Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Comparing the effect of multi-gradient echo and multi-band fMRI during a semantic task.

Imaging neuroscience (Cambridge, Mass.)·2026

Same author

Stable individual differences dominate adult brain volume variation until later life.

Imaging neuroscience (Cambridge, Mass.)·2026

Same author

Genetic risk of Alzheimer's disease is associated with loss of brain network segregation in midlife.

Communications biology·2026

Same author

Dissociable contributions of cortical thickness and surface area to cognitive ageing: evidence from multiple longitudinal cohorts.

bioRxiv : the preprint server for biology·2026

Same author

Does signed prediction error drive declarative memory? Evidence from variable choice paradigms.

Memory & cognition·2026

Same author

Re-visiting cognitive reserve: The importance of multiple brain measures.

Brain and neuroscience advances·2026

Same journal

Dataset of Optimized Structures of Aliphatic Chains Chemisorbed on Si(110) and Si(111) Surfaces via First-Principles Methods.

Scientific data·2026

Same journal

EURO-PROBE - Manual segmentations of the prostate and intraprostatic urethra on T2-weighted MRI.

Scientific data·2026

Same journal

Chromosome-Level Genome Assembly of Southern Africa Mozambique Tilapia (Oreochromis mossambicus) using PacBio HiFi and Omni-C sequencing.

Scientific data·2026

Same journal

Ovarian Stainology: Database of evidence-based immunohistochemical antigen expression in ovarian tumors.

Scientific data·2026

Same journal

A dataset of small protein conformational ensembles from all-atom molecular dynamics simulations.

Scientific data·2026

Same journal

A real-world Fitbit-derived dataset of activity, sleep, and heart rate with matched clinical factors in on-treatment lung cancer patients.

Scientific data·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 12, 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

A multi-subject, multi-modal human neuroimaging dataset.

Daniel G Wakeman¹, Richard N Henson²

¹Athinoula A. Martinos Center for Biomedical Imaging , Charlestown, Massachusetts 02129, USA ; MRC Cognition & Brain Sciences Unit , Cambridge CB2 7EF, England.

Scientific Data

|May 16, 2015

Summary

This summary is machine-generated.

This study presents multimodal neuroimaging data from 19 healthy volunteers, combining Electroencephalography (EEG), Magnetoencephalography (MEG), and functional Magnetic Resonance Imaging (fMRI). The dataset enables advanced methods for integrating brain imaging modalities to enhance spatial and temporal resolution.

More Related Videos

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms

Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms

Published on: March 3, 2023

Related Experiment Videos

Last Updated: Apr 12, 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

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms

Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms

Published on: March 3, 2023

Area of Science:

Neuroscience
Cognitive Science
Medical Imaging

Background:

Multimodal neuroimaging offers complementary insights into brain function and structure.
Integrating data from different modalities can overcome individual limitations in spatial or temporal resolution.

Purpose of the Study:

To present a novel dataset combining functional and structural neuroimaging data from healthy volunteers.
To facilitate the development of methods for multimodal neuroimaging data integration.
To provide a benchmark dataset for comparing neuroimaging analysis techniques.

Main Methods:

Acquisition of Electroencephalography (EEG), Magnetoencephalography (MEG), and functional Magnetic Resonance Imaging (fMRI) data.
Collection of structural data including T1-weighted MPRAGE, Multi-Echo FLASH, and Diffusion-weighted MR sequences.
Participants performed a perceptual task involving familiar, unfamiliar, and scrambled faces across multiple experimental runs and visits.

Main Results:

A comprehensive dataset comprising synchronized functional and structural neuroimaging data was successfully acquired.
The data allow for the exploration of integrated measures of functional and structural connectivity.
The dataset serves as a valuable resource for validating and comparing various neuroimaging analysis pipelines.

Conclusions:

The presented multimodal neuroimaging dataset is a valuable resource for advancing brain imaging research.
It supports the development of advanced analytical methods for improved spatial and temporal resolution in neuroscience.
The freely available data encourage collaborative research and methodological innovation in the field.