
#SBC
First-level analysis [VALUE 1 name]: Seed-based connectivity maps (SBC) were estimated characterizing the spatial pattern of functional connectivity with a seed area[IF measure 2  while controlling for all other seeds][IF measure 4  while controlling for all other seeds]. Seed regions included [VALUE 1 listseeds]. Functional connectivity strength was represented by [IF measure 1 Fisher-transformed bivariate correlation coefficients][IF measure 2 Fisher-transformed semipartial correlation coefficients][IF measure 3 bivariate regression coefficients][IF measure 4 multivariate regression coefficients] from a weighted general linear model (weighted-GLM [CITATION1]), estimated separately for each [IF measure 1 seed area and target voxel, modeling the association between their BOLD signal timeseries][IF measure 2 target voxel, modeling the association between all seeds simultaneously and each individual voxel BOLD signal timeseries][IF measure 3 seed area and target voxel, modeling the association between their BOLD signal timeseries][IF measure 4 target voxel, modeling the association between all seeds simultaneously and each individual voxel BOLD signal timeseries].

#RRC
First-level analysis [VALUE 1 name]: ROI-to-ROI connectivity (RRC) matrices were estimated characterizing the functional connectivity between each pair of regions among [VALUE 1 listseeds][IF measure 2  while controlling for all other ROIs][IF measure 4  while controlling for all other ROIs]. Functional connectivity strength was represented by [IF measure 1 Fisher-transformed bivariate correlation coefficients][IF measure 2 Fisher-transformed semipartial correlation coefficients][IF measure 3 bivariate regression coefficients][IF measure 4 multivariate regression coefficients] from a general linear model (weighted-GLM [CITATION1]), estimated separately for each [IF measure 1 pair of ROIs, characterizing the association between their BOLD signal timeseries][IF measure 2 target ROI, characterizing the multivariate association between all seeds and each target ROI BOLD signal timeseries][IF measure 3 pair of ROIs, characterizing the association between their BOLD signal timeseries][IF measure 4 target ROI, characterizing the multivariate association between all seeds and each target ROI BOLD signal timeseries].

#SBCRRC
First-level analysis [VALUE 1 name]: Seed-based connectivity maps (SBC) and ROI-to-ROI connectivity matrices (RRC) were estimated characterizing the patterns of functional connectivity with [VALUE 1 listseeds][IF measure 2  while controlling for all other ROIs][IF measure 4  while controlling for all other ROIs]. Functional connectivity strength was represented by [IF measure 1 Fisher-transformed bivariate correlation coefficients][IF measure 2 Fisher-transformed semipartial correlation coefficients][IF measure 3 bivariate regression coefficients][IF measure 4 multivariate regression coefficients] from a weighted general linear model (weighted-GLM [CITATION1]), defined separately for each [IF measure 1 pair of seed and target areas, modeling the association between their BOLD signal timeseries][IF measure 2 target area, modeling the association between all seeds simultaneously and each individual target area BOLD signal timeseries][IF measure 3 pair of seed and target areas, modeling the association between their BOLD signal timeseries][IF measure 4 target area, modeling the association between all seeds simultaneously and each individual target area BOLD signal timeseries].

#TMOD
First-level analysis [VALUE 1 name]: Temporal modulation analyses were used to study the changes in functional connectivity with [VALUE 1 modulation]. Seed regions included [VALUE 1 listseeds]. Separately for each [IF measure 1 pair of seed and target areas][IF measure 2 target area (combining all seeds in a single model)][IF measure 3 pair of seed and target areas][IF measure 4 target area (combining all seeds in a single model)], a generalized psychophysiological interaction model (gPPI [CITATION4][CITATION3]) was defined with seed BOLD signals as physiological factors, [VALUE 1 modulation] as a psychological factor, and the product of the two as psychophysiological interaction terms. Functional connectivity changes with [VALUE 1 modulation] were characterized by the [IF measure 1 Fisher-transformed semipartial correlation][IF measure 2 Fisher-transformed semipartial correlation][IF measure 3 multivariate regression][IF measure 4 multivariate regression] coefficient of the psychophysiological interaction term in each model.

