Quantifying brain-functional dynamics using deep dynamical systems: technical considerations
Both mental health and mental illness unfold in complex and unpredictable ways. Novel artificial intelligence approaches from the area of dynamical systems reconstruction can characterize such dynamics and help understand the underlying brain mechanisms, which can also be used as potential biomarker...
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| Main Authors: | , , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
16 August 2024
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| In: |
iScience
Year: 2024, Volume: 27, Issue: 8, Pages: [1], 1-12 |
| ISSN: | 2589-0042 |
| DOI: | 10.1016/j.isci.2024.110545 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1016/j.isci.2024.110545 Verlag, kostenfrei, Volltext: https://www.sciencedirect.com/science/article/pii/S258900422401770X 
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| Author Notes: | Jiarui Chen, Anastasia Benedyk, Alexander Moldavski, Heike Tost, Andreas Meyer-Lindenberg, Urs Braun, Daniel Durstewitz, Georgia Koppe, and Emanuel Schwarz |
| Summary: | Both mental health and mental illness unfold in complex and unpredictable ways. Novel artificial intelligence approaches from the area of dynamical systems reconstruction can characterize such dynamics and help understand the underlying brain mechanisms, which can also be used as potential biomarkers. However, applying deep learning to model dynamical systems at the individual level must overcome numerous computational challenges to be reproducible and clinically useful. In this study, we performed an extensive analysis of these challenges using generative modeling of brain dynamics from fMRI data as an example and demonstrated their impact on classifying patients with schizophrenia and major depression. This study highlights the tendency of deep learning models to identify functionally unique solutions during parameter optimization, which severely impacts the reproducibility of downstream predictions. We hope this study guides the future development of individual-level generative models and similar machine learning approaches aimed at identifying reproducible biomarkers of mental illness. |
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| Item Description: | Gesehen am 04.02.2025 |
| Physical Description: | Online Resource |
| ISSN: | 2589-0042 |
| DOI: | 10.1016/j.isci.2024.110545 |