Projects:fMRIClustering

Back to NA-MIC Collaborations, MIT Algorithms

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.

Clustering for Discovering Structure in the Space of Functional Selectivity

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 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 profiles, we construct a representation of the data which explicitly parametrizes all interesting patterns of activation. Mapping the data into this space, we formulate a model-based clustering algorithm that simultaneously finds a set of activation profiles and their spatial maps. We validate our method on the data from studies of category selectivity in visual cortex, demonstrating good agreement with the findings based on prior hypothesis-driven methods. This model enables functional group analysis independent of spatial correspondence among subjects. We are currently working on a co-clustering extension of this algorithm which can simultaneously find a set of clusters of voxels and meta-categories of stimuli in experiments with diverse sets of stimulus categories.

Fig. 9 compares the map of voxels assigned to a face-selective profile by our algorithm with the t-test's map of voxels with statistically significant (p<0.0001) response to faces when compared with object stimuli. Note that in contrast with the hypothesis testing method, we don't specify the existence of a face-selective region in our algorithm and the algorithm automatically discovers such a profile of activation in the data.

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.

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.

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.

Key Investigators

• MIT: Danial Lashkari, Archana Venkataraman, Ed Vul, Nancy Kanwisher, Polina Golland.
• Harvard: J. Oh, Marek Kubicki, Carl-Fredrik Westin.

Publications

NA-MIC Publications Database on fMRI clustering

Project Week Results: June 2008