Biology subjects

Abbasi, O.

Publications and source records attributed to Abbasi, O..

10 recordsLinked to original sources

bioRxiv2026

Multi-Methodological Characterization of Sleep Deprivation: From Standard EEG Power Spectra to Aperiodic Dynamics in Humans and Mice.

Sleep deprivation is a potent, rapid-acting therapeutic intervention for major depressive disorder, yet its underlying neural mechanisms remain poorly understood, hindering the development of predictive biomarkers. Here, we systematically characterize the electro-physiological signatures of prolonged wakefulness using a multi-methodological approach across three independent datasets in humans and mice. By integrating standard power spectral density analysis with aperiodic component fitting (SpecParam) and highly compar-ative time-series analysis (HCTSA), we identified robust cross-species biomarkers of sleep pressure. Machine learning models revealed that theta power is the most consistent feature for differentiating control and sleep deprivation states, achieving up to 90% classification accuracy. Sleep deprivation significantly increased the spectral offset - suggesting global cortical hyperexcitation - while simultaneously steepening the spectral slope. We interpret this simultaneous shift as a state uncoordinated state of hyperexcited and inefficient neu-ral processing. These findings establish reproducible EEG markers of sleep deprivation that transcend species. Given the clinical utility of wake therapy, we propose that prefrontal the-ta power and spectral offset/slope may serve as mechanism-based predictors of therapeu-tic response. Our results provide a framework for the clinical validation of these biomarkers, potentially enabling personalized chronotherapeutic interventions for psychiatric disorders.

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bioRxiv2025

Respiration as a dynamic modulator of sensory sampling

Respiration dynamically modulates sensory perception by orchestrating transient states of the brain and the body. Using simultaneous recordings of high-density magneto-encephalography (MEG), respiration, and pupillometry, we show that human perceptual sensitivity to near-threshold visual stimuli was enhanced during inspiration, coinciding with respiration-modulated increases in arousal neuromodulation and cortical excitability. Participants adapted their breathing patterns to align with predictable stimulus onset, and this adaptive respiratory control correlated with improved performance. We further reveal that respiration-modulated changes in alpha and beta oscillations reflect distinct shifts in sensory and motor excitability, respectively. Crucially, respiration-resolved multivariate Granger causality analyses demonstrate that the breathing rhythm systematically shapes directed information flow within a widespread interoceptive network. This respiration-brain coupling was flexibly adjusted based on stimulus predictability, highlighting a novel mechanism for active sensing which integrates internal bodily rhythms with external sensory demands to optimize perception.

neuroscience↗

bioRxiv2024

Frequency-specific cortico-subcortical interaction in continuous speaking and listening

Speech production and perception involve complex neural dynamics in the human brain. Using magnetoencephalography (MEG), our study explores the interaction between cortico-cortical and cortico-subcortical connectivities during these processes. Our connectivity findings during speaking revealed a significant connection from the right cerebellum to the left temporal areas in low frequencies, which displayed an opposite trend in high frequencies. Notably, high-frequency connectivity was absent during the listening condition. These findings underscore the vital roles of cortico-cortical and cortico-subcortical connections within the speech production and perception network. The results of our new study enhance our understanding of the complex dynamics of brain connectivity during speech processes, emphasizing the distinct frequency-based interactions between various brain regions.

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bioRxiv2024

Entrainment Echoes in the Cerebellum

Historically, researchers have considered the cerebellum a coordinator of motor programs that ensures precise timing of movements and their adaptation to external events1. However, it has become increasingly clear that this role is not restricted to the motor system2. Rather, the cerebellum seems to play an important role in temporal prediction in general, as shown in its involvement in multiple functions that rely on precise event timing3. Although previous work suggested that the cerebellum exclusively predicts the interval between two events4, rather than tracking a global rhythm, it is also active when a rhythmic stimulus changes in rate5. The latter finding is in line with a cerebellar role in speech processing6 that entails frequent rate changes7. Neural mechanisms underlying the cerebellums involvement in speech processing, however, remain poorly understood. Moreover, there is a lack of studies contrasting speech and non-speech stimuli to establish speech-specificity of the observed effects8. In a re-analysis of magnetoencephalography (MEG) data9, we found that activity in the cerebellum aligned to rhythmic sequences of noise-vocoded speech, irrespective of its intelligibility. We then tested whether these "entrained" responses persist, and how they interact with other brain regions, when the rhythmic stimulus stopped and temporal predictions had to be updated. We found that only intelligible speech produced rhythmic responses in the cerebellum that outlasted the stimulus. During this "entrainment echo", but not during rhythmic speech itself, cerebellar activity was coupled with that in the left inferior frontal gyrus (IFG), and specifically at rates corresponding to the preceding stimulus rhythm. This finding represents unprecedented evidence for specific cerebellum-driven temporal predictions in speech processing and their relay to cortical regions.

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bioRxiv2024

Dissociating endogenous and exogenous delta activity during natural speech comprehension

Decoding human speech requires the brain to segment the incoming acoustic signal into meaningful linguistic units, ranging from syllables and words to phrases. Integrating these linguistic constituents into a coherent percept sets the root of compositional meaning and hence understanding. One important cue for segmentation in natural speech are prosodic cues, such as pauses, but their interplay with higher-level linguistic processing is still unknown. Here we dissociate the neural tracking of prosodic pauses from the segmentation of multi-word chunks using magnetoencephalography (MEG). We find that manipulating the regularity of pauses disrupts slow speech-brain tracking bilaterally in auditory areas (below 2 Hz) and in turn increases left-lateralized coherence of higher frequency auditory activity at speech onsets (around 25 - 45 Hz). Critically, we also find that multi-word chunks--defined as short, coherent bundles of inter-word dependencies--are processed through the rhythmic fluctuations of low frequency activity (below 2 Hz) bilaterally and independently of prosodic cues. Importantly, low-frequency alignment at chunk onsets increases the accuracy of an encoding model in bilateral auditory and frontal areas, while controlling for the effect of acoustics. Our findings provide novel insights into the neural basis of speech perception, demonstrating that both acoustic features (prosodic cues) and abstract processing at the multi-word timescale are underpinned independently by low-frequency electrophysiological brain activity.

