psychology Software

Tools, Sharpsburg, PA, USA), and MRI compatible goggles and headphones (Resonance

Technology, Los Angeles, CA, USA) on the GE scanners, or Nordic NeuroLab audiovisual

presentation software and hardware (NordicNeuroLab, Bergen, Norway) on the Siemens scanners.

The electrostatic headphones are noise-attenuating (30 dB, without earplugs), and the audio system

has a frequency response of 0.008-3.5 kHz designed for hearing speech during fMRI. Sound level

was set individually to accommodate individual differences in hearing, and the patients were

interviewed after the scan to confirm that they were able to properly hear the instructions for each

paradigm, and the movie-clips’ soundtracks during the scan. All functional runs started with 10

sec dummy scans to allow for stabilization of the BOLD signal.

fMRI pre-processing

The data analysis workflow is shown in Supplementary Figure S1. fMRI data were pre-processed

using the FMRI Expert Analysis Tool (FEAT) of FMRIB Software Library (FSL v6.0) [4]. The

pre-processing steps included: (1) realigning functional images to the first image of the run using

rigid body motion correction with FMRIB's Linear Image Registration Tool (MCFLIRT) [5], (2)

head motion estimation and outlier detection, (3) spatial smoothing using a full width half

maximum (FWHM) Gaussian kernel of 5 mm, and (4) high-pass filtering (cutoff=100 sec) to

remove linear drifts and low-frequency noise.

Head motion was quantified using the artifact detection tool (ART,

https://www.nitrc.org/projects/artifact_detect) [6]. In each run, time points with relative framewise

displacement (FD) >0.9 mm or global BOLD signal change >5 SD were flagged as outliers. Scans



with outlier time points amounting to >15% of the total time points were excluded from further

analyses.

Language regions-of-interests (ROIs)

To test for local differences between the paradigms in language mapping sensitivity and specificity,

and language lateralization as measured by a laterality index (LI), three functional language ROIs

were defined in each hemisphere. First, a functionally defined, inclusive language template was

derived by combining the maps associated with the terms “language”, “phonological”, and

“semantic”, in the Neurosynth platform [7]. The functional maps for these terms were based on

meta-analyses of 2509 studies, and adding other terms (e.g., “linguistic” and “sentence

comprehension”) did not expand the combined language template. The template in each

hemisphere was then mirrored to the other hemisphere, to obtain a symmetric bilateral template.

Second, the automated anatomical labeling atlas 3 (AAL 3) [8] was used to segment the template

into three ROIs, frontal, temporal, and inferior parietal lobule (IPL), in each hemisphere (see

Figure S1, panel 3).

Patient-specific language ROIs

Patient-specific language ROIs were generated by transforming the ROIs defined in the Montreal

Neurological Institute (MNI) space to the patient’s native space, using reverse application of the

normalization matrix generated from spatial normalization of the patient’s T1 structural images to

the MNI space [3,9–11]. The cost-function masking method (mutual information algorithm) with

a 12-degrees-of-freedom transformation in the FMRIB’s Linear Image Registration Tool (FLIRT)

was employed and has shown evidence that it could provide higher accurate results in patients with



brain lesions both in terms of deformation field displacement and voxel-wise intensity differences

[12,13]. A lesion mask, including the tumor region and surrounding edema, and any prior surgical

cavities, was generated in each patient using semi-automatic segmentation on the contrast-

enhanced T1 images in 3D Slicer [14] and visual inspection by two neurosurgeons. The lesion

masks were included in the normalization procedure to reduce lesion distortions and were also

used to mask out the lesions from the patient-specific language ROIs [14,15] (Figure 1B, Figure

S1 panel 3).

fMRI data analysis

General linear model (GLM) analysis of tb-fMRI

The tb-fMRI data were analyzed at the single subject level using GLM analysis in FSL, with

regressors representing the task and control conditions, and the 6 parameters derived from rigid-

body head motion correction to account for motion artifacts. The resulting t-maps were

individually thresholded at 10% of the activation distribution within the bilateral language

template.

