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Science. 2016 Apr 8;352(6282):216-20. doi: 10.1126/science.aad8127. Epub 2016 Apr 7.

Task-free MRI predicts individual differences in brain activity during task performance.

Author information

1
Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, Oxford OX3 9DU, UK. Department of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer, 52621, Israel.
2
Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, Oxford OX3 9DU, UK.
3
Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, Oxford OX3 9DU, UK. Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, 6525 EZ Nijmegen, Netherlands.
4
Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, Oxford OX3 9DU, UK. Wellcome Trust Centre for Neuroimaging, University College London, London, WC1N 3BG, UK.
5
Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, Oxford OX3 9DU, UK. saad@fmrib.ox.ac.uk.

Abstract

When asked to perform the same task, different individuals exhibit markedly different patterns of brain activity. This variability is often attributed to volatile factors, such as task strategy or compliance. We propose that individual differences in brain responses are, to a large degree, inherent to the brain and can be predicted from task-independent measurements collected at rest. Using a large set of task conditions, spanning several behavioral domains, we train a simple model that relates task-independent measurements to task activity and evaluate the model by predicting task activation maps for unseen subjects using magnetic resonance imaging. Our model can accurately predict individual differences in brain activity and highlights a coupling between brain connectivity and function that can be captured at the level of individual subjects.

PMID:
27124457
DOI:
10.1126/science.aad8127
[Indexed for MEDLINE]
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