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      Individual word representations dissociate from linguistic context along a cortical unimodal to heteromodal gradient

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          Abstract

          Language comprehension involves multiple hierarchical processing stages across time, space, and levels of representation. When processing a word, the sensory input is transformed into increasingly abstract representations that need to be integrated with the linguistic context. Thus, language comprehension involves both input‐driven as well as context‐dependent processes. While neuroimaging research has traditionally focused on mapping individual brain regions to the distinct underlying processes, recent studies indicate that whole‐brain distributed patterns of cortical activation might be highly relevant for cognitive functions, including language. One such pattern, based on resting‐state connectivity, is the ‘principal cortical gradient’, which dissociates sensory from heteromodal brain regions. The present study investigated the extent to which this gradient provides an organizational principle underlying language function, using a multimodal neuroimaging dataset of functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) recordings from 102 participants during sentence reading. We found that the brain response to individual representations of a word (word length, orthographic distance, and word frequency), which reflect visual; orthographic; and lexical properties, gradually increases towards the sensory end of the gradient. Although these properties showed opposite effect directions in fMRI and MEG, their association with the sensory end of the gradient was consistent across both neuroimaging modalities. In contrast, MEG revealed that properties reflecting a word's relation to its linguistic context (semantic similarity and position within the sentence) involve the heteromodal end of the gradient to a stronger extent. This dissociation between individual word and contextual properties was stable across earlier and later time windows during word presentation, indicating interactive processing of word representations and linguistic context at opposing ends of the principal gradient. To conclude, our findings indicate that the principal gradient underlies the organization of a range of linguistic representations while supporting a gradual distinction between context‐independent and context‐dependent representations. Furthermore, the gradient reveals convergent patterns across neuroimaging modalities (similar location along the gradient) in the presence of divergent responses (opposite effect directions).

          Abstract

          Functional magnetic resonance imaging and magnetoencephalography recordings ( N = 102) reveal that language comprehension during sentence reading is organized along a sensory‐to‐heteromodal cortical gradient. Word representations and linguistic context fall at opposite ends along this gradient, while both ends of the gradient are interactively activated through time.

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          lmerTest Package: Tests in Linear Mixed Effects Models

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            FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data

            This paper describes FieldTrip, an open source software package that we developed for the analysis of MEG, EEG, and other electrophysiological data. The software is implemented as a MATLAB toolbox and includes a complete set of consistent and user-friendly high-level functions that allow experimental neuroscientists to analyze experimental data. It includes algorithms for simple and advanced analysis, such as time-frequency analysis using multitapers, source reconstruction using dipoles, distributed sources and beamformers, connectivity analysis, and nonparametric statistical permutation tests at the channel and source level. The implementation as toolbox allows the user to perform elaborate and structured analyses of large data sets using the MATLAB command line and batch scripting. Furthermore, users and developers can easily extend the functionality and implement new algorithms. The modular design facilitates the reuse in other software packages.
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              Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI.

              A central goal in systems neuroscience is the parcellation of the cerebral cortex into discrete neurobiological "atoms". Resting-state functional magnetic resonance imaging (rs-fMRI) offers the possibility of in vivo human cortical parcellation. Almost all previous parcellations relied on 1 of 2 approaches. The local gradient approach detects abrupt transitions in functional connectivity patterns. These transitions potentially reflect cortical areal boundaries defined by histology or visuotopic fMRI. By contrast, the global similarity approach clusters similar functional connectivity patterns regardless of spatial proximity, resulting in parcels with homogeneous (similar) rs-fMRI signals. Here, we propose a gradient-weighted Markov Random Field (gwMRF) model integrating local gradient and global similarity approaches. Using task-fMRI and rs-fMRI across diverse acquisition protocols, we found gwMRF parcellations to be more homogeneous than 4 previously published parcellations. Furthermore, gwMRF parcellations agreed with the boundaries of certain cortical areas defined using histology and visuotopic fMRI. Some parcels captured subareal (somatotopic and visuotopic) features that likely reflect distinct computational units within known cortical areas. These results suggest that gwMRF parcellations reveal neurobiologically meaningful features of brain organization and are potentially useful for future applications requiring dimensionality reduction of voxel-wise fMRI data. Multiresolution parcellations generated from 1489 participants are publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal).
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                Author and article information

                Contributors
                susanne.eisenhauer@york.ac.uk
                Journal
                Hum Brain Mapp
                Hum Brain Mapp
                10.1002/(ISSN)1097-0193
                HBM
                Human Brain Mapping
                John Wiley & Sons, Inc. (Hoboken, USA )
                1065-9471
                1097-0193
                02 February 2024
                1 February 2024
                : 45
                : 2 ( doiID: 10.1002/hbm.v45.2 )
                : e26607
                Affiliations
                [ 1 ] Department of Psychology University of York York UK
                [ 2 ] York Neuroimaging Centre, Innovation Way York UK
                [ 3 ] Department of Psychology Northumbria University Newcastle upon Tyne UK
                [ 4 ] Department of Psychology Queen's University Kingston Ontario Canada
                Author notes
                [*] [* ] Correspondence

                Susanne Eisenhauer, Department of Psychology, University of York, York, UK.

                Email: susanne.eisenhauer@ 123456york.ac.uk

                Author information
                https://orcid.org/0000-0003-2570-0604
                Article
                HBM26607
                10.1002/hbm.26607
                10836172
                6e7116f0-b53b-4c3d-a60b-c60fca4edd6e
                © 2024 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 30 November 2023
                : 24 April 2023
                : 15 January 2024
                Page count
                Figures: 6, Tables: 0, Pages: 21, Words: 18602
                Funding
                Funded by: European Research Council , doi 10.13039/501100000781;
                Award ID: 771863
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                1 February, 2024
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.3.6 mode:remove_FC converted:02.02.2024

                Neurology
                context,cortical gradients,fmri,language,meg,word representations
                Neurology
                context, cortical gradients, fmri, language, meg, word representations

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