Biology subjects

Sitt, J.

Publications and source records attributed to Sitt, J..

At least 19 recordsLinked to original sources

bioRxiv2026

Hyper-Hierarchical Brain States Are Associated with Disorders of Consciousness

BackgroundConsciousness is increasingly understood as an emergent property of large-scale brain dynamics that depend upon flexible interactions among distributed cortical and subcortical systems. Although disorders of consciousness (DOC) have traditionally been associated with impaired integration and reduced network complexity, the role of hierarchical brain organization in supporting conscious awareness remains poorly understood. Here, we investigated how hierarchical organization relates to behavioral responsiveness in DOC by combining trophic-level analysis, trophic coherence, and whole-brain dynamical metrics. MethodsResting-state functional MRI data were analyzed from healthy controls (CNT), minimally conscious state (MCS) patients, and unresponsive wakefulness syndrome (UWS) patients drawn from a previously published DOC cohort. Static global and regional measures of functional hierarchy were computed from directed effective-connectivity networks. Dynamic trophic states were identified using time-resolved phase-coupling analyses and clustering of recurrent coordination patterns. State occupancy, dwell time, metastability, synchrony, and behavioral associations with Coma Recovery Scale-Revised (CRS-R) scores were evaluated. ResultsRegional trophic levels were positively associated with behavioral responsiveness, with higher frontal and thalamic trophic levels and lower insular trophic levels predicting higher Coma Recovery Scale-Revised (CRS-R) scores. Dynamic trophic-state analysis identified a pathological hyper-hierarchical state, defined by elevated frontal, thalamic, and insular trophic levels, that exhibited progressively greater occupancy and longer dwell times from healthy controls to minimally conscious state and unresponsive wakefulness syndrome patients. In contrast, occupancy and dwell time of this state distinguished diagnostic groups but were not significantly associated with behavioral responsiveness. Independent analyses demonstrated significant reductions in metastability and global synchrony across disorders of consciousness. Anatomical mapping localized elevated trophic levels within the pathological state predominantly to fronto-thalamo-limbic systems. ConclusionsDisorders of consciousness are characterized not simply by loss of hierarchical organization but by prolonged stabilization within recurrent hyper-hierarchical brain states. Conscious awareness appears to depend not only on hierarchical organization itself but also on the capacity to flexibly transition between distinct brain states. Severe disorders of consciousness are associated with persistent occupation of pathological hyper-hierarchical states, potentially restricting the dynamical repertoire available for conscious processing.

neuroscience↗

bioRxiv2026

Structural determinants of dynamical state transitions in disorders of consciousness: a whole-brain modeling approach

Disorders of consciousness (DoC) are associated with large-scale alterations in brain dynamics, yet the structural factors that constrain these changes remain unclear. Here, we investigate how the topology of the structural connectome shapes the sensitivity of brain dynamics to perturbation using a whole-brain computational model constrained by diffusion MRI-derived connectivity. We systematically probed the effects of node removal and targeted modulation of local excitation-inhibition balance on dynamic functional connectivity, quantifying dynamical richness via transitions between recurrent connectivity states and jump length distributions in functional connectivity space. We show that a nodes integration within the structural connectome, quantified using a spectral integration measure, strongly predicts its impact on global brain dynamics. Lesions to highly integrative hubs drive the system toward low-complexity dynamical regimes resembling those observed in DoC, particularly posterior medial regions such as the precuneus and posterior cingulate cortex. Analogously, increasing excitability in these regions restores healthy-like dynamics in silico. In contrast, perturbations to weakly integrated regions have limited global effects. These results demonstrate that generic features of structural connectivity constrain whole-brain dynamical stability and help explain why damage to specific hubs disproportionately disrupts conscious brain activity.

neuroscience↗

bioRxiv2026

The blueprint of human functional architecture shifts from cognition to anatomy during perturbations of consciousness

