Biology subjects

Chennu, S.

Publications and source records attributed to Chennu, S..

11 recordsLinked to original sources

bioRxiv2023

Lateral frontoparietal effective connectivity differentiates and predicts state of consciousness in traumatic disorders of consciousness

Neuroimaging studies have suggested an important role for the default mode network (DMN) in disorders of consciousness (DoC). However, the extent to which DMN connectivity can discriminate DoC states - unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS) - is less evident. Particularly, it is unclear whether effective DMN connectivity, as measured indirectly with dynamic causal modelling (DCM) of resting EEG can disentangle UWS from healthy controls and from patients considered conscious (MCS+). Crucially, this extends to UWS patients with potentially "covert" awareness (minimally conscious star, MCS*) indexed by voluntary brain activity in conjunction with partially preserved frontoparietal metabolism as measured with positron emission tomography (PET+ diagnosis; in contrast to PET-diagnosis with complete frontoparietal hypometabolism). Here, we address this gap by using DCM of EEG data acquired from patients with traumatic brain injury in 11 UWS (6 PET- and 5 PET+) and in 12 MCS+ (11 PET+ and 1 PET-), alongside with 11 healthy controls. We provide evidence for a key difference in left frontoparietal connectivity when contrasting UWS PET- with MCS+ patients and healthy controls. Next, in a leave-one-subject-out cross-validation, we tested the classification performance of the DCM models demonstrating that connectivity between medial prefrontal and left parietal sources reliably discriminates UWS PET- from MCS+ patients and controls. Finally, we illustrate that these models generalize to an unseen dataset: models trained to discriminate UWS PET- from MCS+ and controls, classify MCS* patients as conscious subjects with high posterior probability (pp > .92). These results identify specific alterations in the DMN after severe brain injury and highlight the clinical utility of EEG- based effective connectivity for identifying patients with potential covert awareness. Author SummaryOur study investigates the role of the Default Mode Network (DMN) in individuals with disorders of consciousness (DoC), such as unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS). Previous neuroimaging studies have suggested a role for the DMN in DoC, but its ability to differentiate between UWS and MCS remain unclear. Using advance brain imaging and modelling techniques, we analyzed data from DoC patients with traumatic brain injury and healthy controls. Our findings reveal a key difference in left frontoparietal connectivity when comparing UWS to MCS patients and healthy individuals. To validate our results, we employed a robust cross-validation approach, which demonstrated that the connectivity between frontal and left parietal brain regions reliably discriminates UWS patients from MCS patients and controls. Furthermore, we extended our analysis to include patients with potential covert awareness, showcasing the clinical utility of our findings. We successfully classified these patients as conscious with high accuracy. This research significantly contributes to our understanding of the DMN in DoC and highlights the potential use of electroencephalography-based connectivity analysis in clinical settings. By identifying specific alterations in the DMN after severe brain injury, our study may aid in the accurate diagnosis and management of individuals with disorders of consciousness, potentially improving their overall outcomes.

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bioRxiv2021

Directed information sharing in the brain distinguishes perception from its consequences during perceptual bistability

In the search for the neural basis of conscious experience, perception and the cognitive processes associated with reporting perception are typically confounded as neural activity is recorded while participants explicitly report what they experience. Here we present a novel way to disentangle perception from report using eye-movement analysis techniques based on convolutional neural networks and neurodynamical analyses based on information theory. We use a bistable visual stimulus that instantiates two well-known properties of conscious perception: integration and differentiation. At any given moment, observers either perceive the stimulus as one integrated unitary object or as two differentiated objects that are clearly distinct from each other. Using electroen-cephalography, we show that measures of integration and differentiation based on information theory closely follow participants perceptual experience of those contents when switches were reported. We observed increased information integration between anterior to posterior electrodes (front to back) prior to a switch to the integrated percept, and higher information differentiation of anterior signals leading up to reporting the differentiated percept. Crucially, information integration was closely linked to perception and even observed in a no-report condition when perceptual transitions were inferred from eye movements alone. In contrast, the link between neural differentiation and perception was observed solely in the active report condition. Our results, therefore, suggest that perception and the processes associated with report require distinct amounts of anterior-posterior network communication and anterior information differentiation. While front-to-back directed information is associated with changes in the content of perception when viewing bistable visual stimuli, regardless of report, frontal information differentiation was absent in the no-report condition and therefore is not directly linked to perception per se.

