Biology subjects

Fousek, J.

Publications and source records attributed to Fousek, J..

9 recordsLinked to original sources

bioRxiv2026

EEG dynamics in Lewy body diseases is related to clinical fluctuations and degeneration of locus coeruleus

This work investigates aperiodic bursts of neuronal activity, referred to as neuronal avalanches, which are thought to support flexible information processing in Lewy body (LB) disease. We compared avalanche-derived features across LB disease phenotypes (prodromal dementia with LB and Parkinsons disease, PD), with healthy controls (HC). Based on the premise that focal neurodegeneration alters whole-brain dynamics, we examined whether the "richness" of braindynamics was associated with clinical symptoms and with the microstructural integrity of catecholaminergic nuclei - the right caudal locus coeruleus (rcLC) and the substantia nigra pars compacta (SNpc). The sample comprised 30 HC, 56 cognitively normal individuals with core clinical features of dementia with LB (CN-CCF), 34 individuals with mild cognitive impairment with LB (MCI-LB), and 30 advanced PD patients. Neuronal avalanches were extracted from source-reconstructed resting-state EEG and described by their count, the number of unique spatial patterns (flexibility), hemispheric symmetry of flexibility, and avalanche transition matrices. LC and SNpc integrity were assessed using neuromelanin-sensitive MRI and free-water diffusion imaging, respectively. Lewy body disease groups exhibited increased flexibility relative to HC, which was, in prodromal groups, associated with cognitive fluctuations and REM sleep behavior disorder symptoms, but not parkinsonism. Flexibility was negatively related to rcLC integrity, which was lowest in MCI-LB. These findings suggest that elevated flexibility reflects dysregulated brain dynamics linked to malignant non-motor features of LB diseases and to the caudal LC involvement.

neuroscience↗

bioRxiv2025

Cholinergic heterogeneity facilitates synchronization and information flow in a whole-brain model

The human brain displays substantial regional variability in molecular, anatomical, and physiological organization. Yet, how this heterogeneity shapes large-scale neuronal dynamics remains poorly understood. To address this question, we employed a biologically informed whole-brain computational model capable of generating distinct brain states, from awake-like to sleep-like regimes. Our model was constrained by empirical human structural connectivity and spatial maps of cholinergic receptor gene expression, thereby embedding regional neuromodulatory variability into a macroscopic framework. We found that incorporating cholinergic heterogeneity had a significant impact on brain dynamics: it not only facilitated network synchronization but also enhanced information flow between brain regions. Furthermore, we addressed a particularly intricate dynamic regime characterized by the coexistence of localized sleep-like activity within otherwise awake-like states. We showed that the emergence of these slow waves was a byproduct of both regional levels of neuronal adaptation and structural connectivity. In summary, our findings highlight the critical role of molecular and anatomical heterogeneity in shaping global brain dynamics, suggesting new avenues for linking microscale diversity to macroscale function.

neuroscience↗

bioRxiv2025

Alpha rhythm subharmonics underlie responsiveness to theta burst stimulation via calcium metaplasticity

AO_SCPLOWBSTRACTC_SCPLOWRepetitive transcranial magnetic stimulation (rTMS) is a non-invasive technique to modulate brain activity, often used in treating Major Depressive Disorder (MDD) by targeting fronto-limbic circuitry. Despite its clinical utility, optimizing rTMS protocols remains challenging due to the complex and variable effects of stimulation parameter changes on synaptic plasticity. Oscillatory brain activity, measurable via Electroencephalography (EEG), serves as a biomarker for functional circuits and treatment response. To better understand the impact of rTMS on brain oscillations and connectivity, we used computational modeling of corticothalamic circuits to explore the mechanisms of stimulus-induced plasticity. We integrated calcium-dependent plasticity (CaDP) with Bienenstock-Cooper-Munro (BCM) metaplasticity formulations in a neural population model of resting-state EEG. By varying protocol parameters, we simulated iTBS effects on spectral power, synaptic efficacy, and calcium concentrations. Our findings highlight a resonance between theta stimulation and individual resting-state alpha rhythms, enhancing incoming excitatory long-term depression (LTD) and inhibitory long-term potentiation (LTP), leading to corticothalamic feed-forward inhibition (FFI). Induced effects were encapsulated by a weakening of corticothalamic loops and enhancement of intrathalamic loops. This work offers a novel paradigm for individualizing iTBS treatments, provides insights into the neurophysiological basis of clinical responsiveness, and offers a framework with which to derive tailored protocols.

