Biology subjects

Levi, R.

Publications and source records attributed to Levi, R..

9 recordsLinked to original sources

bioRxiv2026

Understanding cognitive function in post-COVID-19 condition with fatigue: an interdisciplinary multimodal investigation

Lingering cognitive symptoms of post-COVID-19 condition (PCC) continue to challenge our understanding, highlighting the need for interdisciplinary research. We investigated whether individuals with PCC and fatigue differ from matched controls across neurocognitive, neuroimaging, and immunological measures, integrating these findings with qualitative phenomenological analyses through multimodal cross-analysis. Individuals with PCC and clinically significant fatigue (n=34), and age-, sex-, and education-matched controls recovered from COVID-19 without PCC (n=37) were included. Assessments encompassed physician evaluation, neuropsychological and functional tests, structural and functional MRI, immunological tests, and qualitative phenomenological analysis of interviews. Data were analyzed using established methods and an interdisciplinary cross-analysis to examine persistent cognitive symptoms in PCC. Group comparisons revealed subtle differences between individuals with PCC and controls, while neurocognitive performance and immunological markers remained within normative ranges. Modest differences in white matter, myelin related, and global brain measures were observed, alongside differences in associations between network connectivity and neurocognitive performance across groups, suggesting reduced network resilience rather than domain specific deficits. This pattern resonated with qualitative phenomenological findings: altered experiences of concentrating and difficulties to execute decisions and actions, despite preserved performance on standard neurocognitive tests. These findings suggest that cognitive symptoms in post-COVID-19 fatigue are associated with subtle neurobiological alterations not captured by standard cognitive assessments. In this highly fatigued sample, converging neuroimaging, neurocognitive and immunological findings point toward reduced network efficiency rather than overt cognitive impairment. Qualitative phenomenological analysis further highlighted altered modes of being in the world characterized by increased cognitive effort and difficulties executing decisions and actions.

neuroscience↗

bioRxiv2026

Patient-specific computational mechanics of functional lumbar spine units

In the current clinical practice, the diagnosis of spinal disorders and their surgical planning are critically based on imaging data. To complement this data, patient-specific finite element models have been developed and showed to be powerful tools for evaluating spine mechanics. Most of them rely on Computational Tomography (CT) scans - which have a high resolution but are seldom available in routine clinical practice - while only a recent few models are on less invasive Magnetic Resonance Imaging (MRI). Yet, despite the proliferation of these computational models, encompassing detailed anatomical and functional information, the rheological assumptions they are built upon are based on tissue-sample mechanical response data, which leaves a gap in the quantitative analysis on how such assumptions influence the macroscopic response of a functional spinal unit. Aiming at addressing these shortcomings, the main purpose of this work is to introduce a quantitative computational assessment of the macroscopic impact of commonly adopted rheological models - from linear elasticity to fiber-reinforced nonlinear hyperelasticity - in several loading conditions, focusing on a lumbar unit which is considered as a typical benchmark system. We also propose a reconstruction procedure to accurately describe subject-specific anatomy from MRI data, including the intervertebral disc and its nucleus pulposus. Bones are modeled as linear elastic media, whereas for the AF, we consider three different mechanical models - namely, isotropic linear elasticity and the Holzapfel-Gasser-Ogden model with and without fiber reinforcement. Model verification on an idealized geometry demonstrates numerical consistency, while parametric orthostatic simulations highlight the need for nonlinear formulations to capture anisotropy and strain-stiffening behavior of the intervertebral disc. Then, we carry out flexion, lateral bending, and torsion tests on a subject-specific reconstructed functional unit, for which we provide parametric analysis in terms of momentum magnitude and resulting range of motion. These tests further confirm the need for a nonlinear rheology of the annulus fibrosus and provide a quantitative assessment of the differences between the constitutive laws considered. Moreover, successful comparisons with the literature, in terms of macroscopic deformation under several loading conditions, serve as partial validation for our computational model.

