Voxel Localization
Voxel Localization - 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...
13:13
Nano-fEM: Protein Localization Using Photo-activated Localization Microscopy and Electron Microscopy
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Cited by 14 •
2012
We describe a method to localize fluorescently tagged proteins in electron micrographs. Fluorescence is first localized using photo-activated localization microscopy on ultrathin sections. These images are then aligned to electron micrographs of the same section.
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.
07:06
Visualizing RNA Localization in Xenopus Oocytes
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Cited by 5 •
2010
Visualization of in vivo RNA transport is accomplished by microinjection of fluorescently labeled RNA transcripts into Xenopus oocytes, followed by confocal microscopy.
FAQs
Image registration establishes the transformation between voxel indices and anatomical or standard coordinates. That alignment lets software place a measured signal at a corresponding brain location rather than treating the voxel as an isolated array position. In neuroscience, this step is essential when researchers compare activation patterns across participants or relate imaging results to known anatomy.
Spatial resolution determines how finely a dataset can distinguish neighboring locations and therefore constrains the precision of a localized finding. Statistical thresholds add a separate interpretive condition: they determine which measured regional effects are treated as meaningful. Consequently, a reported location reflects both the imaging detail available and the threshold applied to the data.
Spatial misregistration can make a signal appear to arise from the wrong brain region, even when the measured pattern is real. Careful alignment reduces this risk by placing corresponding anatomy and signals in comparable coordinates. This distinction matters because researchers must separate genuine regional effects from apparent findings created by positional mismatch.
A typical workflow starts with voxel indices in an imaging dataset, maps them through image registration to anatomical or standard coordinates, and then associates the localized positions with measured signals. Researchers can use those mapped signals for activation mapping, functional connectivity analyses, or comparisons across participants. The sequence links computational coordinates to interpretable neurobiological locations.
Once signals are assigned to anatomically interpretable locations, voxel localization helps researchers relate neural activity to behavior, anatomy, and disease. It also supports comparisons across participants by providing a common coordinate framework. These uses allow regional effects to be examined in relation to observed traits or conditions rather than only as patterns in raw image arrays.
In functional connectivity analyses, localization supplies the spatial reference for the signals included in the analysis. Researchers can therefore associate connectivity results with identified brain regions and compare those regional patterns across participants. Accurate alignment remains important because a mismatch in position could make a relationship appear to involve a different region than the one actually represented in the dataset.