Shorter night-time sleep duration and later sleep timing from infancy to adolescence.

BACKGROUND: Here, we (a) examined the trajectories of night-time sleep duration, bedtime and midpoint of night-time sleep (MPS) from infancy to adolescence, and (b) explored perinatal risk factors for persistent poor sleep health. METHODS: This study used data from 12,962 participants in the Avon Longitudinal Study of Parents and Children (ALSPAC). Parent or self-reported night-time sleep duration, bedtime and wake-up time were collected from questionnaires at 6, 18 and 30 months, and at 3.5, 4-5, 5-6, 6-7, 9, 11 and 15-16 years. Child's sex, birth weight, gestational age, health and temperament, together with mother's family adversity index (FAI), age at birth, prenatal socioeconomic status and postnatal anxiety and depression, were included as risk factors for persistent poor sleep health. Latent class growth analyses were applied first to detect trajectories of night-time sleep duration, bedtime and MPS, and we then applied logistic regressions for the longitudinal associations between risk factors and persistent poor sleep health domains. RESULTS: We obtained four trajectories for each of the three sleep domains. In particular, we identified a trajectory characterized by persistent shorter sleep, a trajectory of persistent later bedtime and a trajectory of persistent later MPS. Two risk factors were associated with the three poor sleep health domains: higher FAI with increased risk of persistent shorter sleep (OR = 1.20, 95% CI = 1.11-1.30, p < .001), persistent later bedtime (OR = 1.28, 95% CI = 1.19-1.39, p < .001) and persistent later MPS (OR = 1.30, 95% CI = 1.22-1.38, p < .001); and higher maternal socioeconomic status with reduced risk of persistent shorter sleep (OR = 0.99, 95% CI = 0.98-1.00, p = .048), persistent later bedtime (OR = 0.98, 95% CI = 0.97-0.99, p < .001) and persistent later MPS (OR = 0.99, 95% CI = 0.98-0.99, p < .001). CONCLUSIONS: We detected trajectories of persistent poor sleep health (i.e. shorter sleep duration, later bedtime and later MPS) from infancy to adolescence, and specific perinatal risk factors linked to persistent poor sleep health domains.

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.

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.

Evidence for distinct neuro-metabolic phenotypes in humans.

Advances in magnetic resonance imaging have shown how individual differences in the structure and function of the human brain relate to health and cognition. The relationship between individual differences and the levels of neuro-metabolites, however, remains largely unexplored - despite the potential for the discovery of novel behavioural and disease phenotypes. In this study, we measured 14 metabolite levels, normalised as ratios to total-creatine, with 1H magnetic resonance spectroscopy (MRS) acquired from the bilateral anterior cingulate cortices of six healthy participants, repeatedly over a period of four months. ANOVA tests revealed statistically significant differences of 3 metabolites and 3 commonly used combinations (total-choline, glutamate + glutamine and total-N-acetylaspartate) between the participants, with scyllo-inositol (F=85, p=6e-26) and total-choline (F=39, p=1e-17) having the greatest discriminatory power. This was not attributable to structural differences. When predicting individuals from the repeated MRS measurements, a leave-one-out classification accuracy of 88% was achieved using a support vector machine based on scyllo-inositol and total-choline levels. Accuracy increased to 98% with the addition of total-N-acetylaspartate and myo-inositol - demonstrating the efficacy of combining MRS with machine learning and metabolomic methodology. These results provide evidence for the existence of neuro-metabolic phenotypes, which may be non-invasively measured using widely available 3 Tesla MRS. Establishing these phenotypes in a larger cohort and investigating their connection to brain health and function presents an important area for future study.

Temporal organisation of the brain's intrinsic motor network: The relationship with circadian phenotype and motor performance.