Independent component analysis (ICA) of mw-fMRI and rs-fMRI

Mw-fMRI and rs-fMRI data were analyzed at the single-subject level using the Group ICA Of

fMRI Toolbox (GIFT, https://trendscenter.org/software/gift). The number of independent

components was estimated for each dataset using the minimum description length (MDL) criterion

[11,16]. The average number of components was estimated as 41 in the mw-fMRI and 38 in the

rs-fMRI. The Infomax ICA algorithm [17] was applied to decompose the pre-processed fMRI into

spatially-independent components with ICASSO to derive reliable components [18]. Each



component map was converted to a z-map and thresholded at 10% of the individual activation

distribution within the bilateral language template.

For each dataset, the ICA output included a series of components, each associated with a

spatial map and a time-course. To identify the language components from the ICA output, both

temporal and spatial correlation criteria were applied. The temporal criterion consisted of

correlation with two language response models (LRMs), one consisting of the mean time-course

of language response in the frontotemporal cortex (LRM-FT), and the other consisting of a distinct

mean time course of language response in the parietal cortex (LRM-P), derived from an

independent group of 22 healthy subjects who underwent mw-fMRI language mapping with the

same movie stimulus [3]. A primary language component (LC1) was searched for by correlating

the time-course of each component with LRM-FT, and the map of each component with the

patient-specific language template (separately in each hemisphere). A secondary language

component (LC2) was searched for by correlating the time-course of each component with LRM-

P, and the map of each component with the patient-specific IPL ROI (Figure S1, panel 3). The

LC1 and LC2 were selected based on: (1) temporal and spatial correlation coefficients ranked

among the top 5 of all components (the spatial correlation coefficients were examined with both

the left and right hemisphere ROIs); and (2) a stability index (Iq, range: 0-1, generated by the

ICASSO approach) >0.8 [18]. For rs-fMRI, the language component(s) was identified based

strictly on spatial correlation with the patient-specific language template. The selected language

components were visually inspected to confirm that activations were in the putative language areas

in each patient.



In four patients in whom two mw-fMRI LC1s were identified and one in whom three LC2s

were identified, the components were combined for further analyses. In two patients, only the mw-

fMRI LC1 could be identified.

Sensitivity and specificity of language localization

The language mapping sensitivity of each fMRI paradigm in each ROI was calculated as the

proportion of supra-threshold voxels within the ROI. The language mapping specificity was

calculated for the entire brain as the proportion of non-activated voxels outside of the language

ROIs. The sensitivity and specificity were calculated at four thresholds, 5%, 10%, 15%, and 20%

of the top activation (Figure S3) to test for possible influences of the threshold on the results.

Because differences between the paradigms in these metrics were found to be stable across

thresholds, and a 10% threshold is commonly used [19], 10% was used for computing the inter-

subject activation overlap maps and the report of sensitivity and specificity measures shown in

Figure 1C and discussed in the paper.

Language lateralization index (LI)

The language LIs were calculated for each left/right pair of language ROIs, as well as for the entire

language template, using the equation:

LI =sumAmplitude! − sumAmplitude"

sumAmplitude! + sumAmplitude"

where sumAmplitudeL and sumAmplitudeR represent the sum of t or z scores within the left and

right language ROIs, respectively. LI³0.2 was regarded as left-hemisphere language dominance,

LI£-0.2 as right-hemisphere language dominance, and -0.2<LI<0.2 as bilateral language function.

For mw-fMRI, the LI in the frontal and temporal ROIs was computed based on LC1, and in the



IPL ROI based on LC2. LI agreement between paradigms (Figure S4) was computed at a 10%

threshold, as done for the sensitivity and specificity calculation.

Statistical analysis

The patient demographic and clinical characteristics were described using mean±SD, median

(interquartile range, IQR), or frequency according to the variable type (Table S1). Group

differences in head motion were assessed using a paired t-test. Multiple comparisons correction

with Bonferroni was employed for comparisons between language maps of the fMRI paradigm.