Consciousness and cognition arise from the ongoing interactions between brain regions. Synchronous fluctuations of fMRI signals may indicate that two brain regions perform similar cognitive functions, but neural interactions are also constrained by anatomical connectivity and regions molecular, cytoarchitectonic, and metabolic profiles. Here we disentangle the respective contributions of ongoing cognition and multimodal neurobiological constraints in shaping functional connectivity. We jointly contextualise haemodynamic FC against eight distinct multimodal representations of the human connectome: (i) structural connectivity from diffusion tractography; (ii) spatial embedding; (iii) similarity of transcriptional profiles from gene expression; (iv) similarity of receptor profiles from Positron Emission Tomography; (v) laminar profile similarity from histology; (vi) correlated electrophysiological activity from magnetoencephalography; (vii) correlated metabolic activity from PET glucose uptake; (viii) coordinated activation across 123 cognitive operations from the NeuroSynth meta-analytic engine. We demonstrate that cognitive co-activation is the dominant predictor of inter-regional fMRI synchrony in the awake human brain, even when quantified using intracranial electrical stimulation. Crucially, this predominance of cognitive co-activation for shaping functional connectivity is systematically obliterated across five datasets of pharmacological and pathological perturbations of consciousness (chronic disorders of consciousness; anaesthesia with sevoflurane, propofol, or ketamine) when cognition is disconnected from the environment or altogether abolished. Altogether, we show that multimodal predictors of functional architecture shift away from cognitive co-activation and toward anatomicalmolecular constraints during pharmacological and pathological perturbations of consciousness.

neuroscience↗

bioRxiv2026

Decoding the phenomenology of spontaneous thought using large language-model ratings on verbal retrospective free reports

AO_SCPLOWBSTRACTC_SCPLOWSpontaneous thoughts constitute most of everyday inner experience, yet long-standing methodological challenges obscure a thorough exploration of their content and neurophysiological underpinnings. Traditional approaches relying on thought probes impose strict constraints on phenomenological reports, whereas online verbal reports disrupt the natural flow of experience while interfering neural signals with motor artifacts. Here, we designed and tested an alternative approach to assess the neural basis of spontaneous thoughts combining delayed verbal retrospective free reports (RFR) with automated phenomenological ratings generated by large language models (LLMs). Twenty-two participants performed an eyes-closed free-thinking task, providing reports that were evaluated along ten phenomenological dimensions by four state-of-the-art LLMs and a panel of human raters. Machine-learning models (ML) were then trained to decode LLM-derived ratings from EEG spectral, complexity, and connectivity features. Our analyses showed that inter-rater agreement among LLMs exceeded that of human raters whereas ML models achieved above-chance accuracy for the prediction of emotional valence. These findings provide support for the use of LLMs for a scalable phenomenological annotation of spontaneous thoughts and suggest that their affective dimensions can be decoded from concurrent EEG activity.

neuroscience↗

bioRxiv2025

Sustained dynamics and modality-specific network reconfigurations define crossmodal prediction error

Predictive processing theories posit that the brain continuously generates expectations about incoming sensory inputs, updating them through prediction errors. While extensively studied within single modalities, it remains unclear how prediction errors unfold when expectations and violations occur across different senses. Using a local-global hierarchical oddball paradigm combined with high-density EEG in 47 participants, we contrasted unimodal and crossmodal prediction errors across auditory, visual, and somatosensory domains. We found that crossmodal violations elicit temporally sustained cortical responses which diverge from the transient, localised dynamics observed for unimodal prediction errors. Temporal decoding revealed that crossmodal effects maintain shared prolonged neural representations, suggestive of supramodal integration across all levels of cortical processing. Computational modelling further demonstrated that crossmodal prediction errors reorganize effective connectivity within and between sensory hierarchies, engaging distinct early cortical pathways depending on the sensory combination. Our findings refine hierarchical predictive coding for crossmodal transitions by demonstrating that, unlike unimodal prediction errors, crossmodal prediction errors recruit dedicated prolonged supramodal representations and flexibly adapted modality-specific networks.

neuroscience↗

bioRxiv2025

Individualised electrophysiological neural field models for the assessment of thalamocortical function in disorders of consciousness: a multicentre study

