Enhancing cerebral infarct classification by automatically extracting relevant fMRI features
Abstract Accurate detection of cortical infarct is critical for timely treatment and improved patient outcomes. Current brain imaging methods often require invasive procedures that primarily assess blood vessel and structural white matter damage. There is a need for non-invasive approaches, such as...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-06-01
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| Series: | Brain Informatics |
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| Online Access: | https://doi.org/10.1186/s40708-025-00259-w |
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| author | Vitaly I. Dobromyslin Wenjin Zhou for the Alzheimer’s Disease Neuroimaging Initiative |
| author_facet | Vitaly I. Dobromyslin Wenjin Zhou for the Alzheimer’s Disease Neuroimaging Initiative |
| author_sort | Vitaly I. Dobromyslin |
| collection | DOAJ |
| description | Abstract Accurate detection of cortical infarct is critical for timely treatment and improved patient outcomes. Current brain imaging methods often require invasive procedures that primarily assess blood vessel and structural white matter damage. There is a need for non-invasive approaches, such as functional MRI (fMRI), that better reflect neuronal viability. This study utilized automated machine learning (auto-ML) techniques to identify novel infarct-specific fMRI biomarkers specifically related to chronic cortical infarcts. We analyzed resting-state fMRI data from the multi-center ADNI dataset, which included 20 chronic infarct patients and 30 cognitively normal (CN) controls. This study utilized automated machine learning (auto-ML) techniques to identify novel fMRI biomarkers specifically related to chronic cortical infarcts. Surface-based registration methods were applied to minimize partial-volume effects typically associated with lower resolution fMRI data. We evaluated the performance of 7 previously known fMRI biomarkers alongside 107 new auto-generated fMRI biomarkers across 33 different classification models. Our analysis identified 6 new fMRI biomarkers that substantially improved infarct detection performance compared to previously established metrics. The best-performing combination of biomarkers and classifiers achieved a cross-validation ROC score of 0.791, closely matching the accuracy of diffusion-weighted imaging methods used in acute stroke detection. Our proposed auto-ML fMRI infarct-detection technique demonstrated robustness across diverse imaging sites and scanner types, highlighting the potential of automated feature extraction to significantly enhance non-invasive infarct detection. |
| format | Article |
| id | doaj-art-5860a90dbafb4a3f910dbad29aad602c |
| institution | OA Journals |
| issn | 2198-4018 2198-4026 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Brain Informatics |
| spelling | doaj-art-5860a90dbafb4a3f910dbad29aad602c2025-08-20T02:10:32ZengSpringerOpenBrain Informatics2198-40182198-40262025-06-0112111010.1186/s40708-025-00259-wEnhancing cerebral infarct classification by automatically extracting relevant fMRI featuresVitaly I. Dobromyslin0Wenjin Zhou1for the Alzheimer’s Disease Neuroimaging InitiativeUniversity of MassachusettsUniversity of MassachusettsAbstract Accurate detection of cortical infarct is critical for timely treatment and improved patient outcomes. Current brain imaging methods often require invasive procedures that primarily assess blood vessel and structural white matter damage. There is a need for non-invasive approaches, such as functional MRI (fMRI), that better reflect neuronal viability. This study utilized automated machine learning (auto-ML) techniques to identify novel infarct-specific fMRI biomarkers specifically related to chronic cortical infarcts. We analyzed resting-state fMRI data from the multi-center ADNI dataset, which included 20 chronic infarct patients and 30 cognitively normal (CN) controls. This study utilized automated machine learning (auto-ML) techniques to identify novel fMRI biomarkers specifically related to chronic cortical infarcts. Surface-based registration methods were applied to minimize partial-volume effects typically associated with lower resolution fMRI data. We evaluated the performance of 7 previously known fMRI biomarkers alongside 107 new auto-generated fMRI biomarkers across 33 different classification models. Our analysis identified 6 new fMRI biomarkers that substantially improved infarct detection performance compared to previously established metrics. The best-performing combination of biomarkers and classifiers achieved a cross-validation ROC score of 0.791, closely matching the accuracy of diffusion-weighted imaging methods used in acute stroke detection. Our proposed auto-ML fMRI infarct-detection technique demonstrated robustness across diverse imaging sites and scanner types, highlighting the potential of automated feature extraction to significantly enhance non-invasive infarct detection.https://doi.org/10.1186/s40708-025-00259-wCortical infarctInfarct detectionResting-state fMRIMachine learningAuto-ML |
| spellingShingle | Vitaly I. Dobromyslin Wenjin Zhou for the Alzheimer’s Disease Neuroimaging Initiative Enhancing cerebral infarct classification by automatically extracting relevant fMRI features Brain Informatics Cortical infarct Infarct detection Resting-state fMRI Machine learning Auto-ML |
| title | Enhancing cerebral infarct classification by automatically extracting relevant fMRI features |
| title_full | Enhancing cerebral infarct classification by automatically extracting relevant fMRI features |
| title_fullStr | Enhancing cerebral infarct classification by automatically extracting relevant fMRI features |
| title_full_unstemmed | Enhancing cerebral infarct classification by automatically extracting relevant fMRI features |
| title_short | Enhancing cerebral infarct classification by automatically extracting relevant fMRI features |
| title_sort | enhancing cerebral infarct classification by automatically extracting relevant fmri features |
| topic | Cortical infarct Infarct detection Resting-state fMRI Machine learning Auto-ML |
| url | https://doi.org/10.1186/s40708-025-00259-w |
| work_keys_str_mv | AT vitalyidobromyslin enhancingcerebralinfarctclassificationbyautomaticallyextractingrelevantfmrifeatures AT wenjinzhou enhancingcerebralinfarctclassificationbyautomaticallyextractingrelevantfmrifeatures AT forthealzheimersdiseaseneuroimaginginitiative enhancingcerebralinfarctclassificationbyautomaticallyextractingrelevantfmrifeatures |