BACKGROUND: Functional connectivity (FC) of the motor network (MN) is often used to investigate how intrinsic properties of the brain are associated with motor abilities and performance. In addition, the MN is a key feature in clinical work to map the recovery after stroke and aid the understanding of neurodegenerative disorders. Time of day variation and individual differences in circadian timing, however, have not yet been considered collectively when looking at FC. METHODS: A total of 33 healthy, right handed individuals (13 male, 23.1 ± 4.2 years) took part in the study. Actigraphy, sleep diaries and circadian phase markers (dim light melatonin onset and cortisol awakening response) were used to determine early (ECP, n = 13) and late (LCP, n = 20) circadian phenotype groups. Resting state functional MRI testing sessions were conducted at 14:00 h, 20:00 h and 08:00 h and preceded by a maximum voluntary contraction test for isometric grip strength to measure motor performance. RESULTS: Significant differences in FC of the MN between ECPs and LCPs were found, as well as significant variations between different times of day. A higher amplitude in diurnal variation of FC and performance was observed in LCPs compared to ECPs, with the morning being most significantly affected. Overall, lower FC was significantly associated with poorer motor performance. DISCUSSION: Our findings uncover intrinsic differences between times of day and circadian phenotype groups. This suggests that central mechanisms contribute to diurnal variation in motor performance and the functional integrity of the MN at rest influences the ability to perform in a motor task.

Proof-of-concept evidence for trimodal simultaneous investigation of human brain function.

The link between spatial (where) and temporal (when) aspects of the neural correlates of most psychological phenomena is not clear. Elucidation of this relation, which is crucial to fully understand human brain function, requires integration across multiple brain imaging modalities and cognitive tasks that reliably modulate the engagement of the brain systems of interest. By overcoming the methodological challenges posed by simultaneous recordings, the present report provides proof-of-concept evidence for a novel approach using three complementary imaging modalities: functional magnetic resonance imaging (fMRI), event-related potentials (ERPs), and event-related optical signals (EROS). Using the emotional oddball task, a paradigm that taps into both cognitive and affective aspects of processing, we show the feasibility of capturing converging and complementary measures of brain function that are not currently attainable using traditional unimodal or other multimodal approaches. This opens up unprecedented possibilities to clarify spatiotemporal integration of brain function.

fMRI characterisation of widespread brain networks relevant for behavioural variability in fine hand motor control with and without visual feedback.

A bilateral visuo-parietal-motor network is responsible for fine control of hand movements. However, the sub-regions which are devoted to maintenance of contraction stability and how these processes fluctuate with trial-quality of task execution and in the presence/absence of visual feedback remains unclear. We addressed this by integrating behavioural and fMRI measurements during right-hand isometric compression of a compliant rubber bulb, at 10% and 30% of maximum voluntary contraction, both with and without visual feedback of the applied force. We quantified single-trial behavioural performance during 1) the whole task period and 2) stable contraction maintenance, and regressed these metrics against the fMRI data to identify the brain activity most relevant to trial-by-trial fluctuations in performance during specific task phases. fMRI-behaviour correlations in a bilateral network of visual, premotor, primary motor, parietal and inferior frontal cortical regions emerged during performance of the entire feedback task, but only in premotor, parietal cortex and thalamus during the stable contraction period. The trials with the best task performance showed increased bilaterality and amplitude of fMRI responses. With feedback, stronger BOLD-behaviour coupling was found during 10% compared to 30% contractions. Only a small subset of regions in this network were weakly correlated with behaviour without feedback, despite wider network activated during this task than in the presence of feedback. These findings reflect a more focused network strongly coupled to behavioural fluctuations when providing visual feedback, whereas without it the task recruited widespread brain activity almost uncoupled from behavioural performance.

Altered thalamocortical and intra-thalamic functional connectivity during light sleep compared with wake.

