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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: Jun 23, 2025

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

In-Scanner Thoughts shape Resting-state Functional Connectivity: how participants "rest" matters.

J Gonzalez-Castillo¹, M A Spurney¹, K C Lam²

¹Section on Functional Imaging Methods, NIMH, NIH, Bethesda, Maryland, USA.

Biorxiv : the Preprint Server for Biology

|June 21, 2024

Summary

This summary is machine-generated.

Resting-state functional connectivity (rs-FC) is influenced by participants' in-scanner experiences. Mind wandering and other subjective states significantly alter rs-FC patterns, impacting clinical research findings.

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Acquisition of Resting-State Functional Magnetic Resonance Imaging Data in the Rat

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Area of Science:

Neuroimaging
Cognitive Neuroscience
Psychiatry

Background:

Resting-state functional connectivity (rs-FC) analysis is crucial for identifying brain alterations in clinical populations.
Standard preprocessing controls for confounds like motion and demographics, but not subjective experience.

Purpose of the Study:

To investigate the impact of in-scanner subjective experience on rs-FC measures.
To determine if rs-FC can predict specific aspects of in-scanner experience.

Main Methods:

Analysis of 471 resting-state fMRI (rs-fMRI) scans with associated experiential data.
Statistical comparison of FC patterns based on reported in-scanner experiences.
Machine learning models to predict experiential aspects from FC data.

Main Results:

Significant widespread differences in rs-FC were observed between scans with varying in-scanner experiences.
rs-FC successfully predicted demographic, cognitive, and clinical variables, similar to its predictive power for subjective experience.

Conclusions:

In-scanner experience is a critical factor influencing rs-fMRI derived FC.
Future rs-fMRI studies must account for subjective states to improve interpretability and reduce confounds.