Biology subjects

Litvak, V.

Publications and source records attributed to Litvak, V..

At least 19 recordsLinked to original sources

bioRxiv2026

Intracranial Validation of Magnetoencephalography Across Oscillatory Frequency and Depth

Non-invasive, whole-brain neuroimaging methods such as functional magnetic resonance imaging, electroencephalography (EEG), and magnetoencephalography (MEG) are essential tools for studying the basis of human cognition in health and disease. MEG offers the opportunity to study neural activity at its intrinsic timescale, by recording the magnetic fields generated by electrical currents within the brain from outside the skull. Moreover, recently developed optically pumped magnetometers (OPMs) allow these recordings to take place in new settings, for example during naturalistic behaviour and in previously inaccessible populations. These breakthroughs have led to a shift in the neuroimaging landscape, with a global increase in the adoption of MEG. Crucially, however, the extent to which MEG recordings can measure different features of neural activity remains unclear. To address this issue, we leveraged a unique and rare dataset of concurrent MEG and intracranial EEG recordings from a cohort of epileptic patients. We found that group-level inferences of spontaneous oscillatory dynamics made with source-localised MEG, i.e. estimates of power and bursts, accurately reflected the underlying neural activity. As expected, the agreement was strongest for lower-frequency activity (delta, theta, and alpha) and superficial sources, and weakest in the gamma range. Crucially, however, MEG was also sensitive to deep structures: it captured oscillatory power and burst dynamics in the hippocampus, most robustly in the theta band. These findings demonstrate that MEG is sensitive to physiologically meaningful activity in cortical and subcortical regions and establish a foundation for the interpretation of future MEG studies across a wide range of research domains.

neuroscience↗

bioRxiv2026

Low effective dimensionality of the human functional connectome does not invalidate lesion network mapping

Van den Heuvel et al. argued that the low effective dimensionality of the human connectome causes lesion network maps to converge on a common pattern, the connectome degree map, and concluded that the method cannot resolve distinct symptom-specific circuits. Here, we show that, although lesion network maps are low-dimensional and biased towards the connectomes leading eigenvector, they still recover their ground-truth networks well. Moreover, we propose a modification of the connectome that at least halves this bias and can improve recovery. Our results, therefore, reaffirm the validity of lesion network mapping, while acknowledging the important limitations identified by its critics.

neuroscience↗

bioRxiv2026

Can the heartbeat-evoked potential (HEP) be separated from Cardiac Artefact (CA) using beamforming?

The heartbeat-evoked potential (HEP), a cortical response to heartbeats and neural marker of interoception, is clinically relevant but heavily contaminated by scalp cardiac-artefact (CA), making reliable distinction challenging. Because HEP and cardiac potentials are anatomically distinct, they may be separable via beamforming, a source localisation method isolating brain activity while suppressing external interference. Here, the first known validation of EEG beamforming for HEP source reconstruction was attempted, quantifying waveform recovery and spatial accuracy using simulated data. Linearly constrained minimal variance (LCMV) beamforming was used (A) to test signal recovery, 128-channel EEG was simulated for three models with known HEP waveforms: (1) a single right insula (R-Ins) HEP; (2) temporally distinct HEPs in R-Ins and right anterior cingulate cortex (R-ACC); (3) overlapping HEPs in both regions. Recovery was assessed by correlating reconstructed and known waveforms. (B) To test CA suppression, CA from brain-dead isoelectric EEGs was integrated to simulations, with varying source (-10-50dB) and sensor (0-30dB) signal-to-noise ratios (SNR). (C) To analyse empirical EEG from hypertensive and anxiety individuals (n=106). Null-space projection, using subject-specific QRS waveforms and its temporal derivative, removed residual CA in source waveforms. In model-1, beamforming achieved near-perfect recovery without CA (r>0.99, error=0mm) at -30dB source SNR, remaining robust in the presence of CA (r=0.72-0.94, error=0-5.7mm) but degrading below -20dB (r<0.3, error=27mm). Models 2 and 3 showed similar recovery but introduced R-ACC-to-R-Ins leakage. In empirical data, beamforming revealed significant HEP activity in R-Ins and R-ACC (p<0.001). A significant late R-ACC HEP difference (250-500ms post-R-peak) between hypertension and controls emerged after excluding low-SNR subjects and strengthened following QRS cleaning (p=0.035, d=0.96). LCMV beamforming reliably recovers simulated HEPs while suppressing CA given sufficient SNR. Applied empirically, beamforming detected clinically meaningful group differences in interoceptive regions (R-Ins/R-ACC), offering a source-level approach to separate HEP from CA that complements sensor-level methods.

