Projects:fMRIClustering - Revision history

Rameshvs at 20:06, 28 November 2012

2012-11-28T20:06:46Z

Rameshvs at 22:29, 30 March 2011

2011-03-30T22:29:22Z

Pega85: /* Clustering for the Exploration of Functional Connectivity */

2011-03-30T21:19:26Z

Clustering for the Exploration of Functional Connectivity

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FernD at 19:44, 5 November 2010

2010-11-05T19:44:30Z

Melonakos: /* Publications */

2010-05-11T20:05:20Z

Publications

Danial at 20:37, 16 February 2010

2010-02-16T20:37:11Z

Danial at 20:35, 16 February 2010

2010-02-16T20:35:21Z

Polina: /* Publications */

2009-09-29T18:36:27Z

Publications

Polina: /* Key Investigators */

2009-09-29T18:35:46Z

Key Investigators

Pega85: /* Key Investigators */

2009-09-10T19:17:38Z

Key Investigators

@@ Line 81: / Line 81: @@
 = Key Investigators =
-* MIT: Danial Lashkari, Archana Venkataraman, Ed Vul, Nancy Kanwisher, Polina Golland.
+* MIT: Danial Lashkari, Archana Venkataraman, Ramesh Sridharan, Ed Vul, Nancy Kanwisher, Polina Golland.
 * Harvard: J. Oh, Marek Kubicki, Carl-Fredrik Westin.

@@ Line 3: / Line 3: @@
 = 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.
+One of the major goals in the analysis of fMRI data is the detection of regions of 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.
 = Clustering for Discovering Structure in the Space of Functional Selectivity =
-''' ''Clustering Study of Domain Specificity in High Level Visual Cortex'' '''
+We are devising clustering algorithms for discovering structure in the functional organization of the high-level visual cortex. It is suggested that there are regions in the visual cortex with high selectivity to certain categories of visual stimuli; we refer to these regions as /functional units/. Currently, the conventional method for detection of these regions is based on statistical tests comparing response of each voxel in the brain to different visual categories to see if it shows considerably higher activation to one category. For example, the well-known FFA (Fusiform Face Area) is the set of voxels which show high activation to face images. We use a model-based clustering approach to the analysis of this type of data as a means to make this analysis automatic and further discover new structures in the high-level visual cortex.
-As a more specific application of model-based clustering algorithms, we are devising clustering algorithms for discovering structure in the functional organization of the high-level visual cortex. It is suggested that there are regions in the visual cortex with high selectivity to certain categories of visual stimuli. Currently, the conventional method for detection of these methods is based on statistical tests comparing response of each voxel in the brain to different visual categories to see if it shows considerably higher activation to one category. For example, the well-known FFA (Fusiform Face Area) is the set of voxels which show high activation to face images. We use a model-based clustering approach to the analysis of this type of data as a means to make this analysis automatic and further discover new structures in the high-level visual cortex.
+We formulate a model-based clustering algorithm that simultaneously
-Introducing the notion of space of activation
+Fig. 1 shows the categories learned by our algorithm on a study with 8 subjects. We split trials of each image into two groups of equal size and consider
-profiles, we construct a representation of the data which explicitly
+each group as an independent stimulus forming a total of 138
-parametrizes all interesting patterns of activation. Mapping the data into
+stimuli. Hence, we can examine the consistency of our stimulus categorization with respect to identical trials. Stimulus pairs
-this space, we formulate a model-based clustering algorithm that simultaneously
+corresponding to the same image are generally assigned to the same
-finds a set of activation profiles and their spatial maps. We validate
+category, confirming the consistency of the resuls across
-our method on the data from studies of category selectivity in visual
+trials. Category 1 corresponds to the scene images and, interestingly, also includes all images of
-cortex, demonstrating good agreement with the findings based on prior
+trees. This may suggest a high level category structure that is not
-hypothesis-driven methods. This model enables functional group analysis
+merely driven by low level features. Such a structure is even more
-independent of spatial correspondence among subjects. We are currently working on a co-clustering extension of this
+evident in the 4th category where images of a tiger that has a large
-algorithm which can simultaneously find a set of clusters of voxels and meta-categories
+face join human faces. Some other animals are clustered together with human bodies in categories 2 and
-of stimuli in experiments with diverse sets of stimulus categories.
+. Shoes and cars, which have similar shapes, are clustered together
 {|
-|+ '''Fig 9. Spatial maps of the face selective regions found by the statistical test (red) and our mixture model (dark blue). Maps are presented in alternating rows for comparison. Visually responsive mask of voxels used in our experiment is illustrated in yellow and light blue.'''
+|+ '''Fig 1. Categories learned from 8 subjects'''
 |align="center"|[[Image:mit_fmri_clustering_mapffacompare.PNG |thumb|800px]]
 |}
@@ Line 32: / Line 67: @@
 '''''Hierarchical Model for Exploratory fMRI Analysis without Spatial Normalization'''''
-Building on the work on the clustering model for the domain specificity, we develop a hierarchical exploratory method for simultaneous parcellation of multisub ect fMRI data into functionally coherent areas. The method is based on a solely functional representation of the fMRI data and a hierarchical probabilistic model that accounts for both inter-subject and intra-subject forms of variability in fMRI response. We employ a Variational Bayes approximation to ﬁt the model to the data. The resulting algorithm ﬁnds a functional parcellation of the individual brains along with a set of population-level clusters, establishing correspondence between these two levels. The model eliminates the need for spatial normalization while still enabling us to fuse data from several subjects. We demonstrate the application of our method on the same visual fMRI study as before. Fig. 10 shows the scene-selective parcel in 2 different subjects. Parcel-level spatial correspondence is evident in the figure between the subjects.
+Building on the work on the clustering model for the domain specificity, we develop a hierarchical exploratory method for simultaneous parcellation of multisub ect fMRI data into functionally coherent areas. The method is based on a solely functional representation of the fMRI data and a hierarchical probabilistic model that accounts for both inter-subject and intra-subject forms of variability in fMRI response. We employ a Variational Bayes approximation to ﬁt the model to the data. The resulting algorithm ﬁnds a functional parcellation of the individual brains along with a set of population-level clusters, establishing correspondence between these two levels. The model eliminates the need for spatial normalization while still enabling us to fuse data from several subjects. We demonstrate the application of our method on the same visual fMRI study as before. Fig. 6 shows the scene-selective parcel in 2 different subjects. Parcel-level spatial correspondence is evident in the figure between the subjects.
 <table>
-<tr> <th> '''Fig 10. The map of the scene selective parcels in two different subjects. The rough location of the scene-selective areas PPA and TOS, identified by the expert, are shown on the maps by yellow and green circles, respectively.'''
+<tr> <th> '''Fig 6. The map of the scene selective parcels in two different subjects. The rough location of the scene-selective areas PPA and TOS, identified by the expert, are shown on the maps by yellow and green circles, respectively.'''
 <tr>
 <td align="center">

