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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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Updated: Jan 18, 2026

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

Instantaneous Frequency: A New Functional Biomarker for Dynamic Brain Causal Networks.

Haoteng Tang¹, Siyuan Dai², Lei Guo²

¹Department of Computer Science, University of Texas Rio Grande Valley, Edinburg, Texas, USA.

AI in Neuroscience

|September 8, 2025

Summary

This summary is machine-generated.

Instantaneous frequency (IF) analysis offers a new way to study brain networks using fMRI. This method effectively differentiates groups based on cognitive decline, sex, and sleep, showing promise for early diagnosis.

Keywords:

biomarker brain effective network clinical phenotypes cognitive impairment fMRI instantaneous frequency

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Last Updated: Jan 18, 2026

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

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Published on: March 21, 2019

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Published on: October 30, 2018

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

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Published on: June 30, 2018

Area of Science:

Neuroimaging
Network Neuroscience
Signal Processing

Background:

Introduces instantaneous frequency (IF) analysis as a novel method for characterizing dynamic brain causal networks.
Utilizes functional magnetic resonance imaging (fMRI) blood-oxygen-level-dependent (BOLD) signals.

Purpose of the Study:

To present and validate IF analysis for dynamic brain network characterization.
To explore the potential of IF as a biomarker for global network oscillatory behavior.

Main Methods:

Effective connectivity is estimated using dynamic causal modeling (DCM).
IF sequences are derived from DCM, with average IF serving as a key metric.
Analysis includes data from Alzheimer's Disease Neuroimaging Initiative, Open Access Series of Imaging Studies, and Human Connectome Project.

Main Results:

Demonstrates efficacy in distinguishing between clinical and demographic groups.
Identifies significant group differences in IF metrics across cognitive decline stages (normal control, MCI, AD), sex, and sleep quality.
Highlights IF's sensitivity in detecting variations in brain network dynamics.

Conclusions:

IF analysis is a promising, sensitive indicator for early diagnosis and monitoring of neurodegenerative and cognitive conditions.
Average IF shows potential as a biomarker for global network oscillatory behavior.
The method effectively differentiates groups based on cognitive status, sex, and sleep quality.