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Frequency-aware Interpretable Deep Learning Framework For Alzheimer's Disease Classification Using Rs-fmri.

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Frequency-aware Interpretable Deep Learning Framework For Alzheimer's Disease Classification Using Rs-fmri.

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Frequency-Aware Interpretable Deep Learning Framework for Alzheimer's Disease Classification Using rs-fMRI.

Yutong Gao¹, Robyn L Miller¹, Vince D Calhoun¹

¹Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.

Biorxiv : the Preprint Server for Biology

|September 26, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces FINE, a deep learning model that analyzes brain connectivity patterns in Alzheimer's disease (AD). FINE identifies frequency-specific disruptions, offering new biomarkers for AD detection and understanding disease mechanisms.

Keywords:

Brain Dynamics Deep Learning Explainable AI Frequency-Aware rs-fMRI

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Area of Science:

Neuroscience
Artificial Intelligence
Medical Imaging

Background:

Alzheimer's disease (AD) diagnosis relies on identifying alterations in brain connectivity.
Understanding spectral and temporal changes in brain networks is crucial for developing effective biomarkers.

Purpose of the Study:

To introduce FINE (Frequency-aware Interpretable Neural Encoder), a novel deep learning model.
To capture multi-scale temporal and frequency-specific patterns in dynamic functional network connectivity (dFNC) from resting-state fMRI data.
To enhance Alzheimer's disease classification and provide interpretable insights into brain connectivity disruptions.

Main Methods:

Developed FINE, a deep learning model integrating convolutional layers, wavelet layers, transformers, and static encoders.

Employed an end-to-end framework for joint modeling of temporal evolution and spectral content of brain networks.

Utilized gradient-based saliency maps for frequency-wise interpretability, aligned with statistical group differences.

Main Results:

Evaluated on the OASIS-3 dataset (856 subjects), FINE achieved an ROC-AUC of 0.769 for AD classification.
Identified frequency-specific connectivity disruptions in subcortical, sensorimotor, and cerebellar networks.
Demonstrated the model's ability to reveal potential robust, biologically meaningful biomarkers for AD.

Conclusions:

Frequency-aware modeling and interpretable architectures can significantly advance AD classification.
FINE provides valuable insights into the functional disruption of AD-related brain dynamics.
The approach holds promise for developing more informative biomarkers and deepening the understanding of Alzheimer's disease mechanisms.