Understanding the neural mechanisms underlying disorders of consciousness (DoC) remains a major challenge, particularly in distinguishing limited awareness in minimally conscious state (MCS) and complete unawareness in unresponsive wakefulness syndrome, also coined vegetative state (UWS/VS). In this multicentre study, we fitted a biophysically informed corticothalamic neural field model to high-density EEG data from two large independent datasets, comprising 203 UWS patients, 270 MCS patients and 74 healthy controls. We then used the fitted parameters to simulate EEG time series on a per-subject basis and compared empirical and simulated complexity metrics. The model reliably captured the spectral features across different states of consciousness and revealed reduced corticothalamic integrity in DoC patients that was more pronounced in UWS than in MCS, supporting the mesocircuit hypothesis. Furthermore, the simulated EEG reproduced the complexity patterns of the empirical recordings, with permutation entropy emerging as a sensitive marker capable of distinguishing between MCS and UWS for both real and simulated time series.

neuroscience↗

bioRxiv2024

Modeling disorders of consciousness at the patient level reveals the network's influence on the diagnosis vs the local node parameters role in prognosis

The study of disorders of consciousness (DoC) is very complex because patients suffer from a wide variety of lesions, affected brain mechanisms, different severity of symptoms, and are unable to communicate. Combining neuroimaging data and mathematical modeling can help us quantify and better describe some of these alterations. The goal of this study is to provide a new analysis and modeling pipeline for fMRI data leading to new diagnosis and prognosis biomarkers at the individual patient level. To do so, we project patients fMRI data into a low-dimension latent-space. We define the latent spaces dimension as the smallest dimension able to maintain the complexity, non-linearities, and information carried by the data, according to different criteria that we detail in the first part. This dimensionality reduction procedure then allows us to build biologically inspired latent whole-brain models that can be calibrated at the single-patient level. In particular, we propose a new model inspired by the regulation of neuronal activity by astrocytes in the brain. This modeling procedure leads to two types of model-based biomarkers (MBBs) that provide novel insight at different levels: (1) the connectivity matrices bring us information about the severity of the patients diagnosis, and, (2) the local node parameters correlate to the patients etiology, age and prognosis. Altogether, this study offers a new data processing framework for resting-state fMRI which provides crucial information regarding DoC patients diagnosis and prognosis. Finally, this analysis pipeline could be applied to other neurological conditions.

neuroscience↗

bioRxiv2024

Transcranial direct current stimulation modulates primate brain dynamics across states of consciousness.

The resting primate brain is traversed by spontaneous functional connectivity patterns that show striking differences between conscious and unconscious states. Transcranial direct current stimulation, a non-invasive neuromodulatory technique, can improve signs of consciousness in disorders of consciousness; however, can it influence both conscious and unconscious dynamic functional connectivity? We investigated the modulatory effect of prefrontal cortex (PFC) transcranial direct current stimulation (tDCS) on brain dynamics in awake and anesthetized non-human primates using functional MRI. In awake macaques receiving either anodal or cathodal tDCS, we found that cathodal stimulation robustly disrupted the repertoire of functional connectivity patterns, increased structure-function correlation, decreased Shannon entropy, and favored transitions towards anatomically-based patterns. Under deep sedation, anodal tDCS significantly altered brain pattern distribution and reduced structure-function correlation. The prefrontal stimulation also modified dynamic connectivity arrangements typically associated with consciousness and unconsciousness. Our findings offer compelling evidence that PFC tDCS induces striking modifications in the fMRI-based dynamic organization of the brain across different states of consciousness. This study contributes to an enhanced understanding of tDCS neuromodulation mechanisms and has important clinical implications for disorders of consciousness.

neuroscience↗

bioRxiv2024

Dynamic connectivity profiles characteristic of conscious states are associated with enhanced conscious processing of external stimuli

