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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

OPTIMIZING BRAIN CONNECTIVITY NETWORKS FOR DISEASE CLASSIFICATION USING EPIC.

Gautam Prasad¹, Shantanu H Joshi², Paul M Thompson¹

¹Imaging Genetics Center, Inst. for Neuroimaging and Informatics, Keck Sch. of Med. of USC, Los Angeles, CA, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging

|November 19, 2014

Summary

This summary is machine-generated.

We developed a new method, evolving partitions to improve connectomics (EPIC), to optimize brain segmentation for disease classification. This approach achieved 85% accuracy in identifying Alzheimer's disease using brain connectivity data alone.

Keywords:

Cortical parcellation classification connectivity matrix partition simulated annealing

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Area of Science:

Neuroimaging
Computational Neuroscience
Machine Learning in Medicine

Background:

Structural connectivity analysis relies on parcellating the cortex into anatomical regions to generate connectivity matrices.
Standard parcellation methods may not be optimal for disease classification tasks.
Accurate brain connectivity analysis is crucial for understanding neurological disorders.

Purpose of the Study:

To develop and validate a novel method for adaptively selecting optimal cortical segmentations.
To maximize feature-based disease classification performance using brain connectivity data.
To improve the accuracy of identifying neurological diseases through optimized connectomics.

Main Methods:

Proposed evolving partitions to improve connectomics (EPIC), a method for optimizing cortical parcellation.
Represented cortical segmentations as set partitions of anatomical regions.
Utilized support vector machine (SVM) classification and simulated annealing for optimization, validated via cross-validation on the ADNI-2 dataset.

Main Results:

The EPIC method successfully identified an optimal cortical parcellation for disease classification.
Achieved 85% classification accuracy using only structural connectivity information on the ADNI-2 dataset.
Demonstrated the effectiveness of adaptive segmentation in enhancing diagnostic capabilities.

Conclusions:

Adaptive cortical segmentation significantly improves disease classification performance in brain connectivity analysis.
EPIC offers a powerful approach for optimizing connectomics data for clinical applications.
This method holds promise for advancing the diagnosis and understanding of neurological diseases.