Biology subjects

Pinotsis, D.

Publications and source records attributed to Pinotsis, D..

11 recordsLinked to original sources

bioRxiv2026

A mathematical model of the HPA axis demonstrates bifurcations under varying stress and systemic conditions

In this study, we develop a mathematical model of the hypothalamic pituitary adrenal (HPA) axis and systematically investigate the model complexity required to reproduce observed hormone dynamics. The model is fitted to ACTH and cortisol measurements from five healthy volunteers reported in the literature. We show that simultaneous reproduction of both hormone profiles requires a delay differential equation model with at least five state variables among the model architectures considered. Following fitting the volunteer data, we investigate the response of the model to sustained stress using bifurcation analysis. The coupled system exhibits bifurcations over a range of stress intensities. This analysis reveals bifurcations whose critical stress thresholds depend strongly on the modulatory state of the system. These results provide a mathematical framework for investigating transitions between physiological and dysregulated HPA axis dynamics, which may be related to states of health and disease.

bioinformatics↗

bioRxiv2026

Measuring excitation/inhibition balance through field potentials

Several electroencephalography-based metrics have been proposed to index excitation/inhibition balance (E/I). We used single-unit and local field potential recordings from rat medial prefrontal and orbitofrontal cortex and mouse visual cortex and hippocampus to evaluate four candidate metrics against empirically-measured E/I. While 1/f slope and gamma oscillations showed region and context dependencies, broadband (6-80 Hz) and high gamma (80-150 Hz) power consistently correlated with E/I, supporting their use as proxy measures. If at least 30 minutes' data from medial prefrontal cortex were used per animal, broadband power could rank the animals by their E/I ratio, suggesting it could be used as an individual differences measure.

neuroscience↗

bioRxiv2026

Evidence for predictive computations in a brain hierarchy during a visual search task

Many lines of evidence suggest that the cortex functions fundamentally as a predictive system, compressing high-dimensional sensory inputs into low-dimensional representations that support internal models of the environment. These models generate top-down signals that shape and filter incoming sensory information, prioritizing unpredicted, and thus more informative, inputs for further processing. Multiple, mutually compatible computational frameworks have been proposed to explain these mechanisms, including predictive coding, predictive routing, and autoencoder-based approaches. However, a key challenge remains in empirically disentangling their relative contributions. Here, we directly compared three candidate algorithms, predictive coding, routing and autoencoders using LFP data from a brain hierarchy acquired during a visual search task. Notably, predictive coding differs from alternative models in its capacity to dynamically optimize signal passing across brain hierarchies in an input-specific manner, while the other algorithms do not consider constraints on feedback or feedforward inputs. It also goes beyond pairwise interactions, by predicting information flow in triplets of brain areas; here, V4, 7A and PFC. We considered the characteristic patterns of neural dynamics and within-area coupling that distinct algorithms produce. These were subsequently used to assess the relative evidence of each algorithm in the face of the LFP data. Our results support a hybrid account: hierarchical message passing consistent with predictive coding appears necessary to explain deep-layer activity, while predictive suppression mechanisms, aligned with predictive routing, account for superficial-layer dynamics without requiring explicit error computations.

neuroscience↗

bioRxiv2025

Ephaptic coupling can explain variability in neural activity

The waxing and waning cortical oscillatory power correlates with function and disease. This cross-trial variability has been thought to be due to neuromodulation, uncertainty encoding, and/or changes in cortical excitability. Here, we report evidence that it is also due to fluctuations in ephaptic influences of mesoscale electric fields. We analyzed LFP data from the PFC recorded during a spatial delay saccade task. We constructed a model that describes the electric field close to the cortical patch given the neural activity that generates it. This revealed that field-to-neuron interactions (ephaptic coupling strength) was stronger that neuron-to-field and it correlated trial by trial changes in oscillatory power. This suggests a form of circular causality where neural activity and extracellular electric fields continuously shape each other. These results further suggest that mesoscale ephaptic effects help drive the formation of memory ensembles, a prediction of the cytoelectric coupling hypothesis.

