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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Selecting Multiple Node Statistics Jointly from Functional Connectivity Networks for Brain Disorders Identification.

Yangyang Zhang^1,2, Yanfang Xue¹, Xiao Wu¹

¹School of Mathematics Science, Liaocheng University, Liaocheng, China.

Brain Topography

|September 22, 2022

Summary

This summary is machine-generated.

This study introduces a new method, multiple node statistics feature selection (MNSFS), for analyzing functional connectivity networks (FCNs) to detect brain diseases like mild cognitive impairment (MCI) and major depressive disorder (MDD). The approach improves diagnostic accuracy by jointly selecting multiple node statistics.

Keywords:

Brain disorder Feature selection Functional connectivity network Node statistic sgLASSO

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Area of Science:

Neuroscience
Computational Psychiatry
Medical Image Analysis

Background:

Functional connectivity network (FCN) analysis aids in early diagnosis of brain disorders like mild cognitive impairment (MCI) and major depressive disorder (MDD).
Current feature representation methods for FCNs often rely on single node statistics, leading to limitations and incomplete analysis.
Identifying discriminative brain regions and their associated network properties is crucial for understanding neurodevelopmental origins of brain disorders.

Purpose of the Study:

To develop a novel scheme for selecting multiple node statistics jointly from FCNs for automated classification.
To improve the accuracy and interpretability of automated classification for brain diseases.
To identify specific brain regions and network statistics that are indicative of MCI and MDD.

Main Methods:

Proposed a multiple node statistics feature selection (MNSFS) scheme.
Extracted multiple statistics from individual nodes within FCNs, grouping similar statistics.
Utilized sparse group least absolute shrinkage and selection operator (sgLASSO) for joint selection of nodes and statistics.

Main Results:

The MNSFS scheme demonstrated superior classification accuracy in identifying subjects with MCI and MDD compared to existing methods.
The method successfully identified discriminative brain regions and specific associated statistics, enhancing the interpretability of the classification.
Validation was performed on two public benchmark datasets.

Conclusions:

The proposed MNSFS scheme offers a more comprehensive and effective approach to feature representation in FCN analysis.
This method enhances automated classification of brain diseases by leveraging multiple node statistics simultaneously.
The findings contribute to a better understanding of the neurobiological underpinnings of MCI and MDD through interpretable feature selection.