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Tracking whole-brain connectivity dynamics in the resting state.
- E. Allen, E. Damaraju, S. Plis, E. Erhardt, T. Eichele, V. Calhoun
- PsychologyCerebral cortex
- 1 March 2014
It is suggested that the study of time-varying aspects of FC can unveil flexibility in the functional coordination between different neural systems, and that the exploitation of these dynamics in further investigations may improve the understanding of behavioral shifts and adaptive processes.
Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls
Deep learning for neuroimaging: a validation study
- S. Plis, R. Devon Hjelm, R. Salakhutdinov, V. Calhoun
- Computer ScienceFront. Neurosci.
- 20 December 2013
The results show that deep learning methods are able to learn physiologically important representations and detect latent relations in neuroimaging data.
Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data.
- A. Aliper, S. Plis, Artem V. Artemov, Alvaro E. Ulloa, Polina Mamoshina, A. Zhavoronkov
- Computer ScienceMolecular pharmaceutics
- 8 June 2016
This work demonstrates a deep learning neural net trained on transcriptomic data to recognize pharmacological properties of multiple drugs across different biological systems and conditions and proposes using deep neural net confusion matrices for drug repositioning.
End-to-end learning of brain tissue segmentation from imperfect labeling
- A. Fedorov, Jeremy Johnson, E. Damaraju, Alexei Ozerin, V. Calhoun, S. Plis
- Computer ScienceInternational Joint Conference on Neural Networks…
- 3 December 2016
A deep learning model that is based on volumetric dilated convolutions, subsequently reducing both processing time and errors is introduced, which has great potential in a clinical setting where, with little to no substantial delay, a patient and provider can go over test results.
Spatiotemporal Bayesian inference dipole analysis for MEG neuroimaging data
Analysis of Multimodal Neuroimaging Data
- F. Biessmann, S. Plis, F. Meinecke, T. Eichele, K.-R. Muller
- BiologyIEEE Reviews in Biomedical Engineering
- 6 October 2011
A comprehensive overview of mathematical tools reoccurring in multimodal neuroimaging studies for artifact removal, data-driven and model-driven analyses, enabling the practitioner to try established or new combinations from these algorithmic building blocks.
Impact of autocorrelation on functional connectivity
Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments
This work presents eight solutions that used deep reinforcement learning approaches, based on algorithms such as Deep Deterministic Policy Gradient, Proximal Policy Optimization, and Trust Region Policyoptimization, to make it run as fast as possible through an obstacle course.