Biology subjects

Malerba, P.

Publications and source records attributed to Malerba, P..

10 recordsLinked to original sources

bioRxiv2026

Predictability of Sleep Slow Oscillation Emergence and Spatial Extent from Pre-Onset Neural Dynamics

Slow oscillations (SOs; [~]0.5-1.5 Hz) are a hallmark of non-rapid eye movement (NREM) sleep and are known to support memory consolidation and large-scale cortical communication. Although their instantaneous dynamics are well characterized, the neural processes that precede SO initiation--and whether they predict the spatial extent of the upcoming event--remain largely unknown, despite their potential utility for anticipatory closed-loop intervention. Using high-density EEG from 29 healthy adults, we examined neural activity in the 2-s interval preceding the SO trough. Analyses focused on distinct SO subtypes defined by their spatiotemporal properties: Global, Frontal, and Local. We specifically focused on Global and Frontal events, both of which originate frontally but differ in their propagation. We quantified instantaneous spectral power, time-frequency dynamics, phase-amplitude coupling (PAC), and amplitude-amplitude coupling. Across all analyses, theta-band power (4-8 Hz) emerged as the earliest and most robust predictor of SO initiation, remaining informative after restricting analyses to temporally isolated SOs to reduce potential residual-aftereffect confounds from preceding SOs. Theta power exhibited a sustained rise beginning nearly two seconds before the SO trough ([~]1.6 s before onset). Theta power increases were absent in surrogate epochs and reliably differentiated Global from Frontal SOs with moderate-to-strong effect sizes (Cohens d = 0.45-0.77), demonstrating the robustness of theta power as a physiological signature. Mechanistically, Global SOs were preceded by enhanced delta-theta PAC and broad low-frequency synchronization, whereas Frontal SOs were preceded by elevated theta/alpha-to-beta/low-gamma coupling, reflecting a state of locally enhanced coupling at frequencies higher than the SO range, which appears to restrict propagation. A simple logistic regression classifier using only pre-onset theta power achieved >95% accuracy in distinguishing SOs from surrogate events and differentiated Global from Frontal SOs with [~]83% multiclass accuracy, showing further sensitivity improvements when delta power was included. These findings demonstrate that isolated SOs are preceded by structured network dynamics that are tied to their spatial extent. Thus, theta activity can be used to predict SO occurrence and might be leveraged in closed-loop neuromodulation.

neuroscience↗

bioRxiv2024

Predictive Modeling of Sleep Slow Oscillation Emergence on the electrode manifold: Toward Personalized Closed-Loop Brain Stimulation

BackgroundSleep slow oscillations (SOs), characteristic of NREM sleep, are causally tied to cognitive outcomes and the health-promoting homeostatic functions of sleep. Due to these known benefits, brain stimulation techniques aiming to enhance SOs are being developed, with great potential to contribute to clinical interventions, as they hold promise for improving sleep functions in populations with identified SO deficits (e.g., mild cognitive impairment). SO-targeting closed-loop stimulation protocols currently strive to identify SO occurrences in real time, a computationally intensive step that can lead to reduced precision (compared to post-hoc detection). These approaches are also often limited to focusing on only one electrode location, thus inherently precluding targeting of SOs that is informed by the overall organization of SOs in space-time. Prediction of SO emergence across the electrode manifold would establish an alternative to online detection, thus greatly advancing the development of personalized and flexible brain stimulation paradigms. This study presents a computational model that predicts SO occurrences at multiple locations across a night of sleep. In combination with our previous study on optimizing brain stimulation protocols using the spatiotemporal properties of SOs, this model contributes to increasing the accuracy of SO targeting in brain stimulation applications. MethodsSOs were detected in a dataset of nighttime sleep of 22 subjects (9 females), acquired with polysomnography including 64 EEG channels. Modeling of SO occurrence was achieved for SOs in stage N3, or in a combination of stages N2 and N3 (N2&N3). We study SO emergence at progressively more refined time scales. First, the cumulative SO occurrences in successive sleep cycles were successfully fit with exponentials. Secondly, the SO timing in each individual was modeled with a renewal point process. Using an inverse Gaussian model, we estimated the probability density function of SO timing and its parameters (mean) and {lambda} (shape, representing skewness) in successive cycles. ResultsWe observed a declining trend in the SO count across sleep cycles, which we modeled using a power law relationship. The decay rate per cycle was 1.473 for N3 and 1.139 for N2&N3, with variances of the decay rates across participants being 1 and 0.53, respectively. This pattern mirrors the declining trend of slow wave activity (SWA) across sleep cycles, likely due to the inherent relationship between SWA and SO. Additionally, the SO timing model for N3 showed an increasing trend in the model parameters (, {lambda}) across cycles. The increase rate per cycle followed a power law relationship with a rate of 0.83 and an exponential relationship with a rate of 4.59, respectively. The variances of the increase rates were 0.02 for and 0.44 for {lambda} across participants. ConclusionThis study establishes a predictive model for SO occurrence during NREM sleep, providing insights into its organization in successive cycles and at different EEG channels, which is relevant to development of personalized stimulation paradigms. These findings imply that personalized model parameters can be estimated by incorporating SO information in the first sleep cycle, and hence SO timing can be predicted before its occurrence with a probability distribution, enabling more precise targeting of SOs.