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bioRxiv2022

Predictive coordination of breathing during speaking and listening

It has long been known that human breathing is altered during listening and speaking compared to rest. Theoretical models of human communication suggest two distinct phenomena during speaking and listening: During speaking, inhalation depth is adjusted to the air volume required for the upcoming utterance. During listening, inhalation is temporally aligned to inhalation of the speaker. While evidence for the former is relatively strong, it is virtually absent for the latter. We address both questions using recordings of speech envelope and respiration in 30 participants during 14 minutes of speaking and listening. We extend existing evidence for the first phenomenon by using the speech envelope to show that inhalation depth is positively correlated with the total power of the speech envelope in the following utterance. Pertaining to the second phenomenon, we provide first evidence that inhalation during listening to your own speech is significantly more likely at time points of inhalation during speaking. These findings are compatible with models that postulate alignment of internal forward models of interlocutors with the aim to facilitate communication.

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bioRxiv2022

Oscillatory brain networks in continuous speaking and listening

Speech production and perception are fundamental processes of human cognition that both rely on an internal forward model that is still poorly understood. Here, we study this forward model by using Magnetoencephalography (MEG) to comprehensively map connectivity of regional brain activity within the brain and to the speech envelope during continuous speaking and listening. Our results reveal a partly shared neural substrate for both processes but also a dissociation in space, delay and frequency. Neural activity in motor and frontal areas is coupled to succeeding speech in delta band (1-3 Hz), whereas coupling in the theta range follows speech in temporal areas during speaking. Neural connectivity results showed a separation of bottom-up and top-down signalling in distinct frequency bands during speaking. Here, we show that frequency-specific connectivity channels for bottom-up and top-down signalling support continuous speaking and listening in a way that is consistent with the predictive coding framework.

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bioRxiv2022

Modulatory dynamics of periodic and aperiodic activity in respiration-brain coupling

Bodily rhythms such as respiration are increasingly acknowledged to modulate neural oscillations underlying human action, perception, and cognition. Conversely, the link between respiration and aperiodic brain activity - a non-oscillatory reflection of excitation-inhibition (E:I) balance - has remained unstudied. Aiming to disentangle potential respiration-related dynamics of periodic and aperiodic activity, we applied recently developed algorithms of time-resolved parameter estimation to resting-state MEG and EEG data from two labs (total N = 78 participants). We provide evidence that fluctuations of aperiodic brain activity (1/f slope) are phase-locked to the respiratory cycle, which suggests that spontaneous state shifts of excitation-inhibition balance are at least partly influenced by peripheral bodily signals. Moreover, differential temporal dynamics in their coupling to non-oscillatory and oscillatory activity raise the possibility of a functional distinction in the way each component is related to respiration. Our findings highlight the role of respiration as a physiological influence on brain signalling.

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bioRxiv2021

Comparison of undirected frequency-domain connectivity measures for cerebro-peripheral analysis

Analyses of cerebro-peripheral connectivity aim to quantify ongoing coupling between brain activity (measured by MEG/EEG) and peripheral signals such as muscle activity, continuous speech, or physiological rhythms (such as pupil dilation or respiration). Due to the distinct rhythmicity of these signals, undirected connectivity is typically assessed in the frequency domain. This leaves the investigator with two critical choices, namely a) the appropriate measure for spectral estimation (i.e., the transformation into the frequency domain) and b) the actual connectivity measure. As there is no consensus regarding best practice, a wide variety of methods has been applied. Here we systematically compare combinations of six standard spectral estimation methods (comprising fast Fourier and continuous wavelet transformation, bandpass filtering, and short-time Fourier transformation) and six connectivity measures (phase-locking value, Gaussian-Copula mutual information, Rayleigh test, weighted pairwise phase consistency, magnitude squared coherence, and entropy). We provide performance measures of each combination for simulated data (with precise control over true connectivity), a single-subject set of real MEG data, and a full group analysis of real MEG data. Our results show that, overall, wppc and gcmi tend to outperform other connectivity measures, while entropy was the only measure sensitive to bimodal deviations from a uniform phase distribution. For group analysis, choosing the appropriate spectral estimation method appeared to be more critical than the connectivity measure. We discuss practical implications (sampling rate, SNR, computation time, and data length) and aim to provide recommendations tailored to particular research questions.

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bioRxiv2020

Correcting MEG artifacts caused by overt speech

Recording brain activity during speech production using magnetoencephalography (MEG) can help us to understand the dynamics of speech production. However, these measurements are challenging due to the induced artifacts coming from several sources such as facial muscle activity, lower jaw and head movements. Here, we aimed to characterise speech-related artifacts and subsequently present an approach to remove these artifacts from MEG data. We recorded MEG from 11 healthy participants while they pronounced various syllables in different loudness. Head positions/orientations were extracted during speech production to investigate its role in MEG distortions. Finally, we present an artifact rejection approach using the combination of regression analysis and signal space projection (SSP) in order to correct the induced artifact from MEG data. Our results show that louder speech leads to stronger head movements and stronger MEG distortions. Our proposed artifact rejection approach could successfully remove the speech-related artifact and retrieve the underlying neurophysiological signals. As the presented artifact rejection approach was shown to remove artifacts induced by overt speech in the MEG, it will facilitate research addressing the neural basis of speech production with MEG.

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