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Ensemble learning with 3D convolutional neural networks for connectome-based prediction
The specificty and sensitivity of resting state functional MRI (rs-fMRI)...
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Detecting abnormalities in resting-state dynamics: An unsupervised learning approach
Resting-state functional MRI (rs-fMRI) is a rich imaging modality that c...
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Towards Monitoring Parkinson's Disease Following Drug Treatment: CGP Classification of rs-MRI Data
Background and Objective: It is commonly accepted that accurate monitori...
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Bayesian recurrent state space model for rs-fMRI
We propose a hierarchical Bayesian recurrent state space model for model...
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fMRI group analysis based on outputs from Matrix-Variate Dynamic Linear Models
In this work, we describe in more detail how to perform fMRI group analy...
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Ensemble Deep Learning on Large, Mixed-Site fMRI Datasets in Autism and Other Tasks
Deep learning models for MRI classification face two recurring problems:...
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A Coupled Manifold Optimization Framework to Jointly Model the Functional Connectomics and Behavioral Data Spaces
The problem of linking functional connectomics to behavior is extremely ...
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3D Convolutional Neural Networks for Classification of Functional Connectomes
Resting-state functional MRI (rs-fMRI) scans hold the potential to serve as a diagnostic or prognostic tool for a wide variety of conditions, such as autism, Alzheimer's disease, and stroke. While a growing number of studies have demonstrated the promise of machine learning algorithms for rs-fMRI based clinical or behavioral prediction, most prior models have been limited in their capacity to exploit the richness of the data. For example, classification techniques applied to rs-fMRI often rely on region-based summary statistics and/or linear models. In this work, we propose a novel volumetric Convolutional Neural Network (CNN) framework that takes advantage of the full-resolution 3D spatial structure of rs-fMRI data and fits non-linear predictive models. We showcase our approach on a challenging large-scale dataset (ABIDE, with N > 2,000) and report state-of-the-art accuracy results on rs-fMRI-based discrimination of autism patients and healthy controls.
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