Data from: Intrinsic functional connectivity delineates transmodal language functions
Data files
May 15, 2025 version files 129.03 MB
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DBNO_transmodal.zip
129.02 MB
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README.md
4.25 KB
Abstract
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.
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
Source code for earlier versions of Connectome Workbench may be found here:
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>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>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>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>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>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>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