The transition from wakefulness into sleep is accompanied by modified activity in the brain's thalamocortical network. Sleep-related decreases in thalamocortical functional connectivity (FC) have previously been reported, but the extent to which these changes differ between thalamocortical pathways, and patterns of intra-thalamic FC during sleep remain untested. To non-invasively investigate thalamocortical and intra-thalamic FC as a function of sleep stage we recorded simultaneous EEG-fMRI data in 13 healthy participants during their descent into light sleep. Visual scoring of EEG data permitted sleep staging. We derived a functional thalamic parcellation during wakefulness by computing seed-based FC, measured between thalamic voxels and a set of pre-defined cortical regions. Sleep differentially affected FC between these distinct thalamic subdivisions and their associated cortical projections, with significant increases in FC during sleep restricted to sensorimotor connections. In contrast, intra-thalamic FC, both within and between functional thalamic subdivisions, showed significant increases with advancement into sleep. This work demonstrates the complexity and state-specific nature of functional thalamic relationships--both with the cortex and internally--over the sleep/wake cycle, and further highlights the importance of a thalamocortical focus in the study of sleep mechanisms.

Poststimulus undershoots in cerebral blood flow and BOLD fMRI responses are modulated by poststimulus neuronal activity.

fMRI is the foremost technique for noninvasive measurement of human brain function. However, its utility is limited by an incomplete understanding of the relationship between neuronal activity and the hemodynamic response. Though the primary peak of the hemodynamic response is modulated by neuronal activity, the origin of the typically negative poststimulus signal is poorly understood and its amplitude assumed to covary with the primary response. We use simultaneous recordings of EEG with blood oxygenation level-dependent (BOLD) and cerebral blood flow (CBF) fMRI during unilateral median nerve stimulation to show that the poststimulus fMRI signal is neuronally modulated. We observe high spatial agreement between concurrent BOLD and CBF responses to median nerve stimulation, with primary signal increases in contralateral sensorimotor cortex and primary signal decreases in ipsilateral sensorimotor cortex. During the poststimulus period, the amplitude and directionality (positive/negative) of the BOLD signal in both contralateral and ipsilateral sensorimotor cortex depends on the poststimulus synchrony of 8-13 Hz EEG neuronal activity, which is often considered to reflect cortical inhibition, along with concordant changes in CBF and metabolism. Therefore we present conclusive evidence that the fMRI time course represents a hemodynamic signature of at least two distinct temporal phases of neuronal activity, substantially improving understanding of the origin of the BOLD response and increasing the potential measurements of brain function provided by fMRI. We suggest that the poststimulus EEG and fMRI responses may be required for the resetting of the entire sensory network to enable a return to resting-state activity levels.

Comparison of functional thalamic segmentation from seed-based analysis and ICA.

Information flow between the thalamus and cerebral cortex is a crucial component of adaptive brain function, but the details of thalamocortical interactions in human subjects remain unclear. The principal aim of this study was to evaluate the agreement between functional thalamic network patterns, derived using seed-based connectivity analysis and independent component analysis (ICA) applied separately to resting state functional MRI (fMRI) data from 21 healthy participants. For the seed-based analysis, functional thalamic parcellation was achieved by computing functional connectivity (FC) between thalamic voxels and a set of pre-defined cortical regions. Thalamus-constrained ICA provided an alternative parcellation. Both FC analyses demonstrated plausible and comparable group-level thalamic subdivisions, in agreement with previous work. Quantitative assessment of the spatial overlap between FC thalamic segmentations, and comparison of each to a histological "gold-standard" thalamic atlas and a structurally-defined thalamic atlas, highlighted variations between them and, most notably, differences with both histological and structural results. Whilst deeper understanding of thalamocortical connectivity rests upon identification of features common to multiple non-invasive neuroimaging techniques (e.g. FC, structural connectivity and anatomical localisation of individual-specific nuclei), this work sheds further light on the functional organisation of the thalamus and the varying sensitivities of complementary analyses to resolve it.

Influence of epoch length on measurement of dynamic functional connectivity in wakefulness and behavioural validation in sleep.

