Biology subjects

Alipour, M.

Publications and source records attributed to Alipour, M..

3 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↗