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2-CHANNEL CONVOLUTIONAL 3D DEEP NEURAL NETWORK (2CC3D) FOR FMRI ANALYSIS: ASD CLASSIFICATION AND FEATURE LEARNING.

Xiaoxiao Li¹, Nicha C Dvornek², Xenophon Papademetris²

¹Biomedical Engineering, Yale University, New Haven, CT USA.

Proceedings. IEEE International Symposium on Biomedical Imaging

|September 28, 2020

Summary

This study introduces a novel fMRI analysis method, 2CC3D, for identifying autism spectrum disorder (ASD). The approach enhances ASD classification accuracy by integrating spatial and temporal brain data using 3D CNNs.

Keywords:

Brain Machine Learning fMRI analysis

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Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

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Published on: August 5, 2014

Area of Science:

Neuroimaging
Machine Learning
Biomedical Engineering

Background:

Autism spectrum disorder (ASD) diagnosis relies on behavioral assessments, with limited objective biomarkers.
Functional magnetic resonance imaging (fMRI) offers a window into brain activity, but analyzing its complex spatio-temporal data for ASD remains challenging.

Purpose of the Study:

To develop and validate a novel whole-brain fMRI analysis scheme for improved autism spectrum disorder (ASD) identification.
To explore novel biological markers within fMRI data for enhanced ASD classification.
To leverage both spatial and temporal information in fMRI data for a more accurate diagnostic approach.

Main Methods:

A new fMRI analysis scheme, termed 2CC3D, was proposed, integrating sliding window temporal statistics (mean, standard deviation) with 3D convolutional neural networks (CNNs).
Sliding windows were employed to generate 2-channel images capturing temporal dynamics, which served as input for the 3D CNN.
The 3D CNN was utilized to extract spatial features indicative of ASD from the processed fMRI data.

Main Results:

The 2CC3D method successfully deciphered ASD-related fMRI spatial features directly from the CNN's convolutional layers.
Investigation into input formats and sliding window parameters optimized the method's performance.
The proposed method demonstrated the effectiveness of aligning 2-channel images for ASD classification.

Conclusions:

The 2CC3D method significantly improves ASD classification accuracy compared to traditional machine learning models, achieving an 8.5% increase in mean F-scores.
This approach offers a promising tool for identifying biological markers of ASD using fMRI.
The integration of spatio-temporal fMRI analysis holds potential for objective ASD diagnosis and research.