Biology subjects

Manasova, D.

Publications and source records attributed to Manasova, D..

5 recordsLinked to original sources

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.

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

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.

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

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.

neuroscience↗