Biology subjects

Apicella, I.

Publications and source records attributed to Apicella, I..

5 recordsLinked to original sources

bioRxiv2022

Scale-free avalanches in humans NREM sleep and their relationships with sleep macro and microstructure

Sleep plays a key role in preserving brain function, keeping the brain network in a state that ensures optimal computational capabilities. Empirical evidence indicates that such a state is consistent with criticality, where scale-free neuronal avalanches emerge. However, the relationship between sleep, emergent avalanches, and criticality remains poorly understood. Here we fully characterize the critical behavior of avalanches during sleep, and study their relationship with the sleep macro- and micro-architecture, in particular the cyclic alternating pattern (CAP). We show that avalanche size and duration distributions exhibit robust power laws with exponents approximately equal to -3/2 e -2, respectively. Importantly, we find that sizes scale as a power law of the durations, and that all critical exponents for neuronal avalanches obey robust scaling relations, which are consistent with the mean-field directed percolation universality class. Our analysis demonstrates that avalanche dynamics depends on the position within the NREM-REM cycles, with the avalanche density increasing in the descending phases and decreasing in the ascending phases of sleep cycles. Moreover, we show that, within NREM sleep, avalanche occurrence correlates with CAP activation phases, particularly A1, which are the expression of slow wave sleep propensity and have been proposed to be beneficial for cognitive processes. The results suggest that neuronal avalanches, and thus tuning to criticality, actively contribute to sleep development and play a role in preserving network function. Such findings, alongside characterization of the universality class for avalanches, open new avenues to the investigation of functional role of criticality during sleep with potential clinical application. Significance statementWe fully characterize the critical behavior of neuronal avalanches during sleep, and show that avalanches follow precise scaling laws that are consistent with the mean-field directed percolation universality class. The analysis provides first evidence of a functional relationship between avalanche occurrence, slow-wave sleep dynamics, sleep stage transitions and occurrence of CAP phase A during NREM sleep. Because CAP is considered one of the major guardians of NREM sleep that allows the brain to dynamically react to external perturbation and contributes to the cognitive consolidation processes occurring in sleep, our observations suggest that neuronal avalanches at criticality are associated with flexible response to external inputs and to cognitive processes, a key assumption of the critical brain hypothesis.

neuroscience↗

bioRxiv2022

Power spectrum and critical exponents in the 2D stochastic Wilson Cowan model

The power spectrum of brain activity is composed by peaks at characteristic frequencies superimposed to a background that decays as a power law of the frequency, f-{beta}, with an exponent {beta} close to 1 (pink noise). This exponent is predicted to be connected with the exponent{gamma} related to the scaling of the average size with the duration of avalanches of activity. "Mean field" models of neural dynamics predict exponents {beta} and{gamma} equal or near 2 at criticality (brown noise), including the simple branching model and the fully connected stochastic Wilson Cowan model. We here show that a 2D version of the stochastic Wilson Cowan model, where neuron connections decay exponentially with the distance, is characterized by exponents {beta} and{gamma} markedly different from those of mean field, respectively around 1 and 1.3. The exponents and{tau} of avalanche size and duration distributions, equal to 1.5 and 2 in mean field, decrease respectively to 1.29 {+/-} 0.01 and 1.37 {+/-} 0.01. This seems to suggest the possibility of a different universality class for the model in finite dimension.

neuroscience↗

bioRxiv2021

The learning curve of murine subretinal injection among clinically trained ophthalmic surgeons

PO_SCPLOWURPOSEC_SCPLOWSubretinal injection (SRI) in mice is widely used in retinal research, yet the learning curve (LC) of this surgically challenging technique is unknown. MO_SCPLOWETHODSC_SCPLOWTo evaluate the LC for SRI in a murine model, we analyzed training data from 3 clinically trained ophthalmic surgeons from 2018 to 2020. Successful SRI was defined as either the absence of retinal pigment epithelium (RPE) degeneration after phosphate buffer saline injection and the presence of RPE degeneration after Alu RNA injection. Multivariable survival-time regression models were used to evaluate the association between surgeon experience and success rate, with adjustment for injection agents, and to calculate an approximate case number to achieve a 95% success rate. A Cumulative Sum (CUSUM) analysis was performed and plotted individually to monitor each surgeons simultaneous performance. RO_SCPLOWESULTSC_SCPLOWDespite prior microsurgery experience, the combined average success rate of the first 50 cases in mice was only 27%. The predicted SRI success rate did not reach a plateau above 95% until approximately 364 prior cases. Using the 364-training case as a "cutoff" point, the predicted probability of success before and after the 364th case was 65.38% and 99.32%, respectively (P < 0.0001). CUSUM analysis showed an initial upward slope and then remained within the decision intervals with an acceptable success rate set at 95% in the late stage. CO_SCPLOWONCLUSIONSC_SCPLOWThis study demonstrates the complexity and substantial LC for successful SRI in mice with high confidence. A systematic training system could improve the reliability and reproducibility of SRI-related experiments and improve the interpretation of experimental results using this technique. Translational RelevanceOur prediction model and monitor system allow objective quantification of technical proficiency in the field of subretinal drug delivery and gene therapy for the first time.

