Biology subjects

Sitt, J. D.

Publications and source records attributed to Sitt, J. D..

16 recordsLinked to original sources

bioRxiv2026

Spontaneous slow waves index multiple dimensions of recovery in Disorders of Consciousness

Whether and how residual awareness is expressed in patients with Disorders of Consciousness (DoC) depends on the severity of brain damage and the transient functional state of the cortex at assessment. Classically, DoC are associated with electroencephalographic (EEG) slowing, marked by high-amplitude slow waves, even during the day. Yet, slow waves are also a hallmark of healthy sleep, where they are thought to serve restorative functions and to help maintain network homeostasis. Here, we asked whether slow waves in DoC can provide insights into the severity of cortical dysfunction or, conversely, into the preservation of residual awareness and recovery potential, by characterizing their spatial, temporal and morphological properties. We analyzed 256-channel EEG recorded during the Local-Global auditory paradigm in 236 DoC patients, including 123 in Unresponsive Wakefulness Syndrome (UWS), 62 in a Minimally Conscious State minus (MCS-) and 29 in MCS plus (MCS+). High-amplitude slow waves were detected as local events, and their density, amplitude and slopes were quantified scalp-wide and channel-wise, by mean level and temporal variability across the recording. Slow-wave features were related to concurrent (Coma Recovery Scale-Revised - CRS-R, Local-Global paradigm) and follow-up (one-year Glasgow Outcome Scale-Extended - GOS-E) assessments of consciousness and functional recovery. Associations with MRI-derived measures of cortical integrity and structural connectivity, and differences between anoxic and traumatic etiologies, were also examined. Slow waves were more frequent in less severe DoC patients (i.e., MCS) compared to UWS. Slow-wave slopes were also steeper frontally and increased at a faster rate in patients exhibiting higher levels of consciousness and/or markers of conscious access during the Local-Global paradigm. More frequent and steeper slow waves at baseline further predicted recovery of consciousness and of functional independence at one year. At the individual level, slow-wave features classified states of consciousness above chance (AUC = 0.62 and 0.71 for MCS- and MCS+ versus UWS), whereas outcome prediction remained modest (0.56-0.58). These clinical associations had a structural basis: slow-wave occurrence was greater after traumatic than anoxic injury and scaled with the integrity of subcortico-cortical projection fibers and consciousness-related cortical networks. Contrary to the classical view of delta activity as a non-specific marker of impaired consciousness, prominent spontaneous slow waves indexed behavioral and neurophysiological signs of awareness and better recovery. Beyond reflecting a propensity for pathological bistability, they may also signal the integrity of the networks required to generate them, together with their residual capacity for dynamic regulation.

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bioRxiv2026

What drives intersubject correlation of EEG during auditory narratives?

When participants are engaged with auditory narratives, physiological and neural signals exhibit temporal correlations between subjects. The intersubject correlation (ISC) increases when attention is directed to the stories, suggesting that shared neural and bodily dynamics arise from a similar processing of the narratives. Identifying the factors that drive these common responses is clinically relevant for interpreting EEG ISC exhibited in unresponsive patients. In this study, we investigated whether the ISC of the EEG elicited by auditory narratives is driven by low-level acoustic (envelope, spectrogram) and/or higher-level linguistic information (word onset, word surprisal) in two groups of healthy participants during passive, attentive and distracted listening. We use temporal response functions (TRFs) for acoustic, and linguistic features to assess the contribution of each feature to the ISC, measured using correlated component analysis (CorrCA). TRFs derived for acoustic features explained a larger fraction of variance in the EEG than linguistic features and were the main contributors to the ISC. The attention-related increase in ISC was driven by all features. Importantly, word surprisal had an effect on ISC only during active story engagement, with timing and scalp distribution consistent with language processing. Notably, the linear responses captured by TRFs only explained a small amount of the overall ISC, suggesting that ISC is largely driven by nonlinear responses to the narratives. We propose that the combined use of ISC and TRFs has the potential to provide meaningful markers of language processing in patients with disorders of consciousness, and we suggest practical recommendations for their implementation.