neuroscience↗

bioRxiv2026

Temporal-difference valence-partitioned Bayesian brains work out whether others are caring or uncaring

Working out whether others care for us is crucial in personal relationships and when seeking professional help. It is often most difficult for those most in need, e.g. following interpersonal traumata. Here, we introduce a simple Caring Attributions task and elucidate key computational mechanisms involved and, using EEG, the cortical activity representing the degree of belief that another is beneficent or maleficent. We find evidence for a new type of neurocomputational processing: valence-partitioned temporal-difference inference (TD-Bayes). This employs primary processing about latent causes, but also separate channels to represent these different valenced attributions, inspired by value-partitioned associative learning (VPAL). TD-Bayes uses slow propagation of beliefs using temporal-difference updating. These models gave a very good account of behaviour, slightly better than VPAL, but crucially, their partitioned representations have stronger, distinct representations in ERP signals. They provide a promising inroad into the understanding of how people may jump to atttibutions about caring vs. uncaring others.

neuroscience↗

bioRxiv2026

Electrophysiological Correlates of Reinforcement Learning in the Human Ventral Tegmental Area

The ventral tegmental area (VTA) is the principal source of dopaminergic input to the human prefrontal cortex and is hypothesised to play a central role in reward-based learning. However, direct electrophysiological evidence in humans has been lacking. Here, using intracranial recordings obtained during an instrumental learning task in patients undergoing deep brain stimulation surgery for chronic cluster headache (n=13), we show that VTA local field potentials selectively encode rewarding outcomes compared with losses or neutral outcomes independently of whether behavioural learning occurred. In a subset of participants (n= 8) who learned to prefer a high-reward option, VTA responses to unexpected rewards were larger than to expected rewards. In two participants who sufficiently explored both options, VTA activity reflected the expected value of the chosen option during decision-making. These findings provide direct electrophysiological evidence that the human VTA encodes reward-related signals consistent with reinforcement learning mechanisms described in animal models.

neuroscience↗

bioRxiv2025

Beyond beta: Aperiodic broadband power reflects Parkinson's disease severity - a multicenter study

Parkinsons disease is linked to increased beta oscillations in the subthalamic nucleus, which correlate with motor symptoms. However, findings across studies have varied. Our standardized analysis of multicenter datasets shows that small sample sizes contributed to these discrepancies--a challenge we address by pooling datasets into one large cohort (n=119). Moving beyond beta power, we disentangled spectral components reflecting distinct neural processes. Combining aperiodic offset, low beta, and low gamma oscillations explained significantly more variance in symptom severity than beta alone. Moreover, interhemispheric within-patient analyses showed that, unlike beta oscillations, aperiodic broadband power-likely reflecting spiking activity-was increased in the more affected hemisphere. These findings identify aperiodic broadband power as a potential biomarker for adaptive deep brain stimulation and provide novel insights into the relationship between subthalamic hyperactivity and motor symptoms in human Parkinsons disease.

neuroscience↗

bioRxiv2024

Prediction of pathological subthalamic nucleus beta burst occurrence in Parkinson's disease

