Biology subjects

Oveisi, M. P.

Publications and source records attributed to Oveisi, M. P..

5 recordsLinked to original sources

bioRxiv2025

Corticothalamic circuit mechanisms underlying brain region and ageing variations in resting-state alpha activity

Understanding the neural mechanisms underlying oscillations in resting-state brain activity, which exhibit substantial spatial and age-related variations, remains a significant challenge. This study aims to characterize the contributions of neural circuits to the mechanisms governing resting-state alpha oscillations, which are crucial for various neurocognitive processes and pathologies. Using the Cam-CAN dataset, source-space MEG analyses revealed a pronounced posterior-anterior gradient in alpha frequency, alpha power, and aperiodic components, alongside notable age-related changes. Through neurophysiological modelling, we uncover strong corticothalamic interactions in occipital regions, contrasting with predominantly corticocortical interactions in frontal areas. Ageing is associated with reduced intrathalamic activity and increased corticothalamic delay in occipital regions, while fronto-central regions exhibit increased intrathalamic activity. These findings establish how different circuits shape alpha oscillations across posterior-anterior axis and age, providing a mechanistic foundation for targeted clinical interventions and offering benchmarks for future studies in patient populations.

neuroscience↗

bioRxiv2025

Alpha rhythm subharmonics underlie responsiveness to theta burst stimulation via calcium metaplasticity

AO_SCPLOWBSTRACTC_SCPLOWRepetitive transcranial magnetic stimulation (rTMS) is a non-invasive technique to modulate brain activity, often used in treating Major Depressive Disorder (MDD) by targeting fronto-limbic circuitry. Despite its clinical utility, optimizing rTMS protocols remains challenging due to the complex and variable effects of stimulation parameter changes on synaptic plasticity. Oscillatory brain activity, measurable via Electroencephalography (EEG), serves as a biomarker for functional circuits and treatment response. To better understand the impact of rTMS on brain oscillations and connectivity, we used computational modeling of corticothalamic circuits to explore the mechanisms of stimulus-induced plasticity. We integrated calcium-dependent plasticity (CaDP) with Bienenstock-Cooper-Munro (BCM) metaplasticity formulations in a neural population model of resting-state EEG. By varying protocol parameters, we simulated iTBS effects on spectral power, synaptic efficacy, and calcium concentrations. Our findings highlight a resonance between theta stimulation and individual resting-state alpha rhythms, enhancing incoming excitatory long-term depression (LTD) and inhibitory long-term potentiation (LTP), leading to corticothalamic feed-forward inhibition (FFI). Induced effects were encapsulated by a weakening of corticothalamic loops and enhancement of intrathalamic loops. This work offers a novel paradigm for individualizing iTBS treatments, provides insights into the neurophysiological basis of clinical responsiveness, and offers a framework with which to derive tailored protocols.

neuroscience↗

bioRxiv2025

Excitation-Inhibition Balance and Fronto-Limbic Connectivity Drive TMS Treatment Outcomes in Refractory Depression

Depression affects over 350 million people worldwide, with treatment resistance occurring in up to 30% of cases. Intermittent theta burst stimulation (iTBS) targeting the left dorsolateral prefrontal cortex (DLPFC) has emerged as a promising intervention, yet the neurophysiological mechanisms determining which patients will respond remain poorly understood. Here, we combined transcranial magnetic stimulation with electroencephalography and whole-brain computational modeling to uncover the mechanistic basis of treatment efficacy in 90 patients with treatment-resistant depression. We identified two distinct neurophysiological signatures that differentiate responders from non-responders: (1) post-treatment shifts in excitation-inhibition balance toward greater inhibitory control, and (2) a pre-treatment brain state characterized by anticorrelated dynamics between subgenual anterior cingulate cortex and DLPFC. These features were significantly correlated with clinical improvement and could not be explained by non-specific factors. Our findings provide a neurophysiologically-informed framework for developing personalized and optimized neuromodulation approaches in treatment-resistant depression.