bioengineering↗

bioRxiv2023

A Classification-Based Generative Approach to Selective Targeting of Global Slow Oscillations during Sleep

BackgroundGiven sleeps crucial role in health and cognition, numerous sleep-based brain interventions are being developed, aiming to enhance cognitive function, particularly memory consolidation, by improving sleep. Research has shown that Transcranial Alternating Current Stimulation (tACS) during sleep can enhance memory performance, especially when used in a closed-loop (cl-tACS) mode that coordinates with sleep slow oscillations (SOs, 0.5-1.5Hz). However, sleep tACS research is characterized by mixed results across individuals, which are often attributed to individual variability. Objective/HypothesisThis study targets a specific type of SOs, widespread on the electrode manifold in a short delay ("global SOs"), due to their close relationship with long-term memory consolidation. We propose a model-based approach to optimize cl-tACS paradigms, targeting global SOs not only by considering their temporal properties but also their spatial profile. MethodsWe introduce selective targeting of global SOs using a classification-based approach. We first estimate the current elicited by various stimulation paradigms, and optimize parameters to match currents found in natural sleep during a global SO. Then, we employ an ensemble classifier trained on sleep data to identify effective paradigms. Finally, the best stimulation protocol is determined based on classification performance. ResultsOur study introduces a model-driven cl-tACS approach that specifically targets global SOs, with the potential to extend to other brain dynamics. This method establishes a connection between brain dynamics and stimulation optimization. ConclusionOur research presents a novel approach to optimize cl-tACS during sleep, with a focus on targeting global SOs. This approach holds promise for improving cl-tACS not only for global SOs but also for other physiological events, benefiting both research and clinical applications in sleep and cognition.

neuroscience↗

bioRxiv2022

Slow oscillations promote long range effective communication: the key for memory consolidation in a broken down network

One of the most prominent and robust findings in cognitive neuroscience is the strengthening of memories during non-rapid eye movement (NREM) sleep, with slow oscillations (SOs, < 1Hz) playing a critical role in systems-level consolidation. However, NREM is a sleep period generally showing a breakdown in connectivity and reduction of synaptic plasticity with increasing depth: a brain state seemingly unfavorable to memory consolidation. Here, we present a novel approach to address this apparent paradox that leverages an event-related causality measure to estimate directional information flow during NREM sleep in epochs with and without SOs. Our results confirm that NREM is generally a state of dampened neural communication, but reveals that SOs provide two windows of enhanced large-scale communication before and after the SO trough. These peaks in communication are significantly higher when SOs are coupled with sleep spindles, compared with uncoupled SOs. To probe the functional relevance of these SO-selective peaks of information flow, we tested the temporal and topographic conditions that predict overnight episodic memory improvement. Our results show that global, long-range communication during SOs promote sleep-dependent systems consolidation of episodic memories. A significant correlation between peaks of information flow and memory improvement lends a predictive validity to our measurements of effective connectivity. In other words, we were able to predict memory improvement on the basis of independent electrophysiological observations during sleep. This work introduces a non-invasive approach to understanding information processing during sleep, a behavioral stage whose function, until now, has been understood only after synaptic reorganization after waking from sleep. Our findings provide a mechanism for how systems-level brain communication can occur during an otherwise low connectivity sleep state, indicating that SOs are a gating mechanism for large-scale neural communication, which is the necessary substrate for systems consolidation and long-term memory formation.

