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All meta-directing substituents are deactivating groups. These substituents withdraw electrons from the aromatic ring, making the ring less reactive toward electrophilic substitution. For example, the nitration of nitrobenzene is 100,000 times slower than that of benzene because of the deactivating effect of the nitro group. The first step in an electrophilic aromatic substitution is the addition of an electrophile to form a resonance-stabilized carbocation. The energy diagrams for...
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BrainMap VBM: An environment for structural meta-analysis.

Thomas J Vanasse1,2, P Mickle Fox1, Daniel S Barron3

  • 1Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, Texas.

Human Brain Mapping
|May 3, 2018
PubMed
Summary
This summary is machine-generated.

The BrainMap VBM database now includes voxel-based morphometry studies, enabling coordinate-based meta-analysis for brain structure research. This resource aids in distinguishing psychiatric from neurological diseases using advanced data mining techniques.

Keywords:
atrophydata-miningindependent components analysisnetworkspattern analysisstructural covariancestructural magnetic resonance imagingtransdiagnosticvoxel-based morphometry

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Area of Science:

  • Neuroimaging
  • Neuroscience
  • Data Science

Background:

  • The BrainMap database archives human neuroimaging literature.
  • It facilitates coordinate-based meta-analysis (CBMA) of functional neuroimaging studies.
  • BrainMap is expanding to include voxel-based morphometry (VBM) studies.

Purpose of the Study:

  • To introduce and describe the BrainMap VBM database.
  • To detail its meta-data structure, data volume, and reverse inference functions.
  • To demonstrate its utility in analyzing VBM literature and distinguishing disease types.

Main Methods:

  • Development of the BrainMap VBM database for structural neuroimaging.
  • Application of coordinate-based meta-analysis (CBMA) to VBM data.
  • Utilizing independent components analysis (ICA) and hierarchical clustering analysis (HCA) for data mining.

Main Results:

  • The BrainMap VBM database now includes a significant volume of VBM studies.
  • Data-mining procedures successfully distinguished psychiatric from neurological diseases.
  • Two data-filtering approaches were identified to reduce redundancy for ICA.

Conclusions:

  • BrainMap VBM provides a valuable resource for large-scale meta-analysis of brain structure.
  • The database facilitates the exploration of disease-specific neuroanatomical patterns.
  • Further exploration with diverse modeling approaches is encouraged for the neuroscientific community.