Name: basics of multivariate analysis in neuroimaging data
Uploaded: 2010-07-24T17:00:00
Duration: 6 min 35 s
Description: Watch this Scientific Journal Video about Basics of Multivariate Analysis in Neuroimaging Data at JoVE.com

The author is grateful for NIH grant support:
NIH/NIBIB 5R01EB006204-03 Multivariate approaches to neuroimaging analysis
NIH/NIA 5R01AG026114-02 Early AD Detection with ASL MRI &amp; Covariance Analysis
ADNI: Imaging data was provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (NIH U01AG024904). Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics ,Johnson and Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc, F. Hoffman-La Roche, Schering-Plough, Synarc, Inc., and Wyeth, as well as non-profit partners the Alzheimer's Association and Alzheimer's Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health(<a href="http://www.fnih.org">http://www.fnih.org</a>). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH grants P30 AG010129, K01 AG030514, and the Dana Foundation.

<ol>
	<li>Moeller, J. R., Strother, S. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+regional+covariance+approach+to+the+analysis+of+functional+patterns+in+positron+emission+tomographic+data.">A regional covariance approach to the analysis of functional patterns in positron emission tomographic data.</a> J Cereb Blood Flow Metab. 11 (2), A121-A121 (1991).</li><li>Scarmeas, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Covariance+PET+patterns+in+early+Alzheimer's+disease+and+subjects+with+cognitive+impairment+but+no+dementia:+utility+in+group+discrimination+and+correlations+with+functional+performance.">Covariance PET patterns in early Alzheimer's disease and subjects with cognitive impairment but no dementia: utility in group discrimination and correlations with functional performance.</a> Neuroimage. 23 (1), 35-35 (2004).</li><li>Siedlecki, K. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Examining+the+multifactorial+nature+of+cognitive+aging+with+covariance+analysis+of+positron+emission+tomography+data.">Examining the multifactorial nature of cognitive aging with covariance analysis of positron emission tomography data.</a> J Int Neuropsychol Soc. 15 (6), 973-973 (2009).</li><li>Burnham, K. P., Anderson, D. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Model selection and multimodel inference a practical information-theoretic approach. , (2002).</li><li>Moeller, J. R., Strother, S. C., Sidtis, J. J., Rottenberg, D. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Scaled+subprofile+model:+a+statistical+approach+to+the+analysis+of+functional+patterns+in+positron+emission+tomographic+data.">Scaled subprofile model: a statistical approach to the analysis of functional patterns in positron emission tomographic data.</a> J Cereb Blood Flow Metab. 7 (5), 649-649 (1987).</li><li>Habeck, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multivariate+and+univariate+neuroimaging+biomarkers+of+Alzheimer's+disease.">Multivariate and univariate neuroimaging biomarkers of Alzheimer's disease.</a> Neuroimage. 40 (4), 1503-1503 (2008).</li><li>Habeck, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+new+approach+to+spatial+covariance+modeling+of+functional+brain+imaging+data:+ordinal+trend+analysis.">A new approach to spatial covariance modeling of functional brain imaging data: ordinal trend analysis.</a> Neural Comput. 17 (7), 1602-1602 (2005).</li><li>McIntosh, A. R., Bookstein, F. L., Haxby, J. V., Grady, C. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Spatial+pattern+analysis+of+functional+brain+images+using+partial+least+squares.">Spatial pattern analysis of functional brain images using partial least squares.</a> Neuroimage. 3 Pt 1, 143-143 (1996).</li><li>McIntosh, A. R., Lobaugh, N. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Partial+least+squares+analysis+of+neuroimaging+data:+applications+and+advances.">Partial least squares analysis of neuroimaging data: applications and advances.</a> Neuroimage. 23, S250-S250 (2004).</li></ol>

ब्याज की कोई संघर्ष की घोषणा की.