neuroscience↗

bioRxiv2021

Critical behaviour of the stochastic Wilson-Cowan model

Spontaneous brain activity is characterized by bursts and avalanche-like dynamics, with scale-free features typical of critical behaviour. The stochastic version of the celebrated Wilson-Cowan model has been widely studied as a system of spiking neurons reproducing non-trivial features of the neural activity, from avalanche dynamics to oscillatory behaviours. However, to what extent such phenomena are related to the presence of a genuine critical point remains elusive. Here we address this central issue, providing analytical results in the linear approximation and extensive numerical analysis. In particular, we present results supporting the existence of a bona fide critical point, where a second-order-like phase transition occurs, characterized by scale-free avalanche dynamics, scaling with the system size and a diverging relaxation time-scale. Moreover, our study shows that the observed critical behaviour falls within the universality class of the mean-field branching process, where the exponents of the avalanche size and duration distributions are, respectively, -3/2 and -2. We also provide an accurate analysis of the system behaviour as a function of the total number of neurons, focusing on the time correlation functions of the firing rate in a wide range of the parameter space. Author summaryNetworks of spiking neurons are introduced to describe some features of the brain activity, which are characterized by burst events (avalanches) with power-law distributions of size and duration. The observation of this kind of noisy behaviour in a wide variety of real systems led to the hypothesis that neuronal networks work in the proximity of a critical point. This hypothesis is at the core of an intense debate. At variance with previous claims, here we show that a stochastic version of the Wilson-Cowan model presents a phenomenology in agreement with the existence of a bona fide critical point for a particular choice of the relative synaptic weight between excitatory and inhibitory neurons. The system behaviour at this point shows all features typical of criticality, such as diverging timescales, scaling with the system size and scale-free distributions of avalanche sizes and durations, with exponents corresponding to the mean-field branching process. Our analysis unveils the critical nature of the observed behaviours.

neuroscience↗

bioRxiv2021

Identification of Fluoxetine as a direct NLRP3 inhibitor to treat atrophic macular degeneration

The atrophic form of age-related macular degeneration (dry AMD) affects nearly 200 million people worldwide. There is no FDA-approved therapy for this disease, which is the leading cause of irreversible blindness among people over 50 years of age. Vision loss in dry AMD results from degeneration of the retinal pigmented epithelium (RPE). RPE cell death is driven in part by accumulation of Alu RNAs, which are noncoding transcripts of a human retrotransposon. Alu RNA induces RPE degeneration by activating the NLRP3-ASC inflammasome. We report that fluoxetine, an FDA-approved drug for treating clinical depression, binds NLRP3 in silico, in vitro, and in vivo, and that it inhibits activation of the NLRP3-ASC inflammasome in RPE cells and macrophages, two critical cell types in dry AMD. We also demonstrate that fluoxetine, unlike several other anti-depressant drugs, reduces Alu RNA-induced RPE degeneration in mice. Finally, by analyzing two health insurance databases comprising more than 100 million Americans, we report a reduced hazard of developing dry AMD among patients with depression who were treated with fluoxetine. Collectively, these studies triangulate to link fluoxetine as a potential drug repurposing candidate for a major unmet medical need that causes blindness in millions of people in the United States and across the world. Significance StatementDry age-related macular degeneration (AMD) affects the vision of millions of people worldwide. There is currently no FDA-approved treatment for dry AMD. The inflammasome components NLRP3 and ASC have been implicated in the pathogenesis of dry AMD. We report that fluoxetine, which is approved for the treatment of clinical depression, directly binds the NLRP3 protein and prevents the assembly and activation of the NLRP3-ASC inflammasome. As a result, it also blocks the degeneration of retinal pigmented epithelium (RPE) cells in an animal model of dry AMD. Furthermore, we demonstrate through an analysis of health insurance databases that use of this FDA-approved anti-depressant drug is associated with reduced incidence of dry AMD. These studies identify that fluoxetine is a potential repurposing candidate for AMD, a prevalent cause of blindness.

immunology↗