Pairing explainable deep learning classification with clustering to uncover effects of schizophrenia upon whole brain functional network connectivity dynamics
Many studies have analyzed resting state functional magnetic resonance imaging (rs-fMRI) dynamic functional network connectivity (dFNC) data to elucidate the effects of neurological and neuropsychiatric disorders upon the interactions of brain regions over time. Existing studies often use either mac...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Neuroimage: Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666956023000314 |
| _version_ | 1797398442519035904 |
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| author | Charles A. Ellis Robyn L. Miller Vince D. Calhoun |
| author_facet | Charles A. Ellis Robyn L. Miller Vince D. Calhoun |
| author_sort | Charles A. Ellis |
| collection | DOAJ |
| description | Many studies have analyzed resting state functional magnetic resonance imaging (rs-fMRI) dynamic functional network connectivity (dFNC) data to elucidate the effects of neurological and neuropsychiatric disorders upon the interactions of brain regions over time. Existing studies often use either machine learning classification or clustering algorithms. Additionally, several studies have used clustering algorithms to extract features related to brain states trajectories that can be used to train interpretable classifiers. However, the combination of explainable dFNC classifiers followed by clustering algorithms is highly underutilized. In this study, we show how such an approach can be used to study the effects of schizophrenia (SZ) upon brain activity. Specifically, we train an explainable deep learning model to classify between individuals with SZ and healthy controls. We then cluster the resulting explanations, identifying discriminatory states of dFNC. We lastly apply several novel measures to quantify aspects of the classifier explanations and obtain additional insights into the effects of SZ upon brain network dynamics. Specifically, we uncover effects of schizophrenia upon subcortical, sensory, and cerebellar network interactions. We also find that individuals with SZ likely have reduced variability in overall brain activity and that the effects of SZ may be temporally localized. In addition to uncovering effects of SZ upon brain network dynamics, our approach could provide novel insights into a variety of neurological and neuropsychiatric disorders in future dFNC studies. |
| first_indexed | 2024-03-09T01:25:38Z |
| format | Article |
| id | doaj.art-b0d0c4c478754133835a49b5b5c0af78 |
| institution | Directory Open Access Journal |
| issn | 2666-9560 |
| language | English |
| last_indexed | 2024-03-09T01:25:38Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Neuroimage: Reports |
| spelling | doaj.art-b0d0c4c478754133835a49b5b5c0af782023-12-10T06:18:44ZengElsevierNeuroimage: Reports2666-95602023-12-0134100186Pairing explainable deep learning classification with clustering to uncover effects of schizophrenia upon whole brain functional network connectivity dynamicsCharles A. Ellis0Robyn L. Miller1Vince D. Calhoun2Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States; Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States; Corresponding author. Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States.Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States; Department of Computer Science, Georgia State University, Atlanta, GA, United StatesWallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States; Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States; Department of Computer Science, Georgia State University, Atlanta, GA, United StatesMany studies have analyzed resting state functional magnetic resonance imaging (rs-fMRI) dynamic functional network connectivity (dFNC) data to elucidate the effects of neurological and neuropsychiatric disorders upon the interactions of brain regions over time. Existing studies often use either machine learning classification or clustering algorithms. Additionally, several studies have used clustering algorithms to extract features related to brain states trajectories that can be used to train interpretable classifiers. However, the combination of explainable dFNC classifiers followed by clustering algorithms is highly underutilized. In this study, we show how such an approach can be used to study the effects of schizophrenia (SZ) upon brain activity. Specifically, we train an explainable deep learning model to classify between individuals with SZ and healthy controls. We then cluster the resulting explanations, identifying discriminatory states of dFNC. We lastly apply several novel measures to quantify aspects of the classifier explanations and obtain additional insights into the effects of SZ upon brain network dynamics. Specifically, we uncover effects of schizophrenia upon subcortical, sensory, and cerebellar network interactions. We also find that individuals with SZ likely have reduced variability in overall brain activity and that the effects of SZ may be temporally localized. In addition to uncovering effects of SZ upon brain network dynamics, our approach could provide novel insights into a variety of neurological and neuropsychiatric disorders in future dFNC studies.http://www.sciencedirect.com/science/article/pii/S2666956023000314Explainable AIDeep learningClusteringDynamic functional network connectivityResting state functional magnetic resonance imagingSchizophrenia |
| spellingShingle | Charles A. Ellis Robyn L. Miller Vince D. Calhoun Pairing explainable deep learning classification with clustering to uncover effects of schizophrenia upon whole brain functional network connectivity dynamics Neuroimage: Reports Explainable AI Deep learning Clustering Dynamic functional network connectivity Resting state functional magnetic resonance imaging Schizophrenia |
| title | Pairing explainable deep learning classification with clustering to uncover effects of schizophrenia upon whole brain functional network connectivity dynamics |
| title_full | Pairing explainable deep learning classification with clustering to uncover effects of schizophrenia upon whole brain functional network connectivity dynamics |
| title_fullStr | Pairing explainable deep learning classification with clustering to uncover effects of schizophrenia upon whole brain functional network connectivity dynamics |
| title_full_unstemmed | Pairing explainable deep learning classification with clustering to uncover effects of schizophrenia upon whole brain functional network connectivity dynamics |
| title_short | Pairing explainable deep learning classification with clustering to uncover effects of schizophrenia upon whole brain functional network connectivity dynamics |
| title_sort | pairing explainable deep learning classification with clustering to uncover effects of schizophrenia upon whole brain functional network connectivity dynamics |
| topic | Explainable AI Deep learning Clustering Dynamic functional network connectivity Resting state functional magnetic resonance imaging Schizophrenia |
| url | http://www.sciencedirect.com/science/article/pii/S2666956023000314 |
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