Biology subjects

Bagshaw, A. P.

Publications and source records attributed to Bagshaw, A. P..

8 recordsLinked to original sources

bioRxiv2024

Associating EEG Functional Networks and the Effect of Sleep Deprivation as Measured Using Psychomotor Vigilance Tests

People are routinely forced to undertake cognitive challenges under the effect of sleep deprivation, due to professional and social obligations forcing them to ignore their circadian clock. However, low intra-individual and high inter-individual differences in behavioural outcomes are known to occur when people are sleep deprived, leading to the conclusion that trait-like differences to sleep deprivation could explain the differing levels of resilience. Within this study we consider if trait-like resilience to sleep deprivation, measured using psychomotor vigilance tests over a 40h constant routine protocol, could be associated with graph metrics (mean node strength, clustering coefficient, characteristic path length and stability) calculated from EEG functional networks acquired when participants are well rested (baseline). Furthermore, we investigated how stability (the consistency of a participants functional network over time measured using 2-D correlation) changed over the constant routine. We showed evidence of strong significant correlations between high mean node strength, low characteristic path length and high stability at baseline with a general resilience to extended sleep deprivation, although the same features lead to vulnerability during the period of natural sleep onset, highlighting non-uniform correlations over time. We also show significant differences in the levels of stability between resilient and vulnerable groups.

neuroscience↗

bioRxiv2023

Genetically mediated associations between chronotype and neuroimaging phenotypes in the UK Biobank: a Mendelian randomisation study

Chronotype impacts numerous physiological and disease traits, from metabolic syndrome to schizophrenia. The suprachiasmatic nucleus (SCN) maintains transcriptional-translational feedback loop (TTFL) which acts as a central chronobiological pacemaker, regulating 24-hour cycles throughout the human body. However, each tissue maintains its own peripheral clock, and both endogenous hormones and neurotransmitters and exogenous environmental cues regulate the SCNs central clock. The extent to which brain regions outside the SCN influence the core TTFL is unknown. Here, we investigated how genetic variability affecting brain regions outside the SCN may indirectly influence chronotype, using Mendelian randomization and causal inference. We performed genome wide association studies (GWAS) based on image derived phenotypes (IDPs) from neuroimaging data (grey matter volume, thickness and surface area, microstructural white matter measures; 42,062 participants), and additionally for sleep duration and morning/evening chronotype (361,739 participants). Significant, single nucleotide polymorphisms (SNPs) associating with each phenotype were entered into 2-sample Mendelian randomization performed using inverse-variance weighted methods (exposure versus outcome): 1) chronotype versus each IDP, 2) sleep versus each IDP and 3) each IDP versus chronotype. Subsequently, we investigated genes where significant instrumental SNPs were located for circadian periodic cycling, interaction with TTFL genes in common biological pathways (genetic, physical, or functional interaction), and enrichment of traits from UK Biobank and GWAS Catalogs. We found three associations with chronotype (morning/evening diurnal preference) outside the SCN based on genetically predicted (FAM76B, DENND1A, CDH11) regional differences in brain volume. Specifically, genetically predicted lower inferior temporal gyrus volume linked to morning phenotype, while lower volume of the superior parietal lobule and angular gyrus linked to evening preference. In addition, evening chronotype exposure influenced superior temporal gyrus volume, and both increased sleep duration and evening chronotype influenced thalamic volume. We conclude that genetically mediated associations between chronotype and brain regions outside SCN exist suggesting novel zeitgeber mechanisms.

neuroscience↗

bioRxiv2023

The impact of paediatric epilepsy and co-occurring neurodevelopmental disorders on functional brain networks in wake and sleep