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bioRxiv2020

How hot is the hot zone? Computational modelling clarifies the role of parietal and frontoparietal connectivity during anaesthetic-induced loss of consciousness

In recent years, specific cortical networks have been proposed to be crucial for sustaining consciousness, including the posterior hot zone and frontoparietal resting state networks (RSN). Here, we computationally evaluate the relative contributions of three RSNs - the default mode network (DMN), the salience network (SAL), and the central executive network (CEN) - to consciousness and its loss during propofol anaesthesia. Specifically, we use dynamic causal modelling (DCM) of 10 minutes of high-density EEG recordings (N = 10, 4 males) obtained during behavioural responsiveness, unconsciousness and post-anaesthetic recovery to characterise differences in effective connectivity within frontal areas, the posterior "hot zone", frontoparietal connections, and between-RSN connections. We estimate - for the first time - a large DCM model (LAR) of resting EEG, combining the three RSNs into a rich club of interconnectivity. Consistent with the hot zone theory, our findings demonstrate reductions in inter-RSN connectivity in the parietal cortex. Within the DMN itself, the strongest reductions are in feed-forward frontoparietal and parietal connections at the precuneus node. Within the SAL and CEN, loss of consciousness generates small increases in bidirectional connectivity. Using novel DCM leave-one-out cross-validation, we show that the most consistent out-of-sample predictions of the state of consciousness come from a key set of frontoparietal connections. This finding also generalises to unseen data collected during post-anaesthetic recovery. Our findings provide new, computational evidence for the importance of the posterior hot zone in explaining the loss of consciousness, highlighting also the distinct role of frontoparietal connectivity in underpinning conscious responsiveness, and consequently, suggest a dissociation between the mechanisms most prominently associated with explaining the contrast between conscious awareness and unconsciousness, and those maintaining consciousness. HighlightsO_LIModelling shows that connectivity within hot zone tracks change of conscious state C_LIO_LISeparately, frontoparietal connections support maintenance of conscious state C_LIO_LIStrength of frontoparietal connections predicts conscious state in unseen data C_LIO_LIBoth parietal hot zone and frontoparietal connectivity important for consciousness C_LI FundingThis work was supported by the UK Engineering and Physical Sciences Research Council (EP/P033199/1), Belgian National Funds for Scientific Research (FRS-FNRS), the University and University Hospital of Liege, the Fund Generet, the King Baudouin Foundation, the AstraZeneca Foundation, the European Unions Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3), DOCMA project (EU-H2020-MSCA-RISE-778234), the BIAL Foundation, the European Space Agency (ESA) and the Belgian Federal Science Policy Office (BELSPO) in the framework of the PRODEX Programme, the Center-TBI project (FP7-HEALTH-602150), the Public Utility Foundation Universite Europeenne du Travail, "Fondazione Europea di Ricerca Biomedica", the Mind Science Foundation, the European Commission, and the Special Research Fund of Ghent University. O.G. is research associate and S.L. is research director at the F.R.S-FNRS. Declaration of interestNone. Significance StatementVarious connectivity studies have suggested multiple network-level mechanisms driving changes in the state of consciousness, such as the posterior hot zone, frontal-, and large-scale frontoparietal networks. Here, we computationally evaluate evidence for these mechanisms using dynamic causal modeling for resting EEG recorded before and during propofol-anaesthesia, and demonstrate that, particularly, connectivity in the posterior hot zone is impaired during propofol-induced unconsciousness. With a robust cross-validation paradigm, we show that connectivity in the large-scale frontoparietal networks can consistently predict the state of consciousness and further generalise these findings to an unseen state of recovery. These results suggest a dissociation between the mechanisms most prominently associated with explaining the contrast between conscious awareness and unconsciousness, and those maintaining consciousness.

