Evidence map›Paper›PMID 35914669›Full record

ArticleNeuroImage2022

A dynamic gradient architecture generates brain activity states.

Jesse A Brown, Alex J Lee, Lorenzo Pasquini, William W Seeley

Abstract read
In one paragraph

Article in NeuroImage, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing 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

30 citing papers in PubMed.

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  15. Sex-specific topological structure associated with dementia via latent space estimation.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Jesse A BrownMemory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA. Electronic address: jesse.brown@ucsf.edu.
Alex J LeeMemory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA.
Lorenzo PasquiniMemory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA.
William W SeeleyMemory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA.

Funding

Mapping the Human Connectome: Structure, Function, and HeritabilityU54MH091657 · NIMH · WASHINGTON UNIVERSITY · PI UGURBIL, KAMIL, VAN ESSEN, DAVID C · 2010 to 2014
$34.7M
BMI Bioinformatics Training GrantsT32GM067547 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI HERNANDEZ, RYAN D. · 2003 to 2022
$6.6M
Dynamic neural systems underlying socioemotional functionR00AG065457 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI PASQUINI, LORENZO · 2022 to 2024
$898k
Patient-tailored network prediction of neurodegenerative disease progressionK01AG055698 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI BROWN, JESSE AARON · 2017 to 2022
$779k
Dynamic neural systems underlying socioemotional functionK99AG065457 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI PASQUINI, LORENZO · 2020 to 2021
$250k
NIA NIH HHS K01 AG055698NIA NIH HHS K99 AG065457NIA NIH HHS R00 AG065457NIGMS NIH HHS T32 GM067547NIMH NIH HHS U54 MH091657
6 · The paper itself

Abstract

The human brain exhibits a diverse yet constrained range of activity states. While these states can be faithfully represented in a low-dimensional latent space, our understanding of the constitutive functional anatomy is still evolving. Here we applied dimensionality reduction to task-free and task fMRI data to address whether latent dimensions reflect intrinsic systems and if so, how these systems may interact to generate different activity states. We find that each dimension represents a dynamic activity gradient, including a primary unipolar sensory-association gradient underlying the global signal. The gradients appear stable across individuals and cognitive states, while recapitulating key functional connectivity properties including anticorrelation, modularity, and regional hubness. We then use dynamical systems modeling to show that gradients causally interact via state-specific coupling parameters to create distinct brain activity patterns. Together, these findings indicate that a set of dynamic, intrinsic spatial gradients interact to determine the repertoire of possible brain activity states.

Indexed as

BrainNerve NetBrain MappingHumansMagnetic Resonance ImagingDimensionality reductionDynamical systemsFunctional connectivityGlobal signalGradients

Identifiers

PMID35914669
PMCPMC9585924

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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 Socratic graph.