#gPPI
First-level analysis [VALUE 1 name]: Psychophysiological interaction analyses were used to study the changes in functional connectivity across [VALUE 1 listconditions] conditions. Seed regions included [VALUE 1 listseeds]. Separately for each [IF measure 1 pair of seed and target areas][IF measure 2 target area (combining all seeds in a single model)][IF measure 3 pair of seed and target areas][IF measure 4 target area (combining all seeds in a single model)], a generalized psychophysiological interaction model (gPPI [CITATION4][CITATION3]) was defined with seed BOLD signals as physiological factors, boxcar signals characterizing each individual task condition convolved with an SPM canonical hemodynamic response function as psychological factors, and the product of the two as psychophysiological interaction terms. Functional connectivity changes across conditions were characterized by the [IF measure 1 Fisher-transformed semipartial correlation][IF measure 2 Fisher-transformed semipartial correlation][IF measure 3 multivariate regression][IF measure 4 multivariate regression] coefficient of the psychophysiological interaction terms in each model.

#ADDDERIV
In addition, [IF anyderivs 1 first ][IF anyderivs 2 first and second ] order derivatives of each seed BOLD timeseries were included in each individual GLM to model [IF anyderivs 1 temporal lags ][IF anyderivs 2 temporal lags ]in functional connectivity.

#ADDFBAND
In addition, band-pass filtered seed BOLD timeseries across [VALUE anyfbands 1] non-overlapping frequency bands between [VALUE 1 filter] Hz and [VALUE 2 filter] Hz were included in each model to study differences in functional connectivity across frequency bands.

#ADDWEIGHTS
[IF weight 2 Individual scans were weighted by a boxcar signal characterizing each individual task or experimental condition convolved with an SPM canonical hemodynamic response function and rectified.][IF weight 3 Individual scans were weighted by a boxcar signal characterizing individual task or experimental conditions convolved with a hanning window.][IF weight -2 In order to compensate for possible transient magnetization effects at the beginning of each run, individual scans were weighted by a step function convolved with an SPM canonical hemodynamic response function and rectified.][IF weight -3 Individual scans were weighted by a hanning window over the duration of each functional run in order to reduce the influence of the initial and final scans.]

#ADDTMODWEIGHTS
To study task- or condition- specific interactions, gPPI psychological and interaction terms were masked by a boxcar signal characterizing each individual condition convolved with an SPM canonical hemodynamic response function and rectified.

#LCOR
First-level analysis [VALUE 1 name]: Local Correlation maps (LCOR) characterizing local coherence at each voxel were estimated as the weighted average of all short-range connections between a voxel and a [VALUE 1 localsupport] mm FWHM Gaussian neighborhood area [CITATION5]. Short-range connections were computed from the matrix of bivariate correlation coefficients between the BOLD timeseries from each pair of voxels, estimated [IF finitedimensions 1 using a singular value decomposition of the z-score normalized BOLD signal (subject-level SVD) with [VALUE 1 dimensions_in] components ]separately for each subject[IF multipleconditions 1  and condition] [CITATION6].[IF norm 1  Last, LCOR measures across voxels were rank sorted and normalized separately for each individual subject[IF multipleconditions 1  and condition] using a Gaussian inverse cumulative distribution function with zero mean and unit variance.]

#GCOR
First-level analysis [VALUE 1 name]: Global Correlation maps (GCOR) characterizing network centrality at each voxel were estimated as the average of all short- and long- range connections between a voxel and the rest of the brain [CITATION7]. Connections were computed from the matrix of bivariate correlation coefficients between the BOLD timeseries from each pair of voxels, estimated [IF finitedimensions 1 using a singular value decomposition of the z-score normalized BOLD signal (subject-level SVD) with [VALUE 1 dimensions_in] components ]separately for each subject[IF multipleconditions 1  and condition] [CITATION6].[IF norm 1  Last, GCOR measures across voxels were rank sorted and normalized separately for each individual subject[IF multipleconditions 1  and condition] using a Gaussian inverse cumulative distribution function with zero mean and unit variance.]

#IC
First-level analysis [VALUE 1 name]: Intrinsic Connectivity maps (IC) characterizing network centrality at each voxel were estimated as the root mean square (RMS) of all short- and long- range connections between a voxel and the rest of the brain [CITATION8]. Connections were computed from the matrix of bivariate correlation coefficients between the BOLD timeseries from each pair of voxels, estimated [IF finitedimensions 1 using a singular value decomposition of the z-score normalized BOLD signal (subject-level SVD) with [VALUE 1 dimensions_in] components ]separately for each subject[IF multipleconditions 1  and condition] [CITATION6].[IF norm 1  Last, IC measures across voxels were rank sorted and normalized separately for each individual subject[IF multipleconditions 1  and condition] using a Gaussian inverse cumulative distribution function with zero mean and unit variance.]