bioengineering↗

bioRxiv2026

Human Sperm-Induced Cell-Cell Fusion Requiring JUNO (hSPICER): A paradigm shift to test sperm fertilizing potential

Current evaluation of male fertility is largely based on indirect sperm parameters such as viability, concentration, morphology, and motility; however, each of these parameters, alone or combined, has been shown to have limited predictive value for successful fertilization. To address this problem, we introduce hSPICER (human SPerm-Induced CEll-cell fusion Requiring JUNO), an assay that evaluates sperm function based on their ability to induce fusion of somatic cells expressing human JUNO (hJUNO), the egg-specific sperm receptor. Similarly to our previous discovery in mice, we found that human sperm can fuse with somatic cells expressing hJUNO on their surface (pseudo-eggs) and promote content mixing between cells in culture, as measured using a split GFP system. The assay is sensitive, specific, and species-dependent, requiring hJUNO for optimal signal. We generated a stable cell line expressing hJUNO, enhancing reproducibility and sensitivity. We also show that hSPICER is compatible with cryopreserved sperm and consistent over different days. Importantly, hSPICER values correlate with fertilization outcomes of patients during fertility treatments, indicating its potential as a functional diagnostic tool. Beyond diagnostic uses, hSPICER establishes a platform to explore sperm fusion mechanisms and to screen for therapeutic compounds and interventions to treat low fertility, enhance fertilization, and develop non-hormonal contraceptives for males and females, as well as quality assessment of semen samples in fertility clinics and sperm banks.

developmental biology↗

bioRxiv2025

Brain serotonin circuit reverses decline in physical activity with age

Physical inactivity increases with age and is the fourth leading risk factor for mortality. Establishing the mechanisms underpinning declining physical activity (PA) with age has remained both elusive and difficult to overcome. Older (50-70 years old) compared with younger (17-30 years old) UK participants showed reduced PA, and this profile was effectively modelled in mice. Analysis of brain serotonin (5-HT) neurons in the dorsal raphe (DR) revealed altered firing activity in older mice. Chemogenetically mimicking this 5-HTDR tone in young adult mice produced an older adult PA profile. Genetically blocking 5-HT activity at 5-HT2C receptors (5-HT2CRs) prevented the decline in PA with age. Importantly, barring 5-HT action at 5-HT2CRs specifically within the ventral tegmental area restored youthful PA levels and strength in older mice. These data fill a longstanding knowledge gap by defining brain circuitry programming the decline in PA with age, and importantly, a means to reverse it.

neuroscience↗

bioRxiv2024

Efficiency and reliability in biological neural network architectures

Simplified models of neural networks have demonstrated the importance of establishing a reasonable tradeoff between memory capacity and fault-tolerance in cortical coding schemes. The intensity of the tradeoff is mediated by the level of neuronal variability. Indeed, increased redundancy in neuronal activity enhances the robustness of the code at the cost of the its efficiency. We hypothesized that the heterogeneous architecture of biological neural networks provides a substrate to regulate this tradeoff, thereby allowing different subpopulations of the same network to optimize for different objectives. To distinguish between subpopulations, we developed a metric based on the mathematical theory of simplicial complexes that captures the complexity of their connectivity, by contrasting its higher-order structure to a random control. To confirm the relevance of our metric we analyzed several openly available connectomes, revealing that they all exhibited wider distributions of simplicial complexity across subpopulations than relevant controls. Using a biologically detailed cortical model and an electron microscopic data set of cortical connectivity with co-registered functional data, we showed that subpopulations with low simplicial complexity exhibit efficient activity. Conversely, subpopulations of high simplicial complexity play a supporting role in boosting the reliability of the network as a whole, softening the robustness-efficiency tradeoff. Crucially, we found that both types of subpopulations can and do coexist within a single connectome in biological neuronal networks, due to the heterogeneity of their connectivity. Our work thus suggests an avenue for resolving seemingly paradoxical previous results that assume homogeneous connectivity.