neuroscience↗

bioRxiv2025

Ephaptic coupling and power fluctuations in depression

The initial therapeutic exposure to DBS during implantation surgery has reproducible acute behavioral effects that carry over without further stimulation. We analyzed LFP data from the first month following brief therapeutic intraoperative DBS. Data were recorded from the subcallosal cingulate cortex (SCC). During this month no further stimulation was applied. Recent studies have identified beta power fluctuations in LFP data as an acute putative depression biomarker of this exposure. However, a detailed description of neural dynamics underlying brain power fluctuations is missing. Here, we consider how these fluctuations are related to brain itinerancy, that is neural activity changes between stable and unstable states. We also provide a proof of principle study that these dynamics can be described using two new dynamical systems measures: instability frequency and relative wandering time. These capture interactions between neural activity and the mesoscale oscillatory electric fields generated by it. The two measures seem to split low vs. high HDRS scores within a small patient cohort. They are motivated by the cytoelectric coupling hypothesis, that suggests that efficient information processing results from mesoscale electric fields; and that the re-emergence of depression symptoms might result from altered electric fields. Whether the new measures reflect general mechanisms of rapid antidepressant action remains to be tested.

neuroscience↗

bioRxiv2025

Ephaptic-Axonal Interactions Shape Radial Biases DuringNeural Self-Organization

Communication in the brain seems to not just be mediated by axonal action potential transmission resulting in post synaptic modulations, but also by electrical field effects of depolarizing neurons, i.e. ephaptic coupling. The significant role of ephaptic coupling in synchronizing neural ensembles has been conjectured to indirectly affect ontogenetic neural circuit development through sub threshold impacts on spike timing. We therefore hypothesized synchronously firing ensembles to emerge at spatial distances where ephaptic and axonal signals were most temporally correlated. To test our model of ephaptic axonal interactions during neural self-organization, we compared its predictions to developmental outcomes of cortical rat tissue on high-density multielectrode arrays in vitro. We observed a cosinusoidal variation of synchronous activity over radial distances that can be understood to result from the joint effects of ephaptic and axonal signals during gamma-band bursts. Recurrent amplification of this theorized interaction effect during ontogenetic differentiation appears to radially bias the spectral profile of neural activity in the spatial distribution of neural ensembles. While long- term plasticity has been conventionally attributed to synaptic action alone, we show that that the interaction effects with ephaptic waves deserve further investigation.

neuroscience↗

bioRxiv2025

A Canonical Microcircuit for Estimating Excitation/Inhibition (E/I) Balance

Excitation/inhibition (E/I) balance is crucial for maintaining healthy brain function and can be disrupted in various neurological and psychiatric disorders. Despite its importance, there are few tools to study E/I balance non-invasively in humans. Here, we propose a canonical microcircuit model to estimate E/I balance from non-invasive magneto- and electroencephalography (M/EEG) recordings by parameterising global pyramidal and inhibitory cell excitability. We first establish that E/I parameters are identifiable and recoverable. We then explore the effects of these new parameters and their interaction with other parameters in a series of simulations. To highlight the clinical relevance of this new model, we simulate changes in E/I balance and their impact on event-related potentials (ERPs) derived from paired-click, passive and active oddball paradigms, which are among the most robust clinical biomarkers of schizophrenia. Our simulations show that a loss of pyramidal cell excitability can explain reduced ERP amplitudes across all three paradigms, mirroring empirical findings in schizophrenia. This method may serve as a computational assay for estimating synaptopathy and E/I balance from non-invasive M/EEG recordings across various clinical conditions thereby advancing efforts to develop personalised interventions to restore E/I balance.

neuroscience↗

bioRxiv2023

In vivo ephaptic coupling allows memory network formation

It is increasingly clear that memories are distributed across multiple brain areas. Such "engram complexes" are important features of memory formation and consolidation. Here, we test the hypothesis that engram complexes are formed in part by bioelectric fields that sculpt and guide the neural activity and tie together the areas that participate in engram complexes. Like the conductor of an orchestra, the fields influence each musician or neuron and orchestrate the output, the symphony. Our results use the theory of synergetics, machine learning and data from a spatial delayed saccade task and provide evidence for in vivo ephaptic coupling in memory representations.

