What question did this study set out to answer?

The aim is to enhance the classification accuracy of Alzheimer's disease by integrating fMRI data with clinical tests using an explainable deep learning model.

February 2, 2026Brain and Behavior0 citationsOpen Access

Explainable Deep Learning Model for Multimodal Alzheimer's Disease Classification

Functional and Clinical: An Explainable Deep Learning Model for Multimodal Alzheimer's Disease Classification

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Authors

SWSamuel L. Warren AAAhmed Abdelrehem

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Overview

Demonstrates an accurate multimodal classification of Alzheimer's disease, highlighting the integration of imaging and clinical data.

Key Points

The aim is to enhance the classification accuracy of Alzheimer's disease by integrating fMRI data with clinical tests using an explainable deep learning model.
Utilized a 3D convolutional neural network model for data analysis.
Supplemented fMRI data with five clinical tests to address small dataset limitations.
Applied leave-one-out cross-validation to prevent data leakage.
Employed perturbation ranking to identify feature importance in classifications.
Achieved 90% accuracy in classifying Alzheimer's disease versus controls.
Outperformed a model using only fMRI data, which achieved 58% accuracy.
Clinical tests showed varying importance depending on the diagnostic group, especially MoCA for controls.

Cite This Study

Warren et al. (2026) studied this question.

synapsesocial.com/papers/6980ff37c1c9540dea812007 https://doi.org/10.1002/brb3.71240

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February 2, 2026Brain and Behavior0 citationsOpen Access

Explainable Deep Learning Model for Multimodal Alzheimer's Disease Classification

Functional and Clinical: An Explainable Deep Learning Model for Multimodal Alzheimer's Disease Classification

View Full Paper

Ask AI

Bookmark

Authors

SWSamuel L. Warren AAAhmed Abdelrehem

Discussion

Member takes

Overview

Demonstrates an accurate multimodal classification of Alzheimer's disease, highlighting the integration of imaging and clinical data.

Key Points

The aim is to enhance the classification accuracy of Alzheimer's disease by integrating fMRI data with clinical tests using an explainable deep learning model.
Utilized a 3D convolutional neural network model for data analysis.
Supplemented fMRI data with five clinical tests to address small dataset limitations.
Applied leave-one-out cross-validation to prevent data leakage.
Employed perturbation ranking to identify feature importance in classifications.
Achieved 90% accuracy in classifying Alzheimer's disease versus controls.
Outperformed a model using only fMRI data, which achieved 58% accuracy.
Clinical tests showed varying importance depending on the diagnostic group, especially MoCA for controls.

Cite This Study

Warren et al. (2026) studied this question.

synapsesocial.com/papers/6980ff37c1c9540dea812007 https://doi.org/10.1002/brb3.71240

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