Biology subjects

Oswal, A.

Publications and source records attributed to Oswal, A..

12 recordsLinked to original sources

bioRxiv2026

Reduced entropy of subthalamic beta bursts predicts freezing of gait in Parkinsons disease

BackgroundFreezing of gait (FOG) in Parkinsons disease is associated with abnormal beta activity in the subthalamic nucleus (STN), but the temporal structure of burst dynamics remains poorly understood. ObjectivesTo determine whether temporal features of STN beta bursts distinguish pre-freeze from stable gait and predict freezing onset. MethodsSTN recordings during gait from four individuals were analyzed. Temporal features of burst timing, including entropy and variability, were computed across behavioral states. Predictive performance was assessed using leave-one-patient-out classifiers. ResultsEntropy of inter-burst intervals was reduced prior to freezing (p < 0.01), with strong predictive performance (AUC = 0.825; threshold AUC = 0.858). During freezing, variability measures decreased and temporal structure increased, while entropy did not differ from pre-freeze. Phase-amplitude coupling showed frequency-specific but heterogeneous effects across comparisons. ConclusionsReduced temporal variability of STN beta burst timing precedes and predicts freezing, suggesting a transition to constrained neural dynamics.

neuroscience↗

bioRxiv2026

Device-embedded accelerometry complements neural signals for tracking parkinsonian motor states

Adaptive deep brain stimulation (aDBS) relies on physiological biomarkers to infer motor state and guide therapeutic stimulation in Parkinsons disease. However, neural biomarkers may themselves be altered by stimulation, potentially limiting their utility for closed-loop control. We address this limitation by testing whether DBS device-embedded accelerometers can accurately track Parkinsonian motor state across stimulation conditions. We analysed over 1,900 hours of chronic recordings of subthalamic nucleus (STN), sensorimotor cortical and device-embedded accelerometry signals acquired before and during continuous STN stimulation, alongside continuous wearable assessments of bradykinesia and dyskinesia. Across stimulation conditions, accelerometry-derived features robustly tracked motor symptom severity and outperformed neural features for symptom decoding. Mechanistically, total STN beta power - a widely used biomarker for aDBS - proved less informative because it conflates periodic and aperiodic neural processes with opposing relationships to motor state. Under active stimulation, periodic beta activity showed reduced coupling to symptom severity, whereas STN aperiodic activity, cortical periodic activity and cortico-subthalamic coherence remained comparatively stable. Together, these findings demonstrate that neural and behavioural biomarkers exhibit differential robustness during deep brain stimulation and identify device-embedded accelerometry as a robust behavioural biomarker of motor state, motivating its use in next-generation adaptive DBS systems.

bioengineering↗

bioRxiv2025

Dynamic neural states underpin bradykinesia severity in Parkinsons disease

BackgroundBradykinesia in Parkinsons disease (PD) may arise due to transient, network-wide neural dynamics that extend beyond beta-band oscillatory activity within the motor cortical-subthalamic nucleus (STN) circuit. MethodsWe address this question by using Hidden Markov Models (HMMs) to identify neural states from chronic motor cortical and STN recordings in five PD patients (1,046 hours from 10 hemispheres), with concurrent measurements of bradykinesia using wearable sensors. FindingsWe identified four neural states with distinct spectral and temporal features. Two states exhibited spectral signatures--particularly STN low and high gamma, STN delta/alpha, cortical beta, and cortico-STN beta coherence--that predicted worsening bradykinesia. However, STN beta power alone was not consistently predictive, challenging traditional beta-centric views. These states also displayed compensatory features associated with bradykinesia amelioration, including cortical delta/alpha activity, cortical high gamma, and cortico-STN high gamma coherence. Two additional states affected bradykinesia through temporal rather than spectral properties. Prolonged lifetimes of one of these state worsened symptoms, whereas increased occurrences of another, marked by local beta without cortico-STN beta coherence, improved motor function. InterpretationOur findings highlight the multidimensional nature of bradykinesia and suggest that state-aware, adaptive interventions targeting state features--rather than single frequency bands--may offer new opportunities for improved deep brain stimulation in PD. FundingAO is supported by an MRC Clinician Scientist Fellowship (MR/W024810/1) and a Rosetrees Trust/Race Against Dementia Team award. BA and AO acknowledge funding support from the Oxford University Hospitals Charity and Jon Moulton Trust. TL acknowledges funding support from the China Scholarship Council. Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSPrevious work has demonstrated that subthalamic nucleus oscillatory activity at beta (15-30 Hz) frequencies correlates positively with motor symptoms in Parkinsons disease. This has led to beta activity being used as a biomarker for adaptive Deep Brain Stimulation. It remains unclear however whether other oscillatory features within the broader motor cortical-subthalamic nucleus network could provide improved biomarkers for tracking symptom severity. Added value of this studyWe address this by performing chronic motor cortical and subthalamic nucleus recordings in Parkinsons disease patients during activities of daily living. Simultaneous measurements of symptom severity were captured using wearable sensors. We used Hidden Markov Models to identify transient states of neural activity and related these to symptom severity. Although cortical beta and cortico-STN beta coherence predicted worsening motor symptoms, STN beta activity alone was not a consistent predictor. Interestingly, we identified spectral features associated with motor symptom improvements, including cortical delta/alpha activity, cortical high gamma, and cortico-STN high gamma coherence. Additionally, there was a compensatory state characterised by short-lived cortical and subthalamic nucleus beta activity, whose increased occurrence was associated with symptomatic improvements. Implications of all the available evidenceOur findings highlight the importance of prolonged, high temporal resolution measurements of both neural activity and symptom severity for discovering adaptive Deep Brain Stimulation biomarkers. Crucially, we identify new target states and spectral features for improving motor symptoms in Parkinsons disease.