Conventional functional connectivity (FC) analysis of fMRI data derives a single measurement from the entire scan, generally several minutes in duration, which neglects the brain's dynamic behaviour and potentially loses important temporal information. Short-interval dynamic FC is an attractive proposition if methodological issues can be resolved and the approach validated. This was addressed in two ways; firstly we assessed FC of the posterior cingulate cortex (PCC) node of the default mode network (DMN) using differing temporal intervals (8s to 5min) in the waking-resting state. We found that 30-second intervals and longer produce spatially similar correlation topography compared to 15-minute static FC measurements, while providing increased temporal information about changes in FC that were consistent across interval lengths. Secondly, we used NREM sleep as a behavioural validation for the use of 30-second temporal intervals due to the known fMRI FC changes with sleep stage that have been observed in previous studies using intervals of several minutes. We found significant decreases in DMN FC with sleep depth which were most pronounced during stage N2 and N3. Additionally, both the proportion of time with strong PCC-DMN connectivity and the variability in dynamic FC decreased with sleep. We therefore show that dynamic FC with epochs as short as tens of seconds is a viable method for characterising intrinsic brain activity.

Reliability of information-based integration of EEG and fMRI data: a simulation study.

Most studies involving simultaneous electroencephalographic (EEG) and functional magnetic resonance imaging (fMRI) data rely on the first-order, affine-linear correlation of EEG and fMRI features within the framework of the general linear model. An alternative is the use of information-based measures such as mutual information and entropy, which can also detect higher-order correlations present in the data. The estimate of information-theoretic quantities might be influenced by several parameters, such as the numerosity of the sample, the amount of correlation between variables, and the discretization (or binning) strategy of choice. While these issues have been investigated for invasive neurophysiological data and a number of bias-correction estimates have been developed, there has been no attempt to systematically examine the accuracy of information estimates for the multivariate distributions arising in the context of EEG-fMRI recordings. This is especially important given the differences between electrophysiological and EEG-fMRI recordings. In this study, we drew random samples from simulated bivariate and trivariate distributions, mimicking the statistical properties of EEG-fMRI data. We compared the estimated information shared by simulated random variables with its numerical value and found that the interaction between the binning strategy and the estimation method influences the accuracy of the estimate. Conditional on the simulation assumptions, we found that the equipopulated binning strategy yields the best and most consistent results across distributions and bias correction methods. We also found that within bias correction techniques, the asymptotically debiased (TPMC), the jackknife debiased (JD), and the best upper bound (BUB) approach give similar results, and those are consistent across distributions.

The structural and functional connectivity of the posterior cingulate cortex: comparison between deterministic and probabilistic tractography for the investigation of structure-function relationships.

The default mode network (DMN) is one of the most studied resting-state networks, and is thought to be involved in the maintenance of consciousness within the alert human brain. Although many studies have examined the functional connectivity (FC) of the DMN, few have investigated its underlying structural connectivity (SC), or the relationship between the two. We investigated this question in fifteen healthy subjects, concentrating on connections to the precuneus/posterior cingulate cortex (PCC), commonly considered as the central node of the DMN. We used group independent component analysis (GICA) and seed-based correlation analysis of fMRI data to quantify FC, and streamline and probabilistic tractography to identify structural tracts from diffusion tensor imaging (DTI) data. We first assessed the presence of structural connections between the DMN regions identified with GICA. Of the 15 subjects, when using the probabilistic approach 15 (15) demonstrated connections between the PCC and mesial prefrontal cortex (mPFC), 11 (15) showed connections from the PCC to the right inferior parietal cortex (rIPC) and 8 (15) to the left IPC. Next, we assessed the strength of FC (magnitude of temporal correlation) and SC (mean fractional anisotropy of reconstructed tracts (streamline), number of super-threshold voxels within the mask region (probabilistic)). The lIPC had significantly reduced FC to the PCC compared to the mPFC and rIPC. No difference in SC strength between connections was found using the streamline approach. For the probabilistic approach, mPFC had significantly lower SC than both IPCs. The two measures of SC strength were significantly correlated, but not for all paired connections. Finally, we observed a significant correlation between SC and FC for both tractography approaches when data were pooled across PCC-lIPL, PCC-rIPL and PCC-mPFC connections, and for some individual paired connections. Our results suggest that the streamline approach is advantageous for characterising the connectivity of long white matter tracts (PCC-mPFC), whilst the probabilistic approach was more reliable at identifying PCC-IPC connections. The direct comparison of FC and SC indicated that pairs of nodes with stronger structural connections also had stronger functional connectivity, and that this was maintained with both tractography approaches. Whilst the definition of SC strength remains controversial, our results could be considered to provide some degree of validation for the measures of SC strength that we have used. Direct comparisons of SC and FC are necessary in order to understand the structural basis of functional connectivity, and to characterise and quantify the changes in the brain's functional architecture that occur as a result of normal physiology or pathology.

