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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Related Experiment Video

Updated: Aug 1, 2025

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Published on: November 13, 2016

Brain Connectivity Signature Extractions from TMS Invoked EEGs.

Deepa Gupta¹, Xiaoming Du², Ann Summerfelt²

¹Computer Science and Electrical Engineering, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21227, USA.

Sensors (Basel, Switzerland)

|April 28, 2023

Summary

Brain connectivity signatures derived from electroencephalography (EEG) and transcranial magnetic stimulation (TMS) can help identify psychiatric disorders. This study reveals stable signatures for medical applications.

Keywords:

EEG brain connectivity signatures electroencephalography machine learning network neuro-psychiatric diagnosis schizophrenia signal processing

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Last Updated: Aug 1, 2025

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

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Published on: November 13, 2016

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Published on: May 12, 2014

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Published on: October 30, 2018

Area of Science:

Neuroscience
Biophysics
Medical Imaging

Background:

Brain connectivity abnormalities are increasingly linked to psychiatric disorders.
Brain connectivity signatures are valuable for patient identification, monitoring, and treatment.
Electroencephalography (EEG) combined with transcranial magnetic stimulation (TMS) offers high spatiotemporal resolution for analyzing brain connectivity.

Purpose of the Study:

To investigate EEG-based brain connectivity signatures using energy landscape analysis.
To analyze TMS-evoked EEG signals for identifying distinct brain connectivity patterns.
To establish a baseline for future dense electrode studies in medical applications.

Main Methods:

Analysis of EEG-alpha wave activity following TMS to the left motor cortex, left prefrontal cortex, and cerebellar vermis.
Application of energy landscape analysis techniques to source-localized EEG data.
Statistical analysis using two-sample t-tests with Bonferroni correction to identify significant connectivity signatures.

Main Results:

Cerebellar vermis stimulation yielded the most connectivity signatures.
Left motor cortex stimulation identified a sensorimotor network state.
Six reliable and stable connectivity signatures were identified from 29 potential signatures.

Conclusions:

The study identified localized cortical connectivity signatures with potential medical applications.
Findings provide a foundation for future research using denser EEG electrode configurations.
EEG-TMS combined with energy landscape analysis is a promising method for characterizing brain connectivity.