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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: Jan 16, 2026

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Published on: June 13, 2025

Within-individual precision mapping of brain networks exclusively using task data.

Jingnan Du¹, Vaibhav Tripathi¹, Maxwell L Elliott¹

¹Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA.

|September 27, 2025

Summary

This summary is machine-generated.

Brain network mapping is possible using task-based functional connectivity data, not just resting-state data. Network similarity increases with more data, revealing stable individual brain architecture across different states.

Keywords:

association cortex default network functional connectivity individual differences precision functional mapping task fMRI

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Last Updated: Jan 16, 2026

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Published on: June 13, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Published on: September 12, 2011

Area of Science:

Neuroscience
Brain Imaging
Network Science

Background:

Functional connectivity analysis of resting-state data is the standard for mapping individual brain networks.
The utility of task-based data for estimating these precise brain networks remains largely unexplored.

Purpose of the Study:

To investigate if brain networks can be accurately estimated using only task-based functional connectivity data.
To compare network estimations from task data versus resting-state data.
To explore novel applications of task-based network estimation.

Main Methods:

Functional connectivity analysis was performed on both resting-state and task-based fMRI data.
Correlation matrices were computed and compared between data types.
Precision networks were estimated and their spatial overlap and predictive power were assessed.
Thalamic association zones were mapped using pooled resting-state and task data.

Main Results:

Correlation matrices derived from task data showed significant similarity to those from resting-state data.
The amount of data was the primary factor influencing the similarity between task and resting-state network estimations.
Task-derived precision networks exhibited strong spatial overlap with resting-state networks and predicted functional dissociations.
Task data enabled simultaneous network estimation and task response extraction.

Conclusions:

Brain network architecture is stable and idiosyncratic to the individual, persisting across different task states.
Task-based functional connectivity data can be reliably used for precision brain network mapping.
This approach offers new possibilities for studying brain organization, such as mapping thalamic connectivity.