Ramesh Sridharan

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Functional MRI studies have uncovered a number of brain areas that demonstrate highly specific functional patterns. In the case of visual object recognition, small, focal regions have been characterized with selectivity for visual categories such as human faces. In this paper, we develop an algorithm that automatically learns patterns of functional(More)
We present a model that describes the structure in the responses of different brain areas to a set of stimuli in terms of stimulus categories (clusters of stimuli) and functional units (clusters of voxels). We assume that voxels within a unit respond similarly to all stimuli from the same category, and design a nonparametric hierarchical model to capture(More)
We present an analysis framework for large studies of mul-timodal clinical quality brain image collections. Processing and analysis of such datasets is challenging due to low resolution, poor contrast, mis-aligned images, and restricted field of view. We adapt existing registration and segmentation methods and build a computational pipeline for spatial(More)
We consider input streams, which represent underlying shorter signals. We will use a 1 , a represent the input stream, where a t arrives at time t. This stream describes some underlying signal, A[i] for i ∈ [1, N ] for some dimensionality N , which we would like to query. There are three typical models used: • Time Series: a t = A[t] • Cash Register: a t =(More)
We develop a method for unsupervised analysis of functional brain images that learns group-level patterns of functional response. Our algorithm is based on a generative model that comprises two main layers. At the lower level, we express the functional brain response to each stimulus as a binary activation variable. At the next level, we define a prior over(More)
We propose and demonstrate an inference algorithm for the automatic segmentation of cerebrovascular pathologies in clinical MR images of the brain. Identifying and differentiating pathologies is important for understanding the underlying mechanisms and clinical outcomes of cerebral ischemia. Manual delineation of separate pathologies is infeasible in large(More)
Nearest-neighbor inference methods have been widely and successfully used in numerous applications such as forecasting which news topics will go viral, recommending products to people in online stores, and delineating objects in images by looking at image patches. However, there is little theoretical understanding of when, why, and how well these(More)
We present a semi-parametric generative model for predicting anatomy of a patient in subsequent scans following a single baseline image. Such predictive modeling promises to facilitate novel analyses in both voxel-level studies and longitudinal biomarker evaluation. We capture anatomical change through a combination of population-wide regression and a(More)
We present an interactive tool for visualization of medical imaging pipelines that are built with Nipype, a freely available tool for building pipelines programatically. Our tool enables researchers to interact with their pipelines, visualize the pipeline structure, and view their intermediate and final results. We also provide a video and live(More)