DOI: 10.5061/dryad.ngf1vhj4t
title: Data from: Intrinsic functional connectivity delineates transmodal language functions

description: Communication involves the translation of sensory information (e.g., heard
 words) into abstract concepts according to abstract rules (e.g., the
 meaning of those words). Accordingly, using language involves an interplay
 between unimodal brain areas that process sensory information and
 transmodal areas that respond to linguistic input regardless of the input
 modality (e.g., reading sentences vs. listening to speech). Previous work
 has shown that intrinsic functional connectivity (iFC), when performed
 within individuals, can delineate a distributed language network that
 overlaps in detail with regions activated by a reading task. The network
 was widely distributed across multiple brain regions, recapitulating an
 organization that is characteristic of association cortex, and which
 suggests that the distributed language network serves transmodal, not
 unimodal, functions. Here, we tested whether the distributed language
 network encapsulates transmodal functions by assessing its degree of
 overlap with two language tasks, one auditory (i.e., listening to speech)
 and one visual (i.e., reading sentences). The results show that the
 distributed language network aligns well with regions activated by both
 tasks, supporting a transmodal function. Further, the boundaries of the
 distributed language network along the lateral temporal cortex serve as a
 good proxy for the division between transmodal language and auditory
 functions: presentation of sounds (i.e., filtered, incomprehensible
 speech) evoked activity that was largely outside of the distributed
 language network but closely followed the network boundaries. These
 findings support that individualized iFC estimates can delineate the
 division between sensory-linked and abstract linguistic functions. We
 conclude that within-individual iFC may be viable for language mapping in
 individuals with aphasia who cannot perform language tasks in the scanner.
description: # Intrinsic functional connectivity delineates transmodal language
 functions
 [https://doi.org/10.5061/dryad.ngf1vhj4t](https://doi.org/10.5061/dryad.ngf1vhj4t) ## Description of the data and file structure This dataset contains the task activity maps and surface parcellations used in our research paper. All surface files needed to open these maps are also included. First open surface file(s) (e.g. wbtemplates_fsaverage6/lh.pialinfl2.surf.gii) then open data file(s) (e.g. parcellations_and_borders/S1_k14_MSHBM_parcellation_colored.dlabel.nii). Files can be viewed using the free software Connectome Workbench. The latest version can be downloaded here:  [https://www.humanconnectome.org/software/get-connectome-workbench](https://www.humanconnectome.org/software/get-connectome-workbench) Source code for earlier versions of Connectome Workbench may be found here: [https://github.com/Washington-University/workbench/releases](https://github.com/Washington-University/workbench/releases) ### Files and variables #### File: DBNO\_transmodal.zip **Description:**  Folder containing all relevant data from the Detailed Brain Network Organization (3T) dataset.  *parcellations_and_borders:* contains MS-HBM (Multi-Session Hierarchical Bayesian Model) network parcellations and borders on the surface for each subject.  The parcellation is k = 14 for subjects 1-7 and k = 15 for subject 8.  * S[subject number]_k[k value]_MSHBM_parcellation_colored.dlabel.nii: Subject-specific data-driven clustering, calculated from resting-state data using the MS-HBM approach - S[subject number]_k[k value]_MSHBM_parcellation_colored.[hemisphere].border: Borders outlining subject-specific data-driven clustering, calculated from resting-state data using the MS-HBM approach *task_activity:* Folder containing task activity (READLOC, SPEECHLOC, AUDLOC) maps on the surface for each subject. * *task_activity/READLOC_maps:* z-scored composite and run-level visual language activity maps  * S[subject number]_READLOC_SENTENCESmPSEUDO_n[run total]_mean.dscalar.nii: Subject-specific READLOC task activity maps for the contrast sentences&gt;pseudowords, averaged across all runs * S[subject number]_READLOC_SENTENCESmPSEUDO_n[run total]_all-runs.dtseries.nii: Subject-specific READLOC task activity maps for the contrast sentences&gt;pseudowords, with runs separated * *task_activity/SPEECHLOC_maps:* z-scored composite and run-level auditory language activity maps  * S[subject number]_SPEECHLOC_INTACTmDISTORTED_n[run total]_mean.dscalar.nii: Subject-specific SPEECHLOC task activity maps for the contrast intact&gt;distorted speech, averaged across all runs * S[subject number]_SPEECHLOC_INTACTmDISTORTED_n[run total]_all-runs.dtseries.nii: Subject-specific SPEECHLOC task activity maps for the contrast intact&gt;distorted speech, with runs separated * *task_activity/AUDLOC_maps:* z-scored composite and run-level auditory localizer activity maps  * S[subject number]_AUDLOC_DISTORTEDmFIX_n[run total]_mean.dscalar.nii: Subject-specific AUDLOC task activity maps for the contrast distorted speech&gt;fixation, averaged across all runs * S[subject number]_AUDLOC_DISTORTEDmFIX_n[run total]_all-runs.dtseries.nii: Subject-specific AUDLOC task activity maps for the contrast distorted speech&gt;fixation, with runs separated * *task_activity/READLOC-SPEECHLOC_Overlap_maps:* Label files of binarized overlaid task activity from the READLOC and SPEECHLOC maps * S[subject number]_READLOC-SPEECHLOC_Overlap.dlabel.nii: Subject-specific averages of task activity from READLOC and SPEECHLOC, binarized and overlaid * *task_activity/READLOC-AUDLOC_Overlap_maps:* Label files of binarized overlaid task activity from the READLOC and AUDLOC maps * S[subject number]_READLOC-AUDLOC_Overlap.dlabel.nii: Subject-specific averages of task activity from READLOC and AUDLOC, binarized and overlaid *wbtemplates_fsaverage6*: contains template surfaces in fsaverage6 space **Datatypes** * Surface File Types * lh.border: border file showing outline of network parcellations (left hemisphere only) * dlabel.nii: parcellation file * dscalar.nii: scalar image file  * dtseries.nii: time series of each vertex * surf.gii: surface geometry file