Biology subjects

Tisdall, M.

Publications and source records attributed to Tisdall, M..

3 recordsLinked to original sources

bioRxiv2026

A cardiac pulse signal affects local field potentials recorded from deep brain stimulation electrodes across clinical targets

ObjectiveMany deep brain stimulation (DBS) systems sense local field potentials (LFPs) for patient monitoring or closed-loop therapy (CL-DBS). LFPs can be impacted by artifacts, including a recently discovered cardiac non-electrocardiographic pulse signal that can be visually masked by commercial device filters. We aimed to establish its prevalence across patient groups and brain areas, and to investigate its spectral impact. MethodsWe performed a cross-sectional analysis of LFPs recorded from the cranially mounted Picostim from the pedunculopontine nucleus in multiple systems atrophy patients, periacqueductal gray and sensory thalamus in chronic pain patients, and the centromedian thalamic nucleus (CMT) in paediatric epilepsy patients. For comparison, we analyse externalised recordings from the subthalamic nucleus in Parkinsons disease patients. The PulsAr algorithm was developed to detect and extract pulsatile signals, and we characterised contamination level and spectral content. ResultsThough not visually obvious in CMT, the pulsatile signal was algorithmically detected in all targets, with 33% of LFPs across targets classed as contaminated. Pulse signal power was similar across targets and may have been masked by higher endogenous activity in CMT. While its dominant frequencies were in the heartbeat range, the signal had spectral content extending up to >10Hz. ConclusionsA heart pulse signal affects LFP recordings from DBS leads across brain regions and patient groups. While masked by some device filters, spectral content can extend into higher (clinically relevant) frequencies. Researchers and clinicians should exercise caution when sensing lower LFP frequencies, especially for automated control of therapy in CL-DBS. HighlightsO_LIHistorically, electrocardiographic artifacts have been a major source of artifact affecting deep brain stimulation recordings, however pulsatile artifact is less well described. C_LIO_LIA cardiac pulse signal affects local field potentials recorded from deep brain stimulation electrodes across clinical targets. C_LIO_LIWe introduce an ECG-independent algorithm that detects and extracts this pulsatile signal. C_LIO_LIThe heart pulse signal looks like the intracranial pressure waveform and affects spectral frequencies above the heart rate range up to >10Hz. C_LIO_LIClinicians should incorporate screening and procedures to ensure accurate biomarker detection for clinical decision making. C_LI

bioengineering↗

bioRxiv2024

Topographic variation in neurotransmitter receptor densities explains differences in intracranial EEG spectra

Neurotransmitter receptor expression and neuronal population dynamics show regional variability across the human cortex. However, currently there is an explanatory gap regarding how cortical microarchitecture and mesoscopic electrophysiological signals are mechanistically related, limiting our ability to exploit these measures of brain (dys)function for improved treatments of brain disorder; e.g., epilepsy. To bridge this gap, we leveraged dynamic causal modelling (DCM) and fitted biophysically informed neural mass models to a normative set of intracranial EEG data. Subsequently, using a hierarchical Bayesian modelling approach, we evaluated whether model evidence improved when information about regional neurotransmitter receptor densities is provided. We then tested whether the inferred constraints -- furnished by receptor density -- generalise across different electrophysiological recording modalities. The neural mass models explained regionally specific intracranial EEG spectra accurately, when fitted independently. Incorporating prior information on receptor distributions, further improved model evidence, indicating that variability in receptor density explains some variance in cortical population dynamics. The output of this modelling provides a cortical atlas of neurobiologically informed intracortical synaptic connectivity parameters that can be used as empirical priors in future -- e.g., patient specific -- modelling, as demonstrated in a worked example (a single-subject mismatch negativity study). In summary, we show that molecular cortical characteristics (i.e., receptor densities) can be incorporated to improve generative, biophysically plausible models of coupled neuronal populations. This work can help to explain regional variations in human electrophysiology, may provide a methodological foundation to integrate multi-modal data, and might serve as a normative resource for future DCM studies of electrophysiology. Significance StatementUnderstanding the link between measures of brain function and their underlying molecular and synaptic constraints is essential for developing and validating personalised, pharmacological interventions. But despite increasing availability of detailed normative datasets of human brain structure and function -- across modalities and spatial scales -- translating between these remains challenging. Using two large normative datasets -- intracranial EEG recordings and autoradiographic receptor density distributions -- we demonstrate that generative models of these data can link structure to function. Specifically, we show that regional oscillatory neuronal population activity is shaped by the distribution of neurotransmitter receptors. This modelling furnishes an atlas of normative parameter values, which can provide neurobiologically informed priors for in-silico (e.g., Digital Twin) characterisation of normal and disordered brain functioning.

neuroscience↗

bioRxiv2019

Interictal discharges spread along local recurrent networks between tubers and surrounding cortex

The presence of interictal epileptiform discharges on electroencephalography (EEG) may indicate increased epileptic seizure risk and on invasive EEG are the signature of the irritative zone. In highly epileptogenic lesions - such as cortical tubers in tuberous sclerosis - these discharges can be recorded with intracranial stereotactic EEG as part of the evaluation for epilepsy surgery. Yet the network mechanisms that underwrite the generation and spread of these discharges remain poorly understood, limiting their current diagnostic use.\n\nHere, we investigate the dynamics of interictal epileptiform discharges using a combination of quantitative analysis of invasive EEG recordings and mesoscale neural mass modelling of cortical dynamics. We first characterise spatially organised local dynamics of discharges recorded from 36 separate tubers in 8 patients with tuberous sclerosis. We characterise these dynamics with a set of competing explanatory network models using dynamic causal modelling. Bayesian model comparison of plausible network architectures suggests that the recurrent coupling between neuronal populations within - and adjacent to - the tuber core explains the travelling wave dynamics observed in these patient recordings.\n\nOur results - based on interictal activity - unify competing theories about the pathological organisation of epileptic foci and surrounding cortex in patients with tuberous sclerosis. Coupled oscillator dynamics have previously been used to describe ictal activity, where fast travelling ictal discharges are commonly observed within the recruited seizure network. The interictal data analysed here add the insight that this functional architecture is already established in the interictal state. This links observations of interictal EEG abnormalities directly to pathological network coupling in epilepsy, with possible implications for epilepsy surgery approaches in tuberous sclerosis.\n\nSignificance StatementInterictal epileptiform discharges (IEDs) are clinically important markers of an epileptic brain. Here we link local IED spread to network coupling through a combination of clinical recordings in paediatric patients with tuberous sclerosis complex, quantitative EEG analysis of interictal discharges spread, and Bayesian inference on coupled neural mass model parameters. We show that the kinds of interictal discharges seen in our patients require recurrent local network coupling extending beyond the putative seizure focus and that in fact only those recurrent coupled networks can support seizure-like and interictal dynamics when run in simulation. Our findings provide a novel integrated perspective on emergent epileptic dynamics in human patients.

neuroscience↗