arXiv subjects

Christopher Thornton

Publications and source records attributed to Christopher Thornton.

9 recordsLinked to original sources

arXiv2026

Habitual lifestyle timing explains circadian timing, but daily lifestyle changes do not, in free-living humans across 2000 days

Background: Both between- and within-subject variations in circadian timing matter for health. If lifestyle changes could be used to regulate circadian timing, they would offer accessible and scalable routes to chronotherapy, but this link remains unclear under real-life conditions. Here, we explore how lifestyle 'traits' (such as typical wake time) and 'states' (day-to-day deviations from traits, such as waking up later than typical) explain between- and within-subject variation in acrophase (peak time) of the circadian rhythm of heart rate (CRHR). Methods: We collected free-living wearable data (smartwatch, continuous glucose monitor) from healthy volunteers for up to 4 weeks. The CRHR was derived from activity-adjusted heart rate, and acrophase was defined as time-of-day at daily CRHR peak. Sleep, food, and physical activity 'factors' were calculated and split into traits and states. Using a linear mixed-effects model, we tested how traits and states associate with between- and within-subject acrophase variance. Findings: Data from 105 healthy volunteers (66 female, age = 42.5 $\pm$ 15.7 years) spanning ~2000 days (18.8 $\pm$ 8.30 days each) were analysed. Traits were substantially more influential than states, explaining 42.3% versus 0.9% of total acrophase variance. Accordingly, traits explained 86.5% of between-subject variance, whereas states explained only 1.8% of within-subject variance. Sleep, food and physical activity factors contributed both jointly and uniquely, and lifestyle timing mattered most. Interpretation: Between-subject lifestyle traits explained acrophase better than within-subject lifestyle states. This asymmetry, alongside the considerable overlap between factors, supports sustained, holistic, timing-focused lifestyle adjustments as chronotherapy targets, testable through future interventional studies.

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arXiv2025

Multimodal Modeling of Ultradian Rhythms Using the Hankel Alternative View of Koopman (HAVOK) Analysis

Ultradian rhythms - quasi-rhythmic fluctuations in behavior and physiology with periods shorter than 24 hours - are observed across various organisms, including humans. Despite their role in key biological processes such as sleep architecture and hormone regulation, their underlying mechanisms remain poorly understood. Here, we leveraged wearable sensor technology for continuous monitoring of physiological signals in 16 healthy participants over two weeks. By systematically removing circadian and longer-scale rhythms, we isolated ultradian dynamics and modeled them using the Hankel Alternative View of Koopman (HAVOK) framework,a data-driven approach based on Takens' embedding theorem and Koopman operator theory. This allowed us to characterize ultradian rhythms as an intermittently forced linear system and distinguish between regular oscillatory behavior and more complex dynamics. Across participants, ultradian fluctuations were well-described by the HAVOK model, with intermittent forcing consistently observed. The model demonstrated strong forecasting accuracy, with root mean squared error (RMSE) of $0.0315 \pm 0.02$, $0.0306 \pm 0.02$, and $0.0218 \pm 0.02$ in the leading time-delay coordinates. Notably, a significant sex difference in model rank (z = -2.06, p = 0.0396) suggests that sex hormones may play a key role in ultradian dynamics. These findings provide evidence for intermittently forced linear systems as a useful framework for understanding ultradian rhythms and their regulation.

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arXiv2024

More variable circadian rhythms in epilepsy captured by long-term heart rate recordings from wearable sensors

Objective: The circadian rhythm synchronizes physiological and behavioural patterns with the 24-hour light-dark cycle. Disruption to the circadian rhythm is linked to various health conditions, though optimal methods to describe these disruptions remain unclear. An emerging approach is to examine the intra-individual variability in measurable properties of the circadian rhythm over extended periods. Epileptic seizures are modulated by circadian rhythms, but the relevance of circadian rhythm disruption in epilepsy remains unexplored. Our study investigates intra-individual circadian variability in epilepsy and its relationship with seizures. Methods: We retrospectively analyzed over 70,000 hours of wearable smartwatch data (Fitbit) from 143 people with epilepsy (PWE) and 31 healthy controls. Circadian oscillations in heart rate time series were extracted, daily estimates of circadian period, acrophase, and amplitude properties were produced, and estimates of the intra-individual variability of these properties over an entire recording were calculated. Results: PWE exhibited greater intra-individual variability in period (76 min vs. 57 min, d=0.66, p<0.001) and acrophase (64 min vs. 48 min, d=0.49, p=0.004) compared to controls, but not in amplitude (2 bpm, d=-0.15, p=0.49). Variability in circadian properties showed no correlation with seizure frequency, nor any differences between weeks with and without seizures. Significance: For the first time, we show that heart rate circadian rhythms are more variable in PWE, detectable via consumer wearable devices. However, no association with seizure frequency or occurrence was found, suggesting that this variability might be underpinned by the epilepsy aetiology rather than being a seizure-driven effect.

