Algorithm:MIT - Revision history

JChris.FillionR: Fix MediaWiki table formatting issue discovered while converting to GitHub Flavored Markdown using pandoc (via https://github.com/outofcontrol/mediawiki-to-gfm)

2023-04-11T06:17:57Z

Fix MediaWiki table formatting issue discovered while converting to GitHub Flavored Markdown using pandoc (via https://github.com/outofcontrol/mediawiki-to-gfm)

JChris.FillionR: Fix MediaWiki table formatting issue discovered while converting to GitHub Flavored Markdown using pandoc (via https://github.com/outofcontrol/mediawiki-to-gfm)

2023-04-11T06:12:45Z

Fix MediaWiki table formatting issue discovered while converting to GitHub Flavored Markdown using pandoc (via https://github.com/outofcontrol/mediawiki-to-gfm)

Andysweet: /* Quantitative Susceptibility Mapping */

2012-12-15T18:04:22Z

Quantitative Susceptibility Mapping

Pega85 at 19:49, 10 December 2012

2012-12-10T19:49:50Z

Pega85 at 19:48, 10 December 2012

2012-12-10T19:48:52Z

Pega85 at 19:47, 10 December 2012

2012-12-10T19:47:11Z

Polina: /* Generative Models of Brain Connectivity */

2012-12-10T18:30:34Z

Generative Models of Brain Connectivity

Polina at 18:28, 10 December 2012

2012-12-10T18:28:37Z

Polina at 18:21, 10 December 2012

2012-12-10T18:21:06Z

Polina at 17:51, 10 December 2012

2012-12-10T17:51:04Z

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 in magnetic susceptibility cause perturbations in the local magnetic field, which
 can be computed from the phase of the MR signal. [[Projects:QuantitativeSusceptibilityMapping|More...]]
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 == [[Projects:GenerativeBrainConnectivity|Generative Models of Brain Connectivity]] ==
-Our goal is to use measures of connectivity between various ROIs as an avenue for understanding the structural and functional organization of the brain. We assess functional and anatomical connectivity using both fMRI correlations and DWI tractography measures, respectively. [[Projects:GenerativeBrainConnectivity|More...]]
+Our goal is to use measures of connectivity between various ROIs as an avenue for understanding the structural and functional effects of pathology. We assess functional and anatomical connectivity using both fMRI correlations and DWI tractography measures, respectively. Our methods are evaluated on a clinical study of schizophrenia. [[Projects:GenerativeBrainConnectivity|More...]]
 <font color="red">'''New: '''</font>

@@ Line 46: / Line 46: @@
 == [[Projects:GenerativeBrainConnectivity|Generative Models of Brain Connectivity]] ==
-This project uses standard machine learning algorithms to automatically identify relevant patterns in functional connectivity data. Our first application is to determine predictive differences between a control and clinical population. Our second application is to partition the brain into different functional systems. [[Projects:GenerativeBrainConnectivity|More...]]
 <font color="red">'''New: '''</font>
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-== [[Projects:DataDrivenFunctionalConnectivity|Data Driven Functional Connectivity]] ==
+== [[Projects:DataDrivenFunctionalConnectivity|Data Driven Analysis of Functional Connectivity]] ==
 This project uses standard machine learning algorithms to automatically identify relevant patterns in functional connectivity data. Our first application is to determine predictive differences between a control and clinical population. Our second application is to partition the brain into different functional systems. [[Projects:DataDrivenFunctionalConnectivity|More...]]

@@ Line 45: / Line 45: @@
 | |
-== [[Projects:generativeBrainConnectivityConnectivity|generative Models of Functional Connectivity]] ==
+== [[Projects:generativeBrainConnectivityConnectivity|Generative Models of Brain Connectivity]] ==
 This project uses standard machine learning algorithms to automatically identify relevant patterns in functional connectivity data. Our first application is to determine predictive differences between a control and clinical population. Our second application is to partition the brain into different functional systems. [[Projects:DataDrivenFunctionalConnectivity|More...]]

← Older revision		Revision as of 17:51, 10 December 2012
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		+	\| \| [[Image:mit_fmri_clustering_parcellation2_xsub.png\|200px]]
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		+
		+	== [[Projects:fMRIClustering\|Improving fMRI Analysis using Supervised and Unsupervised Learning]] ==
		+
		+	One of the major goals in the analysis of fMRI data is the detection of networks in the brain with similar functional behavior. A wide variety of methods including hypothesis-driven statistical tests, supervised, and unsupervised learning methods have been employed to find these networks. In this project, we develop novel learning algorithms that enable more efficient inferences from fMRI measurements. [[Projects:fMRIClustering\|More...]]
		+
		+	<font color="red">'''New: '''</font> E. Vul, D. Lashkari, P.-J. Hsieh, P. Golland; N.G. Kanwisher. Data-driven functional clustering reveals dominance of face, place, and body selectivity in the ventral visual pathway. Journal of Neurophysiology, 108:2306-2322, 2012.
		+
		+	<font color="red">'''New: '''</font> D. Lashkari, R. Sridharan, E. Vul, P.-J. Hsieh, N.G. Kanwisher, and P. Golland. Search for Patterns of Functional Specificity in the Brain: A Nonparametric Hierarchical Bayesian Model for Group fMRI Data. NeuroImage, 59(2):1348-1368, 2012.
		+
		+	\|-
		+
		+


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−	~~\| \| [[Image:mit_fmri_clustering_parcellation2_xsub.png\|200px]]~~
−	~~\| \|~~
−
−	~~== [[Projects:fMRIClustering\|Improving fMRI Analysis using Supervised and Unsupervised Learning]] ==~~
−
−	One of the major goals in the analysis of fMRI data is the detection of networks in the brain with similar functional behavior. A wide variety of methods including hypothesis-driven statistical tests, supervised, and unsupervised learning methods have been employed to find these networks. In this project, we develop novel learning algorithms that enable more efficient inferences from fMRI measurements. [[Projects:fMRIClustering\|More...]]
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