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Updated: Sep 24, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Published on: October 11, 2018

Multi-Source Domain Adaptation Techniques for Mitigating Batch Effects: A Comparative Study.

Rohan Panda¹, Sunil Vasu Kalmady^2,3, Russell Greiner^3,4,5

¹Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, United States.

Frontiers in Neuroinformatics

|May 9, 2022

Summary

This summary is machine-generated.

Multi-source domain adaptation (MSDA) methods effectively address batch effects in combined neuroimaging datasets. These techniques enable robust deep learning models for analyzing resting-state fMRI data across different sites and conditions.

Keywords:

ADHD ASD batch effects deep learning multi-source domain adaptation resting-state fMRI

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Area of Science:

Neuroimaging
Machine Learning
Biomedical Data Analysis

Background:

Deep learning (DL) in neuroimaging requires large datasets, often infeasible from single sites due to cost and time.
Combining data from multiple sites creates heterogeneous datasets, introducing batch effects that hinder model training.

Purpose of the Study:

To analyze and compare the performance of popular multi-source domain adaptation (MSDA) methods.
To evaluate MSDA effectiveness in predicting labels (illness, age, sex) from resting-state fMRI (rs-fMRI) data.
To assess the impact of class imbalance and the number of data sites on MSDA performance.

Main Methods:

Comparison of four MSDA methods: MDAN, DARN, MDMN, and M³SDA.
Application to two public rs-fMRI datasets (ABIDE 1 and ADHD-200).
Evaluation under varying conditions, including class imbalance and differing numbers of data acquisition sites.

Main Results:

MSDA methods demonstrate effectiveness in mitigating batch effects for neuroimaging analysis.
Performance variations observed among different MSDA techniques under various conditions.
Successful prediction of illness, age, and sex labels using domain-invariant features.

Conclusions:

MSDA techniques are valuable for building robust deep learning models with multi-site neuroimaging data.
The choice of MSDA method and data conditions significantly impact model performance.
This study highlights the potential of MSDA in advancing large-scale rs-fMRI data analysis.