Consistency-based thresholding of the human connectome

Roberts, James A.; Perry, Alistair; Roberts, Gloria; Mitchell, Philip B.; Breakspear, Michael

doi:10.1016/j.neuroimage.2016.09.053

Cited by 169 publications

(212 citation statements)

References 48 publications

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“…Our method took advantage of state-of-the-art approaches to limit false positives, including the use of anatomical constraints (Smith et al, 2012), we also thresholded our matrices based on a measure of edge weight consistency shown to improve estimate of consistent networks across subjects (Roberts et al, 2017). While this process may reduce the subject-level variability in the group, we view it as an essential step to remove possible noise sources from the data.…”

Section: Discussionmentioning

confidence: 99%

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Charting shared developmental trajectories of cortical thickness and structural connectivity in childhood and adolescence

Ball

Beare

Seal

2019

Human Brain Mapping

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The cortex is organised into broadly hierarchical functional systems with distinct neuroanatomical characteristics reflected by macroscopic measures of cortical morphology. Diffusion‐weighted magnetic resonance imaging allows the delineation of areal connectivity, changes to which reflect the ongoing maturation of white matter tracts. These developmental processes are intrinsically linked with timing coincident with the development of cognitive function. In this study, we use a data‐driven multivariate approach, nonnegative matrix factorisation, to define cortical regions that co‐vary together across a large paediatric cohort (n = 456) and are associated with specific subnetworks of cortical connectivity. We find that age between 3 and 21 years is associated with accelerated cortical thinning in frontoparietal regions, whereas relative thinning of primary motor and sensory regions is slower. Together, the subject‐specific weights of the derived set of cortical components can be combined to predict chronological age. Structural connectivity networks reveal a relative increase in strength in connection within, as opposed to between hemispheres that vary in line with cortical changes. We confirm our findings in an independent sample.

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Section: Discussionmentioning

confidence: 99%

“…After discounting connections that were zero across all subjects, connectivity matrices were subject to a consistency-based thresholding, retaining the top 10% most consistent edges across subjects and resulting in n = 1,538 edges in the final set of networks (Roberts, Perry, Roberts, Mitchell, & Breakspear, 2017). …”

Section: Whole-brain Tractographymentioning

confidence: 99%

Charting shared developmental trajectories of cortical thickness and structural connectivity in childhood and adolescence

Ball

Beare

Seal

2019

Human Brain Mapping

View full text Add to dashboard Cite

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“…SC was further either weighted (multiplied) or regressed by the average fiber length. As SC is biased by distance in a logarithmic way (Roberts, Perry, Roberts, Mitchell, & Breakspear, ) (Figure S1), we regressed out the distance on the logarithm of SC and then took the power of the residuals as the corrected SC.…”

Section: Methodsmentioning

confidence: 99%

Parcellation influence on the connectivity‐based structure–function relationship in the human brain

Messé

2019

Human Brain Mapping

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One of the fundamental questions in neuroscience is how brain structure and function are intertwined. MRI‐based studies have demonstrated a close relationship between the physical wiring of the brain (structural connectivity) and the associated patterns of synchronization (functional connectivity). However, little is known about the spatial consistency of such a relationship and notably its potential dependence on brain parcellations. In the present study, we performed a comparison of a set of state‐of‐the‐art group‐wise brain atlases, with various spatial resolutions, to relate structural and functional connectivity derived from high quality MRI data. We aim to investigate if the definition of brain areas influences the relationship between structural and functional connectivity. We observed that there is a significant effect of brain parcellations, which is mainly driven by the number of areas; there are mixed differences in the SC–FC relationship when compared to purely random parcellations; the influence of the number of areas cannot be attributed solely to the reliability of the connectivity estimates; and beyond the influence of the number of regions, the spatial embedding of the brain (distance effect) can explain a large portion of the observed relationship. As such the choice of a brain parcellation for connectivity analyses remains most likely a matter of convenience.

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“…Furthermore we performed a consistency analysis following Roberts et al . 62 (see Supplementary Fig. S4).…”

Section: Methodsmentioning

confidence: 99%

Increased Stability and Breakdown of Brain Effective Connectivity During Slow-Wave Sleep: Mechanistic Insights from Whole-Brain Computational Modelling

Jobst

Hindriks

Laufs

et al. 2017

Sci Rep

105

162

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Recent research has found that the human sleep cycle is characterised by changes in spatiotemporal patterns of brain activity. Yet, we are still missing a mechanistic explanation of the local neuronal dynamics underlying these changes. We used whole-brain computational modelling to study the differences in global brain functional connectivity and synchrony of fMRI activity in healthy humans during wakefulness and slow-wave sleep. We applied a whole-brain model based on the normal form of a supercritical Hopf bifurcation and studied the dynamical changes when adapting the bifurcation parameter for all brain nodes to best match wakefulness and slow-wave sleep. Furthermore, we analysed differences in effective connectivity between the two states. In addition to significant changes in functional connectivity, synchrony and metastability, this analysis revealed a significant shift of the global dynamic working point of brain dynamics, from the edge of the transition between damped to sustained oscillations during wakefulness, to a stable focus during slow-wave sleep. Moreover, we identified a significant global decrease in effective interactions during slow-wave sleep. These results suggest a mechanism for the empirical functional changes observed during slow-wave sleep, namely a global shift of the brain’s dynamic working point leading to increased stability and decreased effective connectivity.

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Consistency-based thresholding of the human connectome

Cited by 169 publications

References 48 publications

Charting shared developmental trajectories of cortical thickness and structural connectivity in childhood and adolescence

Charting shared developmental trajectories of cortical thickness and structural connectivity in childhood and adolescence

Parcellation influence on the connectivity‐based structure–function relationship in the human brain

Increased Stability and Breakdown of Brain Effective Connectivity During Slow-Wave Sleep: Mechanistic Insights from Whole-Brain Computational Modelling

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