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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Spatial structural abnormality maps associated with cognitive and physical performance in relapsing-remitting

Zhizheng Zhuo¹, Ningnannan Zhang², Feng Ao^3,4

¹Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China. 18612161210@163.com.

European Radiology

|October 29, 2024

Summary

This summary is machine-generated.

Deep learning identified five relapsing-remitting multiple sclerosis (RRMS) subtypes based on brain abnormalities, revealing distinct cognitive and physical performance patterns. These spatial maps aid in understanding RRMS heterogeneity and personalized treatment strategies.

Keywords:

Brain abnormality Cognition Deep learning Disability Relapsing-remitting multiple sclerosis

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Area of Science:

Neuroscience
Artificial Intelligence
Medical Imaging

Background:

Relapsing-remitting multiple sclerosis (RRMS) presents significant heterogeneity in cognitive and physical performance.
Understanding the underlying brain abnormalities is crucial for effective patient management.

Purpose of the Study:

To characterize brain abnormalities associated with cognitive and physical performance in RRMS patients.
To utilize a deep learning algorithm for identifying distinct RRMS subtypes.

Main Methods:

A 3D nnU-Net deep learning model was used to generate spatial abnormality maps from T1-weighted MRI scans of 281 RRMS patients.
Patients were categorized into subtypes, and clinical/MRI features were compared using Kruskal-Wallis tests.
Kaplan-Meier analysis assessed disability progression, with validation on two additional datasets.

Main Results:

Five distinct RRMS subtypes were identified, each with unique cognitive and physical performance profiles and disability progression risks.
Subtypes varied in cognitive scores, physical disability severity, brain volume loss, and relapse rates.
Validation on external datasets confirmed the robustness of the identified subtypes.

Conclusions:

Spatial abnormality maps derived from deep learning can effectively explain the heterogeneity in RRMS.
These findings support the potential for stratified management and personalized treatment approaches in RRMS.