@@ Line 1: / Line 1: @@
   Back to [[NA-MIC_Internal_Collaborations:fMRIAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]
 __NOTOC__
-= fMRI Clustering =
+= Improving fMRI Analysis using Supervised and Unsupervised Learning =
-One of the major goals in 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, unsupervised learning methods such as PCA and ICA, and different clustering algorithms have been employed to find these networks. This project aims to particularly study application of model-based clustering algorithms in identification of functional connectivity in the brain.
+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.
 = Clustering for the Exploration of Functional Connectivity =

@@ Line 194: / Line 194: @@
 = Publications =
-[http://www.na-mic.org/publications/pages/display?search=Projects%3AfMRIClustering&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database]
+[http://www.na-mic.org/publications/pages/display?search=Projects%3AfMRIClustering&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database on fMRI clustering]
   Project Week Results: [[2008_Summer_Project_Week:fMRIconnectivity|June 2008]]
 [[Category:fMRI]]

@@ Line 5: / Line 5: @@
 One of the major goals in 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, unsupervised learning methods such as PCA and ICA, and different clustering algorithms have been employed to find these networks. This project aims to particularly study application of model-based clustering algorithms in identification of functional connectivity in the brain.
-= Clustering for Exploration of Functional Connectivity =
+= Clustering for the Exploration of Functional Connectivity =
 '''''Generative Model for Functional Connectivity'''''