One of the goals of the neuroscience of consciousness is to identify neural markers capable of distinguishing brain dynamics in awake, healthy individuals from unconscious conditions. This problem also has a clinical diagnostic interest in disorders of consciousness. Recent research has shown that brain connectivity patterns characterized by long-range interactions and anticorrelations are associated with conscious states and diminish with loss of consciousness in human and non-human primates. However, the precise contribution of these patterns to conscious processing and subjective experience formation remains unclear. In this study, we investigated the functional role of these brain patterns in shaping conscious content by examining their influence on participants ability to process external information during wakefulness. Participants underwent fMRI recordings during an auditory detection task. Phase coherence-based functional connectivity and k-means clustering confirmed that the ongoing dynamics were underpinned by brain patterns consistent with those identified in previous research, including the "high pattern" characteristic of conscious states. We found that the detection of auditory stimuli at threshold was specifically improved when the connectivity pattern at the time of presentation corresponded to this high-pattern. In return, the occurrence of the high-pattern increased after detection, indicating that participants were more likely to transition to a high-pattern following stimulus detection. Our findings suggest that ongoing brain dynamics and conscious perception mutually influence each other and that certain brain configurations are more favorable for conscious processing of external stimuli. In the future, targeting these moments of favorable patterns in patients with disorders of consciousness may help us identify windows of greater receptivity to the external world, paving the way for developing individualized patient care protocols.

neuroscience↗

bioRxiv2023

Dynamical structure-function correlations provide robust and generalizable signatures of consciousness in humans

Resting-state functional magnetic resonance imaging evolves through a repertoire of functional connectivity patterns which might reflect ongoing cognition, as well as the contents of conscious awareness. We investigated whether the dynamic exploration of these states can provide robust and generalizable markers for the state of consciousness in human participants, across loss of consciousness induced by general anaesthesia or slow wave sleep. By clustering transient states of functional connectivity, we demonstrated that brain activity during unconsciousness is dominated by a recurrent pattern primarily mediated by structural connectivity and with a reduced capacity to transition to other patterns. Our results provide evidence supporting the pronounced differences between conscious and unconscious brain states in terms of whole-brain dynamics; in particular, the maintenance of rich brain dynamics measured by entropy is a critical aspect of conscious awareness. Collectively, our results may have significant implications for our understanding of consciousness and the neural basis of human awareness, as well as for the discovery of robust signatures of consciousness that are generalizable among different brain conditions.

neuroscience↗

bioRxiv2023

Heartbeat-evoked potential and ongoing brain dynamics during interoceptive and exteroceptive information processing predict attentional focus

Attention shapes our consciousness content and perception by increasing the probability of becoming aware and, or, better encode a selection of the incoming inner or outer sensory world. We designed a task to engage interoceptive and exteroceptive attention by orienting healthy participants to their heartbeats or auditory stimuli and investigated whether brain dynamics (Kolmogorov complexity - KC, permutation entropy - PE, weighted symbolic mutual information - wSMI, power spectrum density - PSD) and the heartbeat-evoked potential (HEP) distinguished interoceptive from exteroceptive covert attention. Exteroceptive attention yielded an overall flattening of the PSD, whereas during interoceptive attention there was a decrease in complexity, an increase in frontal connectivity and oscillations in the theta range, and a modulation of the HEP. Subject-level classifiers based on HEP features classified the attentional state of 17/20 participants. KC, PE, and wSMI showed comparable accuracy in classifying exteroceptive-interoceptive attention and exhibited a synergic behavior with the HEP features. PSD features demonstrated exceptional performance (20/20). Command-following was assessed in 5 brain-injured patients with a modified version of the task. An Unresponsive Wakefulness Syndrome/Vegetative State patient and a locked-in syndrome patient demonstrated a willful modulation of the HEP and the patient-level classifiers suggest that patients were complying with task instructions. Our findings show that directing attention to bodily rhythms or external stimuli elicits distinct neural responses that can be employed to track covert attention at the individual level. Importantly, the brain markers studied in this work provide multiple layers to explore information processing in disorders of conscious patients. Significance statementWe show that directing attention to bodily rhythms or external stimuli induces specific neural responses, allowing for individual-level tracking of covert attention in healthy participants. The time-locked and dynamical brain markers identified as relevant to distinguish between interoceptive-exteroceptive attention were further assessed in a small cohort of brain-injured patients as an EEG proxy of command-following. We show for the first time the willful modulation of the HEP in an UWS/VS patient and a locked-in syndrome patient, along with significant classification accuracies when combining the explored features. Importantly, the brain markers studied in this work provide multiple layers to explore information processing in disorders of conscious patients, and expand the framework of heart-brain interactions employed for diagnostic purposes.