Multimodal neuroimaging investigations of alterations to consciousness: the relationship between absence epilepsy and sleep.

The link between epilepsy and sleep is well established on many levels. The focus of the current review is on recent neuroimaging investigations into the alterations of consciousness that are observed during absence seizures and the descent into sleep. Functional neuroimaging provides simultaneous cortical and subcortical recording of activity throughout the brain, allowing a detailed definition and characterization of large-scale brain networks and the interactions between them. This has led to the identification of a set of regions which collectively form the consciousness system, which includes contributions from the default mode network (DMN), ascending arousal systems, and the thalamus. Electrophysiological and neuroimaging investigations have also clearly demonstrated the importance of thalamocortical and corticothalamic networks in the evolution of sleep and absence epilepsy, two phenomena in which the subject experiences an alteration to the conscious state and a disconnection from external input. However, the precise relationship between the consciousness system, thalamocortical networks, and consciousness itself remains to be clarified. One of the fundamental challenges is to understand how distributed brain networks coordinate their activity in order to maintain and implement complex behaviors such as consciousness and how modifications to this network activity lead to alterations in consciousness. By taking into account not only the level of activation of individual brain regions but also their connectivity within specific networks and the activity and connectivity of other relevant networks, a more specific quantification of brain states can be achieved. This, in turn, may provide a more fundamental understanding of the alterations to consciousness experienced in sleep and epilepsy.

Exploring an objective measure of overactivity in children with rare genetic syndromes.

BACKGROUND: Overactivity is prevalent in several rare genetic neurodevelopmental syndromes, including Smith-Magenis syndrome, Angelman syndrome, and tuberous sclerosis complex, although has been predominantly assessed using questionnaire techniques. Threats to the precision and validity of questionnaire data may undermine existing insights into this behaviour. Previous research indicates objective measures, namely actigraphy, can effectively differentiate non-overactive children from those with attention-deficit hyperactivity disorder. This study is the first to examine the sensitivity of actigraphy to overactivity across rare genetic syndromes associated with intellectual disability, through comparisons with typically-developing peers and questionnaire overactivity estimates. METHODS: A secondary analysis of actigraphy data and overactivity estimates from The Activity Questionnaire (TAQ) was conducted for children aged 4-15 years with Smith-Magenis syndrome (N=20), Angelman syndrome (N=26), tuberous sclerosis complex (N=16), and typically-developing children (N=61). Actigraphy data were summarized using the M10 non-parametric circadian rhythm variable, and 24-hour activity profiles were modelled via functional linear modelling. Associations between actigraphy data and TAQ overactivity estimates were explored. Differences in actigraphy-defined activity were also examined between syndrome and typically-developing groups, and between children with high and low TAQ overactivity scores within syndromes. RESULTS: M10 and TAQ overactivity scores were strongly positively correlated for children with Angelman syndrome and Smith-Magenis syndrome. M10 did not substantially differ between the syndrome and typically-developing groups. Higher early morning activity and lower evening activity was observed across all syndrome groups relative to typically-developing peers. High and low TAQ group comparisons revealed syndrome-specific profiles of overactivity, persisting throughout the day in Angelman syndrome, occurring during the early morning and early afternoon in Smith-Magenis syndrome, and manifesting briefly in the evening in tuberous sclerosis complex. DISCUSSION: These findings provide some support for the sensitivity of actigraphy to overactivity in children with rare genetic syndromes, and offer syndrome-specific temporal descriptions of overactivity. The findings advance existing descriptions of overactivity, provided by questionnaire techniques, in children with rare genetic syndromes and have implications for the measurement of overactivity. Future studies should examine the impact of syndrome-related characteristics on actigraphy-defined activity and overactivity estimates from actigraphy and questionnaire techniques.

