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Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning

Nature communications, 2021-01, Vol.12 (1), p.353-353, Article 353

2021

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Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls

NeuroImage (Orlando, Fla.), 2017-01, Vol.145 (Pt B), p.137-165

2017

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Tracking whole-brain connectivity dynamics in the resting state

Cerebral cortex (New York, N.Y. 1991), 2014-03, Vol.24 (3), p.663-676

2014

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Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data

Molecular pharmaceutics, 2016-07, Vol.13 (7), p.2524-2530

2016

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Deep residual learning for neuroimaging: An application to predict progression to Alzheimer’s disease

Journal of neuroscience methods, 2020-06, Vol.339, p.108701-108701, Article 108701

2020

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Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks

NeuroImage (Orlando, Fla.), 2017-01, Vol.145 (Pt B), p.314-328

2017

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Deep learning for neuroimaging: a validation study

Frontiers in neuroscience, 2014-08, Vol.8, p.229-229

2014

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Through the looking glass: Deep interpretable dynamic directed connectivity in resting fMRI

NeuroImage (Orlando, Fla.), 2022-12, Vol.264, p.119737-119737, Article 119737

2022

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Independent component analysis for brain FMRI does indeed select for maximal independence

PloS one, 2013-08, Vol.8 (8), p.e73309-e73309

2013

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Group-level component analyses of EEG: validation and evaluation

Frontiers in neuroscience, 2015-07, Vol.9, p.254-254

2015

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Self-supervised multimodal learning for group inferences from MRI data: Discovering disorder-relevant brain regions and multimodal links

NeuroImage (Orlando, Fla.), 2024-01, Vol.285, p.120485-120485, Article 120485

2024

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Restricted Boltzmann machines for neuroimaging: An application in identifying intrinsic networks

NeuroImage (Orlando, Fla.), 2014-08, Vol.96, p.245-260

2014

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Multidataset Independent Subspace Analysis With Application to Multimodal Fusion

IEEE transactions on image processing, 2021-01, Vol.30, p.588-602

2021

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IEEE_Xplore

Interpreting models interpreting brain dynamics

Scientific reports, 2022-07, Vol.12 (1), p.12023-12023, Article 12023

2022

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A deep learning approach to estimating initial conditions of Brain Network Models in reference to measured fMRI data

Frontiers in neuroscience, 2023-07, Vol.17, p.1159914-1159914

2023

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Decentralized distribution-sampled classification models with application to brain imaging

Journal of neuroscience methods, 2020-01, Vol.329, p.108418-108418, Article 108418

2020

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Statelets: Capturing recurrent transient variations in dynamic functional network connectivity

Human brain mapping, 2022-06, Vol.43 (8), p.2503-2518

2022

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Reading the (functional) writing on the (structural) wall: Multimodal fusion of brain structure and function via a deep neural network based translation approach reveals novel impairments in schizophrenia

NeuroImage (Orlando, Fla.), 2018-11, Vol.181, p.734-747

2018

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Application of Machine Learning to Investigation of Arcing on Geosynchronous Satellites

The Journal of the astronautical sciences, 2022-04, Vol.69 (2), p.570-580

2022

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Decentralized dynamic functional network connectivity: State analysis in collaborative settings

Human brain mapping, 2020-08, Vol.41 (11), p.2909-2925

2020

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Three‐way parallel group independent component analysis: Fusion of spatial and spatiotemporal magnetic resonance imaging data

Human brain mapping, 2022-03, Vol.43 (4), p.1280-1294

2022

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Privacy‐preserving quality control of neuroimaging datasets in federated environments

Human brain mapping, 2022-05, Vol.43 (7), p.2289-2310

2022

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NeuroCrypt: Machine Learning Over Encrypted Distributed Neuroimaging Data

Neuroinformatics (Totowa, N.J.), 2022-01, Vol.20 (1), p.91-108

2022

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Disrupted correlation between low frequency power and connectivity strength of resting state brain networks in schizophrenia

Schizophrenia research, 2013-01, Vol.143 (1), p.165-171

2013

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Deep Independence Network Analysis of Structural Brain Imaging: Application to Schizophrenia

IEEE transactions on medical imaging, 2016-07, Vol.35 (7), p.1729-1740

2016

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IEEE_Xplore

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Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning

Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls

Tracking whole-brain connectivity dynamics in the resting state

Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data

Deep residual learning for neuroimaging: An application to predict progression to Alzheimer’s disease

Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks

Deep learning for neuroimaging: a validation study

Through the looking glass: Deep interpretable dynamic directed connectivity in resting fMRI

Independent component analysis for brain FMRI does indeed select for maximal independence

Group-level component analyses of EEG: validation and evaluation

Self-supervised multimodal learning for group inferences from MRI data: Discovering disorder-relevant brain regions and multimodal links

Restricted Boltzmann machines for neuroimaging: An application in identifying intrinsic networks

Multidataset Independent Subspace Analysis With Application to Multimodal Fusion

Interpreting models interpreting brain dynamics

A deep learning approach to estimating initial conditions of Brain Network Models in reference to measured fMRI data

Decentralized distribution-sampled classification models with application to brain imaging

Statelets: Capturing recurrent transient variations in dynamic functional network connectivity

Reading the (functional) writing on the (structural) wall: Multimodal fusion of brain structure and function via a deep neural network based translation approach reveals novel impairments in schizophrenia

Application of Machine Learning to Investigation of Arcing on Geosynchronous Satellites

Decentralized dynamic functional network connectivity: State analysis in collaborative settings

Three‐way parallel group independent component analysis: Fusion of spatial and spatiotemporal magnetic resonance imaging data

Privacy‐preserving quality control of neuroimaging datasets in federated environments

NeuroCrypt: Machine Learning Over Encrypted Distributed Neuroimaging Data

Disrupted correlation between low frequency power and connectivity strength of resting state brain networks in schizophrenia

Deep Independence Network Analysis of Structural Brain Imaging: Application to Schizophrenia

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