Biology subjects

Annen, J.

Publications and source records attributed to Annen, J..

15 recordsLinked to original sources

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

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.

neuroscience↗

bioRxiv2024

Personalized stimulation therapies for disorders of consciousness: A computational approach to inducing healthy-like brain activity based on neural field theory

Disorders of consciousness (DoC) pose significant challenges in neurology. Conventional neuromodulation therapies for DoC have exhibited limited success, with varying effectiveness among patients. In this study, we introduce a computational approach for constructing personalized stimulus signals capable of inducing healthy-like neural activity patterns in DoC patients. Leveraging a simplified brain model based on neural field theory, we fit this model to the power spectrum of a patient with DoC and derive a personalized stimulus time series to induce a healthy-like power spectrum. By applying this stimulus to brain regions typically targeted by stimulation therapies such as deep brain stimulation and repetitive transcranial magnetic stimulation, we demonstrate in silico the ability of our method to elicit EEG power spectra resembling those of healthy individuals. We speculate that in the course of a long-term treatment, when the brain produces healthy-like activity, it may trigger intrinsic plasticity mechanisms, potentially leading to sustained improvements in the patients condition. While further clinical adjustments and validation are needed, this novel approach offers promise in tailoring brain stimulation therapies for DoC patients. Moreover, it presents potential extensions to other conditions that could also benefit from brain stimulation therapies. Author summaryIn this research, we tackled a challenging issue in the field of neurology - disorders of consciousness (DoC). These conditions, which include states like coma and the minimally consciousness state, have been difficult to treat effectively. While traditional brain stimulation treatments have been tried, they do not always work well, and their effects can vary greatly from one person to another. Thus, we set out to find a new way to help these patients. Our approach involves using computer models of the brain to design personalized treatments. We designed a method to generate stimulation signals tailored to individual DoC patients. These signals aim to trigger healthier brain activity patterns, similar to those found in individuals without consciousness disorders. What is exciting is that if the brain begins to produce these healthy-like patterns, it could potentially lead to lasting improvements in the patients consciousness level. We show that this approach works in different simulations, and can be applied in multiple neuromodulation techniques like deep brain stimulation and transcranial magnetic stimulation. This research opens up new possibilities for more effective treatments for people dealing with these challenging disorders. Plus, it might work for other conditions treated with similar brain stimulation therapies.

neuroscience↗

bioRxiv2024

Individual trajectories for recovery of neocortical activity in disorders of consciousness

The evolution from disturbed brain activity to physiological brain rhythms can precede recovery in patients with disorders of consciousness (DoC). Accordingly, intriguing questions arise: What are the pathophysiological factors responsible for disrupted brain rhythms in patients with DoC, and are there potential pathways for individual patients with DoC to return to normal brain rhythms? We addressed these questions at the individual subject level using biophysical simulations based on electroencephalography (EEG). The main findings are that unconscious patients exhibit a loss of excitatory corticothalamic synaptic strength. Synaptic plasticity in this excitatory corticothalamic circuitry fosters physiological brain rhythms in the selection of patients with DoC. The extent to which this occurred was correlated with cerebral glucose uptake. The current findings emphasize the importance of excitatory thalamocortical activity in reestablishing normal brain rhythms after brain injury and show that biophysical modelling of the corticothalamic circuitry could help select patients that might be potentially receptive to treatment and undergo plasticity.

neuroscience↗

bioRxiv2023

Re-awakening the brain: Forcing transitions in disorders of consciousness by external in silico perturbation