neuroscience↗

bioRxiv2024

Assessing the Validity and Reliability of HD-DOT TD-fNIRS Resting-State Measurements in Rapid Succession Data Collection Settings

AO_SCPLOWBSTRACTC_SCPLOWFunctional magnetic resonance imaging (fMRI) has long been a cornerstone in the study of brain activity, but its high operational costs, limited availability, and restricted practical applicability have led researchers to seek alternative neuroimaging technologies. Recent advancements in high-density diffuse optical tomography (HD-DOT) and Time-Domain (TD) functional near-infrared spectroscopy (fNIRS) have emerged as promising solutions, offering the ability to generate detailed tomographic maps of hemodynamic fluctuations associated with neural activity. In this study, using the Kernel Flow device, we assess the performance of HD-DOT TD-fNIRS in terms of signal validity and reliability, particularly in rapid succession data collection settings. We conducted a multiple test-retest experiment involving fNIRS recordings from three participants across 20 ten-minute sessions of eyes-open resting-state brain activity over six days. Our findings indicate that HD-DOT TD-fNIRS systems like the Kernel Flow can reproduce hemodynamic patterns identified by fMRI, albeit with less spatial detail, and can detect resting state networks overall, though some individual network detections are not significant. The system performs consistently over days, with more variability within the time of day, and can capture subject-specific patterns with high accuracy as identified through FC fingerprinting analysis. We conclude that the new generation of HD-DOT TD-fNIRS systems holds significant promise for enhancing and expanding the measurement of functional brain data in both clinical and more naturalistic research settings. This study represents an important step towards a comprehensive understanding of the data quality and consistency achievable with these innovative neuroimaging devices.

neuroscience↗

bioRxiv2024

Corticothalamic modelling of sleep neurophysiology with applications to mobile EEG

AO_SCPLOWBSTRACTC_SCPLOWRecent developments in mathematical modelling of EEG enable the tracking of otherwise-inaccessible neurophysiological parameters throughout sleep. Likewise, advancements in wearable electronics have enabled easy & affordable collection of sleep EEG at home. The convergence of these two advances, namely neurophysiological modelling for mobile sleep EEG, can boost preclinical and clinical assessments of sleep. However, this subject area has received limited attention in existing literature. To address this, we used an established model of the corticothalamic system to analyze EEG power spectra from 5 datasets, spanning from research-grade systems to at-home mobile EEG. In the present work, we compare the convergent and divergent features of the data and the estimated physiological model parameters. While data quality and characteristics differ considerably, key patterns consistent with previous theoretical and empirical work are observed. During the transition from lighter to deeper NREM, i) exponent of the aperiodic (1/f) spectral component is increased, ii) bottom-up thalamocortical drive is reduced, iii) corticocortical connection strengths are increased. This effect is observed in healthy subjects but is interestingly absent when taking SSRI antidepressants, suggesting possible effects of ascending neuromodulation on corticothalamic oscillations. We further show a month-long increase in REM% in one mobile EEG subject, associated with boosted high-frequency activity in spectra and higher thalamothalamic gains in the model, pointing to possible changes of thalamic inhibition in REM parasomnias. Our results provide a proof-of-principle for the utility and feasibility of this physiological modelling-based approach to analyzing mobile EEG data, providing a mechanistic measure of brain physiology during sleep. Statement of significanceWe employ a physiological model of the corticothalamic circuitry to model the EEG power spectra in sleep. We fit this model to 5 EEG datasets, and demonstrate that while mobile and non-mobile EEG recordings differ in their characteristics and quality, they can both robustly represent the changes along sleep stages using the aperiodic (1/f) component. We observe an increased corticocortical connection strength and decreased corticothalamic connection strength as the subject goes into deeper stages of NREM sleep; an effect that is, importantly, not observed in subjects taking SSRIs. This work provides a proof-of-concept for using mathematical modelling, working well for large mobile and non-mobile datasets providing valuable insight into the mechanisms generating sleep EEG.

neuroscience↗