neuroscience↗

bioRxiv2025

The role of symmetry breaking in connectivity and neurodegeneracy in the brain's resting state dynamics

The functioning of brain networks can be broadly categorized as an interplay of two main contributors, namely the neurological processes dictating the local dynamics and the patterns of anatomical connections that enable the interactions between the various local processes, resulting in a global emergent behavior. Using Brain Network Modeling we describe the constraints of the anatomical structure upon the resting state dynamics of the human brain. We identify a low-dimensional representation of the brain states, the Resting State Manifold (RSM), and leveraging network degeneracy and its relationship to structural properties, we identify dynamic patterns supported by the RSM. Noise driven dynamics of the BNM are used to explore the manifold and validate the contours of degeneracy. We demonstrate that the patterns of degeneracy regulate the dynamics of the broader system in the presence of external and/or internal perturbations, revealing the productive relationship between emergent network processes and their constituent network entities.

neuroscience↗

bioRxiv2024

Principles and Operation of Virtual Brain Twins

Current clinical methods often overlook individual variability by relying on population-wide trials, while mechanism-based trials remain underutilized in neuroscience due to the brains complexity. A Virtual Brain Twin (VBT) is a personalized digital replica of an individuals brain, integrating structural and functional brain data into advanced computational models and inference algorithms. By bridging the gap between molecular mechanisms, whole-brain dynamics, and imaging data, VBTs enhance the understanding of (patho)physiological mechanisms, advancing insights into both healthy and disordered brain function. Central to VBT is the network modeling that couples mesoscopic representation of neuronal activity through white matter connectivity, enabling the simulation of brain dynamics at a network level. This transformative approach provides interpretable predictive capabilities, supporting clinicians in personalizing treatments and optimizing interventions. This manuscript outlines the key components of VBT development, covering the conceptual, mathematical, technical, and clinical aspects. We describe the stages of VBT construction-from anatomical coupling and modeling to simulation and Bayesian inference-and demonstrate their applications in resting-state, healthy aging, epilepsy, and multiple sclerosis. Finally, we discuss potential extensions to other neurological disorders, such as Parkinsons disease, and explore future applications in consciousness research and brain-computer interfaces, paving the way for advancements in personalized medicine and brain-machine integration. "The brain is conceived as a self-organizing system operating close to instabilities where its activities are governed by collective variables, the order parameters, that enslave the individual parts, i.e., the neurons." Professor Hermann Haken (1927-2024).

neuroscience↗

bioRxiv2023

Large-scale brain signatures of fluid dynamics and responsiveness linked to consciousness

Signatures of consciousness are found in spectral and temporal properties of neuronal activity. Among these, spatiotemporal complexity after a perturbation has recently emerged as a robust metric to infer levels of consciousness. Perturbation paradigms remain, however, difficult to perform routinely. To discover alternative paradigms and metrics, we systematically explore brain stimulation and resting-state activity in a whole-brain model. We find that perturbational complexity only occurs when the brain model operates within a specific dynamical regime, in which spontaneous activity produces a large degree of functional network reorganizations referred to as being fluid. The regime of high brain fluidity is characterized by a small battery of metrics drawn from dynamical systems theory and predicts the impact of consciousness-altering drugs (Xenon, Propofol, and Ketamine). We validate the predictions in a cohort of 15 subjects at various stages of consciousness and demonstrate their agreement with previously reported perturbational complexity, but in a more accessible paradigm. Beyond the facilitation in clinical use, the metrics highlight complexity properties of brain dynamics in support of the emergence of consciousness.