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bioRxiv2026

The impact of homeostatic inhibitory plasticity in a generative biophysical model

A main characteristic of biological systems is their capacity to dynamically adapt to environmental changes. In the brain, synaptic plasticity enables the strengthening or weakening of connections between neurons, allowing neural circuits to adapt based on experience, learning, and environmental changes. Yet, it is homeostatically regulated such that it avoids excessive proliferation of synaptic contacts. These mechanisms can be studied with large-scale models of brain activity. Here, we embed a biologically grounded inhibitory-homeostatic plasticity rule into the Dynamic Mean Field (DMF) model, creating a Homeostatic Dynamic Mean Field (HDMF) model that dynamically tunes local excitation-inhibition balance. Convergence of excitatory firing rates is reached by mapping a large range of coupling strength to parameters of inhibitory synapses. The HDMF reproduces statistical observables of brain activity as well as the original DMF, and can sustain neuromodulatory perturbations without overhead computations. The HDMF can generate unprecedented sleep-like slow-wave activity, which can also coexist with wake-like asynchronous dynamics, permitting to model dissociated states of consciousness such as parasomnias. Together, these results show that a single homeostatic rule broadens the stability and expressiveness of the DMF, providing a unified platform for studying how local adaptive processes shape the diverse global dynamics of the human brain.

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bioRxiv2025

Disrupted hierarchical organization in disorders of consciousness revealed by fluctuation-dissipation deviations

Evaluating consciousness levels after coma remains clinically challenging, and probing the brains functional hierarchy offers model-based biomarkers of brain states. We characterize the hierarchy loss in disorders of consciousness (DoC) via departures from non-equilibrium dynamics. Irreversible, directed interactions are indexed by deviation from the fluctuation- dissipation theorem (FDT), computed from individualized whole-brain models fit to fMRI from controls and patients in minimally conscious state (MCS) or unresponsive wakefulness syndrome (UWS). Global and resting-state network dynamics in DoC were closer to equilibrium than in controls, decreasing stepwise with decreasing levels of consciousness. Mapping site-specific hierarchical drive over the system revealed disruptions within default-mode network components (e.g., medial and dorsolateral superior frontal gyrus) and subcortical hubs (e.g., thalamus, pallidum and putamen) differentiating between all groups. Recovery of near-control hierarchy in the visual network differentiated MCS from UWS, whereas multiple limbic areas showed similar abnormalities across both DoC groups. Together, these results identify non-equilibrium dynamics as a signature of conscious capacity and stablish FDT deviation as a principled, model-based hierarchy measure that can be operationalised for clinical stratification and monitoring, opening avenues for targeted in silico intervention planing.

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bioRxiv2025

Inception: Simulating Personalized Long-Term Recovery in Disorders of Consciousness using Whole-Brain Computational Perturbations

Advancements in the treatment of Disorders of Consciousness have seen significant progress with per-turbative techniques and pharmacological therapies. Despite their potential, the underlying mechanisms of their variable efficacy remain poorly understood. To address this challenge, recent studies have utilised whole-brain modelling to simulate in silico perturbations. However, existing models focus exclusively on system behaviour during active stimulations, leaving unexplored how the brain dynamics evolve in post-acute and long-term stages, crucial in the recovery of consciousness. Here, we introduce Inception, a novel personalized approach to in silico perturbation modelling. We use a whole-brain models to simulate the perturbations and to unravel information about the long term effects of the proposed intervention. Applied to fMRI data from patients in a minimally conscious state and an unresponsive wakefulness state, our approach effectively simulates the transition to a healthy state, generating perturbed data closely resembling healthy brain activity. Moreover, we show that Inception enhances patient classification through machine learning, outperforming functional connectivity-based approaches. Finally, we investigate the correlation between perturbation responses and brain neuroreceptors, proposing that Inception might capture the long-term effects of pharmacological interventions in Disorders of Consciousness treatment.

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bioRxiv2024

Nonequilibrium dynamics elicited as the origin of perturbative complexity

Assessing the level of consciousness someone is in, is not a trivial question and physicians have to rely on behavioural evaluations instead of quantifiable metrics. Many studies have empirically investigated measures related to the complexity elicited after the brain is stimulated to quantify and assess the level of consciousness across different states. Here we hypothesized that the level of non-equilibrium dynamics of the unperturbed brain already contains the information needed to know how the system will react to an external stimulus. We created personalized whole-brain models fitted to resting state fMRI data recorded in participants in different states of reduced consciousness (such as deep sleep and disorders of consciousness) to infer the effective connections underlying their brain dynamics. We then measured the out-of-equilibrium nature of the unperturbed brain by evaluating the level of asymmetry of the inferred connectivity, the time irreversibility in each model and compared this with the elicited complexity generated after in silico perturbations. Crucially, we found that states of reduced consciousness had a lower level of asymmetry in their effective connectivities compared to control subjects, as well as a lower level of irreversibility in their simulated dynamics, and a lower complexity. We demonstrated that the asymmetry in the underlying connections drives the nonequilibrium state of the system and in turn the differences in complexity as a response to the external stimuli.

