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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 11, 2026

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

Published on: July 14, 2023

Network-based statistic: identifying differences in brain networks.

Andrew Zalesky¹, Alex Fornito, Edward T Bullmore

¹Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Australia. azalesky@unimelb.edu.au

|July 6, 2010

Summary

This summary is machine-generated.

The network-based statistic (NBS) method identifies brain network differences in schizophrenia. NBS found widespread dysconnections in schizophrenia patients, outperforming traditional analyses.

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Last Updated: Jun 11, 2026

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

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Published on: July 14, 2023

Modeling the Functional Network for Spatial Navigation in the Human Brain

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Published on: October 13, 2023

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Area of Science:

Neuroscience
Network Science
Statistical Analysis

Background:

Brain connectivity is often modeled as networks or graphs.
Identifying specific network connections related to diagnostic status or cognitive measures is crucial.

Purpose of the Study:

Introduce the novel network-based statistic (NBS) method.
Evaluate the statistical power of NBS using receiver operating characteristic (ROC) curves.
Demonstrate NBS utility in a schizophrenia case-control study using resting-state functional MRI data.

Main Methods:

The network-based statistic (NBS) controls the family-wise error rate in mass-univariate testing on graph connections.
NBS leverages the interconnectedness of network components for increased statistical power.
Applied NBS to resting-state functional MRI data from schizophrenia patients and controls.

Main Results:

NBS identified a significant, large-scale dysconnected subnetwork in individuals with schizophrenia.
This subnetwork primarily involved fronto-temporal and occipito-temporal connections.
A standard mass-univariate analysis, controlled with the false discovery rate, failed to detect this subnetwork.

Conclusions:

The network-based statistic (NBS) is an effective method for detecting subtle, distributed network alterations in brain connectivity.
NBS offers enhanced power for identifying diagnostic biomarkers in neuroimaging studies.
This approach revealed significant network dysconnectivity in schizophrenia that is missed by conventional methods.