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bioRxiv2020

Sedation modulates fronto-temporal predictive coding circuits and the double surprise acceleration effect

Two important theories in cognitive neuroscience are predictive coding and the global workspace theory. A key research task is to understand how these two theories relate to one another, and particularly, how the brain transitions from a predictive early state to the eventual engagement of a brain-scale state (the global workspace). To address this question, we present a source-localisation of EEG responses evoked by the local-global task - an experimental paradigm that engages a predictive hierarchy, which encompasses the global workspace. The results of our source reconstruction suggest three-phases of processing. The first phase involves the sensory (here auditory) regions of the superior temporal lobe and predicts sensory regularities over a short timeframe (as per the local effect). The third phase is brain-scale, involving inferior frontal, as well as inferior and superior parietal regions; consistent with a global neuronal workspace (as per the global effect). Crucially, our analysis suggests that there is an intermediate (second) phase, involving modulatory interactions between inferior frontal and superior temporal regions. Furthermore, sedation with propofol reduces modulatory interactions in the second phase. This selective effect is consistent with a predictive coding explanation of sedation, with propofol acting on descending predictions of the precision of prediction errors; thereby constraining access to the global neuronal workspace.

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bioRxiv2019

In your phase! Neural phase synchronization underlies visual imagery of faces

Mental imagery is the process through which we retrieve and recombine information from our memory to elicit the subjective impression of "seeing with the minds eye". In the social domain, we imagine other individuals while recalling our encounters with them or modelling alternative social interactions in future. Many studies using imaging and neurophysiological techniques have shown several similarities in brain activity between visual imagery and visual perception, and have identified frontoparietal, occipital and temporal neural components of visual imagery. However, the neural connectivity between these regions during visual imagery of socially relevant stimuli have not been studied. Here we used electroencephalography to investigate neural connectivity and its dynamics between frontal, parietal, occipital and temporal electrodes during visual imagery of faces. We found that voluntary visual imagery of faces is associated with long-range phase synchronisation in the gamma frequency range between frontoparietal electrode pairs and between occipitoparietal electrode pairs. In contrast, no effect of imagery was observed in the connectivity between occipitotemporal electrode pairs. Gamma range synchronisation between occipitoparietal electrode pairs predicted subjective ratings of the contour definition of imagined faces. Furthermore, we found that visual imagery of faces is associated with an increase of short-range frontal synchronisation in the theta frequency range, which temporally preceded the long-range increase in the gamma synchronisation. We speculate that the local frontal synchrony in the theta frequency range might be associated with an effortful top-down mnemonic reactivation of faces. In contrast, the long-range connectivity in the gamma frequency range along the fronto-parieto-occipital axis might be related to the endogenous binding and subjective clarity of facial visual features.

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bioRxiv2018

EEG functional connectivity metrics wPLI and wSMI account for distinct types of brain functional interactions

Functional connectivity (FC) estimation methods are extensively used in neuroimaging to measure brain inter-regional interactions. The weighted Phase Lag Index (wPLI) and the weighted Symbolic Mutual Information (wSMI) represent relatively robust exemplars of spectral (wPLI) and information-theoretic (wSMI) connectivity measures that recently gained increased popularity due to their relative immunity to volume conduction. wPLI and wSMI are posited to have different sensitivity to linear and nonlinear relationships between neural sources, but their performance has never been directly compared. Here, using simulated high-density (hd-)EEG data, we evaluated the accuracy of these two metrics for detecting distinct types of regional interdependencies characterised by different combinations of linear and nonlinear components. Our results demonstrate that while wPLI performs generally better at detecting functional couplings presenting a mixture of linear and nonlinear interdependencies, only wSMI is able to detect exclusively nonlinear interaction dynamics. To evaluate the potential impact of these differences on real experimental data, we computed wPLI and wSMI connectivity in hd-EEG recordings of 12 healthy adults obtained in wakefulness and deep (N3-)sleep. While both wPLI and wSMI revealed a relative decrease in alpha-connectivity during sleep relative to wakefulness, only wSMI identified a relative increase in theta-connectivity, while wPLI detected an increase in delta-connectivity, likely reflecting the occurrence of traveling slow waves. Overall, our findings indicate that wPLI and wSMI provide distinct but complementary information about functional brain connectivity, and that their combined use could advance our knowledge of neural interactions underlying different behavioural states.