neuroscience↗

bioRxiv2021

Slow oscillations provide the spatio-temporal framework for long-range neural communication during sleep

Slow oscillations (SOs, <1Hz) during non-rapid eye movement sleep are thought to reflect sleep homeostasis and support memory consolidation. Yet, the fundamental properties of SOs and their impact on neural network communication are not understood. We used effective connectivity to estimate causal information flow across the electrode manifold during SOs and found two peak of information flow in specific phases of the SO. We show causal communication during non-rapid eye movement sleep peaks during specific phases of the SO, but only across long distances. We confirmed this prediction by cluster analysis demonstrating greater flow in global, compared with local, SOs. Finally, we tested the functional significance of these results by examining which SO properties supported overnight episodic memory improvement, with the underlying assumption that memory consolidation would engage global, long-range communication. Indeed, episodic memory improvement was predicted only by the SO properties with greatest causal information flow, i.e., longest distances between sinks and sources and global, but not local, SOs. These findings explain how NREM sleep (characterized as a state of low brain connectivity) leverages SO-induced selective information flow to coordinate a wide network of brain regions during memory formation.

neuroscience↗

bioRxiv2019

Interaction of Hippocampal Ripples and Cortical Slow Waves Leads to Coordinated Large-Scale Sleep Rhythm

The dialogue between cortex and hippocampus is known to be crucial for sleep dependent consolidation of long lasting memories. During slow wave sleep memory replay depends on slow oscillation (SO) and spindles in the (neo)cortex and sharp wave-ripple complexes (SWR) in the hippocampus, however, the mechanisms underlying interaction of these rhythms are poorly understood. Here, we examined the interaction between cortical SOs and hippocampal SWRs in a computational model of the hippocampo-cortico-thalamic network and compared the results with human intracranial recordings during sleep. We observed that ripple occurrence peaked following the onset of SO (Down-to-Up-state transition) and that cortical input to hippocampus was crucial to maintain this relationship. Ripples influenced the spatiotemporal structure of cortical SO and duration of the Up/Down-states. In particular, ripples were capable of synchronizing Up-to-Down state transition events across the cortical network. Slow waves had a tendency to initiate at cortical locations receiving hippocampal ripples, and these \"initiators\" were able to influence sequential reactivation within cortical Up states. We concluded that during slow wave sleep, hippocampus and neocortex maintain a complex interaction, where SOs bias the onset of ripples, while ripples influence the spatiotemporal pattern of SOs.

neuroscience↗

bioRxiv2018

Large time step discrete-time modeling of sharp wave activity in hippocampal area CA3.

Reduced models of neuronal spiking activity simulated with a fixed integration time step are frequently used in studies of spatio-temporal dynamics of neurobiological networks. The choice of fixed time step integration provides computational simplicity and efficiency, especially in cases dealing with large number of neurons and synapses operating at a different level of activity across the population at any given time. A network model tuned to generate a particular type of oscillations or wave patterns is sensitive to the intrinsic properties of neurons and synapses and, therefore, commonly susceptible to changes in the time step of integration. In this study, we analyzed a model of sharp-wave activity in the network of hippocampal area CA3, to examine how an increase of the integration time step affects network behavior and to propose adjustments of intrinsic properties of neurons and synapses that help minimize or remove the damage caused by the time step increase.\n\nHighlightsO_LISpiking models of neural network activity are sensitive to the integration step\nC_LIO_LILarger integration time steps are preferable in simulating large networks\nC_LIO_LICase study of CA3 sharp waves shows time step increase damages network dynamics\nC_LIO_LINeuronal and synaptic parameters adjustments rescue the dynamics at large time step1\nC_LI