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bioRxiv2024

A virtual clinical trial of psychedelics to treat patients with disorders of consciousness

Disorders of consciousness (DoC), including the unresponsive wakefulness syndrome (UWS) and the minimally conscious state (MCS), have limited treatment options. Recent research suggests that psychedelic drugs, known for their complexity-enhancing properties, could be promising treatments for DoC. This study uses whole-brain computational models to explore this potential. We created individualised models for DoC patients, optimised with empirical fMRI and diffusion-weighted imaging (DWI) data, and simulated the administration of LSD and psilocybin. We used an in-silico perturbation protocol to distinguish between different states of consciousness, including DoC, anaesthesia, and the psychedelic state, and assess the dynamical stability of the brains of DoC patients pre- and post-psychedelic simulation. Our findings indicate that LSD and psilocybin shift DoC patients brains closer to criticality, with a greater effect in MCS patients. In UWS patients, the treatment response correlates with structural connectivity, while in MCS patients, it aligns with baseline functional connectivity. This virtual clinical trial lays a computational foundation for using psychedelics in DoC treatment and highlights the future role of computational modelling in drug discovery and personalised medicine.

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bioRxiv2024

Dynamics of EEG Microstates Change Across the Spectrum of Disorders of Consciousness

As a response to the environment and internal signals, brain networks reorganize on a sub-second scale. To capture this reorganization in patients with disorders of consciousness and understand their residual brain activity, we investigated the dynamics of electroencephalography (EEG) microstates. We analyze EEG microstate markers to quantify the periods of semi-stable topographies and the large-scale cortical networks they may reflect. To achieve this, EEG samples are clustered into four groups and then fit back into each time sample. We then obtain a time series of maps with different frequencies of occurrence and duration. One such occurrence of a map with a given duration is called a microstate. The goal of this work is to study the dynamics of these topographical patterns across patients with disorders of consciousness. Using the microstate time series, we calculate static and dynamic markers. In contrast to the static, the dynamic metrics depend on the specific temporal sequences of the maps. The static measure Ratio of Total Time covered (RTT) shows differences between healthy controls and patients, however, no differences were observed between the groups of patients. In contrast, some dynamic markers capture inter-patient group differences. The dynamic markers we investigated are Mean Microstate Durations (MMD), Microstate Duration Variances (MDV), Microstate Transition Matrices (MTM), and Entropy Production (EP). The MMD and MDV decrease with the state of consciousness, whereas the MTM non-diagonal transitions and EP increase. In other words, DoC patients have slower and closer to equilibrium (time-reversible) brain dynamics. In conclusion, static and dynamic EEG microstate metrics differ across consciousness levels, with the latter capturing the subtitler differences between groups of patients with disorders of consciousness. Abbreviated summaryThis study investigates EEG microstate dynamics in patients with disorders of consciousness (DoC) to understand residual brain activity and network reorganization. Entropy production, in addition to static markers, differs between patients and healthy controls, whereas the rest of the dynamic markers show differences across patient groups.

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bioRxiv2023

Whole-brain modelling supports the use of serotonergic psychedelics for the treatment of disorders of consciousness

Disorders of consciousness (DoC) are a challenging and complex group of neurological conditions characterised by absent or impaired awareness. The current range of therapeutic options for DoC patients is limited, offering few non-invasive pharmacological alternatives. This situation has sprung a growing interest in the development of novel treatments, such as the proposal to study the efficacy of 5HT2A receptor agonists (also known as psychedelics) to restore impaired consciousness. Given the ethical implications of exploring novel compounds in non-communicative individuals, we assessed in silico their effects in the whole-brain dynamics of DoC patients. We embedded the whole-brain activity of patients in a low-dimensional space, and then used this representation to visualise the effects of simulated neuromodulation across a range of receptors representing potential drug targets. Our findings show that activation of serotonergic and opioid receptors shifted brain dynamics of DoC patients towards patterns typically seen in conscious and awake individuals, and that this effect was mediated by the brain-wide density of activated receptors. These results showcase the role of whole-brain models in the discovery of novel pharmacological treatments for neuropsychiatric conditions, while also supporting the feasibility of accelerating the recovery of consciousness with serotonergic psychedelics.

