Classification of ROI-based fMRI data in short-term memory tasks using discriminant analysis and neural networks
Understanding brain function relies on identifying spatiotemporal patterns in brain activity. In recent years, machine learning methods have been widely used to detect connections between regions of interest (ROIs) involved in cognitive functions, as measured by the fMRI technique. However, it'...
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Neuroinformatics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fninf.2024.1480366/full |
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| author | Magdalena Fafrowicz Marcin Tutajewski Igor Sieradzki Jeremi K. Ochab Jeremi K. Ochab Anna Ceglarek-Sroka Koryna Lewandowska Tadeusz Marek Barbara Sikora-Wachowicz Igor T. Podolak Paweł Oświęcimka Paweł Oświęcimka Paweł Oświęcimka |
| author_facet | Magdalena Fafrowicz Marcin Tutajewski Igor Sieradzki Jeremi K. Ochab Jeremi K. Ochab Anna Ceglarek-Sroka Koryna Lewandowska Tadeusz Marek Barbara Sikora-Wachowicz Igor T. Podolak Paweł Oświęcimka Paweł Oświęcimka Paweł Oświęcimka |
| author_sort | Magdalena Fafrowicz |
| collection | DOAJ |
| description | Understanding brain function relies on identifying spatiotemporal patterns in brain activity. In recent years, machine learning methods have been widely used to detect connections between regions of interest (ROIs) involved in cognitive functions, as measured by the fMRI technique. However, it's essential to match the type of learning method to the problem type, and extracting the information about the most important ROI connections might be challenging. In this contribution, we used machine learning techniques to classify tasks in a working memory experiment and identify the brain areas involved in processing information. We employed classical discriminators and neural networks (convolutional and residual) to differentiate between brain responses to distinct types of visual stimuli (visuospatial and verbal) and different phases of the experiment (information encoding and retrieval). The best performance was achieved by the LGBM classifier with 1-time point input data during memory retrieval and a convolutional neural network during the encoding phase. Additionally, we developed an algorithm that took into account feature correlations to estimate the most important brain regions for the model's accuracy. Our findings suggest that from the perspective of considered models, brain signals related to the resting state have a similar degree of complexity to those related to the encoding phase, which does not improve the model's accuracy. However, during the retrieval phase, the signals were easily distinguished from the resting state, indicating their different structure. The study identified brain regions that are crucial for processing information in working memory, as well as the differences in the dynamics of encoding and retrieval processes. Furthermore, our findings indicate spatiotemporal distinctions related to these processes. The analysis confirmed the importance of the basal ganglia in processing information during the retrieval phase. The presented results reveal the benefits of applying machine learning algorithms to investigate working memory dynamics. |
| format | Article |
| id | doaj-art-23a2df9ccea449cda0c0479acdfbee5e |
| institution | OA Journals |
| issn | 1662-5196 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroinformatics |
| spelling | doaj-art-23a2df9ccea449cda0c0479acdfbee5e2025-08-20T02:32:14ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962024-12-011810.3389/fninf.2024.14803661480366Classification of ROI-based fMRI data in short-term memory tasks using discriminant analysis and neural networksMagdalena Fafrowicz0Marcin Tutajewski1Igor Sieradzki2Jeremi K. Ochab3Jeremi K. Ochab4Anna Ceglarek-Sroka5Koryna Lewandowska6Tadeusz Marek7Barbara Sikora-Wachowicz8Igor T. Podolak9Paweł Oświęcimka10Paweł Oświęcimka11Paweł Oświęcimka12Department of Cognitive Neuroscience and Neuroergonomics, Jagiellonian University, Kraków, PolandInstitute of Theoretical Physics, Jagiellonian University, Kraków, PolandGroup of Machine Learning Methods GMUM, Faculty of Mathematics and Computer Science, Jagiellonian University, Kraków, PolandInstitute of Theoretical Physics, Jagiellonian University, Kraków, PolandMark Kac Center for Complex Systems Research, Jagiellonian University, Kraków, PolandDepartment of