Biology subjects

Soekadar, S.

Publications and source records attributed to Soekadar, S..

6 recordsLinked to original sources

bioRxiv2024

Working memory enhancement using real-time phase-tuned transcranial alternating current stimulation

BackgroundPrior work has shown that transcranial alternating current stimulation (tACS) of parietooccipital alpha oscillations (8 - 14 Hz) can modulate working memory (WM) performance as a function of the phase lag to endogenous oscillations. However, leveraging this effect using real-time phase-tuned tACS was not feasible so far due to stimulation artifacts. Objectives/HypothesisWe aimed to develop a system that tracks and adapts the phase lag between tACS and ongoing parietooccipital alpha oscillations in real-time. We hypothesized that such real-time phase-tuned tACS enhances working memory performance, depending on the phase lag. MethodsWe developed real-time phase-tuned closed-loop amplitude-modulated tACS (CLAM-tACS) targeting parietooccipital alpha oscillations. CLAM-tACS was applied at six different phase lags relative to ongoing alpha oscillations while participants (N = 21) performed a working memory task. To exclude that behavioral effects of CLAM-tACS were mediated by other factors such as sensory co-stimulation, a second group of participants (N = 25) received equivalent stimulation of the forehead. ResultsWM accuracy improved in a phase lag dependent manner (p < 0.05) in the group receiving parietooccipital stimulation, with the strongest enhancement observed at 330{degrees} phase lag between tACS and ongoing alpha oscillations (p < 0.01, d = 0.976). Moreover, across participants, modulation of frontoparietal alpha oscillations correlated both in amplitude (p < 0.05) and phase (p < 0.05) with the modulation of WM accuracy. No such effects were observed in the control group receiving frontal stimulation. ConclusionsOur results demonstrate the feasibility and efficacy of real-time phase-tuned CLAM-tACS in modulating both brain activity and behavior, thereby paving the way for further investigation into brain-behavior relationships and the exploration of innovative therapeutic applications.

neuroscience↗

bioRxiv2024

Heartbeat perception is causally linked to frontal delta oscillations

The ability to accurately perceive ones own bodily signals, such as the heartbeat, plays a vital role in physical and mental health. However, the neurophysiological mechanisms underlying this ability, termed interoception, are not fully understood. Converging evidence suggests that cardiac rhythms are linked to frontal brain activity, particularly oscillations in the delta (0.5 - 4 Hz) band, but their causal relationship remained elusive. Using amplitude-modulated transcranial alternating current stimulation (AM-tACS), a method to enhance or suppress brain oscillations in a phase-specific manner, we investigated whether frontal delta oscillations are causally linked to heartbeat perception. We found that enhancement of delta phase synchrony suppressed heartbeat detection accuracy, while suppression of delta phase synchrony enhanced heartbeat detection accuracy. These findings suggest that frontal delta oscillations play a critical role in heartbeat perception, paving the way for causal investigations of interoception and potential clinical applications. SignificanceAlthough bodily signals are known to influence perception and behavior, little is known about the underlying neurophysiological mechanisms. Here, we show that perception of the heartbeat is anticorrelated with phase synchrony of frontal delta oscillations, and that modulating these oscillations with transcranial electric stimulation influences heartbeat perception. Our results suggest that delta oscillations play a key role in processing bodily signals, with potential implications for theories of emotions and clinical neuroscience.

neuroscience↗

bioRxiv2022

In-vivo phase-dependent enhancement and suppression of brain oscillations by transcranial alternating current stimulation (tACS)

Transcranial alternating current stimulation (tACS) can influence human perception and behavior, with recent evidence also suggesting its potential impact in clinical settings, but the underlying mechanisms are poorly understood. Behavioral and indirect physiological evidence indicates that phase-dependent constructive and destructive interference between the tACS electric field and ongoing brain oscillations may play an important role, but direct in-vivo validation was infeasible because stimulation artifacts impeded such assessment. Using stimulation artifact source separation (SASS), a real-time compatible artifact suppression approach, we overcame this limitation and provide direct evidence for millisecond-by-millisecond phase-dependent enhancement and suppression of ongoing brain oscillations during amplitude-modulated tACS (AM-tACS) across 29 healthy human volunteers. We found that AM-tACS enhanced and suppressed targeted brain oscillations by 11.7 {+/-} 5.14% and 10.1 {+/-} 4.07% respectively. Millisecond-precise modulation of oscillations predicted modulation of behavior (r = 0.65, p < 0.001). These results not only provide direct evidence for constructive and destructive interference as a key mechanism of AM-tACS but suggest superiority of phase-locked (closed-loop) AM-tACS over conventional (open-loop) AM-tACS to purposefully enhance or suppress brain oscillations. SignificanceThe presented data provide direct evidence for a key mechanism underlying neurophysiological and behavioral effects of transcranial alternating current stimulation (tACS), a broadly used neuromodulation approach that yields promising clinical results but also raised controversies because of its variable effects. Our findings not only elucidate the underlying mechanisms of tACS, but also provide the rationale for closed-loop tACS protocols that will enable targeted enhancement and suppression of brain oscillations related to various brain functions such perception, memory or cognition. Towards this end, we introduce the technical prerequisites to establish millisecond-to-millisecond precise closed-loop tACS protocols that will be important to advance tACS as a neuroscientific and clinical tool, for example in the treatment of neuropsychiatric disorders.

