Search Results for author: Zening Fu

Found 9 papers, 2 papers with code

Deep Dynamic Effective Connectivity Estimation from Multivariate Time Series

no code implementations4 Feb 2022 Usman Mahmood, Zening Fu, Vince Calhoun, Sergey Plis

To bridge this gap, we developed dynamic effective connectivity estimation via neural network training (DECENNT), a novel model to learn an interpretable directed and dynamic graph induced by the downstream classification/prediction task.

Connectivity Estimation Link Prediction +1

A deep learning model for data-driven discovery of functional connectivity

1 code implementation7 Dec 2021 Usman Mahmood, Zening Fu, Vince Calhoun, Sergey Plis

Functional connectivity (FC) studies have demonstrated the overarching value of studying the brain and its disorders through the undirected weighted graph of fMRI correlation matrix.

Multi network InfoMax: A pre-training method involving graph convolutional networks

no code implementations1 Nov 2021 Usman Mahmood, Zening Fu, Vince Calhoun, Sergey Plis

Since almost every DL model is an ensemble of multiple networks, we take our high-level embeddings from two different networks of a model --a convolutional and a graph network--.

Graph Classification

Brain dynamics via Cumulative Auto-Regressive Self-Attention

no code implementations1 Nov 2021 Usman Mahmood, Zening Fu, Vince Calhoun, Sergey Plis

The supervised training of the model as a classifier between patients and controls results in a model that generates directed connectivity graphs and highlights the components of the time-series that are predictive for each subject.

Time Series

Attend to connect: end-to-end brain functional connectivity estimation

no code implementations ICLR Workshop GTRL 2021 Usman Mahmood, Zening Fu, Vince Calhoun, Sergey Plis

Functional connectivity (FC) studies have demonstrated the benefits of investigating the brain and its disorders through the undirected weighted graph of fMRI correlation matrix.

Connectivity Estimation

Whole MILC: generalizing learned dynamics across tasks, datasets, and populations

1 code implementation29 Jul 2020 Usman Mahmood, Md Mahfuzur Rahman, Alex Fedorov, Noah Lewis, Zening Fu, Vince D. Calhoun, Sergey M. Plis

In this paper we present a novel self supervised training schema which reinforces whole sequence mutual information local to context (whole MILC).

Feature Importance

Transfer Learning of fMRI Dynamics

no code implementations16 Nov 2019 Usman Mahmood, Md Mahfuzur Rahman, Alex Fedorov, Zening Fu, Sergey Plis

In this paper, we demonstrate a self-supervised pre-training method that enables us to pre-train directly on fMRI dynamics of healthy control subjects and transfer the learning to much smaller datasets of schizophrenia.

Small Data Image Classification Transfer Learning

Prediction of Progression to Alzheimer's disease with Deep InfoMax

no code implementations24 Apr 2019 Alex Fedorov, R. Devon Hjelm, Anees Abrol, Zening Fu, Yuhui Du, Sergey Plis, Vince D. Calhoun

Arguably, unsupervised learning plays a crucial role in the majority of algorithms for processing brain imaging.

General Classification

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