neuroscience↗

bioRxiv2022

Processing of the same narrative stimuli elicits common functional connectivity dynamics between individuals

It has been suggested that the richness of conscious experience can be directly linked to the richness of brain state repertories. Brain states change depending on our environment and activities we engage in by taking both external and internally derived information into account. It has been shown that high-level sensory stimulation changes local brain activity and induces neural synchrony across participants. However, the dynamic interplay of cognitive processes that underlie moment-to-moment information processing remains poorly understood. Using naturalistic movies as an ecological laboratory model of the real world, here we assess how the processing of complex naturalistic stimuli alters the dynamics of brain networks interactions, and how these in turn support information processing. Participants underwent fMRI recordings during movie watching, scrambled movie watching, and rest. Measuring phase-synchrony between different brain networks, we computed whole-brain connectivity patterns. We showed that specific connectivity patterns were associated with each experimental condition. We found a higher synchronization of brain patterns across participants during movie watching compared to resting state and scrambled movie conditions. Moreover, synchronization increased during the most engaging parts of the movie. The synchronization dynamics across participants were associated with suspense; more suspenseful scenes induced higher synchronization. These results suggest that processing of the same high-level information elicits common neural dynamics among individuals and that whole-brain functional connectivity tracks variations in the processed information and the subjective experience.

neuroscience↗

bioRxiv2022

Specific and non-uniform brain states during cold perception in mice

The quest to decode the complex supraspinal mechanisms that integrate cutaneous thermal information in the central system is still ongoing. The dorsal horn of the spinal cord is the first hub that encodes thermal input which is then transmitted to brain regions via the spinothalamic and thalamo-cortical pathways. So far, our knowledge about the strength of the interplay between the brain regions during thermal processing is limited. To address this question, we imaged the brains of awake and freely-moving mice using Functional Ultrasound imaging during plantar exposure to constant and varying temperatures. Our study, a synchronous large field investigation of mice brains reveals for the first time the brain states and the specific dynamic interplay between key regions involved in thermal processing. Our study reveals: i) a dichotomy in the response of the somato-motor-cingulate cortices and the hypothalamus, which was never described before, due to the lack of appropriate tools to study such regions with both good spatial and temporal resolutions. ii) We infer that cingulate areas may be involved in the affective responses to temperature changes. iii) Colder temperatures (ramped down) reinforces the disconnection between the somato-motor-cingulate and hypothalamus networks. iv) Finally, we also confirm the existence in the mouse brain of a dynamic brain mode characterized by low cognitive strength, described previously only in non-human primates and humans. The present study points towards the existence of a common hub between somato-motor and cingulate regions, whereas hypothalamus functions are related to a secondary network.

neuroscience↗

bioRxiv2022

Low-dimensional organization of global brain states of reduced consciousness

Brain states are frequently represented using a unidimensional scale measuring the richness of subjective experience (level of consciousness). This description assumes a mapping between the high-dimensional space of whole-brain configurations and the trajectories of brain states associated with changes in consciousness, yet this mapping and its properties remain unknown. We combined whole-brain modelling, data augmentation and deep learning for dimensionality reduction to determine a mapping representing states of consciousness in a low-dimensional space, where distances parallel similarities between states. An orderly trajectory from wakefulness to brain injured patients is revealed in a latent space whose coordinates represent metrics related to functional modularity and structure-function coupling, both increasing alongside loss of consciousness. Finally, we investigated the effects of model perturbations, providing geometrical interpretation for the stability and reversibility of states. We conclude that conscious awareness depends on functional patterns encoded as a low-dimensional trajectory within the vast space of brain configurations.

