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Functional Connectivity Biomarker Extraction for Schizophrenia Based on Energy Landscape Machine Learning Techniques.
Janerra D Allen1, Sravani Varanasi1, Fei Han2
1Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250, USA.
Sensors (Basel, Switzerland)
|December 17, 2024
Summary
Schizophrenia patients show altered brain connectivity patterns, particularly in specific brain regions. Energy landscape analysis reveals these complex abnormalities, offering potential biomarkers for the disorder.
Area of Science:
- Neuroscience
- Psychiatry
- Medical Imaging
Background:
- Brain connectivity is crucial for understanding brain function and neuropsychiatric disorders.
- Schizophrenia is linked to impaired functional connectivity, but its complex patterns are difficult to characterize.
Purpose of the Study:
- To investigate abnormal functional connectivity patterns in schizophrenia using energy landscape analysis.
- To identify potential neuroimaging biomarkers for schizophrenia by analyzing brain connectivity.
Main Methods:
- Resting-state functional magnetic resonance imaging (fMRI) data from 55 schizophrenia patients and 63 healthy controls.
- Analysis of functional connectivity across 246 regions of interest (ROIs) using energy landscape (EL) analysis.
- Comparison of clinical and demographic data, including BPRS, APTS, VPTS, working memory, and processing speed scores.
Main Results:
- Significant differences in clinical measures (BPRS, APTS, VPTS, working memory, processing speed) between patients and controls.
- Abnormal energy landscape patterns identified in schizophrenia patients between the right and left rostral lingual gyrus.
- Aberrant connectivity patterns observed between the left lateral and orbital areas in 12/47 ROIs.
Conclusions:
- Energy landscape analysis effectively captures functional brain complexity in schizophrenia.
- The study highlights potential connectivity biomarkers for schizophrenia, linked to specific clinical features.
- The proposed imaging analysis workflow shows promise for identifying novel biomarkers in schizophrenia research.
