Explainable machine learning algorithm predicting working memory performance in Parkinson’s disease using task-fMRI

Background Parkinson’s disease (PD) is a neurodegenerative disorder that affects both motor and cognitive functions, particularly working memory (WM). Machine learning offers an advantage for decoding complex brain activity patterns, but its application to task-based functional magnetic resonance im...

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Vydáno v:	Journal of neurology Ročník 272; číslo 10; s. 692
Hlavní autoři:	Yasuda, Eiji, Hattori, Takaaki, Shimano, Kaoru, Hase, Takeshi, Oyama, Jun, Yamagiwa, Ken, Kawauchi, Miho, Horovitz, Silvina G., Lungu, Codrin, Matsuzawa, Hitoshi, Hallett, Mark
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 14.10.2025 Springer Nature B.V
Témata:	Activity patterns Aged Algorithms Alzheimer's disease Brain - diagnostic imaging Brain - physiopathology Brain research Cognition & reasoning Cognitive ability Consent Cortex (parietal) Data processing Decision making Deep learning Female Functional magnetic resonance imaging Humans Learning algorithms Machine Learning Magnetic resonance imaging Magnetic Resonance Imaging - methods Male Medical imaging Medicine Medicine & Public Health Memory Memory, Short-Term - physiology Middle Aged Movement disorders Neural networks Neural Networks, Computer Neurodegenerative diseases Neuroimaging Neurological disorders Neurology Neuroradiology Neurosciences Original Communication Parkinson Disease - complications Parkinson Disease - diagnostic imaging Parkinson Disease - physiopathology Parkinson Disease - psychology Parkinson's disease Prefrontal cortex Scanners Working memory Task-based fMRI Parkinson’s disease 3D convolutional neural network Explainable machine learning 3D convolutional autoencoder
ISSN:	0340-5354, 1432-1459, 1432-1459
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