A fundamental challenge in neuroscience is accurately defining brain states and predicting how and where to perturb the brain to force a transition. The ability to promote a transition from one brain state to another by externally driven stimulation could significantly impact rehabilitation and treatments for patients suffering from complex brain injury cases. Thus, it is crucial to find therapeutic interventions able to re-balance the dynamics of brain disorders towards more healthy regimes. Here, we investigated resting-state fMRI data of patients suffering from disorders of consciousness (DoC) after coma (minimally conscious and unresponsive wakefulness states) and healthy controls. We applied model-free and model-based approaches to help elucidate the underlying brain mechanisms of patients with DoC. The model-free approach allowed us to characterize brain states in DoC and healthy controls as a probabilistic metastable substate (PMS) space. The PMS of each group was characterized by a repertoire of unique patterns (i.e., metastable substates) with different probabilities of occurrence. In the model-based approach, we adjusted the PMS of each DoC group to a causal whole-brain model. This allowed us to explore optimal strategies for promoting a transition to the PMS of the control group by applying off-line in silico probing. Furthermore, this approach enabled us to evaluate the impact of all possible local perturbations in terms of their global effects and sensitivity to stimulation, which is a biomarker providing a deeper understanding of the mechanisms underlying DoC. Our results show that transitions from DoC to more healthy regimes were obtained in a synchronous protocol, in which areas from the motor and subcortical networks were the most sensitive to perturbation. This motivates further work to continue understanding brain function and treatments of disorders of consciousness by external stimulation. Author summaryWe studied disorders of consciousness by defining a brain state as a repertoire of metastable substates with different probabilities of occurrence. We created whole-brain computational models of DoC to uncover the causal mechanisms underlying recovery. These models allowed us to transition from DoC to a control healthy state by studying the effects of artificial individual local perturbations under different protocol regimes. We demonstrated successful transitions in the synchronization protocol and showed that the most sensitive areas were located in the motor network and subcortical regions. We believe this could be very valuable for developing clinical treatments and has a great deal for future therapies.

neuroscience↗

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.

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↗

bioRxiv2022

Single session of rTMS enhances brain metastability and intrinsic ignition

BackgroundEmerging evidence support the view that brain stimulation might improve essential tremor (ET) by altering brain networks and facilitating plasticity. Yet, we are still missing a mechanistic explanation of the whole brain dynamics underlying these plasticity defining changes. MethodIn this study, we explored the effect of low-frequency repetitive transcranial magnetic stimulation (rTMS) over left primary motor cortex (L-M1) on functional connectivity dynamics (FCD) in patients with ET. Resting-state fMRI (RsfMRI) was acquired before and after a single session of rTMS in 30 patients with ET and compared with RsfMRI of 20 age and gender matched healthy controls (HCs). We have measured the effect of brain stimulation using network topological re-organization through whole brain integration and segregation, brain stability and capacity of neural propagation through metastability and intrinsic ignition. ResultsPatients with ET had altered FCD measures compared to controls. After a single session rTMS, the brain connectivity measures approached normality and patients with ET revealed significantly higher integration, lower segregation with higher metastability and increased intrinsic ignition. ConclusionBrain metastability and intrinsic ignition measures could be valuable tools in appreciating mechanisms of brain stimulation in ET and other neurological diseases.

neuroscience↗

bioRxiv2021

Disruption in structural-functional network repertoire and time resolved subcortical-frontoparietal connectivity in disorders of consciousness

Understanding recovery of consciousness and elucidating its underlying mechanism is believed to be crucial in the field of basic neuroscience and medicine. Ideas such as the global neuronal workspace and the mesocircuit theory hypothesize that failure of recovery in conscious states coincide with loss of connectivity between subcortical and frontoparietal areas, a loss of the repertoire of functional networks states and metastable brain activation. We adopted a time-resolved functional connectivity framework to explore these ideas and assessed the repertoire of functional network states as a potential marker of consciousness and its potential ability to tell apart patients in the unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS). In addition, prediction of these functional network states by underlying hidden spatial patterns in the anatomical network, i.e. so-called eigenmodes, were supplemented as potential markers. By analysing time-resolved functional connectivity from fMRI data, we demonstrated a reduction of metastability and functional network repertoire in UWS compared to MCS patients. This was expressed in terms of diminished dwell times and loss of nonstationarity in the default mode network and fronto-parietal subcortical network in UWS compared to MCS patients. We further demonstrated that these findings co-occurred with a loss of dynamic interplay between structural eigenmodes and emerging time-resolved functional connectivity in UWS. These results are, amongst others, in support of the global neuronal workspace theory and the mesocircuit hypothesis, underpinning the role of time-resolved thalamo-cortical connections and metastability in the recovery of consciousness.