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bioRxiv2023

Distinct Electrophysiological Signatures of Intentional and Unintentional Mind-Wandering Revealed by Low-Frequency EEG Markers

Mind-wandering is typically characterized by the common experience wherein attention veers off into thoughts unrelated to the task at hand. Recent research highlights the intentionality dimension of mind-wandering as a key predictor of adverse functional outcomes with intentional and unintentional task-unrelated thought (TUT) differentially linked to neural, behavioral, clinical, and functional correlates. We here aimed to elucidate the electrophysiological underpinnings of intentional and unintentional TUT by systematically examining the individual and collective discriminative power of a large set of EEG markers to distinguish between attentional states. Univariate and multivariate analyses were conducted on 54 predefined markers belonging to four conceptual families: ERP, spectral, information theory and connectivity measures, extracted from scalp EEG recordings prior to multidimensional reports of ongoing thought from participants performing a sustained attention task. We report here that on-task, intentional and unintentional TUT exhibit distinct electrophysiological signatures in the low frequency range. More specifically, increased features of the theta frequency range were found to be most discriminative between on-task and off-task states, while features within the alpha band were characteristic of intentional TUT when compared to unintentional TUT. This result is theoretically well aligned with contemporary accounts describing alpha activity as an index of internally oriented attention and a potential mechanism to shield internal processes from sensory input. Our study verifies the validity of the intentionality dimension of mind-wandering and represents a step forward towards real-time detection and mitigation of maladaptive mind-wandering.

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bioRxiv2022

Behavioral and brain responses to verbal stimuli reveal transient periods of cognitive integration of external world in all sleep stages

Sleep has long been considered as a state of disconnection from the environment, with absent reactivity to external stimuli. Here, we questioned this sleep disconnection dogma by directly investigating behavioral responsiveness in 49 napping subjects (27 with narcolepsy and 22 healthy volunteers) engaged in a lexical decision task. Participants were instructed to frown or smile depending on the stimulus type (words vs pseudo-words). We found accurate behavioral responses, visible via contractions of the corrugator or zygomatic muscles, in all sleep stages in both groups (except slow-wave sleep for healthy volunteers). Stimuli presented during states with high (vs. low) values of neural markers indexing rich cognitive states more often yielded responses. Our findings suggest that transient windows of reactivity to external stimuli exist in all sleep stages, even in healthy individuals. Such windows of reactivity could be used to probe sleep-related mental and cognitive processes in real-time across all sleep stages.

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bioRxiv2022

The Interplay of Attention and Conscious Perception: Evidence from Human Intracerebral Recordings and Computational Modeling

How do attention and consciousness interact in the human brain? Rival theories of consciousness disagree on the role of fronto-parietal attentional networks in conscious perception. We recorded neural activity from 727 intracerebral contacts in 13 epileptic patients, while they detected near-threshold targets preceded by attentional cues. Unsupervised clustering revealed three patterns: (1) Attention-enhanced conscious report accompanied sustained right-hemisphere fronto-temporal activity, in networks connected by the superior longitudinal fasciculus (SLF) II-III, and late accumulation in bilateral dorso-prefrontal and right-hemisphere orbitofrontal cortex (SLF I-III). (2) Attentional reorienting affected conscious report through early, sustained activity in a right-hemisphere network (SLF III). (3) Conscious report accompanied left-hemisphere dorsolateral-prefrontal activity. Task modeling with recurrent neural networks identified specific excitatory and inhibitory interactions between attention and consciousness, and their causal contribution to conscious perception of near-threshold targets. Thus, distinct, hemisphere-asymmetric fronto-parietal networks support attentional gain and reorienting in shaping human conscious experience. One-Sentence SummaryIntracerebral recordings, tractography and modeling reveal the interaction of attention and consciousness in the human brain.

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bioRxiv2022

Numerical discrimination in Drosophila melanogaster

Sensitivity to numbers is a crucial and evolutionarily conserved cognitive ability. The lack of experimental models amenable to systematic genetic and neural manipulation has precluded discovering circuits required for numerical cognition. Here, we demonstrate that in a two-choice task Drosophila fruit flies spontaneously prefer sets containing more objects. This preference is determined by the ratio between the two numerical quantities tested, a characteristic signature of numerical cognition across species. Individual flies maintained their numerical choice over consecutive days. Using a numerical visual conditioning paradigm, we found that flies are capable of associating sucrose with numerical quantities and can be trained to reverse their spontaneous preference for large quantities. Finally, we show that silencing LC11 neurons reduces the preference for more objects, thus identifying a neuronal substrate for numerical cognition in invertebrates. This discovery paves the way for the systematic analysis of the behavioral and neural mechanisms underlying sensitivity to numerosity.

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bioRxiv2021

Spatiotemporal dynamics of human attention revealed byintracerebral recording

Exogenous attention, the process that makes external salient stimuli pop-out of a visual scene, is essential for survival. How attention-capturing events modulate human brain processing remains unclear. Here we show how the psychological construct of exogenous attention gradually emerges over large-scale gradients in the human cortex, by analyzing activity from 1,403 intracortical contacts implanted in 28 individuals, while they performed an exogenous attention task. The timing, location and task-relevance of attentional events defined a spatiotemporal gradient of three neural clusters, which mapped onto cortical gradients and presented a hierarchy of timescales. Visual attributes modulated neural activity at one end of the gradient, while at the other end it reflected the upcoming response timing, with attentional effects occurring at the intersection of visual and response signals. These findings challenge multi-step models of attention, and suggest that frontoparietal networks, which process sequential stimuli as separate events sharing the same location, drive exogenous attention phenomena such as inhibition of return.

