Biology subjects

Ferster, M. L.

Publications and source records attributed to Ferster, M. L..

4 recordsLinked to original sources

bioRxiv2025

Local modulation of sleep slow waves depends on timing between auditory stimuli

Conflicting evidence exists regarding the role of the targeted slow-wave phase in determining the direction and spatial specificity of slow-wave activity (SWA) modulation via phase-targeted auditory stimulation (PTAS) during sleep. To reconcile these discrepancies, we re-analyzed high-density electroencephalography (hd-EEG) data from previous studies, focusing on SWA responses to auditory stimuli presented with varying inter-stimulus intervals (ISIs). Our analysis reveals that ISI is a primary determinant of PTAS-induced SWA modulation, exceeding the influence of targeted phase alone. Specifically, auditory stimulation with longer ISIs evoked a global increase in SWA, consistent with a stereotypical auditory-evoked K-complex (KC), independent of targeted phase. Conversely, longer stimulus trains with rapid successive stimulus presentation resulted in spatially localized, phase-dependent SWA modulation, with up-PTAS enhancing and down-PTAS reducing SWA locally around the targeted area. This distinction resolves inconsistencies in prior PTAS studies by demonstrating that phase alone in insufficient in predicting slow-wave responses. Rather, it was the ISI which determined whether PTAS resulted in a global, KC-mediated response or a local, phase-specific modulation of SWA. Consequently, our findings refine the mechanistic understanding of PTAS, suggesting that ISI regulates the engagement of distinct neural circuits and thereby potentially enables the targeted manipulation of specific slow-wave subtypes and their associated functions.

neuroscience↗

bioRxiv2024

Longer Interstimulus Intervals Enhance Efficacy of Automated Phase-Targeted Auditory Stimulation on Procedural Memory Consolidation

Up-phase-targeted auditory stimulation (up-PTAS) during slow-wave sleep has become a valuable tool for modulating slow oscillations and slow-oscillation-spindle-coupling in favor of overnight memory retention. Developing effective, automated protocols for translation into more naturalistic or clinical settings is an ongoing challenge, especially given that current PTAS protocols and their behavioral effects vary greatly between different studies. Here, we assessed the electrophysiological and behavioral effects of systematically varying interstimulus intervals (ISIs) in automated up-PTAS in the home setting, using a mobile PTAS device and app-based behavioral tasks. Building on studies suggesting a non-linear relationship between stimulus number and PTAS effects, we show that applying fewer stimuli with longer ISIs enhanced overnight memory consolidation of a finger-tapping sequence more effectively than applying more stimuli with shorter ISIs. The behavioral response was predicted by the number of stimuli with auditory evoked K-complexes relative to the number of stimuli without K-complexes. PTAS stimuli applied at longer ISIs (> 1.25) were associated with a higher likelihood of K-complex responses and fast spindles nesting in the K-complex up-phase. Our results suggest that up-PTAS can be optimized for overnight memory consolidation by introducing ISIs of at least 1.25s. Our study highlights the feasibility of longitudinal at-home PTAS combined with app-based behavioral tasks in healthy participants while leveraging the mechanistic insights such data can offer. Statement of SignificancePhase-targeted auditory stimulation (PTAS) holds great promise for non-invasively enhancing essential functions of slow-wave sleep. However, current protocols have produced variable results and are often confined to laboratory settings. Our study demonstrates the feasibility of a mobile application of automated PTAS and provides experimental evidence that prolonging interstimulus intervals positively affects overnight procedural memory consolidation via K-complexes and coupled sleep spindles. As K-complexes may also be involved in cardiovascular function and brain waste clearance, the proposed protocol optimization may have effects beyond memory enhancement. Together, our findings lay a foundation for a broader application of PTAS in clinical longitudinal studies to improve patient care and recovery outcomes.

neuroscience↗

bioRxiv2023

Acoustically evoked K-complexes are sufficient to boost verbal memory consolidation during sleep

Over the past decade, phase-targeted auditory stimulation (PTAS), a neuromodulation approach which presents auditory stimuli locked to the ongoing phase of slow waves during sleep, has shown potential to enhance specific aspects of sleep functions. However, the complexity of PTAS responses complicates the establishment of causality between specific electroencephalographic events and observed benefits. Here, we used down-PTAS during sleep to specifically evoke the early, K-complex (KC)-like response following PTAS without leading to a sustained increase in slow-wave activity throughout the stimulation window. Over the course of two nights, one with down-PTAS, the other without, high-density electroencephalography (hd-EEG) was recorded from 14 young healthy adults. The early response exhibited striking similarities to evoked KCs and was associated with improved verbal memory consolidation via stimulus-evoked spindle events nested into the up-phase of ongoing 1 Hz waves in a central region. These findings suggest that the early, KC-like response is sufficient to boost memory, potentially by orchestrating aspects of the hippocampal-neocortical dialogue.

neuroscience↗

bioRxiv2021

Get rid of the beat in mobile EEG applications: A framework towards automated cardiogenic artifact detection and removal in single-channel EEG

Brain activity recordings outside clinical or laboratory settings using mobile EEG systems have recently gained popular interest allowing for realistic long-term monitoring and eventually leading to identification of possible biomarkers for diseases. The less obtrusive, minimized systems (e.g. single-channel EEG, no ECG reference) have the drawback of artifact contamination with varying intensity that are particularly difficult to identify and remove. We developed brMEGA, the first algorithm for automated detection and removal of cardiogenic artifacts using non-linear time-frequency analysis and machine learning to (1) detect whether and where cardiogenic artifacts exist, and (2) remove those artifacts. We compare our algorithm against visual artifact identification and a previously established approach and validate it in one real and semi-real datasets. We demonstrated that brMEGA successfully identifies and substantially removes cardiogenic artifacts in single-channel EEG recordings. Moreover, recovery of cardiogenic artifacts gives the opportunity for future extraction of heart rate features without ECG measurement.

neuroscience↗