#RCOR
First-level analysis [VALUE 1 name]: Radial Correlation maps (RCOR) characterizing functional connectivity gradients among short-range connections were estimated as the weighted average of the spatial gradient of all short-range connections between a voxel and a [VALUE 1 localsupport] mm FWHM Gaussian neighborhood area [CITATION9]. Connections were computed from the matrix of bivariate correlation coefficients between the BOLD timeseries from each pair of voxels, estimated [IF finitedimensions 1 using a singular value decomposition of the z-score normalized BOLD signal (subject-level SVD) with [VALUE 1 dimensions_in] components ]separately for each subject[IF multipleconditions 1  and condition] [CITATION6].[IF norm 1  Last, RCOR measures across voxels were rank sorted and normalized separately for each individual subject[IF multipleconditions 1  and condition] using a Gaussian inverse cumulative distribution function with zero mean and unit variance.]

#RSIM
First-level analysis [VALUE 1 name]: Radial Similarity maps (RSIM) characterizing the sensitivity of seed-based functional connectivity patterns to changes in seed location were estimated as the root mean square (RMS) of the spatial gradient of all short- and long- range connections between a voxel and the rest of the brain [CITATION6]. Connections were computed from the matrix of bivariate correlation coefficients between the BOLD timeseries from each pair of voxels, estimated [IF finitedimensions 1 using a singular value decomposition of the z-score normalized BOLD signal (subject-level SVD) with [VALUE 1 dimensions_in] components ]separately for each subject[IF multipleconditions 1  and condition].[IF norm 1  Last, RSIM measures across voxels were rank sorted and normalized separately for each individual subject[IF multipleconditions 1  and condition] using a Gaussian inverse cumulative distribution function with zero mean and unit variance.]

#ALFF
First-level analysis [VALUE 1 name]: Amplitude of low frequency fluctuations (ALFF) maps characterizing low-frequency BOLD signal variability at each voxel were estimated as the root mean square (RMS) of the BOLD signal after denoising and band-pass filtering between [VALUE 1 bpfilter] Hz and [VALUE 2 bpfilter] Hz [CITATION10].[IF norm 1  ALFF measures across voxels were then rank sorted and normalized separately for each individual subject[IF multipleconditions 1  and condition] using a Gaussian inverse cumulative distribution function with zero mean and unit variance.]

#fALFF
First-level analysis [VALUE 1 name]: Fractional amplitude of low frequency fluctuations (fALFF) maps characterizing low-frequency BOLD signal variability at each voxel were estimated as the ratio between the root mean square (RMS) of the BOLD signal after denoising and band-pass filtering between [VALUE 1 bpfilter] Hz and [VALUE 2 bpfilter] Hz, divided by the same measure computed before band-pass filtering [CITATION11].[IF norm 1  FALFF measures across voxels were then rank sorted and normalized separately for each individual subject[IF multipleconditions 1  and condition] using a Gaussian inverse cumulative distribution function with zero mean and unit variance.]

#IHC
First-level analysis [VALUE 1 name]: Interhemispheric correlation maps (IHC) characterizing the strength of homotopic functional connectivity between the two hemispheres were estimated as the Fisher-transformed bivariate correlation coefficient between the BOLD signal at each voxel and at the same anatomical location in the contralateral hemisphere [IF volumespace 0 (contralateral voxels with matching anatomical features in fsaverage cortical curvature template)][IF volumespace 1 (voxels with the same y&z MNI coordinates and opposite-sign x MNI coordinates)][CITATION12][CITATION1].[IF norm 1  IHC measures across voxels were then rank sorted and normalized separately for each individual subject[IF multipleconditions 1  and condition] using a Gaussian inverse cumulative distribution function with zero mean and unit variance.]