@@ Line 5: / Line 5: @@
 One of the major goals in 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, unsupervised learning methods such as PCA and ICA, and different clustering algorithms have been employed to find these networks. This project aims to particularly study application of model-based clustering algorithms in identification of functional connectivity in the brain.
-= Description =
+= Clustering for Exploration of Functional Connectivity =
 '''''Generative Model for Functional Connectivity'''''
@@ Line 62: / Line 62: @@
 |}
-'''''Exploring Functional Connectivity in fMRI via Clustering'''''
+'''''Comparison between Different Clustering Schemes'''''
 As a continuation to the above experiments, we apply two distinct clustering algorithms to functional connectivity analysis: K-Means clustering and Spectral Clustering. The K-Means algorithm assumes that each voxel time course is drawn independently from one of <em>k</em> multivariate Gaussian distributions with unique means and spherical covariances. In contrast, Spectral Clustering does not presume any parametric form for the data. Rather it captures the underlying signal geometry by inducing a low-dimensional representation based on a pairwise affinity matrix constructed from the data. Without placing any <em>a priori</em> constraints, both clustering methods yield partitions that are associated with brain systems traditionally identified via seed-based correlation analysis. Our empirical results suggest that clustering provides a valuable tool for functional connectivity analysis.
@@ Line 142: / Line 142: @@
 Fig 8. shows clearly that both Spectral Clustering and K-Means can identify well-known structures such as the default network, the visual cortex, the motor cortex, and the dorsal attention system. Spectral Clustering and K-Means also identified white matter. In general, one would not attempt to delineate this region using seed-based correlation analysis because we regress out the white matter signal during the preprocessing. In our experiments Spectral Clustering and K-Means achieve similar clustering results across participants. Furthermore, both methods identify the same functional systems as seed-based analysis without requiring <em>a priori</em> knowledge about the brain and without significant computation. Thus, clustering algorithms offer a viable alternative to standard functional connectivity analysis techniques.
 ''' ''Clustering Study of Domain Specificity in High Level Visual Cortex'' '''
-As a more specific application of model-based clustering algorithms, we are devising clustering algorithms for detection of functional connectivity in high-level visual cortex. It is suggested that there are regions in the visual cortex with high selectivity to certain categories of visual stimuli. Currently, the conventional method for detection of these methods is based on statistical tests comparing response of each voxel in the brain to different visual categories to see if it shows considerably higher activation to one category. For example, the well-known FFA (Fusiform Face Area) is the set of voxels which show high activation to face images. We use a model-based clustering approach to the analysis of this type of data as a means to make this analysis automatic and further discover new structures in the high-level visual cortex.
+As a more specific application of model-based clustering algorithms, we are devising clustering algorithms for discovering structure in the functional organization of the high-level visual cortex. It is suggested that there are regions in the visual cortex with high selectivity to certain categories of visual stimuli. Currently, the conventional method for detection of these methods is based on statistical tests comparing response of each voxel in the brain to different visual categories to see if it shows considerably higher activation to one category. For example, the well-known FFA (Fusiform Face Area) is the set of voxels which show high activation to face images. We use a model-based clustering approach to the analysis of this type of data as a means to make this analysis automatic and further discover new structures in the high-level visual cortex.
 Introducing the notion of space of activation
@@ Line 165: / Line 172: @@
 |align="center"|[[Image:mit_fmri_clustering_mapffacompare.PNG |thumb|800px]]
 |}
 '''''Hierarchical Model for Exploratory fMRI Analysis without Spatial Normalization'''''
@@ Line 183: / Line 185: @@
 [[Image:mit_fmriclustering_hierarchicalppamapsubject2.jpg |650px]]
 </table>
@@ Line 194: / Line 195: @@
 [http://www.na-mic.org/publications/pages/display?search=Projects%3AfMRIClustering&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database]
   Project Week Results: [[2008_Summer_Project_Week:fMRIconnectivity|June 2008]]
 [[Category:fMRI]]

@@ Line 188: / Line 188: @@
 = Key Investigators =
-* MIT Algorithms: Danial Lashkari, Archana Venkataraman, Polina Golland, Nancy Kanwisher
+* MIT CSAIL: Danial Lashkari, Archana Venkataraman, Polina Golland, Nancy Kanwisher
-* Harvard DBP 2: J. Oh, Marek Kubicki, Carl-Fredrik Westin
+* BWH/Harvard: J. Oh, Marek Kubicki, Carl-Fredrik Westin
 = Publications =