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Niharika Shimona D'Souza
Johns Hopkins University
5Publications
1H-index
4Citations
Publications 5
Newest
#1Niharika Shimona D'Souza (Johns Hopkins University)H-Index: 1
#2Mary Beth Nebel (ICSCI: Kennedy Krieger Institute)H-Index: 20
Last.Archana Venkataraman (Johns Hopkins University)H-Index: 9
view all 5 authors...
We propose a unified optimization framework that combines neural networks with dictionary learning to model complex interactions between resting state functional MRI and behavioral data. The dictionary learning objective decomposes patient correlation matrices into a collection of shared basis networks and subject-specific loadings. These subject-specific features are simultaneously input into a neural network that predicts multidimensional clinical information. Our novel optimization framework ...
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Abstract We propose a novel optimization framework to predict clinical severity from resting state fMRI (rs-fMRI) data. Our model consists of two coupled terms. The first term decomposes the correlation matrices into a sparse set of representative subnetworks that define a network manifold. These subnetworks are modeled as rank-one outer-products which correspond to the elemental patterns of co-activation across the brain; the subnetworks are combined via patient-specific non-negative coefficien...
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#1Niharika Shimona D'Souza (Johns Hopkins University)H-Index: 1
#2Mary Beth Nebel (ICSCI: Kennedy Krieger Institute)H-Index: 20
Last.Archana Venkataraman (Johns Hopkins University)H-Index: 9
view all 5 authors...
The problem of linking functional connectomics to behavior is extremely challenging due to the complex interactions between the two distinct, but related, data domains. We propose a coupled manifold optimization framework which projects fMRI data onto a low dimensional matrix manifold common to the cohort. The patient specific loadings simultaneously map onto a behavioral measure of interest via a second, non-linear, manifold. By leveraging the kernel trick, we can optimize over a potentially in...
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Sep 20, 2018 in MICCAI (Medical Image Computing and Computer-Assisted Intervention)
#1Naresh Nandakumar (Johns Hopkins University)H-Index: 1
#2Niharika Shimona D'Souza (Johns Hopkins University)H-Index: 1
Last.Archana Venkataraman (Johns Hopkins University)H-Index: 9
view all 8 authors...
We propose a hierarchical Bayesian model that refines a population-based atlas using resting-state fMRI (rs-fMRI) coherence. Our method starts from an initial parcellation and then iteratively reassigns the voxel memberships at the subject level. Our algorithm uses a maximum a posteriori inference strategy based on the neighboring voxel assignments and the Pearson correlation coefficients between the voxel time series and the parcel reference signals. Our method is generalizable to different ini...
1 CitationsSource
Sep 16, 2018 in MICCAI (Medical Image Computing and Computer-Assisted Intervention)
#1Niharika Shimona D'Souza (Johns Hopkins University)H-Index: 1
#2Mary Beth Nebel (Johns Hopkins University)H-Index: 20
Last.Archana Venkataraman (Johns Hopkins University)H-Index: 9
view all 5 authors...
We propose a matrix factorization technique that decomposes the resting state fMRI (rs-fMRI) correlation matrices for a patient population into a sparse set of representative subnetworks, as modeled by rank one outer products. The subnetworks are combined using patient specific non-negative coefficients; these coefficients are also used to model, and subsequently predict the clinical severity of a given patient via a linear regression. Our generative-discriminative framework is able to exploit t...
2 CitationsSource
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