Difference between revisions of "Multi-Voxel Pattern Analysis"

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== '''Task Switching''' ==
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== '''Multi-Voxel Pattern Analysis''' ==
  
 
==== Basic Characteristics ====
 
==== Basic Characteristics ====
 
* Description
 
* Description
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Task switching is a construct which refers to the executive functioning of perception and action. Central to task switching is the idea that stimuli or responses become relevant due to changing task demands. Task switching refers to a person's ability to reconfigure perceptual and response sets to match changing environmental demands.Task switching creates longer latencies and higher error rates, also known as the task switch cost. In such tasks a person has to respond to an attribute of a stimulus by making a speeded response. For example, a person might be shown "6B" and be asked if the given number is even or odd. Next they might be shown "3C" and asked if the given letter is a vowel or a consonant. Other deviations of task switching include shape/size and horizontal/vertical stimuli. Successful performance regarding task switching requires flexible, context-dependent goal setting and execution. Task switching is considered a useful measure of fundamental types of cognitive control.  
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Multi-Voxel Pattern Analysis is a way to analyze brain scans obtained using fMRI. In contrast to fMRI analysis which focuses on individual brain voxels (volumetric pixels), MVPA utilizes pattern-classification algorithms to figure out the information that is represented in a specific brain activity pattern. The benefits to using MVPA  over traditional single voxel analysis is that MVPA is a more sensitive analytical tool. In particular, it is more useful for hypothesis that think of cognitive states as a result of activity in many interacting parts of the brain rather than a centralized location. Another important benefit of the MVPA approach is the ability to correlate classifier estimates made by the algorithm to the subject's behavior. Thus this technique allows us to sensitively detect and track cognitive states as well as see how these states are represented in the brain. <sup>1</sup>
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<b> Method:</b>
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#<b>Feature Selection:</b> Decide which voxels to include in the classification analysis.
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#<b>Pattern Assembly:</b> Sort data into discrete "brain patterns" corresponding to pattern of activity across selected voxels at a particular time in the experiment. Label brain patterns activity to the experiment while accounting for delays in scanners. 
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#<b>Classifier Training:</b> give a subset of labeled patterns to a multivariate pattern classification algorithm. This allows the algorithm to figure out a formula that relates the experimental condition and the voxel activity patterns.
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#<b>Generalization Testing</b> Give the classification algorithm a new pattern of brain activity that it hasn't seen. and see if the it is able to determine the experimental condition associated with the pattern.
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* History
 
* History
  
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Multivariate methods used to analyze fMRI data has been used to figure out the relationships between different brain regions. In the last 5 years, scientists have realized that these same principles could be used to characterize cognitive states as patterns of brain activity.  This is advantageous because many complex cognitive states have widely distributed brain activity patterns; for example, the activity pattern associated with thinking about a nail may elicit brain activity in more than just, say, the visual cortex.  So determining cognitive states from a brain activity pattern can not be associated to only one area of activity.
  
 
* References
 
* References
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<sup>1</sup> Norman, K.A. et. al.''Beyond mind-reading: multi-voxel pattern analysis of fMRI data.''TRENDS in Cognitive Sciences Vol. 10 No.9. pg. 424-430.  
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Cepeda et al, 2000. Task switching and attention deficit hyperactivity disorder PMID 10885680
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Liefooghe et al, 2008. Working memory csots of task switching PMID 1748298
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Wagner et al, 2006. Individual differences in multiple types of shifting attention PMID 17489298
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Yehene & Meiran, 2007. Is there a general task switching ability? PMID 17223059
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==== Related Information ====
 
==== Related Information ====

Revision as of 14:21, 30 March 2009


Papers List | Variables List

Multi-Voxel Pattern Analysis

Basic Characteristics

  • Description

Multi-Voxel Pattern Analysis is a way to analyze brain scans obtained using fMRI. In contrast to fMRI analysis which focuses on individual brain voxels (volumetric pixels), MVPA utilizes pattern-classification algorithms to figure out the information that is represented in a specific brain activity pattern. The benefits to using MVPA over traditional single voxel analysis is that MVPA is a more sensitive analytical tool. In particular, it is more useful for hypothesis that think of cognitive states as a result of activity in many interacting parts of the brain rather than a centralized location. Another important benefit of the MVPA approach is the ability to correlate classifier estimates made by the algorithm to the subject's behavior. Thus this technique allows us to sensitively detect and track cognitive states as well as see how these states are represented in the brain. 1

Method:

  1. Feature Selection: Decide which voxels to include in the classification analysis.
  2. Pattern Assembly: Sort data into discrete "brain patterns" corresponding to pattern of activity across selected voxels at a particular time in the experiment. Label brain patterns activity to the experiment while accounting for delays in scanners.
  3. Classifier Training: give a subset of labeled patterns to a multivariate pattern classification algorithm. This allows the algorithm to figure out a formula that relates the experimental condition and the voxel activity patterns.
  4. Generalization Testing Give the classification algorithm a new pattern of brain activity that it hasn't seen. and see if the it is able to determine the experimental condition associated with the pattern.


  • History

Multivariate methods used to analyze fMRI data has been used to figure out the relationships between different brain regions. In the last 5 years, scientists have realized that these same principles could be used to characterize cognitive states as patterns of brain activity. This is advantageous because many complex cognitive states have widely distributed brain activity patterns; for example, the activity pattern associated with thinking about a nail may elicit brain activity in more than just, say, the visual cortex. So determining cognitive states from a brain activity pattern can not be associated to only one area of activity.

  • References

1 Norman, K.A. et. al.Beyond mind-reading: multi-voxel pattern analysis of fMRI data.TRENDS in Cognitive Sciences Vol. 10 No.9. pg. 424-430.

Related Information

  • Task or test associated with this construct (vote for your favorite, or nominate a new one by editing this page)
  • Indicators (dependent variables, conditions, or contrasts; measurement variables used for analysis) associated with this construct (vote or nominate by editing this page):
  • Closely related pages (vote or nominate related pages by editing this page):

External Resources

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