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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Shrnutí:	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 imaging (task-based fMRI) has been limited. This study aimed to develop an explainable machine learning model to classify WM performance levels in PD based on task-based fMRI data. Methods We enrolled 45 patients with PD and 15 healthy controls (HCs), all of whom performed an n -back WM task in an MRI scanner. Patients were stratified into three subgroups based on their 3-back task performance: better, intermediate, and worse WM. A three-dimensional convolutional neural network (3D-CNN) model, pre-trained with a 3D convolutional autoencoder, was developed to perform binary classifications between group pairs. Results The model achieved an accuracy of 93.3% in discriminating task-based fMRI images of PD patients with worse WM from HCs, surpassing the mean accuracy of three expert radiologists (70.0%). Saliency maps identified brain regions influencing the model’s decisions, including the dorsolateral prefrontal cortex and superior/inferior parietal lobules. These regions were consistent with both the areas with intergroup differences in the task-based fMRI data and the anatomical areas that are crucial for better WM performance. Conclusions We developed an explainable deep learning model that is capable of classifying WM performance levels in PD using task-based fMRI. This approach may enhance the objective and interpretable assessment of brain function in clinical neuroimaging practice.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	0340-5354 1432-1459 1432-1459
DOI:	10.1007/s00415-025-13438-w