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arXiv2024

Comparing Methodological Variations in Seizure Onset Localisation Algorithms using intracranial EEG

During clinical treatment for epilepsy, the area of the brain thought to be responsible for pathological activity is identified. This identification is typically performed through visual assessment of EEG recordings; however, this is time consuming and prone to subjective inconsistency. Automated onset localisation algorithms provide objective identification of the onset location by highlighting changes in signal features associated with seizure onset. In this work we investigate how methodological differences in such algorithms can result in different onset locations being identified. We analysed ictal intracranial EEG (icEEG) recordings in 16 subjects (100 seizures) with drug-resistant epilepsy from the SWEZ-ETHZ public database. We identified a series of key methodological differences that must be considered when designing or selecting an onset localisation algorithm. These differences were demonstrated using three distinct algorithms that capture different, but complementary, seizure onset features: Imprint, Epileptogenicity Index, and Low Entropy Map. We assessed methodological differences (or Decision Points), and their impact on the identified onset locations. Our independent application of all three algorithms to the same ictal icEEG dataset revealed low agreement between them: 27-60% of onset channels showed minimal or no overlap. Therefore, we investigated the effect of three key differences: (i) how to define a baseline, (ii) whether low-frequency components are considered, and finally (iii) whether electrodecrement is considered. Changes at each Decision Point were found to substantially influence resultant onset channels (r>0.3). Our results demonstrate how seemingly small methodological changes can result in large differences in onset locations. We propose that key Decision Points must be considered when using or designing an onset localisation algorithm.

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arXiv2024

Unsupervised Machine Learning Identifies Latent Ultradian States in Multi-Modal Wearable Sensor Signals

Wearable sensors such as smartwatches have become ubiquitous in recent years, allowing the easy and continual measurement of physiological parameters such as heart rate, physical activity, body temperature, and blood glucose in an every-day setting. This multi-modal data offers the potential to identify latent states occurring across physiological measures, which may represent important bio-behavioural states that could not be observed in any single measure. Here we present an approach, utilising a hidden semi-Markov model, to identify such states in data collected using a smartwatch, electrocardiogram, and blood glucose monitor, over two weeks from a sample of 9 participants. We found 26 latent ultradian states across the sample, with many occurring at particular times of day. Here we describe some of these, as well as their association with subjective mood and time use diaries. These methods provide a novel avenue for developing insights into the physiology of everyday life.

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arXiv2023

Incomplete resection of the icEEG seizure onset zone is not associated with post-surgical outcomes

Delineation of seizure onset regions from EEG is important for effective surgical workup. However, it is unknown if their complete resection is required for seizure freedom, or in other words, if post-surgical seizure recurrence is due to incomplete removal of the seizure onset regions. Retrospective analysis of icEEG recordings from 63 subjects (735 seizures) identified seizure onset regions through visual inspection and algorithmic delineation. We analysed resection of onset regions and correlated this with post-surgical seizure control. Most subjects had over half of onset regions resected (70.7% and 60.5% of subjects for visual and algorithmic methods, respectively). In investigating spatial extent of onset or resection, and presence of diffuse onsets, we found no substantial evidence of association with post-surgical seizure control (all AUC<0.7, p>0.05). Seizure onset regions tends to be at least partially resected, however a less complete resection is not associated with worse post-surgical outcome. We conclude that seizure recurrence after epilepsy surgery is not necessarily a result of failing to completely resect the seizure onset zone, as defined by icEEG. Other network mechanisms must be involved, which are not limited to seizure onset regions alone.

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arXiv2023

Diminished circadian and ultradian rhythms of human brain activity in pathological tissue in vivo

Chronobiological rhythms, such as the circadian rhythm, have long been linked to neurological disorders, but it is currently unknown how pathological processes affect the expression of biological rhythms in the brain. Here, we use the unique opportunity of long-term, continuous intracranially recorded EEG from 38 patients (totalling 6338 hours) to delineate circadian (daily) and ultradian (minute to hourly) rhythms in different brain regions. We show that functional circadian and ultradian rhythms are diminished in pathological tissue, independent of regional variations. We further demonstrate that these diminished rhythms are persistent in time, regardless of load or occurrence of pathological events. These findings provide evidence that brain pathology is functionally associated with persistently diminished chronobiological rhythms in vivo in humans, independent of regional variations or pathological events. Future work interacting with, and restoring, these modulatory chronobiological rhythms may allow for novel therapies.

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arXiv2020

Predicting the Impact of Electric Field Stimulation in a Detailed Computational Model of Cortical Tissue

Neurostimulation using weak electric fields has generated excitement in recent years due to its potential as a medical intervention. However, study of this stimulation modality has been hampered by inconsistent results and large variability within and between studies. In order to begin addressing this variability we need to properly characterise the impact of the current on the underlying neuron populations. To develop and test a computational model capable of capturing the impact of electric field stimulation on networks of neurons. We construct a cortical tissue model with distinct layers and explicit neuron morphologies. We then apply a model of electrical stimulation and carry out multiple test case simulations. The cortical slice model is compared to experimental literature and shown to capture the main features of the electrophysiological response to stimulation. Namely, the model showed 1) a similar level of depolarisation in individual pyramidal neurons, 2) acceleration of intrinsic oscillations, and 3) retention of the spatial profile of oscillations in different layers. We then apply alternative electric fields to demonstrate how the model can capture differences in neuronal responses to the electric field. We demonstrate that the tissue response is dependent on layer depth, the angle of the apical dendrite relative to the field, and stimulation strength. We present publicly available computational modelling software that predicts the neuron network population response to electric field stimulation.

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arXiv2017

Excitation and Control of Plasma Wakefields by Multiple Laser Pulses

We demonstrate experimentally the resonant excitation of plasma waves by trains of laser pulses. We also take an important first step to achieving an energy recovery plasma accelerator by showing that unused wakefield energy can be removed by an out-of-resonance trailing laser pulse. The measured laser wakefields are found to be in excellent agreement with analytical and numerical models of wakefield excitation in the linear regime. Our results indicate a promising direction for achieving highly controlled, GeV-scale laser-plasma accelerators operating at multi-kilohertz repetition rates. This article was published in Physical Review Letters 119, 044802 on 27 July 2017. DOI: 10.1103/PhysRevLett.119.044802 Copyright 2017 American Physical Society.

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