Functional Connectivity Biomarker Extraction for Schizophrenia Based on Energy Landscape Machine Learning Techniques
Brain connectivity represents the functional organization of the brain, which is an important indicator for evaluating neuropsychiatric disorders and treatment effects. Schizophrenia is associated with impaired functional connectivity but characterizing the complex abnormality patterns has been chal...
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/23/7742 |
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| author | Janerra D. Allen Sravani Varanasi Fei Han L. Elliot Hong Fow-Sen Choa |
| author_facet | Janerra D. Allen Sravani Varanasi Fei Han L. Elliot Hong Fow-Sen Choa |
| author_sort | Janerra D. Allen |
| collection | DOAJ |
| description | Brain connectivity represents the functional organization of the brain, which is an important indicator for evaluating neuropsychiatric disorders and treatment effects. Schizophrenia is associated with impaired functional connectivity but characterizing the complex abnormality patterns has been challenging. In this work, we used resting-state functional magnetic resonance imaging (fMRI) data to measure functional connectivity between 55 schizophrenia patients and 63 healthy controls across 246 regions of interest (ROIs) and extracted the disease-related connectivity patterns using energy landscape (EL) analysis. EL analysis captures the complexity of brain function in schizophrenia by focusing on functional brain state stability and region-specific dynamics. Age, sex, and smoker demographics between patients and controls were not significantly different. However, significant patient and control differences were found for the brief psychiatric rating scale (BPRS), auditory perceptual trait and state (APTS), visual perceptual trait and state (VPTS), working memory score, and processing speed score. We found that the brains of individuals with schizophrenia have abnormal energy landscape patterns between the right and left rostral lingual gyrus, and between the left lateral and orbital area in 12/47 regions. The results demonstrate the potential of the proposed imaging analysis workflow to identify potential connectivity biomarkers by indexing specific clinical features in schizophrenia patients. |
| format | Article |
| id | doaj-art-c1e1bb2612de44da9102f5861d8dab4c |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-c1e1bb2612de44da9102f5861d8dab4c2025-08-20T02:50:37ZengMDPI AGSensors1424-82202024-12-012423774210.3390/s24237742Functional Connectivity Biomarker Extraction for Schizophrenia Based on Energy Landscape Machine Learning TechniquesJanerra D. Allen0Sravani Varanasi1Fei Han2L. Elliot Hong3Fow-Sen Choa4Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250, USADepartment of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250, USAThe Hilltop Institute, University of Maryland, Baltimore County, Baltimore, MD 21250, USADepartment of Psychiatry, University of Texas Health Science Center, Houston, TX 77030, USADepartment of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250, USABrain connectivity represents the functional organization of the brain, which is an important indicator for evaluating neuropsychiatric disorders and treatment effects. Schizophrenia is associated with impaired functional connectivity but characterizing the complex abnormality patterns has been challenging. In this work, we used resting-state functional magnetic resonance imaging (fMRI) data to measure functional connectivity between 55 schizophrenia patients and 63 healthy controls across 246 regions of interest (ROIs) and extracted the disease-related connectivity patterns using energy landscape (EL) analysis. EL analysis captures the complexity of brain function in schizophrenia by focusing on functional brain state stability and region-specific dynamics. Age, sex, and smoker demographics between patients and controls were not significantly different. However, significant patient and control differences were found for the brief psychiatric rating scale (BPRS), auditory perceptual trait and state (APTS), visual perceptual trait and state (VPTS), working memory score, and processing speed score. We found that the brains of individuals with schizophrenia have abnormal energy landscape patterns between the right and left rostral lingual gyrus, and between the left lateral and orbital area in 12/47 regions. The results demonstrate the potential of the proposed imaging analysis workflow to identify potential connectivity biomarkers by indexing specific clinical features in schizophrenia patients.https://www.mdpi.com/1424-8220/24/23/7742energy landscapefMRIfunctional connectivitybiomarkerschizophrenia |
| spellingShingle | Janerra D. Allen Sravani Varanasi Fei Han L. Elliot Hong Fow-Sen Choa Functional Connectivity Biomarker Extraction for Schizophrenia Based on Energy Landscape Machine Learning Techniques Sensors energy landscape fMRI functional connectivity biomarker schizophrenia |
| title | Functional Connectivity Biomarker Extraction for Schizophrenia Based on Energy Landscape Machine Learning Techniques |
| title_full | Functional Connectivity Biomarker Extraction for Schizophrenia Based on Energy Landscape Machine Learning Techniques |
| title_fullStr | Functional Connectivity Biomarker Extraction for Schizophrenia Based on Energy Landscape Machine Learning Techniques |
| title_full_unstemmed | Functional Connectivity Biomarker Extraction for Schizophrenia Based on Energy Landscape Machine Learning Techniques |
| title_short | Functional Connectivity Biomarker Extraction for Schizophrenia Based on Energy Landscape Machine Learning Techniques |
| title_sort | functional connectivity biomarker extraction for schizophrenia based on energy landscape machine learning techniques |
| topic | energy landscape fMRI functional connectivity biomarker schizophrenia |
| url | https://www.mdpi.com/1424-8220/24/23/7742 |
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