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Related Concept Videos

Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
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A DEEP LEARNING FRAMEWORK TO LOCALIZE THE EPILEPTOGENIC ZONE FROM DYNAMIC FUNCTIONAL CONNECTIVITY USING A COMBINED

Naresh Nandakumar1, David Hsu2, Raheel Ahmed3

  • 1Department of Electrical and Computer Engineering, Johns Hopkins University, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging
|October 25, 2024
PubMed
Summary

This study introduces an automated framework using dynamic functional connectivity from resting-state fMRI (rs-fMRI) to pinpoint the epileptogenic zone (EZ) in epilepsy patients. The novel approach combines graph convolutional and transformer networks for improved localization accuracy.

Keywords:
Deep LearningEpilepsyFunctional ConnectivityGraph ConvolutionsTransformer Models

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Area of Science:

  • Neuroimaging
  • Epilepsy Research
  • Machine Learning in Medicine

Background:

  • Accurate localization of the epileptogenic zone (EZ) is crucial for treating drug-resistant epilepsy.
  • Resting-state functional MRI (rs-fMRI) reveals dynamic brain connectivity patterns.
  • Existing methods for EZ localization have limitations.

Purpose of the Study:

  • To develop and validate the first automated framework for EZ localization using dynamic functional connectivity from rs-fMRI.
  • To leverage advanced deep learning techniques for improved diagnostic accuracy in epilepsy.

Main Methods:

  • An automated framework integrating graph convolutional networks (GCN) for feature extraction and transformer networks with attention mechanisms.
  • Utilized dynamic functional connectivity derived from rs-fMRI data.
  • Trained on augmented data from the Human Connectome Project and evaluated on a clinical epilepsy dataset.

Main Results:

  • The developed framework demonstrated superior performance in localizing the EZ compared to ablated and baseline models.
  • The combination of GCN and transformer networks significantly enhanced localization accuracy.
  • Data augmentation strategies proved beneficial for model training and generalization.

Conclusions:

  • The proposed automated framework effectively utilizes dynamic functional connectivity from rs-fMRI for EZ localization.
  • This approach offers a promising non-invasive tool for epilepsy diagnosis and surgical planning.
  • The findings highlight the potential of deep learning in advancing neurological disorder research.