हम दर्शक बहुभिन्नरूपी विश्लेषण की मूल बातें की एक स्वाद दे दिया है उम्मीद है, इच्छुक दर्शकों के लिए हमारी वेबसाइट बाहर की जाँच करने के लिए प्रोत्साहित किया जाता है. बहुभिन्नरूपी विश्लेषण में पैरामीटर के लिए कुछ विकल्प बना रहे थे कि काफी बहस के अधीन बहस किया जा सकता है. हम इस आलेख में इन मुद्दों की चर्चा बख्शा प्रमुख मुद्दों से व्याकुलता से बचने के. सबसे पहले, हम पहले 6 प्रमुख घटकों को चुना हमारे सहप्रसरण ई. से संबंधित पैटर्न का निर्माण. इस विकल्प के लिए सैद्धांतिक कारण है कि हम 4 चर्चा नहीं कर रहे हैं . 6 प्रधान घटकों के विशेष पसंद है, हालांकि हमारे तर्क के लिए महत्वपूर्ण नहीं है: एक रेंज में 2 से 20 पीसी के लिए चुना है और अभी भी विभाजन नमूना सिमुलेशन में बहुभिन्नरूपी मार्कर के बेहतर सामान्यीकरण प्रदर्शन प्राप्त कर सकते हैं. परिणाम व्युत्पत्ति और नकल के नमूने में विषयों की संख्या के चुनाव के लिए सम्मान के साथ इसी तरह मजबूत कर रहे हैं. हम प्रतिकृति नमूने में दोनों समूहों के लिए 20 विषयों को चुना है, लेकिन इस गणितीय संगणना गति की सुविधा के लिए विशुद्ध रूप से किया गया था. दोनों तकनीकों के रिश्तेदार गुण के बारे में हमारी परिणाम इसी तरह पकड़ अगर व्युत्पत्ति नमूनों में विषयों की संख्या बढ़ रहे थे. दूसरा, हम केवल बहुभिन्नरूपी विश्लेषण का सबसे बुनियादी प्रकार प्रस्तुत किया. साहित्य मशीन लर्निंग, पहले पीसीए रैखिक और गैर रेखीय परिवर्तनों, और विभिन्न अन्य झुर्रियों से उधार तकनीक के साथ काफी जटिलता संभव है कि सामान्यीकरण प्रदर्शन और भी अधिक बढ़ावा देने सकता हैं. सादगी के लिए हम इस आलेख में इन संभावनाओं पर छुआ तक नहीं है.

basics of multivariate analysis in neuroimaging data

Multivariate analysis techniques for neuroimaging data have recently received increasing attention as they have many attractive features that cannot be easily realized by the more commonly used univariate, voxel-wise, techniques1,5,6,7,8,9. Multivariate approaches evaluate correlation/covariance of activation across brain regions, rather than proceeding on a voxel-by-voxel basis. Thus, their results can be more easily interpreted as a signature of neural networks. Univariate approaches, on the other hand, cannot directly address interregional correlation in the brain. Multivariate approaches can also result in greater statistical power when compared with univariate techniques, which are forced to employ very stringent corrections for voxel-wise multiple comparisons. Further, multivariate techniques also lend themselves much better to prospective application of results from the analysis of one dataset to entirely new datasets. Multivariate techniques are thus well placed to provide information about mean differences and correlations with behavior, similarly to univariate approaches, with potentially greater statistical power and better reproducibility checks. In contrast to these advantages is the high barrier of entry to the use of multivariate approaches, preventing more widespread application in the community. To the neuroscientist becoming familiar with multivariate analysis techniques, an initial survey of the field might present a bewildering variety of approaches that, although algorithmically similar, are presented with different emphases, typically by people with mathematics backgrounds. We believe that multivariate analysis techniques have sufficient potential to warrant better dissemination. Researchers should be able to employ them in an informed and accessible manner. The current article is an attempt at a didactic introduction of multivariate techniques for the novice. A conceptual introduction is followed with a very simple application to a diagnostic data set from the Alzheimer s Disease Neuroimaging Initiative (ADNI), clearly demonstrating the superior performance of the multivariate approach.

Watch this Scientific Journal Video about Basics of Multivariate Analysis in Neuroimaging Data at JoVE.com

Basics of Multivariate Analysis in Neuroimaging Data

वर्तमान लेख बहुभिन्नरूपी विश्लेषण के मूल का वर्णन है और यह अधिक सामान्यतः प्रयोग किया voxel वार univariate विश्लेषण विरोधाभासों. विश्लेषण के दोनों प्रकार के एक नैदानिक ​​तंत्रिका विज्ञान डेटा सेट के लिए लागू कर रहे हैं. पूरक विभाजन आधा सिमुलेशन स्वतंत्र डेटा सेट में बहुभिन्नरूपी परिणामों के बेहतर प्रतिकृति दिखा.

बहुभिन्नरूपी विश्लेषण के डेटा न्यूरोइमेजिंग में मूल बातें

Multivariate analysis techniques for neuroimaging data have recently received increasing attention as they have many attractive features that cannot be ...

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