neuroscience↗

bioRxiv2022

Dynamic causal modelling shows a prominent role of local inhibition in alpha power modulation in higher visual cortex

During resting-state EEG recordings, alpha activity is more prominent over the posterior cortex in eyes-closed (EC) conditions compared to eyes-open (EO). In this study, we characterized the difference in spectra between EO and EC conditions using dynamic causal modelling. Specifically, we investigated the role of intrinsic and extrinsic connectivity--within the visual cortex--in generating EC-EO alpha power differences over posterior electrodes. The primary visual cortex (V1) and the bilateral middle temporal visual areas (V5) were equipped with bidirectional extrinsic connections using a canonical microcircuit. The states of four intrinsically coupled subpopulations--within each occipital source--were also modelled. Using Bayesian model selection, we tested whether modulations of the intrinsic connections in V1, V5 or extrinsic connections (or a combination thereof) provided the best evidence for the data. In addition, using parametric empirical Bayes (PEB), we estimated group averages under the winning model. Bayesian model selection showed that the winning model contained both extrinsic connectivity modulations, as well as intrinsic connectivity modulations in all sources. The PEB analysis revealed increased extrinsic connectivity during EC. Overall, we found a reduction in the inhibitory intrinsic connections during EC. The results suggest that the intrinsic modulations in V5 played the most important role in producing EC-EO alpha differences, suggesting an intrinsic disinhibition in higher order visual cortex, during EC resting state. Author summaryOne of the strongest signals that can be measured using EEG are so called alpha rhythms. These are neural oscillations that fall within the 8-12Hz frequency range. Alpha rhythms are most prominent when the eyes are closed and are seen at the electrodes placed at the back of the head. In this study, we studied the mechanism of alpha rhythms changes when going from eyes-open to an eyes-closed state. We used a biologically plausible model including different neural populations. We focused on modelling connections within and between different neural sources of the visual cortex and how they are modulated when going from eyes-open to an eyes closed state. We found evidence that inhibitory neurons play an important role in alpha rhythms.

neuroscience↗

bioRxiv2021

Towards Biophysical Markers of Depression Vulnerability

A major difficulty with treating psychiatric disorders is their heterogeneity: different neural causes can lead to the same phenotype. To address this, we propose describing the underlying pathophysiology in terms of interpretable, biophysical parameters of a neural model derived from the electroencephalogram. We analyzed data from a small patient cohort of patients with depression and controls. We constructed biophysical models that describe neural dynamics in a cortical network activated during a task that is used to assess depression state. We show that biophysical model parameters are biomarkers, that is, variables that allow subtyping of depression at a biological level. They yield a low dimensional, interpretable feature space that allowed description of differences between individual patients with depressive symptoms. They capture internal heterogeneity/variance of depression state and achieve significantly better classification than commonly used EEG features. Our work is a proof of concept that a combination of biophysical models and machine learning may outperform earlier approaches based on classical statistics and raw brain data.

neuroscience↗

bioRxiv2021

Beyond dimension reduction: Stable electric fields emerge from and allow representational drift

It is known that the exact neurons maintaining a given memory (the neural ensemble) change from trial to trial. This raises the question of how the brain achieves stability in the face of this representational drift. Here, we demonstrate that this stability emerges at the level of the electric fields that arise from neural activity. We show that electric fields carry information about working memory content. The electric fields, in turn, can act as "guard rails" that funnel higher dimensional variable neural activity along stable lower dimensional routes. We obtained the latent space associated with each memory. We then confirmed the stability of the electric field by mapping the latent space to different cortical patches (that comprise a neural ensemble) and reconstructing information flow between patches. Stable electric fields can allow latent states to be transferred between brain areas, in accord with modern engram theory.

neuroscience↗