#fcMVPA
First-level analysis [VALUE 1 name]: Functional connectivity multivariate pattern analyses (fc-MVPA [CITATION13]) were performed to estimate the first [VALUE 1 dimensions_out] eigenpatterns characterizing the principal axes of heterogeneity in functional connectivity across subjects[IF multipleconditions 1  and conditions]. From these eigenpatterns, [VALUE 1 dimensions_out] associated eigenpattern-score images were derived for each individual subject [IF multipleconditions 1 and condition ]characterizing their brain-wide functional connectome state. Eigenpatterns and eigenpattern-scores were computed separately for each individual seed voxel as the left- and right- singular vectors, respectively, from a singular value decomposition (group-level SVD) of the matrix of functional connectivity values between this seed voxel and [IF ismasked 1 all voxels within a custom ROI mask][IF ismasked 0 the rest of the brain] (a matrix with one row per target voxel, and one column per subject[IF multipleconditions 1  and condition]). Individual functional connectivity values were computed from the matrices of bivariate correlation coefficients between the BOLD timeseries from each pair of voxels, estimated [IF finitedimensions 1 using a singular value decomposition of the z-score normalized BOLD signal (subject-level SVD) with [VALUE 1 dimensions_in] components ]separately for each subject[IF multipleconditions 1  and condition] [CITATION6]. 

#groupICA
First-level analysis [VALUE 1 name]: Group-level independent component analyses (group-ICA [CITATION14]) were performed to estimate [VALUE 1 dimensions_out] temporally coherent networks from the fMRI data combined across all subjects[IF multipleconditions 1  and conditions]. The BOLD signal from every timepoint and voxel [IF ismasked 0 in the brain ][IF ismasked 1 within a custom ROI mask ]was concatenated across subjects and conditions along the temporal dimension. [IF finitedimensions 1  A singular value decomposition of the z-score normalized BOLD signal (subject-level SVD) with [VALUE 1 dimensions_in] components separately for each subject[IF multipleconditions 1  and condition] was used as a subject-specific dimensionality reduction step.] The dimensionality of the concatenated data was [IF finitedimensions 1 further ]reduced using a singular value decomposition (group-level SVD) with [VALUE 1 dimensions_out] components, and a fast-ICA fixed-point algorithm [CITATION15] with [VALUE 1 algtype] contrast function was used to identify spatially independent group-level networks from the resulting components. Last, [VALUE 1 bprtype] back-projection [CITATION16] was used to compute ICA maps associated with these same networks separately for each individual subject[IF multipleconditions 1  and condition].

#groupPCA
First-level analysis [VALUE 1 name]: Group-level principal component analyses (group-PCA [CITATION17][CITATION14]) were performed to estimate [VALUE 1 dimensions_out] principal components from the fMRI data combined across all subjects[IF multipleconditions 1  and conditions]. The BOLD signal from every timepoint and voxel [IF ismasked 0 in the brain ][IF ismasked 1 within a custom ROI mask ]was concatenated across subjects and conditions along the temporal dimension. [IF finitedimensions 1  A singular value decomposition of the z-score normalized BOLD signal (subject-level SVD) with [VALUE 1 dimensions_in] components separately for each subject[IF multipleconditions 1  and condition] was used as a subject-specific dimensionality reduction step.] A singular value decomposition (group-level SVD) with [VALUE 1 dimensions_out] components was then used to identify spatially and temporally orthogonal networks characterizing the largest variance in the BOLD signal. Last, [VALUE 1 bprtype] back-projection [CITATION16] was used to compute PCA maps associated with these same networks separately for each individual subject[IF multipleconditions 1  and condition].

#dynICA
First-level analysis [VALUE 1 name]: Dynamic independent component analyses (dyn-ICA [CITATION1]) were performed to estimate the first [VALUE 1 Ncomponents] temporal modulation factors characterizing the observed dynamic changes in functional connectivity within each functional run, together with the circuits (groups of connections) modulated by each of these factors. The BOLD signal within [VALUE 1 listseeds] was concatenated across all subjects[IF multipleconditions 1  and conditions]. Functional connectivity between each pair of regions was modeled by a generalized psychophysiological interaction model (gPPI [CITATION4][CITATION3]) with [VALUE 1 Ncomponents] unknown psychological factors, and an interative dual regression algorithm with a hanning regularization kernel of [VALUE 1 window]s full width half maximum (FWHM) was used to compute maximum likelihood estimates of these factors. Last, a fast-ICA fixed-point algorithm [CITATION15] with hyperbolic tangent contrast function was used on the regression coefficients of the resulting group-level gPPI model interaction terms to identify spatially independent group-level circuits, and a separate gPPI model for each individual subject[IF multipleconditions 1  and condition] was used to compute subject-specific ICA maps associated with these same circuits.

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