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bioRxiv2017

Transient topographical dynamics of the electroencephalogram predict brain connectivity and behavioural responsiveness during drowsiness

As we fall sleep, our brain traverses a series of gradual changes at physiological, behavioural and cognitive levels, which are not yet fully understood. The loss of responsiveness is a critical event in the transition from wakefulness to sleep. Here we seek to understand the electrophysiological signatures that reflect the loss of capacity to respond to external stimuli during drowsiness using two complementary methods: spectral connectivity and EEG microstates. Furthermore, we integrate these two methods for the first time by investigating the connectivity patterns captured during individual microstate lifetimes. While participants performed an auditory semantic classification task, we allowed them to become drowsy and unresponsive. As they stopped responding to the stimuli, we report the breakdown of frontoparietal alpha networks and the emergence of frontoparietal theta connectivity. Further, we show that the temporal dynamics of all canonical EEG microstates slow down during unresponsiveness. We identify a specific microstate (D) whose occurrence and duration are prominently increased during this period. Employing machine learning, we show that the temporal properties of microstate D, particularly its prolonged duration, predicts the response likelihood to individual stimuli. Finally, we find a novel relationship between microstates and brain networks as we show that microstate D uniquely indexes significantly stronger theta connectivity during unresponsiveness. Our findings demonstrate that the transition to unconsciousness is not linear, but rather consists of an interplay between transient brain networks reflecting different degrees of sleep depth.\n\nAuthor summaryHow do we lose responsiveness as we fall asleep? As we become sleepy, our ability to react to external stimuli disappears gradually. Here we sought to understand the rapid fluctuations in brain electrical activity that predict the loss of responsiveness as participants fell asleep while performing a word classification task. We analysed the patterns of connectivity between anterior and posterior brain regions observed during wakefulness in alpha band and showed that this connectivity shifted to slower theta frequencies as participants became unresponsive. We also investigated the dynamics of brain electrical microstates, which represent an alphabet of quasi-stable global brain states with lifetimes of 10-100 milliseconds, and found that the temporal dynamics of microstates slowed down when participants became unresponsive. Using machine learning, we further showed that microstate dynamics prior to a stimulus predict whether subjects will respond to it. We integrated microstates and connectivity for the first time to show that a specific microstate captures connectivity patterns correlated with unresponsiveness during this transition. We conclude that falling asleep is accompanied by a millisecond-level interplay between distinct brain networks, and suggest a renewed focus on fine-grained temporal scales in the study of transitions between levels of consciousness.

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bioRxiv2017

Wakefulness state modulates conscious access: Suppression of auditory detection in the transition to sleep

Mapping the reports of awareness and its neural underpinnings is instrumental to understand the limits of human perception. The capacity to become aware of objects in the world may be studied by suppressing faint target stimuli with strong masking stimuli, or - alternatively - by manipulating the level of wakefulness from full alertness to mild drowsiness. By combining these two approaches, we studied how perceptual awareness is modulated by decreasing wakefulness. We found dynamic changes in behavioural and neural signatures of conscious access in humans between awake and drowsy states. Behaviourally, we show a decrease in the steepness of the psychophysical function for conscious access in drowsy trials. Neural mapping showed delayed processing of target-mask interaction as the consciousness transition progressed, suggesting that the brain resolution of conscious access shifts from early sensory/perceptual to decision-making stages of processing. Once the goal to report the awareness of a target is set, the system behaviourally adapts to rapid changes in wakefulness, revealing the flexibility of the neural signatures of conscious access, and its suppression, to maintain performance. Significance statementMaintaining full alertness for long periods of time in attentionally demanding situations is challenging and may lead to a decrease in performance. We show the effect of wakefulness fluctuations on behaviour and brain dynamics that humans use to maintain performance. We reveal the neural strategies we have to cope with drowsiness by shifting the weights to more flexible brain processes and relaxing the precision of the decisions we take.

