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Published on: September 8, 2021
Functional brain networks in schizophrenia: a review
Vince D Calhoun1, Tom Eichele, Godfrey Pearlson
1The Mind Research Network Albuquerque, NM, USA.
Frontiers in Human Neuroscience
|September 10, 2009
Summary
Functional magnetic resonance imaging (fMRI) reveals brain network disruptions in schizophrenia. Independent component analysis (ICA) aids in identifying these functional connectivity alterations for potential disease biomarkers.
Area of Science:
- Neuroimaging
- Cognitive Neuroscience
- Psychiatric Disorders
Background:
- Functional magnetic resonance imaging (fMRI) is crucial for studying cognitive function and mental illness, particularly schizophrenia.
- Traditional fMRI analysis focuses on hemodynamic response amplitudes, but temporal correlations offer deeper insights.
Purpose of the Study:
- To review functional connectivity measures using fMRI, emphasizing Independent Component Analysis (ICA).
- To explore the potential of brain network analysis for schizophrenia biomarker discovery.
Main Methods:
- Estimating temporal correlations and computing functional connectivity maps.
- Utilizing multivariate decomposition techniques like ICA to identify brain networks.
- Analyzing functional network connectivity to evaluate inter-network interplay.
Main Results:
- Brain networks are consistently found during rest and task performance, offering robust measures of brain activity.
- Disrupted local processing and altered global connectivity characterize schizophrenia.
- Functional network connectivity analysis reveals disease-specific inter-network alterations.
Conclusions:
- fMRI-based brain network identification holds significant potential for understanding schizophrenia.
- New ICA variants show promise for extracting group-discriminative networks for disease classification.
- Functional connectivity analysis provides a comprehensive view of brain alterations in schizophrenia.
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