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Bridging Continuous and Discrete Models of the Anterior Temporal Lobe via Cortical Gradients

Overview

  1. Bangor University
  2. Institut des Maladies Neurodégénératives-UMR 5293
  3. Institute of Psychology, Chinese Academy of Sciences
  4. University of York
Institutions: Bangor University (United Kingdom); Institut des Maladies Neurodégénératives (France); Chinese Academy of Sciences (China); University of York (United Kingdom)
Dates: published online 24 March 2026
Type: Preprint · Language: English
License: CC BY
Identifiers: DOI 10.21203/rs.3.rs-9023856/v1 · OpenAlex W7140209385
Open access: green, a free copy (OpenAlex)
Status: code on request (how the code is found and verified)
Categories: structural MRI / diffusion (modality), cognitive (subfield)
Methods: Spectral & time-frequency, Statistics, Preprocessing, Connectivity, fMRI & imaging, Physiology & signal measures
Keywords: Anterior temporal lobe, DWI, tractography, cortical organisation, large-scale networks, semantic cognition, semantic memory, semantic control
Topic: Neuroscience and Music Perception (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: European Commission (866533); National Natural Science Foundation of China (1U54MH091657, 32300881); National Institutes of Health (5u54mh091657-05, 1U54‐MH‐091657); McDonnell Center for Systems Neuroscience (1U54MH091657)
Citations: not cited yet (Europe PMC); 88 references in the paper

Abstract

The anterior temporal lobe (ATL) is critical for semantic cognition, yet its organisation remains debated. Some evidence supports a patchwork of discrete, domain-specific parcels, while the graded hub hypothesis proposes smooth, ordered transitions. We examined regions along ATL’s curvature, testing whether connectivity profiles varied systematically or discontinuously along whole-brain dimensions of functional connectivity. The structural and functional connectivity of ATL varied largely systematically on the heteromodal-unimodal dimension, with intermediate ventral parcels showing stronger heteromodal connectivity; however, medial parcels deviated from this pattern. On the auditory-motor to visual dimension, there were partial dorsal-to-medial shifts in connectivity, but parcels at both ends did not follow this trend. There were also systematic dorsal-to-medial connectivity shifts between default and control networks. Individual differences in structural connectivity predicted activation in auditory tasks. These results largely support a graded hub model of ATL, while also revealing non-systematic functional variation that may enable flexible semantic processing.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

The paper says that its authors' code is available on request: it was not published with the paper, so there is nothing to verify.

Code availability

Whole-brain deterministic tractography was performed using StarTrack software (https://www.mr-startrack.com). Diffusion geometry processing and template generation were conducted using ANTs (https://github.com/ANTsX/ANTs) and the Tract Querier package (https://github.com/demianw/tract_querier). Functional connectivity preprocessing and analysis used the CONN toolbox version 20a (http://www.nitrc.org/projects/conn) and SPM12 (https://www.fil.ion.ucl.ac.uk/spm/), running in MATLAB 2019a. Statistical analyses were performed using SPSS v28 (statistical analysis). The custom scripts used to project tract terminations into gradient space and perform the statistical comparisons reported in this study are available from the corresponding author upon request.

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

A tracing map links a paper to the code its authors published: this paper has none (its code is available on request), so it has no map.

Data

Datasets cited

Data availability

The diffusion data used in this study are publicly available from the Human Connectome Project (HCP) database (https://db.humanconnectome.org). Users must agree to the HCP Open Access Data Use Terms to access the dataset. The functional connectivity data used in this study can be accessed via written request to the ethics committee at the York Neuroimaging Centre.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 30 September 2026: the first record

Recorded: type, language, journal, dates, 6 authors, 8 keywords, 4 funders, 81 references.

Cite

This paper

Alam, T. G., de Schotten, M. T., Binney, R., Wang, X., Margulies, D., & Jefferies, B. (2026). Bridging Continuous and Discrete Models of the Anterior Temporal Lobe via Cortical Gradients. Research Square (preprint). https://doi.org/10.21203/rs.3.rs-9023856/v1

BibTeX

@article{alam2026bridging,
author = {Alam, Tirso Gonzalez and de Schotten, Michel Thiebaut and Binney, Richard and Wang, Xiuyi and Margulies, Daniel and Jefferies, Beth},
title = {{Bridging Continuous and Discrete Models of the Anterior Temporal Lobe via Cortical Gradients}},
journal = {Research Square (preprint)},
year = {2026},
month = mar,
publisher = {Research Square},
issn = {2693-5015},
doi = {10.21203/rs.3.rs-9023856/v1},
url = {https://doi.org/10.21203/rs.3.rs-9023856/v1}
}

RIS

TY - JOUR
AU - Alam, Tirso Gonzalez
AU - de Schotten, Michel Thiebaut
AU - Binney, Richard
AU - Wang, Xiuyi
AU - Margulies, Daniel
AU - Jefferies, Beth
TI - Bridging Continuous and Discrete Models of the Anterior Temporal Lobe via Cortical Gradients
T2 - Research Square (preprint)
J2 - Res Sq
PY - 2026
DA - 2026/03/24
SN - 2693-5015
PB - Research Square
DO - 10.21203/rs.3.rs-9023856/v1
UR - https://doi.org/10.21203/rs.3.rs-9023856/v1
LA - en
ER -

CSL-JSON

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