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Neuroimage. 2018 May 1;171:402-414. doi: 10.1016/j.neuroimage.2018.01.003. Epub 2018 Jan 5.

Thresholding functional connectomes by means of mixture modeling.

Author information

1
Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands; Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Geert Groteplein Zuid 10, 6525GA Nijmegen, The Netherlands. Electronic address: natalia.bielczyk@gmail.com.
2
Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands; University of Osnabrück, Neuer Graben 29/Schloss, 49074 Osnabrück, Germany.
3
Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands; Radboud University Nijmegen, Comeniuslaan 4, 6525 HP Nijmegen, The Netherlands.
4
Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands; Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Geert Groteplein Zuid 10, 6525GA Nijmegen, The Netherlands.
5
Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands; Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Geert Groteplein Zuid 10, 6525GA Nijmegen, The Netherlands; Oxford Centre for Functional MRI of the Brain, John Radcliffe Hospital, Oxford OX3 9DU, United Kingdom.

Abstract

Functional connectivity has been shown to be a very promising tool for studying the large-scale functional architecture of the human brain. In network research in fMRI, functional connectivity is considered as a set of pair-wise interactions between the nodes of the network. These interactions are typically operationalized through the full or partial correlation between all pairs of regional time series. Estimating the structure of the latent underlying functional connectome from the set of pair-wise partial correlations remains an open research problem though. Typically, this thresholding problem is approached by proportional thresholding, or by means of parametric or non-parametric permutation testing across a cohort of subjects at each possible connection. As an alternative, we propose a data-driven thresholding approach for network matrices on the basis of mixture modeling. This approach allows for creating subject-specific sparse connectomes by modeling the full set of partial correlations as a mixture of low correlation values associated with weak or unreliable edges in the connectome and a sparse set of reliable connections. Consequently, we propose to use alternative thresholding strategy based on the model fit using pseudo-False Discovery Rates derived on the basis of the empirical null estimated as part of the mixture distribution. We evaluate the method on synthetic benchmark fMRI datasets where the underlying network structure is known, and demonstrate that it gives improved performance with respect to the alternative methods for thresholding connectomes, given the canonical thresholding levels. We also demonstrate that mixture modeling gives highly reproducible results when applied to the functional connectomes of the visual system derived from the n-back Working Memory task in the Human Connectome Project. The sparse connectomes obtained from mixture modeling are further discussed in the light of the previous knowledge of the functional architecture of the visual system in humans. We also demonstrate that with use of our method, we are able to extract similar information on the group level as can be achieved with permutation testing even though these two methods are not equivalent. We demonstrate that with both of these methods, we obtain functional decoupling between the two hemispheres in the higher order areas of the visual cortex during visual stimulation as compared to the resting state, which is in line with previous studies suggesting lateralization in the visual processing. However, as opposed to permutation testing, our approach does not require inference at the cohort level and can be used for creating sparse connectomes at the level of a single subject.

KEYWORDS:

False discovery rate; Functional connectivity; Mixture modeling

PMID:
29309896
PMCID:
PMC5981009
DOI:
10.1016/j.neuroimage.2018.01.003
[Indexed for MEDLINE]
Free PMC Article

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