The cortico-basal ganglia network in Parkinsons disease (PD) is characterized by the emergence of transient episodes of exaggerated beta frequency oscillatory synchrony known as bursts. Although it is well established that bursts of prolonged duration associate closely with motor impairments, the mechanisms leading to burst initiation remain poorly understood. Crucially, it is unclear whether there are features of basal ganglia activity which reliably predict burst onset. Current adaptive Deep Brain Stimulation (aDBS) approaches can only reactively deliver stimulation following burst detection and are unable to stimulate proactively to prevent burst onset. The discovery of predictive biomarkers could allow for such proactive stimulation, thereby offering potential for improvements in therapeutic efficacy. Here, using deep learning, we show that the timing of subthalamic nucleus (STN) beta bursts can be accurately predicted up to 60 ms prior to onset. Furthermore, we highlight that a dip in the beta amplitude - which is likely to be indicative of a phase reset of oscillatory populations occurring between 80-100 ms prior to burst onset - is a predictive biomarker for burst occurrence. These findings demonstrate proof-of-principle for the feasibility of beta burst prediction for DBS and provide insights into the mechanisms of burst initiation.

neuroscience↗

bioRxiv2024

Generators of the frequency-following response in the subthalamic nucleus: implications for non-invasive deep brain stimulation

While Deep Brain Stimulation (DBS) is effective treatment for several movement disorders, non-invasive stimulation modes have major clinical relevance. We report on a novel method holding potential for non-invasive subthalamic nucleus (STN) stimulation. We used an auditory frequency-following response task (FFR), a popular tool for studying the auditory brainstem as the neural response in the cortical and midbrain generator, as it precisely reflects the ongoing dynamics of a speech or non-speech sound. We recorded EEG and DBS electrodes from 5 patients, in 4 from the STN, and one from the anterior thalamus and a number of cortical and subcortical areas located in the hippocampus and frontal regions, during an FFR at a frequency higher than the upper limit of phase-locking in the cortex (333Hz). Our results revealed a neural response local to the STN, but not other structures. This finding is novel. Auditory perception in the basal ganglia is rather unexplored, and the STN generator of the FFR has likely gone unseen due to the limitations of our tools and research focus. The potential clinical implications are far-reaching. Future research should investigate whether auditory stimuli at common electrical stimulation frequencies and waveforms of electrical DBS stimulation can induce clinical improvement.

neuroscience↗

bioRxiv2024

Measuring the orientation of segmented Deep Brain Stimulation leads using electroencephalography

Segmented Deep Brain Stimulation (DBS) leads enable current steering in specific directions, but this comes with an increased level of programming complexity. Precise measurement of lead orientation is crucial for facilitating stimulation programming. Presently employed methods involve radiation technology posing inherent risks and limitations. Additionally, the potential rotation of leads post-implantation may require repeated measurements. To address these challenges, we propose an outpatient-friendly, radiation-free method using post-operative imaging-informed electroencephalography (EEG). The method was tested in an EEG phantom yielding maximal errors of under 10 {degrees} with under 30s of data. It works with as few as 4 EEG electrodes with only a small error increase. Measurement variance was of the order of a few degrees, indicating that the method could reach this precision if all the sources of bias are removed. Thus, with optimised hardware, software and measurement protocol, the method is feasible for routine use in a clinical setting.

bioengineering↗

bioRxiv2023

Combining Magnetoencephalography with Telemetric Streaming of Intracranial Recordings and Deep Brain Stimulation - a Feasibility Study

