Voxel Based Analysis
Voxel Based 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...
06:26
Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)
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Cited by 100 •
2019
We detail how to conduct a meta-analysis of voxel-based neuroimaging studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI).
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.
13:51
Cross-Modal Multivariate Pattern Analysis
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Cited by 5 •
2011
Classical multivariate pattern analysis predicts sensory stimuli a subject perceives from neural activity in the corresponding cortices (e.g. visual stimuli from activity in visual cortex). Here, we apply pattern analysis cross-modally and show that sound- and touch-implying visual stimuli can be predicted from activity in auditory and somatosensory cortices, respectively.
FAQs
Alignment places each participant’s scan in a common anatomical space, allowing a voxel location in one brain to be compared with the corresponding location in another. Without this correspondence, group-level tests could mix anatomically different regions. The registration step therefore supports valid comparisons across participants or conditions before voxelwise statistics are performed.
Spatial smoothing and tissue-specific processing are preprocessing choices that shape how the data are analyzed. Smoothing is included in the typical workflow, while tissue-specific handling is especially relevant when the question concerns measures such as gray-matter volume. Their use should match the imaging measure and research question, rather than being treated as interchangeable steps.
Multiple-comparison correction is essential because voxelwise testing examines many locations at once. A pattern can otherwise appear noteworthy simply because numerous statistical tests were conducted. Corrected results, interpreted alongside anatomical location and the experimental context, provide a stronger basis for deciding whether a relationship with behavior is robust rather than a chance finding.
Researchers typically align individual scans to a common anatomical space, apply spatial smoothing and any appropriate tissue-specific processing, then run voxelwise statistical tests across participants or conditions. They next evaluate the findings with multiple-comparison correction and interpret surviving patterns in relation to anatomical location and the experimental context.
Structural analyses can examine regional gray-matter volume, whereas functional analyses can examine voxelwise signals. Either type may be related to behavioral measures including learning, memory, symptoms, or decision-making. The distinction matters because the resulting brain-behavior relationship describes different kinds of measurements and should be interpreted according to the imaging measure analyzed.
The approach is useful when researchers want to test whether variation across the brain relates to measured behavior, or whether brain patterns differ across experimental conditions or participants. Findings should be anchored to the relevant anatomical region and experimental design so statistical associations are not interpreted without context.