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

Beta burst characteristics and coupling within the sensorimotor cortical-subthalamic nucleus circuit in Parkinson's disease

BackgroundBursts of exaggerated subthalamic nucleus (STN) beta activity contribute to clinical impairments in Parkinsons disease (PD). Few studies have explored the characteristics and coupling of bursts across the sensorimotor cortical-STN circuit. ObjectiveWe sought to (1) establish the characteristics of sensorimotor cortical and STN bursts during naturalistic behaviours, and (2) determine the predictability of STN bursts from motor cortical recordings. MethodsWe analysed 1,478 hours of wirelessly streamed bilateral sensorimotor cortical and STN recordings from 5 PD patients. ResultsSTN bursts were longer than cortical bursts and had shorter inter-burst intervals. Long bursts (>200ms) in both structures displayed temporal overlap (>30%), with an estimated cortico-STN conduction delay of 8ms. Furthermore, approximately 27% of all STN bursts were preceded by a cortical burst. ConclusionCortical beta bursts tend to precede STN beta bursts, with short delays. However, subcortical mechanisms are also likely to contribute to STN burst initiation and propagation.

neuroscience↗

bioRxiv2024

Large-Scale Motor Dynamics Associated with Reaching are Altered in Essential Tremor

Essential Tremor (ET) is a very common neurological disorder characterised by involuntary rhythmic movements attributable to pathological synchronization within corticothalamic circuits. Previous work has focused on tremor in isolation, overlooking broader disturbances to motor control during naturalistic movements such as reaching. We hypothesised that ET disrupts the sequential engagement of large-scale rhythmic brain networks, leading to both tremor and deficits in motor planning and execution. To test this, we performed whole-head neuroimaging during an upper-limb reaching task using high-density electroencephalography in ET patients and healthy controls, alongside optically pumped magnetoencephalography in a smaller cohort. Key motor regions--including the supplementary motor area, premotor cortex, posterior parietal cortex, and motor cerebellum--were synchronized to tremor rhythms. Patients exhibited a 15% increase in low beta (14-21 Hz) desynchronization over the supplementary motor area during movement, which strongly correlating with tremor severity (R2 = 0.85). A novel dimensionality reduction technique revealed four distinct networks accounting for 97% of the variance in motor-related brain-wide oscillations, with ET altering their sequential engagement. Consistent with our hypothesis, the frontoparietal beta network- normally involved in motor planning-exhibited additional desynchronization during movement execution in ET patients. This altered engagement correlated with slower movement velocities, suggesting an adaptation towards feedback-driven motor control. These findings reveal fundamental disruptions in distributed motor control networks in ET and identify novel biomarkers as targets for next-generation brain stimulation therapies.

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↗

bioRxiv2023

Auditory cues modulate the short timescale dynamics of STN activity during stepping in Parkinson's disease

BackgroundGait impairment has a major impact on motor performance and quality of life in patients with Parkinsons disease (PD). The neurophysiological mechanisms of gait impairment remain poorly understood, meaning that treatment options are currently limited. It is believed that basal ganglia oscillatory activity at {beta} frequencies (15-30 Hz) may be a contributor to gait impairment in PD, but the precise dynamics of this oscillatory activity during gait remain unclear. Auditory cues are known to lead to improvements in gait kinematics in PD. If the neurophysiological mechanisms of this cueing effect were better understood they could be leveraged to treat gait impairments using brain-computer interface (BCI) technologies. ObjectiveWe aimed to characterize the dynamics of subthalamic nucleus (STN) oscillatory activity during stepping movements in PD and to establish the neurophysiological mechanisms by which auditory cues modulate gait. MethodsWe used local field potentials (LFPs) to study STN oscillatory activity in eight PD patients while stepping in place with auditory cueing. Hidden Markov Models (HMMs) were used to discover dynamic brain states that occurred pre-sound, on-sound, and post-sound cues. ResultsThe occurrence of {beta} bursts was suppressed during and after auditory cues. This manifested as a decrease in their fractional occupancy and state lifetimes. Interestingly, transients showed the opposite effect, with fractional occupancy and state lifetimes increasing during and after auditory cues. ConclusionsWe found transient oscillatory states in the STN LFP during stepping and showed that and {beta} oscillations are differentially modulated by auditory cues during stepping in PD.

neuroscience↗

bioRxiv2023

Predicting Long and Short Duration Beta Bursts from Subthalamic Nucleus Local Field Potential Activity in Parkinson's Disease

Neural activities within the beta frequency range (13-30 Hz) are not stationary, but occur in transient packets known as beta bursts. Parkinsons disease (PD) is characterized by the occurrence of beta bursts of increased duration and amplitude within the cortico-basal ganglia network. The pathophysiological importance of beta bursts is exemplified by the fact that they serve as a clinically useful feedback signal in beta amplitude triggered adaptive Deep Brain Stimulation (aDBS). Prolonged duration beta bursts are closely associated with motor impairments in PD, whilst bursts of shorter duration may have a physiological role. Consequently, we aimed to develop a deep learning-based pipeline capable of predicting long (> 150ms) and short (< 150ms) duration beta bursts from subthalamic nucleus local field potential (LFP) recordings. Our approach achieved promising accuracy values of 87% and 85.2% in two patients implanted with a DBS device that was capable of long-term wireless LFP sensing. Our findings highlight the feasibility of prolonged beta burst prediction and could inform the development of a new type of intelligent DBS approach with the capability of delivering stimulation only during the occurrence of prolonged bursts.

bioengineering↗

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↗

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↗