Multi-voxel Pattern
Multi-voxel Pattern - Related Videos
07:12
Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
0 Views •
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.
03:32
Constructing Patterned Neuronal Circuits on a Multi-Electrode Array
0 Views •
2025
This video demonstrates the process of culturing neuronal cells on a patterned multi-electrode array (MEA) to establish modular neuronal networks. This method allows the study of neural signal transmission and cellular interactions.
11:46
Decoding Auditory Imagery with Multivoxel Pattern Analysis
0 Views •
2023
Source: Laboratories of Jonas T. Kaplan and Sarah I. Gimbel—University of Southern California Imagine the sound of a bell ringing. What is happening in the brain when we conjure up a sound like this in the "mind's ear?" There is growing evidence that the brain uses the same mechanisms for imagination that it uses for perception.1 For example, when imagining visual images, the visual cortex becomes activated, and when imagining sounds, the auditory cortex is engaged. However, to what extent are...
13:51
Cross-Modal Multivariate Pattern Analysis
0 Views •
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.
09:17
Measuring Grey Matter Differences with Voxel-based Morphometry: The Musical Brain
0 Views •
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...
FAQs
Its informative feature is the configuration of activity values across voxels, rather than the magnitude of activity in one location or the average across a region. Two conditions can produce similar overall signal levels while arranging activity differently across a distributed network. Comparing these spatial configurations can therefore expose distinctions that region-by-region analyses may overlook.
Statistical models and classifiers evaluate activity values across multiple voxels to identify patterns associated with particular stimuli, cognitive states, or behaviors. They provide a systematic way to distinguish conditions based on distributed activity rather than relying on a single signal measurement. The resulting pattern-based distinctions help researchers examine how neural information relates to cognition and behavior.
A pattern may reflect information represented across a network rather than within one isolated brain region. This distributed organization allows analyses to examine how coordinated spatial arrangements correspond to perception, memory, decision-making, or behavior. It also explains why subtle relationships can remain informative even when no individual region shows a uniquely large or distinctive average response.
The method compares the arrangement of activity across voxels, so it can detect differences in which units are relatively more or less active within the measured pattern. Consequently, two conditions with comparable total or average signal may still be separable if their spatial configurations differ. This sensitivity supports investigation of fine-grained representations that univariate summaries may not reveal.
A typical analysis compares activity values across many voxels and then applies a statistical model or classifier to identify patterns linked with the conditions under study. Researchers can relate the resulting distinctions to stimuli, cognitive states, or behavior. The workflow therefore moves from distributed neuroimaging measurements to interpretable associations between neural patterns and experimental or behavioral variables.
These patterns support studies of perception, memory, and decision-making by helping researchers examine how distributed activity relates to different mental processes. They also contribute to research on brain connectivity and to efforts to link neural activity with cognition and behavior. Their value lies in analyzing information carried by spatially distributed signals rather than restricting interpretation to isolated regional averages.