Epilepsy is one of the most common neurological disorders in children. Diagnosing epilepsy in children can be very challenging, especially as it often coexists with neurodevelopmental conditions like autism and ADHD. Functional brain networks obtained from neuroimaging and electrophysiological data in wakefulness and sleep have been shown to contain signatures of neurological disorders, and can potentially support the diagnosis and management of co-occurring neurodevelopmental conditions. In this work, we use electroencephalography (EEG) recordings from children, in restful wakefulness and sleep, to extract functional connectivity networks in different frequency bands. We explore the relationship of these networks with epilepsy diagnosis and with measures of neurodevelopmental traits, obtained from questionnaires used as screening tools for autism and ADHD. We explore differences in network markers between children with and without epilepsy in wake and sleep, and quantify the correlation between such markers and measures of neurodevelopmental traits. Our findings highlight the importance of considering the interplay between epilepsy and neurodevelopmental traits when exploring network markers of epilepsy.

neuroscience↗

bioRxiv2023

White matter properties in fronto-parietal tracts predict maladaptive functional activation and deficient response inhibition in ADHD

Response inhibition, defined as the ability to suppress inappropriate responses, is a key characteristic of adaptive human behaviour. However, in individuals with attention deficit hyperactivity disorder (ADHD) it is often impaired and is linked to broad life outcomes. Previous neuroimaging investigations have indicated a myriad of brain networks in response inhibition, which limit its utility in understanding and overcoming response inhibition difficulties. More recently, it has been suggested that a specific fronto-parietal functional circuitry between the inferior frontal gyrus (IFG) and the intraparietal sulcus (IPS), dictates the recruitment of the IPS in response inhibition in ADHD. To ascertain the critical role of the IFG-IPS functional circuit and its relevance to response inhibition in ADHD, it is crucial to understand the underlying structural architecture of this circuit so that the functional relevance could be interpreted correctly. Here we investigated the white matter pathways connecting the IFG and IPS using seed-based probabilistic tractography on diffusion data in 42 ADHD and 24 neurotypicals and assessed their impact on both the recruitment of IPS in response inhibition scenarios and on response inhibition performance in a Go/No-go task. Our results showed that individual differences in the structural properties of the IPS-IFG circuit, including tract volume and diffusivity, were linked to IPS activation and even predicted response inhibition performance outside the scanner. These findings highlight the structural-functional coupling of the IFG-IPS circuit in response inhibition in ADHD and confirm a structural basis for maladaptive functional top-down control in deficient inhibition in ADHD. Our results also support the notion of ADHD as a continuum and suggest that individual differences in tract-specific functional and structural connectivity could serve as neuromarkers of ADHD.

neuroscience↗

bioRxiv2022

SLIPMAT: a pipeline for extracting tissue-specific spectral profiles from 1H MR spectroscopic imaging data

1H Magnetic Resonance Spectroscopy (MRS) is an important non-invasive tool for measuring brain metabolism, with numerous applications in the neuroscientific and clinical domains. In this work we present a new analysis pipeline (SLIPMAT), designed to extract high-quality, tissue-specific, spectral profiles from MR spectroscopic imaging data (MRSI). Spectral decomposition is combined with spatially dependant frequency and phase correction to yield high SNR white and grey matter spectra without partial-volume contamination. A subsequent series of spectral processing steps are applied to reduce unwanted spectral variation, such as baseline correction and linewidth matching, before direct spectral analysis with machine learning and traditional statistical methods. The method is validated using a 2D semi-LASER MRSI sequence, with a 5-minute duration, from data acquired in triplicate across 8 healthy participants. Reliable spectral profiles are confirmed with principal component analysis, revealing the importance of total-choline and scyllo-inositol levels in distinguishing between individuals - in good agreement with our previous work. Furthermore, since the method allows the simultaneous measurement of metabolites in grey and white matter, we show the strong discriminative value of these metabolites in both tissue types for the first time. In conclusion, we present a novel and time efficient MRSI acquisition and processing pipeline, capable of detecting reliable neuro-metabolic differences between healthy individuals, and suitable for the sensitive neurometabolic profiling of in-vivo brain tissue.

neuroscience↗

bioRxiv2022

Circadian distribution of epileptiform discharges in epilepsy: candidate mechanisms of variability