The combination of subcortical Local Field Potential (LFP) recordings and stimulation with Magnetoencephalography (MEG) in Deep Brain Stimulation (DBS) patients enables the investigation of cortico-subcortical communication patterns and provides insights into DBS mechanisms. Until now, these recordings have been carried out in post-surgical patients with externalised leads. However, a new generation of telemetric stimulators makes it possible to record and stream LFP data in chronically implanted patients. Nevertheless, whether such streaming can be combined with MEG has not been tested. In the present study, we tested the most commonly implanted telemetric stimulator - Medtronic Percept PC with a phantom in three different MEG systems: two cryogenic scanners (CTF and MEGIN) and an experimental Optically Pumped Magnetometry (OPM)-based system. We found that when used in combination with the new SenSight segmented leads, Percept PC telemetric streaming only generates band-limited interference in the MEG at 123Hz and harmonics. However, the legacy streaming mode used with older lead models generates multiple, dense artefact peaks in the physiological range of interest (below 50Hz). The effect of stimulation on MEG critically depends on whether it is done in bipolar (between two contacts on the lead) or monopolar (between a lead contact and the stimulator case) mode. Monopolar DBS creates severe interference in the MEG as previously reported. However, we found that the OPM system is more resilient to this interference and could provide artefact-free measurements at least for limited frequency ranges. A resting measurement in the MEGIN system from a Parkinsons patient implanted with Percept PC and subthalamic SenSight leads revealed artefact patterns consistent with our phantom recordings. Moreover, analysis of LFP-MEG coherence in this patient showed oscillatory coherent networks consistent in their frequency and topography with those described in published group studies done with externalised leads. In conclusion, Percept PC telemetric streaming with SenSight leads is compatible with MEG. Furthermore, OPM sensors could provide additional new opportunities for studying DBS effects.

neuroscience↗

bioRxiv2023

Robust estimation of brain stimulation evoked responses using magnetoencephalography

Magnetoencephalography (MEG) recordings are often contaminated by interference that can exceed the amplitude of physiological brain activity by several orders of magnitude. Furthermore, activity of interference sources may leak into the activity of brain signals of interest, resulting in source estimation inaccuracies. This problem is particularly apparent when using MEG to interrogate the effects of brain stimulation on large scale cortical networks. This technical report offers two contributions. Firstly, using phantom MEG recordings we describe an approach for validating the estimation accuracy of brain stimulation evoked responses. Secondly, we propose a novel denoising method for suppressing the leakage of stimulation related signal into recorded brain activity. This approach leverages spatial and temporal domain projectors for signal arising from prespecified anatomical regions of interest. We highlight its advantages compared to the benchmark - spatiotemporal signal space separation (tSSS) - and show that it can more accurately reveal brain stimulation evoked responses.

bioengineering↗

bioRxiv2022

Multi-modal and multi-model interrogation of large-scale functional brain networks

Current whole-brain models are generally tailored to the modelling of a particular modality of data (e.g., fMRI or MEG/EEG). Although different imaging modalities reflect different aspects of neural activity, we hypothesise that this activity arises from common network dynamics. Building on the universal principles of self-organising delay-coupled nonlinear systems, we aim to link distinct electromagnetic and metabolic features of brain activity to the dynamics on the brains macroscopic structural connectome. To jointly predict dynamical and functional connectivity features of distinct signal modalities, we consider two large-scale models generating local short-lived 40 Hz oscillations with various degrees of realism - namely Stuart Landau (SL) and Wilson and Cowan (WC) models. To this end, we measure features of functional connectivity and metastable oscillatory modes (MOMs) in fMRI and MEG signals - and compare them against simulated data. We show that both models can represent MEG functional connectivity (FC) and functional connectivity dynamics (FCD) to a comparable degree, by varying global coupling and mean conduction time delay. For both models, the omission of delays dramatically decreased the performance. For fMRI, the SL model performed worse for FCD, highlighting the importance of balanced dynamics for the emergence of spatiotemporal patterns of ultra-slow dynamics. Notably, optimal working points varied across modalities and no model was able to achieve a correlation with empirical FC higher than 0.45 across modalities for the same set of parameters. Nonetheless, both displayed the emergence of FC patterns beyond the anatomical framework. Finally, we show that both models can generate MOMs with empirical-like properties. Our results demonstrate the emergence of static and dynamic properties of neural activity at different timescales from networks of delay-coupled oscillators at 40 Hz. Given the higher dependence of simulated FC on the underlying structural connectivity, we suggest that mesoscale heterogeneities in neural circuitry may be critical for the emergence of parallel cross-modal functional networks and should be accounted for in future modelling endeavours.

