Biology subjects

Polyakov, D.

Publications and source records attributed to Polyakov, D..

4 recordsLinked to original sources

bioRxiv2024

Neural field theory as a framework for modeling and understanding consciousness states in the brain

Understanding the neural correlates of consciousness remains a central challenge in neuroscience. In this study, we explore the potential of neural field theory (NFT) as a computational framework for representing consciousness states. While prior research has validated NFTs capacity to differentiate between normal and pathological states of consciousness, the relationship of its parameters to the representation of consciousness levels remains unclear. Here, we fitted a corticothalamic NFT model to EEG data collected from healthy individuals and patients with disorders of consciousness. We then comprehensively explored the correlations between the fitted NFT parameters and features extracted from both experimental and simulated EEG data, across various states of consciousness. The identified correlations not only highlight the models ability to differentiate between states of consciousness, but also shed light on the physiological bases of these states, pinpointing potential biomarkers. Our results provide valuable insights into how consciousness levels are represented within the NFT framework and into the dynamics of brain activity across various consciousness states. This underscores the potential of NFT as a useful tool for consciousness research, facilitating in-silico experimentation.

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↗

bioRxiv2020

Receptors, circuits and neural dynamics for prediction

Learned associations between stimuli allow us to model the world and make predictions, crucial for efficient behavior; e.g., hearing a siren, we expect to see an ambulance and quickly make way. While there are theoretical and computational frameworks for prediction, the circuit and receptor-level mechanisms are unclear. Using high-density EEG, Bayesian modeling and machine learning, we show that inferred "causal" relationships between stimuli and frontal alpha activity account for reaction times (a proxy for predictions) on a trial-by-trial basis in an audio-visual delayed match-to-sample task which elicited predictions. Predictive beta feedback activated sensory representations in advance of predicted stimuli. Low-dose ketamine, a NMDA receptor blocker - but not the control drug dexmedetomidine - perturbed behavioral indices of predictions, their representation in higher-order cortex, feedback to posterior cortex and pre-activation of sensory templates in higher-order sensory cortex. This study suggests predictions depend on alpha activity in higher-order cortex, beta feedback and NMDA receptors, and ketamine blocks access to learned predictive information.

neuroscience↗

bioRxiv2019

High Resolution Imaging Mass Spectrometry of Bacterial Microcolonies at Ecological Scales

Microbes interact with the world around them at the chemical level. However, directly examining the chemical exchange between microbes, and microbes and their environment, at ecological scales, i.e. the scale of a single bacterial cell or small groups of cells, remains a key challenge. Here we address this obstacle by presenting a methodology that enables Matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) of bacterial microcolonies. By combining optimized sample preparation with sub-atmospheric pressure MALDI, we demonstrate that chemical output from groups of as few as ~50 cells can be visualized with MALDI-IMS. Application of this methodology to Bacillus subtilis and Streptomyces coelicolor revealed heterogeneity in chemical output across microcolonies, and asymmetrical metabolite production when cells grew within physiological gradients produced by Medicago sativa roots. Taken together, these results indicate that MALDI-IMS can readily visualize metabolites made by very small assemblages of bacterial cells, and that even these small groups of cells can differentially produce metabolites in response to local chemical gradients.

microbiology↗