Epilepsy is a serious neurological disorder characterised by a tendency to have recurrent, spontaneous, seizures. Classically, seizures are assumed to occur at random. However, recent research has uncovered underlying rhythms both in seizures and in key signatures of epilepsy - so-called interictal epileptiform activity - with timescales that vary from hours and days through to months. Understanding the physiological mechanisms that determine these rhythmic patterns of epileptiform discharges remains an open question. Many people with epilepsy identify precipitants of their seizures, the most common of which include stress, sleep deprivation and fatigue. To quantify the impact of these physiological factors, we analysed 24-hour EEG recordings from a cohort of 107 people with idiopathic generalized epilepsy. We found two subgroups with distinct distributions of epileptiform discharges: one with highest incidence during sleep and the other during day-time. We interrogated these data using a mathematical model that describes the transitions between background and epileptiform activity in large-scale brain networks. This model was extended to include a time-dependent forcing term, where the excitability of nodes within the network could be modulated by other factors. We calibrated this forcing term using independently-collected human cortisol (the primary stress-responsive hormone characterised by circadian and ultradian patterns of secretion) data and sleep-staged EEG from healthy human participants. We found that either the dynamics of cortisol or sleep stage transition, or a combination of both, could explain most of the observed distributions of epileptiform discharges. Our findings provide conceptual evidence for the existence of underlying physiological drivers of rhythms of epileptiform discharges. These findings should motivate future research to explore these mechanisms in carefully designed experiments using animal models or people with epilepsy. Author summary65 million people have epilepsy worldwide. Many of these people report specific triggers that make their seizures (the primary symptom of epilepsy) more likely. Here, we use a mathematical model to understand the relationship between possible triggers and rhythms in epileptiform activity observed across the day. The mathematical model describes the activity of connected brain regions, and how the excitability of these regions can change in response to different stimuli. Based on data collected from people with idiopathic generalized epilepsy, we identify transitions between sleep stages and variation in concentration of the stress-hormone cortisol as candidate factors that influence how likely it is for epileptiform activity to occur. By including those factors into the model, we show they can explain most of the daily variability. More broadly, our approach provides a framework for better understanding what factors drive the occurrence of epileptiform activity and offers the potential to suggest experiments that can validate model predictions.

neuroscience↗

bioRxiv2022

Classification of Human Chronotype Based on fMRINetwork-Based Statistics

Chronotype - the relationship between the internal circadian physiology of an individual and the external 24-hour light-dark cycle - is increasingly implicated in mental health and cognition. Individuals presenting with a late chronotype have an increased likelihood of developing depression, and can display reduced cognitive performance during the societal 9-5 day. However, the interplay between physiological rhythms and the brain networks that underpin cognition and mental health are not well understood. To address this issue, we use resting state fMRI collected from 16 people with an early chronotype and 22 people with a late chronotype to study if differentiable information about chronotype is embedded in functional brain networks. We develop a classifier utilising the Network Based-Statistic (NBS) methodology, using rigorous selection criteria to select t-statistic thresholds within the NBS approach. We find significant differences in functional networks measured in early and late chronotypes and describe conditions under which the classifier achieves 97.3% accuracy. Revealing differences in functional brain networks based on extreme chronotype suggests future avenues of research that may ultimately better characterise the relationship between internal physiology, external perturbations, brain networks and disease.

neuroscience↗

bioRxiv2018

A perceptual decision making EEG/fMRI data set

We present a neuroimaging data set comprising behavioural, electroencephalographic (EEG), and functional magnetic resonance imaging (fMRI) data that were acquired from human subjects performing a perceptual decision making task. EEG data were acquired both independently and simultaneously with fMRI data. Potential data usages include the validation of biocomputational accounts of human perceptual decision making or the empirical validation of simultaneous EEG/fMRI data processing algorithms. The dataset is available from the Open Science Framework and organized according to the Brain Imaging Data Structure standard.

neuroscience↗