Abnormal dynamic properties of functional connectivity in disorders of consciousness

Golkowski

Ranft

et al. 2021

Human Brain Mapping

The dynamic interplay of integration and segregation in the brain is at the core of leading theoretical accounts of consciousness. The human brain dynamically alternates between a sub‐state where integration predominates, and a predominantly segregated sub‐state, with different roles in supporting cognition and behaviour. Here, we combine graph theory and dynamic functional connectivity to compare resting‐state functional MRI data from healthy volunteers before, during, and after loss of responsiveness induced with different concentrations of the inhalational anaesthetic, sevoflurane. We show that dynamic states characterised by high brain integration are especially vulnerable to general anaesthesia, exhibiting attenuated complexity and diminished small‐world character. Crucially, these effects are reversed upon recovery, demonstrating their association with consciousness. Higher doses of sevoflurane (3% vol and burst‐suppression) also compromise the temporal balance of integration and segregation in the human brain. Additionally, we demonstrate that reduced anticorrelations between the brain's default mode and executive control networks dynamically reconfigure depending on the brain's state of integration or segregation. Taken together, our results demonstrate that the integrated sub‐state of brain connectivity is especially vulnerable to anaesthesia, in terms of both its complexity and information capacity, whose breakdown represents a generalisable biomarker of loss of consciousness and its recovery.

Section: Discussionmentioning

confidence: 99%

Brain network integration dynamics are associated with loss and recovery of consciousness induced by sevoflurane

Golkowski

Ranft

et al. 2021

Human Brain Mapping

“…Relating subjective mental states to the underlying brain states remains a key challenge for contemporary neuroscience 1 . Despite recent advances, what remains elusive is a principled account of how the spatio-temporal brain dynamics that support consciousness [1][2][3][4][5][6][7] , arise from the interplay of cortical dynamics across the brain's anatomical connections 8 .…”

Section: Main Textmentioning

confidence: 99%

Distributed harmonic patterns of structure-function dependence orchestrate human consciousness

Vohryzek

Kringelbach

et al. 2020

Preprint

A central question in neuroscience is how cognition and consciousness arise from human brain activity. Here, we decompose cortical dynamics of resting-state functional MRI into their constituent elements: the harmonics of the human connectome. Mapping a wide spectrum of consciousness onto these elementary brain states reveals a generalisable connectome harmonic signature of loss of consciousness, whether due to anaesthesia or severe brain injury. Remarkably, its mirror-reversed image corresponds to the harmonic signature of the psychedelic state induced by ketamine or LSD, identifying meaningful relationships between neurobiology, brain function, and conscious experience. The repertoire of connectome harmonics further provides a fine-tuned indicator of level of consciousness, sensitive to differences in anaesthetic dose and clinically relevant sub-categories of patients with disorders of consciousness. Overall, we reveal that the emergence of consciousness from human brain dynamics follows the same universal principles shared by a multitude of physical and biological phenomena: the mathematics of harmonic modes.

“…Existing work comparing aspects of brain network construction has sought to rank pipelines based on reproducibility or test-retest reliability (Andellini, Cannatà, Gazzellini, Bernardi, & Napolitano, 2015 ; Arslan et al, 2018 ; Braun et al, 2012 ; B. Cao et al, 2019 ; H. Cao et al, 2014 ; Du et al, 2015 ; Hu et al, 2019 ; Messaritaki, Dimitriadis, & Jones, 2019 ; Termenon, Jaillard, Delon-Martin, & Achard, 2016 ; J.-H. Wang et al, 2011 ; J. Wang et al, 2017 ; Welton, Kent, Auer, & Dineen, 2015 ), or in terms of maximizing discriminative ability or informativeness of the resulting networks (Arslan et al, 2018 ; De Vico Fallani et al, 2017 ; Dimitriadis, Salis, Tarnanas, & Linden, 2017 ). However, to our knowledge a systematic evaluation of the representativeness of brain networks, in terms of similarity between networks obtained from different construction pipelines applied to the same brain data, has not been undertaken before.…”

Section: Introductionmentioning

confidence: 99%

Combining network topology and information theory to construct representative brain networks

Stamatakis

2021

Network Neuroscience

Network neuroscience employs graph theory to investigate the human brain as a complex network, and derive generalisable insights about the brain’s network properties. However, graph-theoretical results obtained from network construction pipelines that produce idiosyncratic networks, may not generalise when alternative pipelines are employed. This issue is especially pressing because a wide variety of network construction pipelines have been employed in the human network neuroscience literature, making comparisons between studies problematic. Here, we investigate how to produce networks that are maximally representative of the broader set of brain networks obtained from the same neuroimaging data. We do so by minimising an information-theoretic measure of divergence between network topologies, known as the Portrait Divergence. Based on functional and diffusion MRI data from the Human Connectome Project, we consider anatomical, functional and multimodal parcellations at three different scales, and 48 distinct ways of defining network edges. We show that the highest representativeness can be obtained by using parcellations in the order of 200 regions and filtering functionalnetworks based on Efficiency-Cost Optimisation – though suitable alternatives are also highlighted. Overall, we identify specific node definition and thresholding procedures that neuroscientists can follow in order to derive representative networks from their human neuroimaging data.