Voxel Based Morphometry
Voxel Based Morphometry - Related Videos
09:17
Measuring Grey Matter Differences with Voxel-based Morphometry: The Musical Brain
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2023
Source: Laboratories of Jonas T. Kaplan and Sarah I. Gimbel—University of Southern California Experience shapes the brain. It is well understood that our brains are different as a result of learning. While many experience-related changes manifest themselves at the microscopic level, for example by neurochemical adjustments in the behavior of individual neurons, we may also examine anatomical changes to the structure of the brain at a macroscopic level. One famous example of this kind of change...
07:12
Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
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Cited by 23 •
2014
Informational connectivity measures the correspondence between time courses of multi-voxel information across different brain regions. Multi-voxel pattern discriminability time series are extracted from regions and compared, revealing networks that are not identified in a typical functional connectivity approach.
09:23
Shunt Surgery, Right Heart Catheterization, and Vascular Morphometry in a Rat Model for Flow-induced Pulmonary Arterial Hypertension
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Cited by 15 •
2017
This protocol describes a surgical procedure to create a model for flow-induced pulmonary arterial hypertension (PAH) in rats and the procedures to analyze the principle hemodynamic and histological end-points in this model.
08:52
3D Ultrasound Imaging: Fast and Cost-effective Morphometry of Musculoskeletal Tissue
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Cited by 34 •
2017
3D ultrasound imaging (3DUS) allows fast and cost-effective morphometry of musculoskeletal tissues. We present a protocol to measure muscle volume and fascicle length using 3DUS.
12:08
From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
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Cited by 14 •
2014
The bottleneck for cellular 3D electron microscopy is feature extraction (segmentation) in highly complex 3D density maps. We have developed a set of criteria, which provides guidance regarding which segmentation approach (manual, semi-automated, or automated) is best suited for different data types, thus providing a starting point for effective segmentation.
FAQs
Voxel Based Morphometry first converts each high-resolution structural MRI scan into tissue-specific information, then aligns those data to a common anatomical space. This alignment allows corresponding voxels to be compared across individuals or study conditions rather than treating each brain’s native geometry as directly equivalent. The resulting comparisons support systematic localization of structural differences associated with pharmacological exposure or intervention.
Segmentation separates the MRI data into gray-matter and white-matter components, enabling analyses of these tissue classes rather than undifferentiated brain images. Smoothing is applied before voxel-wise statistical testing as part of the data-processing sequence. Together, these steps prepare tissue-specific maps for detecting regional differences and relating them to drug exposure or treatment effects.
Voxel-wise differences identify brain regions where gray- or white-matter properties vary between relevant groups or conditions. In pharmacology, those patterns can be associated with drug exposure, substance use, or an intervention, but the method itself reports structural relationships rather than establishing that a drug directly caused them. Interpretation therefore centers on regional associations and their research context.
A typical workflow begins with high-resolution structural MRI acquisition, followed by segmentation of brain tissues. The tissue data are then aligned to a common anatomical space, smoothed, and submitted to voxel-wise statistical analysis. This sequence produces regional comparisons that can be examined for structural differences linked to pharmacological conditions, treatment interventions, or substance exposure.
Researchers can use Voxel Based Morphometry when they need to examine how a drug, substance exposure, or treatment intervention relates to regional brain remodeling or volume changes. The approach is useful for investigating neuroanatomical drug effects, comparing treatment-related structural patterns, and exploring whether regional MRI findings are associated with therapeutic benefit or adverse outcomes.
The analysis can reveal regional gray- and white-matter differences that help characterize neuroanatomical effects of pharmacological exposure or treatment. These findings may support investigations of treatment response and the search for imaging biomarkers linked to therapeutic benefit or adverse outcomes. Its value lies in connecting structural MRI patterns with clinically or experimentally relevant drug-related questions.