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Hum Brain Mapp. 2017 Aug;38(8):4125-4156. doi: 10.1002/hbm.23653. Epub 2017 May 23.

Challenges in measuring individual differences in functional connectivity using fMRI: The case of healthy aging.

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MRC Cognition and Brain Sciences Unit, Cambridge, United Kingdom.
Cambridge Centre for Ageing and Neuroscience (Cam-CAN), University of Cambridge and MRC Cognition and Brain Sciences Unit, Cambridge, United Kingdom.
Donders Institute for Brain, Cognition and Behaviour, Radboud University, the Netherlands.
Centre for Speech, Language and the Brain, Department of Psychology, University of Cambridge, Cambridge, United Kingdom.
Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom.


Many studies report individual differences in functional connectivity, such as those related to age. However, estimates of connectivity from fMRI are confounded by other factors, such as vascular health, head motion and changes in the location of functional regions. Here, we investigate the impact of these confounds, and pre-processing strategies that can mitigate them, using data from the Cambridge Centre for Ageing & Neuroscience ( This dataset contained two sessions of resting-state fMRI from 214 adults aged 18-88. Functional connectivity between all regions was strongly related to vascular health, most likely reflecting respiratory and cardiac signals. These variations in mean connectivity limit the validity of between-participant comparisons of connectivity estimates, and were best mitigated by regression of mean connectivity over participants. We also showed that high-pass filtering, instead of band-pass filtering, produced stronger and more reliable age-effects. Head motion was correlated with gray-matter volume in selected brain regions, and with various cognitive measures, suggesting that it has a biological (trait) component, and warning against regressing out motion over participants. Finally, we showed that the location of functional regions was more variable in older adults, which was alleviated by smoothing the data, or using a multivariate measure of connectivity. These results demonstrate that analysis choices have a dramatic impact on connectivity differences between individuals, ultimately affecting the associations found between connectivity and cognition. It is important that fMRI connectivity studies address these issues, and we suggest a number of ways to optimize analysis choices. Hum Brain Mapp 38:4125-4156, 2017.


aging; filtering; functional connectivity; functional magnetic resonance imaging; head motion; nuisance regression; pre-processing; resting state; smoothing; vascular health

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