Ben D. Fulcher
University of Sydney
111 Papers
307 Citations
Ben D. Fulcher is an academic researcher from University of Sydney. The author has contributed to research in topics: Computer science & Biology. The author has an hindex of 29, co-authored 91 publications. Previous affiliations of Ben D. Fulcher include Australian Research Council & Monash University.
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Papers
An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI.
TL;DR: These results indicate that simple linear regression of regional fMRI time series against head motion parameters and WM/CSF signals (with or without expansion terms) is not sufficient to remove head motion artefacts, and group comparisons in functional connectivity between healthy controls and schizophrenia patients are highly dependent on preprocessing strategy.
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A practical guide to linking brain-wide gene expression and neuroimaging data.
TL;DR: It is suggested that studies using the AHBA should work towards a unified data processing pipeline to ensure consistent and reproducible results in this burgeoning field of brain structure and function.
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An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI
TL;DR: These results indicate that simple linear regression of regional fMRI time series against head motion parameters and WM/CSF signals is not sufficient to remove head motion artefacts, aCompCor pipelines may only be viable in low-motion data, and group comparisons in functional connectivity between healthy controls and schizophrenia patients are highly dependent on preprocessing strategy.
426
Highly comparative time-series analysis: the empirical structure of time series and their methods
TL;DR: Reduced representations of both time series, in terms of their properties measured by diverse scientific methods, and of time-series analysis methods, interms of their behaviour on empirical time series are introduced and used to organize these interdisciplinary resources.
Bridging the Gap between Connectome and Transcriptome
TL;DR: These analyses have revealed that spatial patterning of gene expression and neuronal connectivity are closely linked, following broad spatial gradients that track regional variations in microcircuitry, inter-regional connectivity, and functional specialisation.
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