neuroscience↗

bioRxiv2021

Unifying turbulent dynamics framework distinguishes different brain states

Recently, significant advances have been made by identifying the levels of synchronicity of the underlying dynamics of a given brain state. This research has demonstrated that unconscious dynamics tend to be more synchronous than those found in conscious states, which are more asynchronous. Here we go beyond this dichotomy to demonstrate that the different brain states are always underpinned by spatiotemporal chaos but with dissociable turbulent dynamics. We investigated human neuroimaging data from different brain states (resting state, meditation, deep sleep, and disorders of consciousness after coma) and were able to distinguish between them using complementary model-free and model-based measures of turbulent information transmission. Our model-free approach used recent advances describing a measure of information cascade across spatial scales using tools from turbulence theory. Complementarily, our model-based approach used exhaustive in silico perturbations of whole-brain models fitted to the empirical neuroimaging data, which allowed us to study the information encoding capabilities of the brain states. Overall, the current framework demonstrates that different levels of turbulent dynamics are fundamental for describing and differentiating between brain states.

neuroscience↗

bioRxiv2021

Ecdysone exerts biphasic control of regenerative signaling, coordinating the completion of regeneration with developmental progression

In Drosophila melanogaster, loss of regenerative capacity in wing imaginal discs coincides with an increase in systemic levels of the steroid hormone ecdysone, a key coordinator of their developmental progression. Regenerating discs release the relaxin hormone Dilp8, which limits ecdysone synthesis and extends the regenerative period. Here, we describe how regenerating tissues produce a biphasic response to ecdysone levels: lower concentrations of ecdysone promote local and systemic regenerative signaling, whereas higher concentrations suppress regeneration through the expression of broad splice isoforms. Ecdysone also promotes the expression of wingless during both regeneration and normal development through a distinct regulatory pathway. This dual role for ecdysone explains how regeneration can still be completed successfully in dilp8- mutant larvae: higher ecdysone levels increase the regenerative activity of tissues, allowing regeneration to reach completion in a shorter time. From these observations, we propose that ecdysone hormone signaling functions to coordinate regeneration with developmental progression. Summary StatementEcdysone coordinates regenerative activity with developmental progression through the biphasic, concentration-dependent activation, and suppression of regenerative signaling.

developmental biology↗

bioRxiv2021

Deep learning the arrow of time in brain activity: characterising brain-environment behavioural interactions in health and disease

The complex intrinsic and extrinsic forces from the body and environment push the brain into non-equilibrium. The arrow of time, central to thermodynamics in physics, is a hallmark of non-equilibrium and serves to distinguish between reversible and non-reversible dynamics in any system. Here, we use a deep learning Temporal Evolution NETwork (TENET) framework to discover the asymmetry in the flow of events, arrow of time, in human brain signals, which provides a quantification of how the brain is driven by the interplay of the environment and internal processes. Specifically, we show in large-scale HCP neuroimaging data from a thousand participants that the levels of non-reversibility/non-equilibrium change across time and cognitive state with higher levels during tasks than when resting. The level of non-equilibrium also differentiates brain activity during the seven different cognitive tasks. Furthermore, using the large-scale UCLA neuroimaging dataset of 265 participants, we show that the TENET framework can distinguish with high specificity and sensitivity resting state in control and different neuropsychiatric diseases (schizophrenia, bipolar disorders and ADHD) with higher levels of non-equilibrium found in health. Overall, the present thermodynamics-based machine learning framework provides vital new insights into the fundamental tenets of brain dynamics for orchestrating the interactions between behaviour and brain in complex environments.

neuroscience↗

bioRxiv2020

Perturbations in dynamical models of whole-brain activity dissociate between the level and stability of consciousness

Consciousness transiently fades away during deep sleep, more stably under anesthesia, and sometimes permanently due to brain injury. The development of an index to quantify the level of consciousness across these different states is regarded as a key problem both in basic and clinical neuroscience. We argue that this problem is ill-defined since such an index would not exhaust all the relevant information about a given state of consciousness. While the level of consciousness can be taken to describe the actual brain state, a complete characterization should also include its potential behavior against external perturbations. We developed and analyzed whole-brain computational models to show that the stability of conscious states provides information complementary to their similarity to conscious wakefulness. Our work leads to a novel methodological framework to sort out different brain states by their stability and reversibility, and illustrates its usefulness to dissociate between physiological (sleep), pathological (brain-injured patients), and pharmacologically-induced (anesthesia) loss of consciousness.

neuroscience↗