Search Results for author: Vince Calhoun

Found 13 papers, 5 papers with code

Improved Differentially Private Decentralized Source Separation for fMRI Data

no code implementations28 Oct 2019 Hafiz Imtiaz, Jafar Mohammadi, Rogers Silva, Bradley Baker, Sergey M. Plis, Anand D. Sarwate, Vince Calhoun

In this work, we propose a differentially private algorithm for performing ICA in a decentralized data setting.

Tracing Network Evolution Using the PARAFAC2 Model

1 code implementation23 Oct 2019 Marie Roald, Suchita Bhinge, Chunying Jia, Vince Calhoun, Tülay Adalı, Evrim Acar

For instance, how spatial networks of functional connectivity in the brain evolve during a task is not well-understood.

Improving Classification Rate of Schizophrenia Using a Multimodal Multi-Layer Perceptron Model with Structural and Functional MR

no code implementations4 Apr 2018 Alvaro Ulloa, Sergey Plis, Vince Calhoun

We propose the use of a multimodal multi-layer perceptron model to enhance the predictive power of structural and functional magnetic resonance imaging (sMRI and fMRI) combined.

General Classification

Almost instant brain atlas segmentation for large-scale studies

1 code implementation1 Nov 2017 Alex Fedorov, Eswar Damaraju, Vince Calhoun, Sergey Plis

Complexity of the task increases even further when segmenting structural MRI of the brain into an atlas with more than 50 regions.

Kernel Method for Detecting Higher Order Interactions in multi-view Data: An Application to Imaging, Genetics, and Epigenetics

no code implementations14 Jul 2017 Md. Ashad Alam, Hui-Yi Lin, Vince Calhoun, Yu-Ping Wang

In this study, we tested the interaction effect of multimodal datasets using a novel method called the kernel method for detecting higher order interactions among biologically relevant mulit-view data.

FDR-Corrected Sparse Canonical Correlation Analysis with Applications to Imaging Genomics

1 code implementation11 May 2017 Alexej Gossmann, Pascal Zille, Vince Calhoun, Yu-Ping Wang

Here we propose a way of applying the FDR concept to sparse CCA, and a method to control the FDR.

End-to-end learning of brain tissue segmentation from imperfect labeling

1 code implementation3 Dec 2016 Alex Fedorov, Jeremy Johnson, Eswar Damaraju, Alexei Ozerin, Vince Calhoun, Sergey Plis

Segmenting a structural magnetic resonance imaging (MRI) scan is an important pre-processing step for analytic procedures and subsequent inferences about longitudinal tissue changes.

Spatio-temporal Dynamics of Intrinsic Networks in Functional Magnetic Imaging Data Using Recurrent Neural Networks

no code implementations3 Nov 2016 R. Devon Hjelm, Eswar Damaraju, Kyunghyun Cho, Helmut Laufs, Sergey M. Plis, Vince Calhoun

We introduce a novel recurrent neural network (RNN) approach to account for temporal dynamics and dependencies in brain networks observed via functional magnetic resonance imaging (fMRI).

Learning Schizophrenia Imaging Genetics Data Via Multiple Kernel Canonical Correlation Analysis

no code implementations15 Sep 2016 Owen Richfield, Md. Ashad Alam, Vince Calhoun, Yu-Ping Wang

Kernel and Multiple Kernel Canonical Correlation Analysis (CCA) are employed to classify schizophrenic and healthy patients based on their SNPs, DNA Methylation and fMRI data.

Classification General Classification

Rate-Agnostic (Causal) Structure Learning

no code implementations NeurIPS 2015 Sergey Plis, David Danks, Cynthia Freeman, Vince Calhoun

That is, these algorithms all learn causal structure without assuming any particular relation between the measurement and system timescales; they are thus rate-agnostic.

Time Series

Iterative Refinement of the Approximate Posterior for Directed Belief Networks

1 code implementation NeurIPS 2016 R. Devon Hjelm, Kyunghyun Cho, Junyoung Chung, Russ Salakhutdinov, Vince Calhoun, Nebojsa Jojic

Variational methods that rely on a recognition network to approximate the posterior of directed graphical models offer better inference and learning than previous methods.

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