The McNemar–Bowker test was performed to assess the agreement between LI results from fMRI

maps and the clinical report. Significance was set at p<0.05 (two-tailed). Statistical analyses and

results visualization were performed using R software (version 3.6.3, R Foundation for Statistical

Computing) and Python (version 3.7.6, https://www.python.org/).



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Table S1. Demographics and clinical characteristics of brain tumor patients (N=34).

Category Distribution

Age (years): mean ± SD 46.9 ± 12.7

Male/Female 14/20

Handedness (EHI)

Left (EHI £ -25) 6

Right (EHI ³ +25) 24

Ambidextrous (-25 < EHI < +25) 2

Unknown 2

Language dominance (based on clinical fMRI report)

Left hemisphere 27

Right hemisphere 3

Bilateral 1

Undetermined 3

Preoperative language deficits (clinical report)

Normal 17

Mild aphasia (expressive/receptive/mixed) 10 (4/1/5)

Severe aphasia (expressive/receptive/mixed) 7 (5/2/0)

Postoperative language outcome at 3-6 months follow-up (clinical report) *

Normal 17

Mild aphasia (expressive/receptive/mixed) 13 (6/2/5)

Severe aphasia (expressive/receptive/mixed) 3 (3/0/0)

Tumor location (L/R)

Frontal lobe 12 (9/3)

Temporal lobe 13 (10/3)

Insula 1 (1/0)

Parietal lobe 3 (1/2)

Frontotemporal area 3 (3/0)

Temporoparietal area 2 (2/0)



*N=33, one patient did not undergo surgery.

Abbreviations:

AED, antiepileptic drug; DNET, dysembryplastic neuroepithelial tumor; EHI, Edinburgh

Handedness Inventory; SD, standard deviation; WHO, World Health Organization; L, left

hemisphere; R, right hemisphere.

Extent of tumor resection

Gross total resection 20

Subtotal resection 11

Partial resection 0

Biopsy only 1

Pathological diagnosis: total number / number of recurrent cases

Glioblastoma (WHO IV) 10/9

Anaplastic oligoastrocytoma (WHO III) 4/2

Low-grade glioma (astrocytoma, glioneuronal, oligoastrocytoma) (WHO I-II) 7/2

Meningioma (WHO I-II) 2/0

Benign lesion (cyst, DNET, atypical cell mass) (WHO I) 8/1

Metastasis (WHO IV) 3/1

With seizures 18

Taking AED 14

Underwent electrocortical stimulation (ECS) mapping 8



Figure S1. Workflow of movie-watching fMRI (mw-fMRI) language mapping.

Step 1. fMRI preprocessing was performed in the FMRI Expert Analysis Tool (FEAT) of FMRIB

Software Library (FSL v6.0), including: (1) realigning functional images to the first image of the

run using rigid body motion correction, (2) head motion estimation and outlier detection which

were quantified using the Artifact Detection Tools (ART). In each run, time points with relative

framewise displacement (FD) >0.9 mm or global BOLD signal change >5 SD were flagged as

outliers. Scans with outlier time points amounting to >15% of the total time points were excluded

from further analyses, (3) spatial smoothing using a full width half maximum (FWHM) Gaussian

kernel of 5 mm, and (4) high-pass filtering (cutoff=100 sec) to remove linear drifts and low-

frequency noise.

Step 2. Single-subject independent component analysis (ICA) of mw-fMRI. Mw-fMRI and rs-

fMRI data were analyzed at the single-subject level using the Group ICA Of fMRI Toolbox (GIFT).

The number of independent components was estimated for each dataset using the minimum

description length (MDL) criterion. The average number of components was estimated as 41 in

the mw-fMRI and 38 in the rs-fMRI. Data dimension reduction was performed using principal

component analysis (PCA). The Infomax ICA algorithm [38] was applied to decompose the pre-

processed fMRI into spatially-independent components with ICASSO to derive reliable

components. Each component map was converted into a z-map and thresholded at 10% of the

activation distribution within the language template.