neuroscience↗

bioRxiv2020

Specific changes in sleep oscillations after blocking human metabotropic glutamate receptor 5 in the absence of altered memory function

BackgroundSleep consolidates declarative memory by repeated replay linked to the cardinal oscillations of NonREM sleep. However, there is so far little evidence of classical glutamatergic plasticity induced by this replay. Rather, we have previously reported that blocking NMDA or AMPA receptors does not affect sleep-dependent consolidation of declarative memory. AimsInvestigate the role of metabotropic glutamate receptor 5 (mGluR5) on memory processing during sleep. MethodsIn two placebo-controlled within-subject cross-over experiments with 20 healthy humans each, we used fenobam to block mGluR5 during sleep. In Experiment I, participants learned word-pairs (declarative task) and a finger sequence (procedural task) in the evening, then received the drug and recall was tested in the next morning. To cover possible effects on synaptic renormalization processes during sleep, in Experiment II, participants learned new word-pairs in the morning after sleep. Results/OutcomesSurprisingly, fenobam neither reduced retention of memory across sleep nor new learning after sleep, although it severely altered sleep architecture and memoryrelevant EEG oscillations. In NonREM sleep, fenobam suppressed 12-15 Hz spindles but augmented 2-4 Hz delta waves, whereas in REM sleep it suppressed 4-8 Hz theta and 16-22 Hz beta waves. Notably, under Fenobam NonREM spindles became more consistently phase-coupled to the slow oscillation. Conclusions/InterpretationsOur findings indicate that mGluR5-related plasticity is not essential for memory processing during sleep, even though mGlurR5 are strongly implicated in the regulation of the cardinal sleep oscillations. Declaration of interest/FundingThe authors have nothing to disclose and funders had no influence on the research presented here.

neuroscience↗

bioRxiv2020

Stimulation Artifact Source Separation (SASS) for assessing electric brain oscillations during transcranial alternating current stimulation (tACS)

Brain oscillations, e.g. measured by electro- or magnetoencephalography (EEG/MEG), are causally linked to brain functions that are fundamental for perception, cognition and learning. Recent advances in neurotechnology provide means to non-invasively target these oscillations using frequency-tuned amplitude-modulated transcranial alternating current stimulation (AM-tACS). However, online adaptation of stimulation parameters to ongoing brain oscillations remains an unsolved problem due to stimulation artifacts that impede such adaptation, particularly at the target frequency. Here, we introduce a real-time compatible artifact rejection algorithm (Stimulation Artifact Source Separation, SASS) that overcomes this limitation. SASS is a spatial filter (linear projection) removing EEG signal components that are maximally different in the presence versus absence of stimulation. This enables the reliable removal of stimulation-specific signal components, while leaving physiological signal components unaffected. For validation of SASS, we evoked brain activity with known phase and amplitude using 10 Hz visual flickers across 7 healthy human volunteers. 64-channel EEG was recorded during and in absence of 10 Hz AM-tACS targeting the visual cortex. Phase differences between AM-tACS and the visual stimuli were randomized, so that steady-state visually evoked potentials (SSVEPs) were phase-locked to the visual stimuli but not to the AM-tACS signal. For validation, distributions of single-trial amplitude and phase of EEG signals recorded during and in absence of AM-tACS were compared for each participant. When no artifact rejection method was applied, AM-tACS stimulation artifacts impeded assessment of single-trial SSVEP amplitude and phase. Using SASS, amplitude and phase of single trials recorded during and in absence of AM-tACS were comparable. These results indicate that SASS can be used to establish adaptive (closed-loop) AM-tACS, a potentially powerful tool to target various brain functions, and to investigate how AM-tACS interacts with electric brain oscillations. Highlights- Stimulation Artifact Source Separation (SASS), a real-time compatible signal decomposition algorithm for separating electric brain activity and stimulation signal artifacts related to amplitude-modulated transcranial alternating current stimulation (AM-tACS), is introduced - Employing SASS, phase and amplitude of single-trial steady state visual evoked potentials (SSVEPs) were reliably recovered from electroencephalography (EEG) recordings at the frequency targeted with AM-tACS - SASS enables assessment of single-trial oscillatory brain activity at the target frequency during stimulation and paves the way for online adaptation of stimulation parameters to ongoing brain oscillations

neuroscience↗

bioRxiv2017

The Anatomical Tracings of Lesions After Stroke (ATLAS) Dataset - Release 1.1

Stroke is the leading cause of adult disability worldwide, with up to two-thirds of individuals experiencing long-term disabilities. Large-scale neuroimaging studies have shown promise in identifying robust biomarkers (e.g., measures of brain structure) of long-term stroke recovery following rehabilitation. However, analyzing large rehabilitation-related datasets is problematic due to barriers in accurate stroke lesion segmentation. Manually-traced lesions are currently the gold standard for lesion segmentation on T1-weighted MRIs, but are labor intensive and require anatomical expertise. While algorithms have been developed to automate this process, the results often lack accuracy. Newer algorithms that employ machine-learning techniques are promising, yet these require large training datasets to optimize performance. Here we present ATLAS (Anatomical Tracings of Lesions After Stroke), an open-source dataset of 304 T1-weighted MRIs with manually segmented lesions and metadata. This large, diverse dataset can be used to train and test lesion segmentation algorithms and provides a standardized dataset for comparing the performance of different segmentation methods. We hope ATLAS release 1.1 will be a useful resource to assess and improve the accuracy of current lesion segmentation methods.

neuroscience↗