neuroscience↗

bioRxiv2022

Synchronization in the connectome: Metastable oscillatory modes emerge from interactions in the brain spacetime network

A rich repertoire of oscillatory signals is detected from human brains with electro- and magnetoencephalography (EEG/MEG). However, the principles underwriting coherent oscillations and their link with neural activity remain under debate. Here, we revisit the mechanistic hypothesis that transient brain rhythms are a signature of metastable synchronization, occurring at reduced collective frequencies due to delays between brain areas. We consider a system of damped oscillators in the presence of background noise - approximating the short-lived gamma-frequency oscillations generated within neuronal circuits - coupled according to the diffusion weighted tractography between brain areas. Varying the global coupling strength and conduction speed, we identify a critical regime where spatially and spectrally resolved metastable oscillatory modes (MOMs) emerge at sub-gamma frequencies, approximating the MEG power spectra from 89 healthy individuals at rest. Further, we demonstrate that the frequency, duration, and scale of MOMs - as well as the frequency-specific envelope functional connectivity - can be controlled by global parameters, while the connectome structure remains unchanged. Grounded in the physics of delay-coupled oscillators, these numerical analyses demonstrate how interactions between locally generated fast oscillations in the connectome spacetime structure can lead to the emergence of collective brain rhythms organized in space and time.

neuroscience↗

bioRxiv2021

Separating neural oscillations from aperiodic 1/f activity: challenges and recommendations

Electrophysiological power spectra typically consist of two components: An aperiodic part usually following an 1/f power law P{propto}1/f{beta}and periodic components appearing as spectral peaks. While the investigation of the periodic parts, commonly referred to as neural oscillations, has received considerable attention, the study of the aperiodic part has only recently gained more interest. The periodic part is usually quantified by center frequencies, powers, and bandwidths, while the aperiodic part is parameterized by the y-intercept and the 1/f exponent {beta}. For investigation of either part, however, it is essential to separate the two components. In this article, we scrutinize two frequently used methods, FOOOF (Fitting Oscillations & One-Over-F) and IRASA (Irregular Resampling Auto-Spectral Analysis), that are commonly used to separate the periodic from the aperiodic component. We evaluate these methods using diverse spectra obtained with electroencephalography (EEG), magnetoencephalography (MEG), and local field potential (LFP) recordings relating to three independent research datasets. Each method and each dataset poses distinct challenges for the extraction of both spectral parts. The specific spectral features hindering the periodic and aperiodic separation are highlighted by simulations of power spectra emphasizing these features. Through comparison with the simulation parameters defined a priori, the parameterization error of each method is quantified. Based on the real and simulated power spectra, we evaluate the advantages of both methods, discuss common challenges, note which spectral features impede the separation, assess the computational costs, and propose recommendations on how to use them.

neuroscience↗

bioRxiv2020

Intersection of structural and functional connectivity of the nucleus basalis of Meynert in Parkinson's disease dementia and Lewy body dementia

Parkinsons disease dementia (PDD) and dementia with Lewy bodies (DLB) are related conditions that are associated with cholinergic system dysfunction. Dysfunction of the nucleus basalis of Meynert (NBM), a basal forebrain structure that provides the dominant source of cortical cholinergic innervation, has been implicated in the pathogenesis of both PDD and DLB. Here we leverage the temporal resolution of magnetoencephalography (MEG) with the spatial resolution of MRI tractography in order to explore the intersection of functional and structural connectivity of the NBM in a unique cohort of PDD and DLB patients undergoing Deep Brain Stimulation (DBS) of this structure. We observe that NBM-cortical structural and functional connectivity correlate within spatially and spectrally segregated networks including: 1) a beta band network to supplementary motor area (SMA), where activity in the SMA was found to drive activity in the NBM, 2) a delta/theta band network to medial temporal lobe structures encompassing the parahippocampal gyrus and 3) a delta/theta band network to visual areas including lingual gyrus. These findings reveal functional networks of the NBM that are likely to subserve important roles in motor control, memory and visual function respectively. Furthermore, they motivate future studies aimed at disentangling network contribution to disease phenotype.