neuroscience↗

bioRxiv2017

Selective Recruitment of Cortical Neurons by Electrical Stimulation

Despite its critical importance in experimental and clinical neuroscience, at present there is no systematic method to predict which neural elements will be activated by a given stimulation regime. Here we develop a novel approach to model the effect of cortical stimulation on spiking probability of neurons in a volume of tissue, by applying an analytical estimate of stimulation-induced activation of different cell types across cortical layers. We utilize the morphology and properties of axonal arborization profiles obtained from publicly available anatomical reconstructions of the twelve main categories of neocortical neurons to derive the dependence of activation probability on cell type, layer and distance from the source. We then propagate this activity through the local network incorporating connectivity, synaptic and cellular properties. Our work predicts that (a) intracranial cortical stimulation induces selective activation across cell types and layers; (b) superficial anodal stimulation is more effective than cathodal at cell activation; (c) cortical surface stimulation focally activates layer I axons, and (d) an optimal stimulation intensity exists capable of eliciting cell activation lasting beyond the end of stimulation.

neuroscience↗

bioRxiv2017

Learning-induced sequence reactivation during sharp-wave ripples: a computational study

During sleep, memories formed during the day are consolidated in a dialogue between cortex and hippocampus. The reactivation of specific neural activity patterns - replay - during sleep has been observed in both structures and is hypothesized to represent a neuronal substrate of consolidation. In the hippocampus, replay happens during sharp wave - ripples (SWR), short bouts of excitatory activity in area CA3 which induce high frequency oscillations in area CA1. In particular, recordings of hippocampal cells which spike at a specific location ( place cells) show that recently learned trajectories are reactivated during SWR in the following sleep SWR. Despite the importance of sleep replay, its underlying neural mechanisms are still poorly understood.\n\nWe developed a model of SWR activity, to study the effects of learning-induced synaptic changes on spontaneous sequence reactivation during SWR. The model implemented a paradigm including three epochs: Pre-sleep, learning and Post-sleep activity. We first tested the effects of learning on the hippocampal network activity through changes in a minimal number of synapses connecting selected pyramidal cells. We then introduced an explicit trajectory-learning task to the model, to obtain behavior-induced synaptic changes. The model revealed that the recently learned trajectory reactivates during sleep more often than other trajectories in the training field. The study predicts that the gain of reactivation rate during sleep following vs sleep preceding learning for a trained sequence of pyramidal cells depends on Pre-sleep activation of the same sequence, and on the amount of trajectory repetitions included in the training phase.

neuroscience↗

bioRxiv2017

Defining the synaptic mechanisms that tune CA3-CA1 reactivation during sharp-wave ripples.

During non-REM sleep, memory consolidation is driven by a dialogue between hippocampus and cortex involving the reactivation of specific neural activity sequences ( replay). In the hippocampus, replay occurs during sharp-wave ripples (SWRs), short bouts of excitatory activity in area CA3 which induce high frequency oscillations in the inhibitory interneurons of area CA1. Despite growing evidence for the functional importance of replay, its neural mechanisms remain poorly understood. Here, we develop a novel theoretical model of hippocampal spiking during SWRs. In our model, noise-induced activation of CA3 pyramidal cells triggered an excitatory cascade capable of inducing local ripple events in CA1. Ripples occurred stochastically, with Schaffer Collaterals driving their coordination, so that localized sharp waves in CA3 produced consistently localized CA1 ripples. In agreement with experimental data, the majority of pyramidal cells in the model showed low reactivation probabilities across SWRs. We found, however, that a subpopulation of pyramidal cells had high reactivation probabilities, which derived from fine-tuning of the network connectivity. In particular, the excitatory inputs along synaptic pathway(s) converging onto cells and cell pairs controlled emergent single cell and cell pair reactivation, with inhibitory inputs and intrinsic cell excitability playing differential roles in CA3 vs. CA1. Our model predicts (1) that the hippocampal network structure driving the emergence of SWR is also able to generate and modulate reactivation, (2) inhibition plays a particularly prominent role in CA3 reactivation and (3) CA1 sequence reactivation is reliant on CA3-CA1 interactions rather than an intrinsic CA1 process.

neuroscience↗