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Exploiting macro- and micro-structural brain changes for improved Parkinson's disease classification from MRI data.
Milton Camacho1,2, Matthias Wilms3,4,5,6, Hannes Almgren6,7
1Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB, Canada. milton.camachocamach@ucalgary.ca.
NPJ Parkinson'S Disease
|February 26, 2024
Summary
This study developed an explainable deep learning model using multimodal MRI to accurately classify Parkinson's disease (PD). The model highlights micro-structural brain changes, crucial for early PD diagnosis and prognosis.
Area of Science:
- Neuroimaging
- Machine Learning
- Neurology
Background:
- Parkinson's disease (PD) is a common neurodegenerative disorder.
- Early and accurate diagnosis of PD is critical for effective management and improving patient outcomes.
- Diagnosing PD, especially in its early stages, presents significant challenges.
Purpose of the Study:
- To develop and validate an explainable deep learning model for classifying Parkinson's disease (PD) using multimodal magnetic resonance imaging (MRI) data.
- To enhance the interpretability of the classification model by identifying key brain regions contributing to PD diagnosis.
- To leverage a large, diverse dataset for robust model training and evaluation.
Main Methods:
- Utilized a large dataset of 1264 multimodal MRI scans (T1-weighted and diffusion-tensor imaging) from 611 PD patients and 653 healthy controls.
- Employed a convolutional neural network (CNN) trained on pre-processed imaging data and demographic information.
- Implemented SmoothGrad saliency maps for model explainability, identifying critical brain regions for PD classification.
Main Results:
- Achieved a high classification performance with ROC-AUC of 0.89, 80.8% accuracy, 82.4% specificity, and 79.1% sensitivity on the test set.
- Saliency maps indicated that diffusion tensor imaging (DTI) metrics, particularly fractional anisotropy, were more influential than T1-weighted MRI.
- Identified key brain regions, including the brainstem, thalamus, amygdala, hippocampus, and cortical areas, as important for PD classification.
Conclusions:
- The developed explainable deep learning model accurately classifies PD patients from healthy controls using multimodal MRI.
- Micro-structural brain changes, as detected by DTI, play a significant role in the progression of Parkinson's disease.
- The model's explainability provides clinically relevant insights into the neuroimaging biomarkers of PD.