neuroscience↗

bioRxiv2022

The virtual aging brain: a model-driven explanation for cognitive decline in older subjects

Healthy aging is accompanied by heterogeneous decline of cognitive abilities among individuals, especially during senescence. The mechanisms of this variability are not understood, but have been associated with the reorganization of white matter fiber tracts and the functional co-activations of brain regions. Here, we built a causal inference framework to provide mechanistic insight into the link between structural connectivity and brain function, informed by brain imaging data and network modeling. By applying various degrees of interhemispheric degradation of structural connectivity, we were not only able to reproduce the age-related decline in interhemispheric functional communication and the associated dynamical flexibility, but we obtained an increase of global modulation of structural connectivity over the brain function during senescence. Notably, the increase in modulation between structural connectivity and brian function was higher in magnitude and steeper in its increase in older adults with poor cognitive performance. We independently validated the causal hypothesis of our framework via a Bayesian approach based on deep-learning. The current results might be the first mechanistic demonstration of dedifferentiation and scaffolding during aging leading to cognitive decline demonstrated in a large cohort.

neuroscience↗

bioRxiv2022

The structured flow on the brain's resting state manifold

Spontaneously fluctuating brain activity patterns that emerge at rest have been linked to brains health and cognition. Despite detailed descriptions of the spatio-temporal brain patterns, our understanding of their generative mechanism is still incomplete. Using a combination of computational modeling and dynamical systems analysis we provide a mechanistic description of the formation of a resting state manifold via the network connectivity. We demonstrate that the symmetry breaking by the connectivity creates a characteristic flow on the manifold, which produces the major data features across scales and imaging modalities. These include spontaneous high amplitude co-activations, neuronal cascades, spectral cortical gradients, multistability and characteristic functional connectivity dynamics. When aggregated across cortical hierarchies, these match the profiles from empirical data. The understanding of the brains resting state manifold is fundamental for the construction of task-specific flows and manifolds used in theories of brain function such as predictive coding. In addition, it shifts the focus from the single recordings towards brains capacity to generate certain dynamics characteristic of health and pathology.

neuroscience↗

bioRxiv2020

Neuronal cascades shape whole-brain functional dynamics at rest

At rest, mammalian brains display remarkable spatiotemporal complexity, evolving through recurrent brain states on a slow timescale of the order of tens of seconds. While the phenomenology of the resting state dynamics is valuable in distinguishing healthy and pathological brains, little is known about its underlying mechanisms. Here, we identify neuronal cascades as a potential mechanism. Using full-brain network modeling, we show that neuronal populations, coupled via a detailed structural connectome, give rise to large-scale cascades of firing rate fluctuations evolving at the same time scale of resting-state networks. The ignition and subsequent propagation of cascades depend upon the brain state and connectivity of each region. The largest cascades produce bursts of Blood-Oxygen-Level-Dependent (BOLD) co-fluctuations at pairs of regions across the brain, which shape the simulated resting-state network dynamics.We experimentally confirm these theoretical predictions. We demonstrate the existence and stability of intermittent epochs of functional connectivity comprising BOLD co-activation bursts in mice and human fMRI. We then provide evidence for the existence and leading role of the neuronal cascades in humans with simultaneous EEG/fMRI recordings. These results show that neuronal cascades are a major determinant of spontaneous fluctuations in brain dynamics at rest. 1 Significance StatementFunctional connectivity and its dynamics are widely used as a proxy of brain function and dysfunction. Their neuronal underpinnings remain unclear. Using connectome-based modeling, we link the fast microscopic neuronal scale to the slow emergent whole-brain dynamics. We show that cascades of neuronal activations spontaneously propagate in resting state-like conditions. The largest neuronal cascades result in the co-fluctuation of Blood-Oxygen-Level-Dependent signals at pairs of brain regions, which in turn translate to stable brain states. Thus, we provide a theoretical framework for the emergence and the dynamics of resting-state networks. We verify these predictions in empirical mouse fMRI and human EEG/fMRI datasets measured in resting states conditions. Our work sheds light on the multiscale mechanisms of brain function.

neuroscience↗