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bioRxiv2019

Combined behavioral and electrophysiological evidence for a direct cortical effect of prefrontal tDCS on disorders of consciousness

Severe brain injuries can lead to long-lasting disorders of consciousness (DoC) such as vegetative state/unresponsive wakefulness syndrome (VS/UWS) or minimally conscious state (MCS). While behavioral assessment remains the gold standard to determine conscious state, EEG has proven to be a promising complementary tool to monitor the effect of new therapeutics. Encouraging results have been obtained with invasive electrical stimulation of the brain, and recent studies identified transcranial direct current stimulation (tDCS) as an effective approach in randomized controlled trials. This non-invasive and inexpensive tool may turn out to be the preferred treatment option. However, its mechanisms of action and physiological effects on brain activity remain unclear and debated. Here, we stimulated 60 DoC patients with the anode placed over left-dorsolateral prefrontal cortex in a prospective open-label study. Clinical behavioral assessment improved in twelve patients (20%) and none deteriorated. This behavioral response after tDCS coincided with an enhancement of putative EEG markers of consciousness: in comparison with non-responders, responders showed increases of power and long-range cortico-cortical functional connectivity in the theta-alpha band, and a larger and more sustained P300 suggesting improved conscious access to auditory novelty. The EEG changes correlated with electric fields strengths in prefrontal cortices, and no correlation was found on the scalp. Taken together, this prospective intervention in a large cohort of DoC patients strengthens the validity of the proposed EEG signatures of consciousness, and is suggestive of a direct causal effect of tDCS on consciousness.

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bioRxiv2018

\"DoC-feeling\": a new behavioural tool to help diagnose the Minimally Conscious State.

ObjectivesThe clinical distinction between vegetative state/unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS) is a key step to elaborate a prognosis and formulate an appropriate medical plan for any patient suffering from disorders of consciousness (DoC). However, this assessment is often challenging and may require specialized expertise. In this study, we hypothesized that pooling subjective reports of the level of consciousness of a given patient across several nursing staff members can be used to clinically detect MCS.\n\nSetting and ParticipantsPatients referred for consciousness assessment were prospectively screened. MCS (target condition) was defined according to the best Coma Recovery Scale-Revised score (CRS-R) obtained from expert physicians (reference standard). \"DoC-feeling\" score consisted in the median value of multiple ratings of patients behavior observation pooled from multiple staff members during a week of hospitalisation (index test). Individual ratings were collected at the end of each shift using a 100mm visual analog scale, blinded from the reference standard. Diagnostic accuracy was evaluated using area under the receiver operating characteristic curve (AUC), sensitivity and specificity metrics.\n\nResults692 ratings performed by 83 nursing staff members were collected from 47 patients. Twenty patients were in a UWS and 27 in a MCS. DoC-feeling scores obtained by pooling all individual ratings obtained for a given patient were significantly greater in MCS than in UWS patients (59.2 mm [IQR: 27.3-77.3] vs. 7.2 mm [IQR: 2.4-11.4]; p<0.001) yielding an AUC of 0.92 (95%CI: 0.84-0.99).\n\nConclusionsDoC-feeling capitalizes on the expertise of nursing staff to evaluate patients consciousness. Together with the CRS-R as well as with brain imaging, DoC-feeling might improve diagnostic and prognostic accuracy of DoC patients.\n\nStrengths and limitations of this studyO_LIWe designed a new behavioural tool called \"DoC-feeling\" to help face the clinical challenge of the detection of Minimally Conscious State in patients suffering from disorders of consciousness (DoC)\nC_LIO_LI\"DoC-feeling score\" quantifies nursing staffs subjective perception of patients consciousness by pooling multiple assessments obtained from multiple caregivers (\"wisdom of the crowds\")\nC_LIO_LIThis score which requires no particular training showed a very good accuracy when compared to the gold standard (repeated expert clinical assessment using the Coma Recovery Scale - Revised (CRS-R))\nC_LIO_LIA validation in a separate cohort would help to determine its place in consciousness assessment\nC_LIO_LIThis score should be tested not only against the CRS-R but also against brain-imaging techniques to test for its capacity to detect covert signs of consciousness\nC_LI

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