Evidence map›Paper›PMID 39045900›Full record

ArticleHuman brain mapping2024

4D dynamic spatial brain networks at rest linked to cognition show atypical variability and coupling in schizophrenia.

Krishna Pusuluri, Zening Fu, Robyn Miller, Godfrey Pearlson, Peter Kochunov, Theo G M Van Erp, Armin Iraji, Vince D Calhoun

Abstract read
In one paragraph

Article in Human brain mapping, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Krishna PusuluriTri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA.ORCID 0000-0001-6234-8393
Zening FuTri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA.
Robyn MillerTri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA.ORCID 0000-0002-4679-7567
Godfrey PearlsonDepartment of Psychiatry, Yale School of Medicine, New Haven, Connecticut, USA.
Peter KochunovDepartment of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA.
Theo G M Van ErpClinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California, USA.
Armin IrajiTri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA.
Vince D CalhounTri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA.

Funding

SOLAR-Eclipse Computational Tools for Imaging GeneticsR01EB015611 · NIBIB · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI KOCHUNOV, PETER V. · 2012 to 2024
$5.0M
Towards Multisystem-Brain Successful Aging in Schizophrenia SpectrumR01MH116948 · NIMH · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI HONG, L ELLIOT ELLIOT · 2018 to 2022
$3.7M
Data-driven solutions for temporal, spatial, and spatiotemporal dynamic functional connectivityR01MH123610 · NIMH · GEORGIA STATE UNIVERSITY · PI ADALI, TULAY, CALHOUN, VINCE D · 2021 to 2025
$3.1M
Lifespan Vascular Biology on White MatterRF1NS114628 · NINDS · UNIVERSITY OF MARYLAND BALTIMORE · PI HONG, L ELLIOT ELLIOT, KOCHUNOV, PETER V. · 2020 to 2020
$3.1M
The Vascular Axis in Schizophrenia Brain-Body AgingR01MH133812 · NIMH · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI L Elliot Elliot Hong · 2024 to 2026
$2.2M
Redefine Trans-Neuropsychiatric Disorder Brain Patterns through Big-Data and Machine LearningRF1MH123163 · NIMH · UNIVERSITY OF MARYLAND BALTIMORE · PI KOCHUNOV, PETER V., THOMPSON, PAUL M · 2021 to 2021
$1.2M
Mapping the Infant Brain Developmental Connectome: Temporally Precise Growth Trajectories of Changing Infant Brain TopologyR01MH119251 · NIMH · EMORY UNIVERSITY · PI IRAJI, ARMIN, SHULTZ, SARAH · 2020 to 2023
$1.0M
Lifespan Vascular Biology on White MatterR01NS114628 · NINDS · UNIVERSITY OF MARYLAND BALTIMORE · PI HONG, L ELLIOT ELLIOT, KOCHUNOV, PETER V. · 2024 to 2024
$770k
Hybrid GPU/CPU Computing Resource to Support Connectomic and GenomicsS10OD023696 · OD · UNIVERSITY OF MARYLAND BALTIMORE · PI KOCHUNOV, PETER V. · 2018 to 2018
$593k
National Science Foundation 2112455NIBIB NIH HHS R01 EB015611NIH HHS 5R01MH119251NIH HHS R01MH123610NIH HHS S10 OD023696NIMH NIH HHS R01 MH116948NIMH NIH HHS R01 MH119251NIMH NIH HHS R01 MH123610NIMH NIH HHS R01 MH133812NIMH NIH HHS RF1 MH123163NINDS NIH HHS R01 NS114628NINDS NIH HHS RF1 NS114628
6 · The paper itself

Abstract

Despite increasing interest in the dynamics of functional brain networks, most studies focus on the changing relationships over time between spatially static networks or regions. Here we propose an approach to study dynamic spatial brain networks in human resting state functional magnetic resonance imaging (rsfMRI) data and evaluate the temporal changes in the volumes of these 4D networks. Our results show significant volumetric coupling (i.e., synchronized shrinkage and growth) between networks during the scan, that we refer to as dynamic spatial network connectivity (dSNC). We find that several features of such dynamic spatial brain networks are associated with cognition, with higher dynamic variability in these networks and higher volumetric coupling between network pairs positively associated with cognitive performance. We show that these networks are modulated differently in individuals with schizophrenia versus typical controls, resulting in network growth or shrinkage, as well as altered focus of activity within a network. Schizophrenia also shows lower spatial dynamical variability in several networks, and lower volumetric coupling between pairs of networks, thus upholding the role of dynamic spatial brain networks in cognitive impairment seen in schizophrenia. Our data show evidence for the importance of studying the typically overlooked voxel-wise changes within and between brain networks.

Indexed as

ConnectomeMagnetic Resonance ImagingNerve NetSchizophreniaAdultBrainCognitive DysfunctionFemaleHumansMaleYoung Adultbrain networksdynamic spatial network connectivity (dSNC)inter‐network dynamic volumetric couplingresting state fMRI (rsfMRI)schizophreniaspatial network dynamics

Identifiers

PMID39045900
PMCPMC11267451

What OpenQuestion holds

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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.