Cognitive Neuroscience and Neuroergonomics, Jagiellonian University, Kraków, PolandDepartment of Cognitive Neuroscience and Neuroergonomics, Jagiellonian University, Kraków, PolandFaculty of Psychology, SWPS University, Katowice, PolandDepartment of Cognitive Neuroscience and Neuroergonomics, Jagiellonian University, Kraków, PolandGroup of Machine Learning Methods GMUM, Faculty of Mathematics and Computer Science, Jagiellonian University, Kraków, PolandInstitute of Theoretical Physics, Jagiellonian University, Kraków, PolandGroup of Machine Learning Methods GMUM, Faculty of Mathematics and Computer Science, Jagiellonian University, Kraków, PolandComplex Systems Theory Department, Institute of Nuclear Physics, Polish Academy of Sciences, Kraków, PolandUnderstanding brain function relies on identifying spatiotemporal patterns in brain activity. In recent years, machine learning methods have been widely used to detect connections between regions of interest (ROIs) involved in cognitive functions, as measured by the fMRI technique. However, it's essential to match the type of learning method to the problem type, and extracting the information about the most important ROI connections might be challenging. In this contribution, we used machine learning techniques to classify tasks in a working memory experiment and identify the brain areas involved in processing information. We employed classical discriminators and neural networks (convolutional and residual) to differentiate between brain responses to distinct types of visual stimuli (visuospatial and verbal) and different phases of the experiment (information encoding and retrieval). The best performance was achieved by the LGBM classifier with 1-time point input data during memory retrieval and a convolutional neural network during the encoding phase. Additionally, we developed an algorithm that took into account feature correlations to estimate the most important brain regions for the model's accuracy. Our findings suggest that from the perspective of considered models, brain signals related to the resting state have a similar degree of complexity to those related to the encoding phase, which does not improve the model's accuracy. However, during the retrieval phase, the signals were easily distinguished from the resting state, indicating their different structure. The study identified brain regions that are crucial for processing information in working memory, as well as the differences in the dynamics of encoding and retrieval processes. Furthermore, our findings indicate spatiotemporal distinctions related to these processes. The analysis confirmed the importance of the basal ganglia in processing information during the retrieval phase. The presented results reveal the benefits of applying machine learning algorithms to investigate working memory dynamics.https://www.frontiersin.org/articles/10.3389/fninf.2024.1480366/fullexplainabilityfMRIworking memoryROImachine learningneural network |
| spellingShingle | Magdalena Fafrowicz Marcin Tutajewski Igor Sieradzki Jeremi K. Ochab Jeremi K. Ochab Anna Ceglarek-Sroka Koryna Lewandowska Tadeusz Marek Barbara Sikora-Wachowicz Igor T. Podolak Paweł Oświęcimka Paweł Oświęcimka Paweł Oświęcimka Classification of ROI-based fMRI data in short-term memory tasks using discriminant analysis and neural networks Frontiers in Neuroinformatics explainability fMRI working memory ROI machine learning neural network |
| title | Classification of ROI-based fMRI data in short-term memory tasks using discriminant analysis and neural networks |
| title_full | Classification of ROI-based fMRI data in short-term memory tasks using discriminant analysis and neural networks |
| title_fullStr | Classification of ROI-based fMRI data in short-term memory tasks using discriminant analysis and neural networks |
| title_full_unstemmed | Classification of ROI-based fMRI data in short-term memory tasks using discriminant analysis and neural networks |
| title_short | Classification of ROI-based fMRI data in short-term memory tasks using discriminant analysis and neural networks |
| title_sort | classification of roi based fmri data in short term memory tasks using discriminant analysis and neural networks |
| topic | explainability fMRI working memory ROI machine learning neural network |
| url | https://www.frontiersin.org/articles/10.3389/fninf.2024.1480366/full |
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