neuroscience↗

bioRxiv2020

Neural signatures of pathological hyperdirect pathway activity in Parkinson's disease

Parkinsons disease (PD) is characterised by the emergence of pathological patterns of oscillatory synchronisation across the cortico-basal-ganglia circuit. The relationship between anatomical connectivity and oscillatory synchronisation within this system remains poorly understood. We address this by integrating evidence from invasive electrophysiology, magnetoencephalography, tractography and computational modelling in patients. Coupling between supplementary motor area (SMA) and subthalamic nucleus (STN) within the high beta frequency (21-30 Hz) range correlated with fibre tract densities between these two structures. Additionally within the STN, non-linear waveform features suggestive of cortical synchronisation correlated with cortico-STN fibre densities. Finally, computational modelling revealed that exaggerated hyperdirect cortical inputs to the STN in the upper beta frequency range can provoke the generation of widespread pathological synchrony at lower beta (13-20 Hz) frequencies. These observations reveal a spectral signature of the hyperdirect pathway at high beta frequencies and provide evidence for its pathophysiological role in oscillatory network dysfunction in PD. One sentence summarySignatures of the hyperdirect pathway and its likely role in pathological network disruption in Parkinsons disease are identified.

neuroscience↗

bioRxiv2020

Conflict detection in a sequential decision task is associated with increased cortico-subthalamic coherence and prolonged subthalamic oscillatory response in the beta band.

Making accurate decisions often involves the integration of current and past evidence. Here we examine the neural correlates of conflict and evidence integration during sequential decision making. Patients implanted with deep-brain stimulation (DBS) electrodes and age-matched healthy controls performed an expanded judgement task, in which they were free to choose how many cues to sample. Behaviourally, we found that while patients sampled numerically more cues, they were less able to integrate evidence and showed suboptimal performance. Using recordings of Magnetoencephalography (MEG) and local field potentials (LFP, in patients) in the subthalamic nucleus (STN), we found that beta oscillations signalled conflict between cues within a sequence. Following cues that differed from previous cues, beta power in the STN and cortex first decreased and then increased. Importantly, the conflict signal in the STN outlasted the cortical one, carrying over to the next cue in the sequence. Furthermore, after a conflict, there was an increase in coherence between the dorsal premotor cortex and subthalamic nucleus in the beta band. These results extend our understanding of cortico-subcortical dynamics of conflict processing, and do so in a context where evidence must be accumulated in discrete steps, much like in real life. Thus, the present work leads to a more nuanced picture of conflict monitoring systems in the brain and potential changes due to disease.

neuroscience↗

bioRxiv2020

Cortical beta oscillations reflect the contextual gating of visual action feedback

In sensorimotor integration, the brain needs to decide how its predictions should accommodate novel evidence by gating sensory data depending on the current context. Here, we examined the oscillatory correlates of this process using magnetoencephalography (MEG). We used virtual reality to decouple visual (virtual) and proprioceptive (real) hand postures during a task requiring matching either modalitys grasping movements to a target oscillation. Thus, we rendered visual information either task-relevant or a (to-be-ignored) distractor. Under visuo-proprioceptive incongruence, occipital beta power decreased relative to congruence when vision was task-relevant but increased when it had to be ignored. Dynamic causal modelling (DCM) revealed that this interaction was best explained by diametrical, task-dependent changes in visual gain. These results suggest a crucial role for beta oscillations in sensorimotor integration; particularly, in the contextual gating (i.e., gain or precision control) of visual vs proprioceptive action feedback, depending on concurrent behavioral demands.

neuroscience↗