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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: Sep 11, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Precision brain morphometry using cluster scanning.

Maxwell L Elliott¹, Jared A Nielsen², Lindsay C Hanford¹

¹Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA, United States.

Imaging Neuroscience (Cambridge, Mass.)

|August 13, 2025

Summary

This summary is machine-generated.

Cluster scanning with rapid MRI acquisitions significantly reduces measurement error in brain morphometry. This technique enhances precision, boosting statistical power for detecting group differences and disease progression biomarkers.

Keywords:

ADNI Alzheimer’s disease MRI aging gray-to-white matter signal intensity ratio hippocampus

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Last Updated: Sep 11, 2025

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3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

Published on: May 12, 2019

Area of Science:

Neuroimaging
Radiology
Biomarkers

Background:

Measurement error in structural MRI limits detecting group differences and longitudinal changes.
Accelerated MRI sequences (~1 minute) offer morphometric errors comparable to traditional scans.

Purpose of the Study:

To evaluate the efficacy of cluster scanning for improving brain morphometric precision.
To assess the impact of pooling estimates from rapid MRI acquisitions on measurement error.

Main Methods:

A test-retest study involving 40 individuals across different age and cognitive groups.
Acquisition of single and clustered compressed sensing (CS) T1-weighted MRI scans.
Comparison of morphometric errors from CS scans with a traditional Alzheimer's Disease Neuroimaging Initiative (ADNI) protocol.

Main Results:

Single CS scans showed ~12% larger morphometric errors than ADNI scans.
Pooling estimates from four clustered CS acquisitions reduced errors by 34% compared to ADNI.
Pooling estimates from eight CS scans further reduced errors by 51% compared to ADNI.

Conclusions:

Cluster scanning with accelerated MRI significantly enhances measurement precision in brain morphometry.
This approach offers a method to improve statistical power for detecting neurodegenerative changes.
Cluster scanning holds potential for developing more sensitive biomarkers of disease progression.