Voxel Level Analysis
Voxel Level Analysis - Related Videos
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.
09:17
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...
11:52
Analysis of LINE-1 Retrotransposition at the Single Nucleus Level
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2016
Here we employ FISH methodology to track LINE-1 retrotransposition at the single nuclei level in chromosome spreads of HepG2 cell lines stably expressing synthetic LINE-1.
14:53
Analysis of Global RNA Synthesis at the Single Cell Level following Hypoxia
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Cited by 2 •
2014
We describe a technique for analysis of global RNA synthesis in hypoxia using imaging. Click-chemistry labeling of RNA has not previously been performed under hypoxia and allows visualization of global RNA changes at the single cell level. This approach complements the existing averaged RNA techniques, allowing direct visualization of cell-to-cell changes in global RNA synthesis.
07:32
Genome-wide Analysis of Aminoacylation (Charging) Levels of tRNA Using Microarrays
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Cited by 11 •
2010
We describe a method for microarray analysis to determine relative aminoacylation levels of all tRNAs from S. cerivisiae.
FAQs
The method first spatially aligns images so that corresponding three-dimensional locations can be compared across participants or experimental conditions. It then evaluates measurements at each voxel with statistical models, allowing researchers to identify localized differences rather than averaging results across an entire anatomical region. This alignment-and-comparison process supports more spatially specific investigation of brain-behavior relationships.
Analyzing individual voxels can reveal localized patterns that may be obscured when measurements are combined into a larger anatomical region. That finer spatial resolution is useful when behavioral research asks whether neural variation relates to a particular aspect of learning, memory, decision-making, or motor performance. Regional analyses remain broader, whereas voxel-wise analysis retains more information about where differences occur.
Statistical models provide the framework for evaluating voxel measurements across participants or conditions and for identifying differences that are localized in the brain. Without this modeling step, aligned images would only show corresponding positions, not whether their measurements differ in a meaningful analytical comparison. The resulting patterns can then be examined in relation to behavioral measures.
A typical workflow uses three central stages supported by the method: spatial alignment of brain images, comparison of corresponding voxels across participants or conditions, and statistical modeling of those measurements. Researchers can then examine the localized patterns produced by the analysis alongside behavioral measures. The workflow preserves three-dimensional spatial information while connecting image-based variation with observed performance or behavior.
It is useful when a study seeks to connect localized neural variation with behavior rather than relying only on broad anatomical summaries. Behavioral applications described for this approach include learning, memory, decision-making, and motor performance. The same strategy also supports investigations of neurological or psychiatric conditions, where spatially detailed brain differences may be relevant to behavioral patterns.
It can identify spatially localized patterns in brain structure or activity and relate those patterns to measured behaviors. For example, researchers may examine whether voxel-wise variation corresponds with differences in learning, memory, decision-making, or motor performance. The outcome is a detailed view of brain-behavior relationships that preserves location-specific information instead of reducing findings to one summary for a large anatomical region.