Evidence map›Paper›PMID 37609246›Full record

ArticlebioRxiv : the preprint server for biology2023

Within-Individual Organization of the Human Cerebral Cortex: Networks, Global Topography, and Function.

Jingnan Du, Lauren M DiNicola, Peter A Angeli, Noam Saadon-Grosman, Wendy Sun, Stephanie Kaiser, Joanna Ladopoulou, Aihuiping Xue, B T Thomas Yeo, Mark C Eldaief and 1 more

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Jingnan DuDepartment of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA.
Lauren M DiNicolaDepartment of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA.
Peter A AngeliDepartment of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA.
Noam Saadon-GrosmanDepartment of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA.
Wendy SunDepartment of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA.
Stephanie KaiserDepartment of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA.
Joanna LadopoulouDepartment of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA.
Aihuiping XueCentre for Sleep & Cognition & Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.
B T Thomas YeoCentre for Sleep & Cognition & Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.
Mark C EldaiefDepartment of Psychiatry, Massachusetts General Hospital, Charlestown, MA 02129, USA.
Randy L BucknerDepartment of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA.

Funding

Precision Mapping the Human Cerebellum for Neuromodulation and Understanding of Brain DisordersR01MH124004 · NIMH · HARVARD UNIVERSITY · PI BUCKNER, RANDY L, POLIMENI, JONATHAN RIZZO · 2020 to 2022
$1.8M
Upgrade Siemens MAGNETOM Trio to MAGNETOM Prisma Fit 3T Human MRI SystemS10OD020039 · OD · HARVARD UNIVERSITY · PI BUCKNER, RANDY L · 2015 to 2015
$600k
NIH HHS S10 OD020039NIMH NIH HHS R01 MH124004
6 · The paper itself

Abstract

The human cerebral cortex is populated by specialized regions that are organized into networks. Here we estimated networks using a Multi-Session Hierarchical Bayesian Model (MS-HBM) applied to intensively sampled within-individual functional MRI (fMRI) data. The network estimation procedure was initially developed and tested in two participants (each scanned 31 times) and then prospectively applied to 15 new participants (each scanned 8 to 11 times). Detailed analysis of the networks revealed a global organization. Locally organized first-order sensory and motor networks were surrounded by spatially adjacent second-order networks that also linked to distant regions. Third-order networks each possessed regions distributed widely throughout association cortex. Moreover, regions of distinct third-order networks displayed side-by-side juxtapositions with a pattern that repeated similarly across multiple cortical zones. We refer to these as Supra-Areal Association Megaclusters (SAAMs). Within each SAAM, two candidate control regions were typically adjacent to three separate domain-specialized regions. Independent task data were analyzed to explore functional response properties. The somatomotor and visual first-order networks responded to body movements and visual stimulation, respectively. A subset of the second-order networks responded to transients in an oddball detection task, consistent with a role in orienting to salient or novel events. The third-order networks, including distinct regions within each SAAM, showed two levels of functional specialization. Regions linked to candidate control networks responded to working memory load across multiple stimulus domains. The remaining regions within each SAAM did not track working memory load but rather dissociated across language, social, and spatial / episodic processing domains. These results support a model of the cerebral cortex in which progressively higher-order networks nest outwards from primary sensory and motor cortices. Within the apex zones of association cortex there is specialization of large-scale networks that divides domain-flexible from domain-specialized regions repeatedly across parietal, temporal, and prefrontal cortices. We discuss implications of these findings including how repeating organizational motifs may emerge during development.

Identifiers

PMID37609246
PMCPMC10441314

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.