neuroscience↗

bioRxiv2021

Posterior integration and thalamo-frontotemporal broadcasting are impaired in disorders of consciousness

The study of the brains static and dynamical activity is opening a valuable source of assistance for the clinical assessment of patients with disorders of consciousness. For example, glucose uptake and dysfunctional spread of naturalistic and synthetic stimuli has proven useful to characterize hampered consciousness. However, understanding of the mechanisms behind loss of consciousness following brain injury is still missing. Here, we study the propagation of endogenous and in-silico exogenous perturbations in patients with disorders of consciousness, based upon directed and causal interactions estimated from resting-state fMRI. We found that patients with disorders of consciousness suffer decreased capacity for neural propagation and responsiveness to events, and that this can be related to glucose metabolism as measured with [18F]FDG-PET. In particular, we show that loss of consciousness is related to the malfunctioning of two neural circuits: the posterior cortical regions failing to convey information, in conjunction with reduced broadcasting of information from subcortical, temporal, parietal and frontal regions. These results seed light on the mechanisms behind disorders of consciousness, triangulating network function with basic measures of brain integrity and behavior. HighlightsO_LIPropagation of neural events and network responses are disrupted in patients with DoC. C_LIO_LILoss of consciousness is related to the malfunctioning of two neural circuits. C_LIO_LIPosterior cortical regions lack to integrate information in altered consciousness. C_LIO_LIBreakdown of information broadcasting of subcortical cortical areas in DoC. C_LIO_LILoss of network responses in DoC patients is related to glucose metabolism. C_LI

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↗

bioRxiv2020

Loss of consciousness reduces the stability of brain hubs and the heterogeneity of brain dynamics

Low-level states of consciousness are characterised by disruptions of brain dynamics that sustain arousal and awareness. Yet, how structural, dynamical, local and network brain properties interplay in the different levels of consciousness is unknown. Here, we studied the fMRI brain dynamics from patients that suffered brain injuries leading to a disorder of consciousness and from subjects undergoing propofol-induced anaesthesia. We showed that pathological and pharmacological low-level states of consciousness displayed less recurrent, less diverse, less connected, and more segregated synchronization patterns than conscious states. We interpreted these effects using whole-brain models built on healthy and injured connectomes. We showed that altered dynamics arise from a global reduction of network interactions, together with more homogeneous and more structurally constrained local dynamics. These effects were accentuated using injured connectomes. Notably, these changes lead the hub regions to lose their stability during low-level states of consciousness, thus attenuating the core-periphery structure of brain dynamics.

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↗

bioRxiv2018

Decreased integration of EEG source-space networks in disorders of consciousness

Increasing evidence links disorders of consciousness (DOC) with disruptions in functional connectivity between distant brain areas. However, to which extent the balance of brain network segregation and integration is modified in DOC patients remains unclear. Using high-density electroencephalography (EEG), the objective of our study was to characterize the local and global topological changes of DOC patients functional brain networks.\n\nResting state high-density-EEG data were collected and analyzed from 82 participants: 61 DOC patients recovering from coma with various levels of consciousness (EMCS (n=6), MCS+ (n=29), MCS- (n=17) and UWS (n=9)), and 21 healthy subjects (i.e., controls). Functional brain networks in five different EEG frequency bands and the broadband signal were estimated using an EEG connectivity approach at the source level. Graph theory-based analyses were used to evaluate group differences between healthy volunteers and patient groups.\n\nResults showed that networks in DOC patients are characterized by impaired global information processing (network integration) and increased local information processing (network segregation) as compared to controls. The large-scale functional brain networks had integration decreasing with lower level of consciousness.

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