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Updated: Nov 26, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

A diagnostic unified classification model for classifying multi-sized and multi-modal brain graphs using graph

Abdullah Yalçin¹, Islem Rekik²

¹BASIRA lab, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey.

Journal of Neuroscience Methods

|December 14, 2020

Summary

This summary is machine-generated.

This study introduces a unified brain graph classification model for multimodal neuroimaging data. The method aligns unpaired brain graphs to a common template, enabling robust classification across different modalities and sizes.

Keywords:

Graph node embedding Linear discriminant analysis Multi-modal brain connectome classification Multi-sized brain graph alignment Neurological disorder diagnosis

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Last Updated: Nov 26, 2025

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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

Area of Science:

Neuroimaging
Machine Learning
Graph Theory

Background:

Unified classification of multimodal brain graphs is challenging due to varying sizes and modalities.
Existing methods require modality-specific models or joint-modality datasets, limiting applicability.

Purpose of the Study:

To develop a unified brain graph classification model for unpaired multimodal data.
To enable classification of brain graphs regardless of size or neuroimaging modality.

Main Methods:

Proposed a graph alignment step to map heterogeneous multimodal brain graphs to a common template.
Applied Linear Discriminant Analysis (LDA) for supervised dimensionality reduction on aligned graphs.

Main Results:

Tested the unified model on unpaired functional and morphological MRI data for autism classification.
Achieved comparable performance to modality-specific models.

Conclusions:

The unified model shows promise for classifying unpaired multimodal brain graphs.
The graph alignment strategy effectively handles heterogeneous brain graph data.