Author

Usama Pervaiz

University of Oxford

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Neuroscience· 4

Signature Assays, Organisms & Methods

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Approach:statistical model· 4 Research Focus:benchmarking· 3 Organism:human· 3 Research Focus:trajectory inference· 2 Approach:Bayesian inference· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Assay:electronic health records· 1

Frequent Co-Authors

Mark W. WoolrichiD· 4 shared Diego VidaurreiD· 3 shared Chetan GohiliD· 2 shared Stephen M. SmithiD· 2 shared Alex Skates· 1 shared Andrew J. QuinniD· 1 shared Angus B. A. StevneriD· 1 shared Cameron HigginsiD· 1 shared

Activity by Year

2020-2022 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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4 shared papersNeuroscience

Mark W. WoolrichORCID

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4 papers

Recent·Most cited

Mixtures of large-scale dynamic functional brain network modes

Chetan Gohil, Evan Roberts, Ryan C. Timms, Alex Skates, Cameron Higgins, Andrew J. Quinn, Usama Pervaiz, Joost van Amersfoort, Pascal Notin, Yarin Gal, Stanisław Adaszewski, Mark W. Woolrich

This paper introduces DyNeMo, a model for dynamic functional brain networks that outperforms HMM in capturing non-exclusive network modes in MEG data.

NeuroImage · Aug 27, 2022Peer-reviewedNeuroscience

Data and model considerations for estimating time-varying functional connectivity in fMRI

Christine Ahrends, Angus B. A. Stevner, Usama Pervaiz, ML Kringelbach, Peter Vuust, Mark W. Woolrich, Diego Vidaurre

This paper investigates how data characteristics and model parameters affect the ability of time-varying functional connectivity models to detect temporal changes in fMRI data.

NeuroImage · Feb 22, 2022Peer-reviewedNeuroscience

Multi-dynamic Modelling Reveals Strongly Time-varying Resting fMRI Correlations

Usama Pervaiz, Diego Vidaurre, Chetan Gohil, Stephen M. Smith, Mark W. Woolrich

This paper introduces a multi-dynamic model to separately analyze time-varying brain activity and functional connectivity in resting fMRI, revealing stronger temporal changes in connectivity and better prediction of behavioral variability.

Medical Image Analysis · Jan 29, 2022Peer-reviewedNeuroscience

Optimising network modelling methods for fMRI

Usama Pervaiz, Diego Vidaurre, Mark W. Woolrich, Stephen M. Smith

The paper evaluates and optimizes network modelling methods for fMRI by testing various parcellation, connectivity measures, and classifiers across multiple datasets to find the best predictive pipeline.

NeuroImage · Feb 13, 2020Peer-reviewedNeuroscience