.png){kind=icon}
.png){kind=icon}
Return
Explainable machine learning algorithm predicting working memory performance in Parkinson’s disease using task-fMRI
.png){kind=icon}
PRE
AI
DOI：10.1007/s00415-025-13438-w.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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. 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. 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. 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.
Keywords:
Working memory
Parkinson’s disease
3D convolutional neural network
3D convolutional autoencoder
Task-based fMRI
Explainable machine learning
Journal
IF:
4.6
Papers:
1.6K
Citations:
2.5W
View more organizations