1 code implementation • 28 Jan 2025 • Keqi Han, Yao Su, Lifang He, Liang Zhan, Sergey Plis, Vince Calhoun, Carl Yang
Functional brain connectome is crucial for deciphering the neural mechanisms underlying cognitive functions and neurological disorders.
no code implementations • 6 Nov 2024 • Praveen Nair, Payal Bhandari, Mohammadsajad Abavisani, Sergey Plis, David Danks
In many causal learning problems, variables of interest are often not all measured over the same observations, but are instead distributed across multiple datasets with overlapping variables.
no code implementations • 14 Oct 2024 • Oktay Agcaoglu, Rogers F. Silva, Deniz Alacam, Sergey Plis, Tulay Adali, Vince Calhoun
Different brain imaging modalities offer unique insights into brain function and structure.
no code implementations • 15 May 2024 • Riyasat Ohib, Bishal Thapaliya, Gintare Karolina Dziugaite, Jingyu Liu, Vince Calhoun, Sergey Plis
In this work, we propose Salient Sparse Federated Learning (SSFL), a streamlined approach for sparse federated learning with efficient communication.
no code implementations • 20 Dec 2023 • Mrinal Mathur, Sergey Plis
Deep learning models have become increasingly computationally intensive, requiring extensive computational resources and time for both training and inference.
1 code implementation • 24 Oct 2023 • Mohamed Masoud, Pratyush Reddy, Farfalla Hu, Sergey Plis
Performing volumetric image processing directly within the browser, particularly with medical data, presents unprecedented challenges compared to conventional backend tools.
no code implementations • 18 May 2023 • Eloy Geenjaar, Donghyun Kim, Riyasat Ohib, Marlena Duda, Amrit Kashyap, Sergey Plis, Vince Calhoun
We evaluate our approach on various task-fMRI datasets, including motor, working memory, and relational processing tasks, and demonstrate that it outperforms widely used dimensionality reduction techniques in how well the latent timeseries relates to behavioral sub-tasks, such as left-hand or right-hand tapping.
no code implementations • 15 Apr 2023 • Riyasat Ohib, Bishal Thapaliya, Pratyush Gaggenapalli, Jingyu Liu, Vince Calhoun, Sergey Plis
Federated learning (FL) enables the training of a model leveraging decentralized data in client sites while preserving privacy by not collecting data.
no code implementations • 29 Nov 2022 • Zafar Iqbal, Usman Mahmood, Zening Fu, Sergey Plis
In this paper, we demonstrate that a model trained on the time direction of functional neuro-imaging data could help in any downstream task, for example, classifying diseases from healthy controls in fMRI data.
no code implementations • 27 Aug 2022 • Xinhui Li, Alex Fedorov, Mrinal Mathur, Anees Abrol, Gregory Kiar, Sergey Plis, Vince Calhoun
We next propose two pipeline-invariant representation learning methodologies, MPSL and PXL, to improve robustness in classification performance and to capture similar neural network representations.
no code implementations • 13 Jun 2022 • Md Mahfuzur Rahman, Noah Lewis, Sergey Plis
We demonstrate through extensive experiments that the proposed approach outperforms vanilla and integrated gradients in subjective and quantitative assessment.
no code implementations • 18 May 2022 • Mohammadsajad Abavisani, David Danks, Sergey Plis
Graphical structures estimated by causal learning algorithms from time series data can provide misleading causal information if the causal timescale of the generating process fails to match the measurement timescale of the data.
no code implementations • 4 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.
no code implementations • 31 Dec 2021 • Samin Yeasar Arnob, Riyasat Ohib, Sergey Plis, Doina Precup
We leverage a fixed dataset to prune neural networks before the start of RL training.
1 code implementation • 7 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.
no code implementations • 1 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.
no code implementations • 1 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--.
no code implementations • 3 May 2021 • Eloy Geenjaar, Noah Lewis, Zening Fu, Rohan Venkatdas, Sergey Plis, Vince Calhoun
Neuroimaging studies often involve the collection of multiple data modalities.
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.
no code implementations • 6 Jan 2020 • Haleh Falakshahi, Victor M. Vergara, Jingyu Liu, Daniel H. Mathalon, Judith M. Ford, James Voyvodic, Bryon A. Mueller, Aysenil Belger, Sarah McEwen, Steven G. Potkin, Adrian Preda, Hooman Rokham, Jing Sui, Jessica A. Turner, Sergey Plis, Vince D. Calhoun
Through simulation and real data, we show our approach reveals important information about disease-related network disruptions that are missed with a focus on a single modality.
no code implementations • 9 Dec 2019 • Riyasat Ohib, Nicolas Gillis, Niccolò Dalmasso, Sameena Shah, Vamsi K. Potluru, Sergey Plis
Instead, in our approach we set the sparsity level for the whole set explicitly and simultaneously project a group of vectors with the sparsity level of each vector tuned automatically.
no code implementations • 16 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.
no code implementations • 24 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.
no code implementations • 4 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.
2 code implementations • 2 Apr 2018 • Łukasz Kidziński, Sharada Prasanna Mohanty, Carmichael Ong, Zhewei Huang, Shuchang Zhou, Anton Pechenko, Adam Stelmaszczyk, Piotr Jarosik, Mikhail Pavlov, Sergey Kolesnikov, Sergey Plis, Zhibo Chen, Zhizheng Zhang, Jiale Chen, Jun Shi, Zhuobin Zheng, Chun Yuan, Zhihui Lin, Henryk Michalewski, Piotr Miłoś, Błażej Osiński, Andrew Melnik, Malte Schilling, Helge Ritter, Sean Carroll, Jennifer Hicks, Sergey Levine, Marcel Salathé, Scott Delp
In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle course.
1 code implementation • 1 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.
2 code implementations • 3 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.
no code implementations • 25 Feb 2016 • Antti Hyttinen, Sergey Plis, Matti Järvisalo, Frederick Eberhardt, David Danks
This paper focuses on causal structure estimation from time series data in which measurements are obtained at a coarser timescale than the causal timescale of the underlying system.
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.