Networks extracted from nonlinear fMRI connectivity exhibit unique spatial variation and enhanced sensitivity to differences between individuals with schizophrenia and controls

Schizophrenia is a chronic brain disorder associated with widespread alterations in functional brain connectivity. Although data-driven approaches such as independent component analysis are often used to study how schizophrenia impacts linearly connected networks, alterations within the underlying nonlinear functional connectivity structure remain largely unknown. Here we report the analysis of networks from explicitly nonlinear functional magnetic resonance imaging connectivity in a case-control dataset. We found systematic spatial variation, with higher nonlinear weight within core regions, suggesting that linear analyses underestimate functional connectivity within network centers. We also found that a unique nonlinear network incorporating default-mode, cingulo-opercular and central executive regions exhibits hypoconnectivity in schizophrenia, indicating that typically hidden connectivity patterns may reflect inefficient network integration in psychosis. Moreover, nonlinear networks including those previously implicated in auditory, linguistic and self-referential cognition exhibit heightened statistical sensitivity to schizophrenia diagnosis, collectively underscoring the potential of our methodology to resolve complex brain phenomena and transform clinical connectivity analysis.

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Publication Details

Journal
Nature Mental Health
Published
2024-11-21
DOI
https://doi.org/10.1038/s44220-024-00341-y
Citations
5
Primary Topic
Functional Brain Connectivity Studies
Type
article
Field-Weighted Citation Impact
1.71

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article

Networks extracted from nonlinear fMRI connectivity exhibit unique spatial variation and enhanced sensitivity to differences between individuals with schizophrenia and controls

Judith M. Ford, Pablo Andrés-Camazón, Peter Kochunov, Mukesh Dhamala et al.
5 citations
Nature Mental Health
Functional Brain Connectivity Studies
1.71
article

Networks extracted from nonlinear fMRI connectivity exhibit unique spatial variation and enhanced sensitivity to differences between individuals with schizophrenia and controls

Judith M. Ford, Pablo Andrés-Camazón, Peter Kochunov, Mukesh Dhamala, Vince D. Calhoun, Tülay Adalı, Katarzyna Kazimierczak, Bhim M. Adhikari, Armin Iraji, Theo G.M. van Erp, Jiayu Chen, Spencer Kinsey
article en
5 citations

Abstract

Schizophrenia is a chronic brain disorder associated with widespread alterations in functional brain connectivity. Although data-driven approaches such as independent component analysis are often used to study how schizophrenia impacts linearly connected networks, alterations within the underlying nonlinear functional connectivity structure remain largely unknown. Here we report the analysis of networks from explicitly nonlinear functional magnetic resonance imaging connectivity in a case-control dataset. We found systematic spatial variation, with higher nonlinear weight within core regions, suggesting that linear analyses underestimate functional connectivity within network centers. We also found that a unique nonlinear network incorporating default-mode, cingulo-opercular and central executive regions exhibits hypoconnectivity in schizophrenia, indicating that typically hidden connectivity patterns may reflect inefficient network integration in psychosis. Moreover, nonlinear networks including those previously implicated in auditory, linguistic and self-referential cognition exhibit heightened statistical sensitivity to schizophrenia diagnosis, collectively underscoring the potential of our methodology to resolve complex brain phenomena and transform clinical connectivity analysis.

Nature Mental HealthVol. 2(12)
University of Maryland, Baltimore (US), San Francisco VA Medical Center (US), University of California, San Francisco (US), Georgia State University (US), University of California, Irvine (US), Hospital General Universitario Gregorio Marañón (ES), Centro de Investigación Biomédica en Red de Salud Mental (ES), Center for Translational Research in Neuroimaging and Data Science (US), University of Bergen (NO), University of Baltimore (US), The University of Texas Health Science Center at Houston (US)
National Science Foundation, U.S. Department of Veterans Affairs, Georgia State University, Instituto de Investigación Sanitaria Gregorio Marañón, National Institutes of Health, National Center for Research Resources
Openalex Percentile: Top 15%
Functional Brain Connectivity Studies
1.71
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