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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 Stimulation (TMS).

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Related Experiment Video

Updated: Jun 25, 2026

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Functional brain segmentation using inter-subject correlation in fMRI.

Jukka-Pekka Kauppi^1,2, Juha Pajula^3,4, Jari Niemi³

¹Department of Mathematical Information Technology, University of Jyväskylä, Jyväskylä, Finland.

Human Brain Mapping

|March 16, 2017

Summary

This summary is machine-generated.

A new method called functional segmentation inter-subject correlation analysis (FuSeISC) analyzes brain activity from fMRI scans. It identifies brain areas with similar or variable processing across individuals, aiding the study of complex sensory information.

Keywords:

Gaussian mixture model functional magnetic resonance imaging functional segmentation human brain inter-subject correlation inter-subject variability naturalistic stimulation shared nearest-neighbor graph

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Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Published on: July 21, 2021

Area of Science:

Neuroimaging
Cognitive Neuroscience
Data Analysis

Background:

The human brain processes vast sensory information, necessitating advanced analysis techniques for neuroimaging data.
Modern experiments increasingly use naturalistic stimuli to better understand brain function in daily-life contexts.
Analyzing functional magnetic resonance imaging (fMRI) data from such complex experiments presents significant challenges.

Purpose of the Study:

To introduce and evaluate a novel exploratory data-analysis approach, functional segmentation inter-subject correlation analysis (FuSeISC).
To enable the analysis of fMRI datasets collected during naturalistic, daily-life-mimicking experimental setups.
To characterize brain areas based on both consistent and variable processing patterns across subjects.

Main Methods:

Functional segmentation inter-subject correlation analysis (FuSeISC) was developed for analyzing fMRI data.
The FuSeISC method was tested on fMRI datasets from block-design stimuli (37 subjects) and naturalistic auditory narratives (19 subjects).
A criterion-based sparsification of the shared nearest-neighbor graph was employed for cluster detection in noisy data, outperforming existing methods.

Main Results:

FuSeISC identified spatially local and/or bilaterally symmetric clusters in cortical areas relevant to stimulus processing.
The method successfully characterized brain regions exhibiting similar and highly variable processing across subjects.
The proposed sparsification technique demonstrated superior performance in cluster detection compared to Ward's method, affinity propagation, and K-means ++ on synthetic data.

Conclusions:

FuSeISC is a valuable tool for spatial exploration of fMRI data, particularly with naturalistic stimuli.
The method offers potential applications in generating functional brain atlases that include diverse processing areas.
FuSeISC provides a novel approach to understanding inter-subject variability in brain function.