Step 3. Identification of language components. Spatial Language Template: The canonical

language template (in standard Montreal Neurological Institute (MNI) space), comprising regions

of the frontal (red), temporal (green), and inferior parietal (yellow) cortex (left panel), is

transformed into a patient-specific language template (in patient native space and with the lesion



region masked out), as shown for a patient with a left temporal glioblastoma (right panel). Time-

course Language Response Models (LRMs): LRM-FT corresponding to the mean time-course in

the frontotemporal ROIs (left panel), and LRM-P corresponding to the mean time-course in the

parietal ROI, derived from mw-fMRI in an independent group of neurologically healthy subjects

watching the same movie clip [3]. Primary and secondary language components: The primary

language component (LC1, left panel) and secondary language component (LC2, right panel), each

consisting of a spatial map and a time-course, are selected based on both spatial and temporal

criteria consisting of correlation with the patient-specific language template and the time-course

LRMs.



Figure S2. Examples of individual language maps in each paradigm.

Three examples of individual language maps resulting from tb-fMRI (blue), mw-fMRI (red), and

rs-fMRI (green), overlaid on the patient’s anatomical image. The cases were selected based on

having data, and high mapping sensitivity and specificity, in all three paradigms.

Case 1 (patient 06): 53-year-old, left-handed (EHI=-40) female presenting with a left temporal

anaplastic oligoastrocytoma (WHO grade III) and severe mixed language deficits. Language

laterality was indeterminate according to the preoperative clinical report (which is based on tb-

fMRI), while mw-fMRI indicated left-hemispheric dominance. She received a gross total tumor

resection and language improved to mild expressive dysfunction after the surgery (as tested at 3-6

months follow-up).

Case 2 (patient 15): 60-year-old, right-handed (EHI=90) female presenting with a left frontal

atypical meningioma (WHO grade II) and mild expressive language dysfunction. Language was

left-hemisphere dominant according to both the clinical report and mw-fMRI. She received a gross

total tumor resection and language function returned to normal after the surgery.

Case 3 (patient 34): 45-year-old, left-handed (EHI=-60) female presenting with a right frontal

anaplastic oligoastrocytoma (WHO grade III) and without any language complaints. Language

was left-hemisphere dominant according to both the clinical report and mw-fMRI. She received a

gross total tumor resection and language function remained normal after the surgery.



Figure S4. Language laterality index in each paradigm.

Language laterality index (LI) agreement between the movie primary language component (movie-

LC1) and task-based (tb-fMRI) and resting-state fMRI (rs-fMRI) activation in the frontal and

temporal ROIs, and between the movie secondary language component (movie-LC2) and task-

based and resting-state fMRI activation in the inferior parietal lobule (IPL) ROI. Patients with

LI≥0.20 were categorized as left hemisphere dominant for language, LI≤−0.20 as right hemisphere

dominant, and −0.20<LI<0.20 as bilateral. Full agreement (same laterality category), partial

agreement (bilateral versus right or left laterality category), and disagreement (different laterality

category) between the compared paradigms correspond to the graph areas colored in deep green,

light green, and white, respectively.

The McNemar–Bowker test of agreement revealed significant differences between the LIs of tb-

fMRI and mw-fMRI in the IPL (p=0.039), and marginal differences in the temporal (p=0.063) and

frontal (p=0.058) ROIs. Specifically, between the two paradigms, 7 of 30 patients showed

disagreement in the temporal, 8 of 30 in the frontal, and 7 of 28 in the parietal ROIs. Of the

disagreement cases, all but 1 in the temporal and 2 in the frontal ROIs showed left language

dominance in tb-fMRI and right dominance in mw-fMRI. Finally, there were no significant

differences between the LIs of rs-fMRI and mw-fMRI in any of the ROIs.



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