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Individualized rTMS Treatment for Depression using an fMRI-Based Targeting Method

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Published on: August 2, 2021

Protocol for a machine learning algorithm predicting depressive disorders using the T1w/T2w ratio.

David A A Baranger¹, Yaroslav O Halchenko², Skye Satz¹

¹Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA, USA.

|January 10, 2022

Summary

This summary is machine-generated.

The T1w/T2w ratio, a novel MRI measure sensitive to cortical myelin, can classify depressive disorders. We present a data processing pipeline for this MRI analysis, aiding future research and clinical applications.

Keywords:

Cortical myelin Depression Elastic net LDA MRI Machine learning T1w/T2w ratio

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Published on: January 11, 2020

Area of Science:

Neuroimaging
Biomedical signal processing

Background:

Cortical myelin changes are implicated in various neurological and psychiatric disorders.
The T1w/T2w ratio is a promising magnetic resonance imaging (MRI) biomarker for assessing cortical myelin content.
Standardized pipelines are needed for reliable T1w/T2w ratio analysis in clinical research.

Purpose of the Study:

To introduce a comprehensive data processing pipeline for T1w/T2w ratio analysis.
To validate the utility of the T1w/T2w ratio in classifying individuals with depressive disorders.
To provide accessible scripts for the T1w/T2w ratio and machine learning pipeline.

Main Methods:

Development of novel quality assurance and data analysis pipelines for T1w/T2w MRI data.
Application of the T1w/T2w ratio in conjunction with machine learning algorithms.
Classification of individuals with depressive disorders versus healthy controls using the developed pipeline.

Main Results:

The developed pipeline enables robust T1w/T2w ratio computation and analysis.
The T1w/T2w ratio, analyzed via machine learning, demonstrated potential in classifying depressive disorders.
The study provides reproducible scripts for T1w/T2w ratio-based classification.

Conclusions:

The T1w/T2w ratio is a valuable MRI measure for detecting myelin-related changes in disorders like depression.
The presented pipeline facilitates the application of T1w/T2w ratio analysis in clinical and research settings.
This work supports the use of advanced neuroimaging biomarkers for psychiatric disorder classification.