Spontaneous EEG alpha oscillation interacts with positive and negative BOLD responses in the visual-auditory cortices and default-mode network.

The human brain is continually, dynamically active and spontaneous fluctuations in this activity play a functional role in affecting both behavioural and neuronal responses. However, the mechanisms through which this occurs remain poorly understood. Simultaneous EEG-fMRI is a promising technique to study how spontaneous activity modulates the brain's response to stimulation, as temporal indices of ongoing cortical excitability can be integrated with spatially localised evoked responses. Here we demonstrate an interaction between the ongoing power of the electrophysiological alpha oscillation and the magnitude of both positive (PBR) and negative (NBR) fMRI responses to two contrasts of visual checkerboard reversal. Furthermore, the amplitude of pre-stimulus EEG alpha-power significantly modulated the amplitude and shape of subsequent PBR and NBR to the visual stimulus. A nonlinear reduction of visual PBR and an enhancement of auditory NBR and default-mode network NBR were observed in trials preceded by high alpha-power. These modulated areas formed a functionally connected network during a separate resting-state recording. Our findings suggest that the "baseline" state of the brain exhibits considerable trial-to-trial variability which arises from fluctuations in the balance of cortical inhibition/excitation that are represented by respective increases/decreases in the power of the EEG alpha oscillation. The consequence of this spontaneous electrophysiological variability is modulated amplitudes of both PBR and NBR to stimulation. Fluctuations in alpha-power may subserve a functional relationship in the visual-auditory network, acting as mediator for both short and long-range cortical inhibition, the strength of which is represented in part by NBR.

Intrinsic variability in the human response to pain is assembled from multiple, dynamic brain processes.

The stimulus-evoked response is the principle measure used to elucidate the timing and spatial location of human brain activity. Brain and behavioural responses to pain are influenced by multiple intrinsic and extrinsic factors and display considerable, natural trial-by-trial variability. However, because the neuronal sources of this variability are poorly understood the functional information it contains is under-exploited for understanding the relationship between brain function and behaviour. We recorded simultaneous EEG-fMRI during rest and noxious thermal stimulation to characterise the relationship between natural fluctuations in behavioural pain-ratings, the spatiotemporal dynamics of brain network responses and intrinsic connectivity. We demonstrate that fMRI response variability in the pain network is: dependent upon its resting-state functional connectivity; modulated by behaviour; and correlated with EEG evoked-potential amplitude. The pre-stimulus default-mode network (DMN) fMRI signal predicts the subsequent magnitude of pain ratings, evoked-potentials and pain network BOLD responses. Additionally, the power of the ongoing EEG alpha oscillation, an index of cortical excitability, modulates the DMN fMRI response to pain. The complex interaction between alpha-power, DMN activity and both the behavioural report of pain and the brain's response to pain demonstrates the neurobiological significance of trial-by-trial variability. Furthermore, we show that multiple, interconnected factors contribute to both the brain's response to stimulation and the psychophysiological emergence of the subjective experience of pain.

