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Psychiatry Res Neuroimaging. 2018 Dec 30;282:90-102. doi: 10.1016/j.pscychresns.2018.09.008. Epub 2018 Sep 28.

Neu3CA-RT: A framework for real-time fMRI analysis.

Author information

1
Department of Electrical Engineering, Eindhoven University of Technology, Postal address: PO box 513; Flux buidling, room 7.066, 5600MB Eindhoven, The Netherlands. Electronic address: j.s.heunis@tue.nl.
2
Department of Electrical Engineering, Eindhoven University of Technology, Postal address: PO box 513; Flux buidling, room 7.066, 5600MB Eindhoven, The Netherlands; Department of Research and Development, Epilepsy Centre Kempenhaeghe, Heeze, The Netherlands.
3
Department of Electrical Engineering, Eindhoven University of Technology, Postal address: PO box 513; Flux buidling, room 7.066, 5600MB Eindhoven, The Netherlands; Philips Research Laboratories Eindhoven, Eindhoven, The Netherlands.
4
Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands; Philips Healthcare, Best, The Netherlands.
5
Department of Electrical Engineering, Eindhoven University of Technology, Postal address: PO box 513; Flux buidling, room 7.066, 5600MB Eindhoven, The Netherlands; Department of Research and Development, Epilepsy Centre Kempenhaeghe, Heeze, The Netherlands; Laboratory for Clinical and Experimental Neurophysiology, Neurobiology and Neuropsychology, Ghent University Hospital, Ghent, Belgium; Department of Neurology, Maastricht University Medical Center, Maastricht, The Netherlands.
6
Department of Electrical Engineering, Eindhoven University of Technology, Postal address: PO box 513; Flux buidling, room 7.066, 5600MB Eindhoven, The Netherlands.

Abstract

Real-time functional magnetic resonance imaging (rtfMRI) allows visualisation of ongoing brain activity of the subject in the scanner. Denoising algorithms aim to rid acquired data of confounding effects, enhancing the blood oxygenation level-dependent (BOLD) signal. Further image processing and analysis methods, like general linear models (GLM) or multivariate analysis, then present application-specific information to the researcher. These processes are typically applied to regions of interest but, increasingly, rtfMRI techniques extract and classify whole brain functional networks and dynamics as correlates for brain states or behaviour, particularly in neuropsychiatric and neurocognitive disorders. We present Neu3CA-RT: a Matlab-based rtfMRI analysis framework aiming to advance scientific knowledge on real-time cognitive brain activity and to promote its translation into clinical practice. Design considerations are listed based on reviewing existing rtfMRI approaches. The toolbox integrates established SPM preprocessing routines, real-time GLM mapping of fMRI data to a basis set of spatial brain networks, correlation of activity with 50 behavioural profiles from the BrainMap database, and an intuitive user interface. The toolbox is demonstrated in a task-based experiment where a subject executes visual, auditory and motor tasks inside a scanner. In three out of four experiments, resulting behavioural profiles agreed with the expected brain state.

KEYWORDS:

GUI; Matlab; Real-time fMRI; SPM12; Whole brain functional networks

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