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bioRxiv2017

Neurobehavioral dynamics of drowsiness

Transcranial magnetic stimulation (TMS) has been widely used in human cognitive neuroscience to examine the causal role of distinct cortical areas in perceptual, cognitive and motor functions. However, it is widely acknowledged that the effects of focal cortical stimulation on behaviour can vary substantially between participants and even from trial to trial within individuals. Here we asked whether spontaneous fluctuations in alertness can account for the variability in behavioural and neurophysiological responses to TMS. We combined single-pulse TMS with neural recording via electroencephalography (EEG) to quantify changes in motor and cortical reactivity with fluctuating levels of alertness defined objectively on the basis of ongoing brain activity. We observed rapid, non-linear changes in TMS-evoked neural responses - specifically, motor evoked potentials and TMS-evoked cortical potentials - as EEG activity indicated decreasing levels of alertness, even while participants remained awake and responsive in the behavioural task.\n\nIMPACT STATEMENTA substantial proportion of inter-trial variability in neurophysiological responses to TMS is due to spontaneous fluctuations in alertness, which should be controlled for during experimental and clinical applications of TMS.

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bioRxiv2017

Altered neurocognitive processing of tactile stimuli in patients with Complex Regional Pain Syndrome (CRPS)

Chronic pain in CRPS has been linked to tactile misperceptions and deficits in somatotopic representation of the affected limb. Here, we identify altered cognitive processing of tactile stimuli in CRPS patients that we propose marks heterogeneity in tactile decision-making mechanisms. In a case-control design, we compared middle and late-latency somatosensory-evoked potentials (SEPs) in response to pseudo-randomised mechanical stimulation of the digits of both hands (including CRPS-affected and non-affected sides) between 13 CRPS patients and 13 matched healthy controls. During a task to discriminate the digit simulated, patients (compared to controls) had significantly lower accuracy and slowed response times but with high between-subject variability. At middle latencies (124-132 ms), tactile processing in patients relative to controls showed decrements in superior parietal lobe and precuneus (that were independent of task demands) but enhanced activity in superior frontal lobe (that were task-dependent). At late latencies, patients showed an augmented P300-like response under task demands that localised to supplementary motor area (SMA). Source activity in SMA correlated with slowed response times, while its scalp representation intriguingly correlated with better functioning of the affected limb, suggesting a compensatory mechanism. Future research should investigate the clinical utility of these putative markers of tactile decision-making mechanisms in CRPS.\n\nDisclosuresThe research was supported by funding from Cambridge Arthritis Research Endeavour (CARE). The study was sponsored by Cambridge University Hospitals NHS Foundation Trust and supported by its Rheumatology department. The authors report no conflicts of interest.\n\nPerspectiveWe present evidence of altered but highly variable cognitive processing (124 - 268ms latency) in response to mechanical tactile stimuli in patients with CRPS compared to healthy controls. Such mid-to-late latency responses could potentially provide convenient and robust biomarkers of abnormal perceptual decision-making mechanisms in CRPS to aid in clinical detection and treatment.

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bioRxiv2017

Integration And Differentiation Of Neural Information Dissociate Between Conscious Percepts

At any given moment, we experience a perceptual scene as a single whole and yet we may distinguish a variety of objects within it. This phenomenon instantiates two properties of conscious perception: integration and differentiation. Integration to experience a collection of objects as a unitary percept, and differentiation to experience these objects as distinct from each other. Here we evaluated the neural information dynamics underlying integration and differentiation of perceptual contents during bistable perception. Participants listened to a sequence of tones (auditory bistable stimuli) experienced either as a single stream (perceptual integration) or as two parallel streams (perceptual differentiation) of sounds. We computed neurophysiological indices of information integration and information differentiation with electroencephalographic and intracranial recordings. When perceptual alternations were endogenously driven, the integrated percept was associated with an increase in neural information-integration and a decrease in neural differentiation across frontoparietal regions, whereas the opposite pattern was observed for the differentiated percept. However, when perception was exogenously driven by a change in the sound stream (no bistability) neural oscillatory power distinguished between percepts but information measures did not. We demonstrate that perceptual integration and differentiation can be mapped to theoretically-motivated neural information signatures, suggesting a direct relationship between phenomenology and neurophysiology.

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