Resting state networks in paroxysmal disorders of consciousness.

Functional MRI (fMRI) has transformed academic neuroscience in the last decade and is now the most widely used non-invasive functional brain imaging technique. However, it has had much less of an impact in clinical neuroscience. While a majority of fMRI applications examine brain function in response to an externally driven task, an alternative approach characterizes the brain's intrinsic functional architecture. This involves fMRI scanning in the absence of an explicit task (i.e., in the resting state) and is, therefore, much more easily performed and tolerated by neurological and neuropsychiatric patient groups. The data are easily acquired, and the functional networks extracted are reproducible and reliable. However, quantifying networks of distributed brain activity and identifying the most informative features in a particular disorder remain a challenge. Progress has been made in this direction in recent years, with the adoption of mathematical tools from communications engineering. Specific alterations to the brain's functional connectivity at rest have been observed in generalized and focal epilepsies, as well as in non-epileptic attack disorder. The challenge for the future is to exploit knowledge of how the brain works as a complex system in order to develop more accurate and sensitive diagnostic tests for neurological and neuropsychiatric disorders.

The contribution of sleep and co-occurring neurodevelopmental conditions to quality of life in children with epilepsy.

BACKGROUND: Health-related quality of life (HRQOL) in children with epilepsy (CWE) is multifactorial and can be affected not only by epilepsy-specific variables but also co-occurring conditions such as sleep disturbances, autism, and attention deficit hyperactivity disorder (ADHD). While highly prevalent in CWE, these conditions are underdiagnosed despite having a significant impact on HRQOL. Sleep problems have a complex relationship with epilepsy and neurodevelopmental characteristics. However, little is known about how these issues interact and contribute to HRQOL. OBJECTIVES: The current study aims to explore the relationship between sleep and neurodevelopmental characteristics on HRQOL in CWE. METHODS: 36 CWE aged 4-16 years old were recruited from two hospitals and asked to wear an actiwatch for a period of 14 days and caregivers completed a series of questionnaires assessing co-occurrences and epilepsy-specific variables. RESULTS: A high proportion of CWE (78.13%) presented significant sleep problems. Informant-reported sleep problems were significantly predictive of HRQOL above seizure severity and the number of antiseizure medications. Interestingly, informant-reported sleep problems were no longer significantly predictive of HRQOL when neurodevelopmental characteristics were considered, indicating a possible mediating effect. Similarly, actigraphy-defined sleep (variability in sleep onset latency) displayed a similar effect but only for ADHD characteristics, whereas autistic characteristics and variability in sleep onset latency continued to exert an individual effect on HRQOL. CONCLUSION: These data from our study shed light on the complicated relationship between sleep, neurodevelopmental characteristics and epilepsy. Findings suggest that the impact of sleep on HRQOL in CWE is possibly mediated by neurodevelopmental characteristics. Furthermore, the impact this triangular relationship exerts on HRQOL is dependent on the type of tool used to measure sleep. These findings highlight the importance of a multidisciplinary approach to epilepsy management.

Classification of human chronotype based on fMRI network-based statistics.

Chronotype-the relationship between the internal circadian physiology of an individual and the external 24-h 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 is not well-understood. To address this issue, we use rs-fMRI collected from 16 people with an early chronotype and 22 people with a late chronotype over three scanning sessions. We develop a classification framework utilizing the Network Based-Statistic methodology, to understand if differentiable information about chronotype is embedded in functional brain networks and how this changes throughout the day. We find evidence of subnetworks throughout the day that differ between extreme chronotypes such that high accuracy can occur, describe rigorous threshold criteria for achieving 97.3% accuracy in the Evening and investigate how the same conditions hinder accuracy for other scanning sessions. Revealing differences in functional brain networks based on extreme chronotype suggests future avenues of research that may ultimately better characterize the relationship between internal physiology, external perturbations, brain networks, and disease.