neuroscience↗

bioRxiv2022

Modeling and Simulation of Rat Non-Barrel Somatosensory Cortex. Part I: Modeling Anatomy

The function of the neocortex is fundamentally determined by its repeating microcircuit motif, but also by its rich, interregional connectivity. We present a data-driven computational model of the anatomy of non-barrel primary somatosensory cortex of juvenile rat, integrating whole-brain scale data while providing cellular and subcellular specificity. The model consists of 4.2 million morphologically detailed neurons, placed in a digital brain atlas. They are connected by 14.2 billion synapses, comprising local, mid-range and extrinsic connectivity. We delineated the limits of determining connectivity from neuron morphology and placement, finding that it reproduces targeting by Sst+ neurons, but requires additional specificity to reproduce targeting by PV+ and VIP+ interneurons. Globally, connectivity was characterized by local clusters tied together through hub neurons in layer 5, demonstrating how local and interegional connectivity are complicit, inseparable networks. The model is suitable for simulation-based studies, and a 211,712 neuron subvolume is made openly available to the community.

neuroscience↗

bioRxiv2022

Counting Is Almost All You Need

The immune memory repertoire encodes the history of present and past infections and immunological attributes of the individual. As such, multiple methods were proposed to use T-cell receptor (TCR) repertoires to detect disease history. We here show that the counting method outperforms all existing algorithms. We then show that the counting can be further improved using a novel attention model to weight the different TCRs. The attention model is based on the projection of TCRs using a Variational AutoEncoder (VAE). Both counting and attention algorithms predict better than any current algorithm whether the host had CMV and its HLA alleles. As an intermediate solution between the complex attention model and the very simple counting model, we propose a new Graph Convolutional Network approach that obtains the accuracy of the attention model and the simplicity of the counting model. The code for the models used in the paper are provided in: https://github.com/louzounlab/CountingIsAlmostAllYouNeed

immunology↗

bioRxiv2020

Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies

1In motor-related brain regions, movement intention has been successfully decoded from in-vivo spike train by isolating a lower-dimension manifold that the high-dimensional spiking activity is constrained to. The mechanism enforcing this constraint remains unclear, although it has been hypothesized to be implemented by the connectivity of the sampled neurons. We test this idea and explore the interactions between local synaptic connectivity and its ability to encode information in a lower dimensional manifold through simulations of a detailed microcircuit model with realistic sources of noise. We confirm that even in isolation such a model can encode the identity of different stimuli in a lower-dimensional space. We then demonstrate that the reliability of the encoding depends on the connectivity between the sampled neurons by specifically sampling populations whose connectivity maximizes certain topological metrics. Finally, we developed an alternative method for determining stimulus identity from the activity of neurons by combining their spike trains with their recurrent connectivity. We found that this method performs better for sampled groups of neurons that perform worse under the classical approach, predicting the possibility of two separate encoding strategies in a single microcircuit.

neuroscience↗

bioRxiv2018

Robust dynamical invariants in sequential neural activity

By studying different sources of temporal variability in central pattern generator circuits, in this paper we unveil distinct aspects of the instantaneous balance between flexibility and robustness in sequential dynamics -a property that characterizes many systems that display neural rhythms. The level of irregularity and coordination was characterized using intrinsic time references and intervals in long recordings of the pyloric central pattern generator. The analysis demonstrated strong robustness of transient dynamics in keeping not only the activation sequences but also specific cycle-by-cycle temporal relationships in the form of dynamical invariants. The rich dynamics of neurons and connections balance flexibility and coordination to readily negotiate the interactions between neurons and produce the resultant rhythm. In particular, two dynamical invariants were identified between time intervals that build the sequence, existing even outside steady states. We suggest that invariant temporal sequence relationships could be present in other networks, including those related to brain rhythms, and underlie rhythm programming and functionality.

neuroscience↗