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Automated regional behavioral analysis for human brain images
Behavioral categories of functional imaging experiments along with standardized brain coordinates of associated activations were used to develop a method to automate regional behavioral analysis of human brain images. Behavioral and coordinate data were taken from the BrainMap database (http://www.b...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3428588/ https://www.ncbi.nlm.nih.gov/pubmed/22973224 http://dx.doi.org/10.3389/fninf.2012.00023 |
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author | Lancaster, Jack L. Laird, Angela R. Eickhoff, Simon B. Martinez, Michael J. Fox, P. Mickle Fox, Peter T. |
author_facet | Lancaster, Jack L. Laird, Angela R. Eickhoff, Simon B. Martinez, Michael J. Fox, P. Mickle Fox, Peter T. |
author_sort | Lancaster, Jack L. |
collection | PubMed |
description | Behavioral categories of functional imaging experiments along with standardized brain coordinates of associated activations were used to develop a method to automate regional behavioral analysis of human brain images. Behavioral and coordinate data were taken from the BrainMap database (http://www.brainmap.org/), which documents over 20 years of published functional brain imaging studies. A brain region of interest (ROI) for behavioral analysis can be defined in functional images, anatomical images or brain atlases, if images are spatially normalized to MNI or Talairach standards. Results of behavioral analysis are presented for each of BrainMap's 51 behavioral sub-domains spanning five behavioral domains (Action, Cognition, Emotion, Interoception, and Perception). For each behavioral sub-domain the fraction of coordinates falling within the ROI was computed and compared with the fraction expected if coordinates for the behavior were not clustered, i.e., uniformly distributed. When the difference between these fractions is large behavioral association is indicated. A z-score ≥ 3.0 was used to designate statistically significant behavioral association. The left-right symmetry of ~100K activation foci was evaluated by hemisphere, lobe, and by behavioral sub-domain. Results highlighted the classic left-side dominance for language while asymmetry for most sub-domains (~75%) was not statistically significant. Use scenarios were presented for anatomical ROIs from the Harvard-Oxford cortical (HOC) brain atlas, functional ROIs from statistical parametric maps in a TMS-PET study, a task-based fMRI study, and ROIs from the ten “major representative” functional networks in a previously published resting state fMRI study. Statistically significant behavioral findings for these use scenarios were consistent with published behaviors for associated anatomical and functional regions. |
format | Online Article Text |
id | pubmed-3428588 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-34285882012-09-12 Automated regional behavioral analysis for human brain images Lancaster, Jack L. Laird, Angela R. Eickhoff, Simon B. Martinez, Michael J. Fox, P. Mickle Fox, Peter T. Front Neuroinform Neuroscience Behavioral categories of functional imaging experiments along with standardized brain coordinates of associated activations were used to develop a method to automate regional behavioral analysis of human brain images. Behavioral and coordinate data were taken from the BrainMap database (http://www.brainmap.org/), which documents over 20 years of published functional brain imaging studies. A brain region of interest (ROI) for behavioral analysis can be defined in functional images, anatomical images or brain atlases, if images are spatially normalized to MNI or Talairach standards. Results of behavioral analysis are presented for each of BrainMap's 51 behavioral sub-domains spanning five behavioral domains (Action, Cognition, Emotion, Interoception, and Perception). For each behavioral sub-domain the fraction of coordinates falling within the ROI was computed and compared with the fraction expected if coordinates for the behavior were not clustered, i.e., uniformly distributed. When the difference between these fractions is large behavioral association is indicated. A z-score ≥ 3.0 was used to designate statistically significant behavioral association. The left-right symmetry of ~100K activation foci was evaluated by hemisphere, lobe, and by behavioral sub-domain. Results highlighted the classic left-side dominance for language while asymmetry for most sub-domains (~75%) was not statistically significant. Use scenarios were presented for anatomical ROIs from the Harvard-Oxford cortical (HOC) brain atlas, functional ROIs from statistical parametric maps in a TMS-PET study, a task-based fMRI study, and ROIs from the ten “major representative” functional networks in a previously published resting state fMRI study. Statistically significant behavioral findings for these use scenarios were consistent with published behaviors for associated anatomical and functional regions. Frontiers Media S.A. 2012-08-28 /pmc/articles/PMC3428588/ /pubmed/22973224 http://dx.doi.org/10.3389/fninf.2012.00023 Text en Copyright © 2012 Lancaster, Laird, Eickhoff, Martinez, Fox and Fox. http://www.frontiersin.org/licenseagreement This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc. |
spellingShingle | Neuroscience Lancaster, Jack L. Laird, Angela R. Eickhoff, Simon B. Martinez, Michael J. Fox, P. Mickle Fox, Peter T. Automated regional behavioral analysis for human brain images |
title | Automated regional behavioral analysis for human brain images |
title_full | Automated regional behavioral analysis for human brain images |
title_fullStr | Automated regional behavioral analysis for human brain images |
title_full_unstemmed | Automated regional behavioral analysis for human brain images |
title_short | Automated regional behavioral analysis for human brain images |
title_sort | automated regional behavioral analysis for human brain images |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3428588/ https://www.ncbi.nlm.nih.gov/pubmed/22973224 http://dx.doi.org/10.3389/fninf.2012.00023 |
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