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.

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.

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.

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.

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.

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.

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.

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.

Role of the Ipsilateral Primary Motor Cortex in the Visuo-Motor Network During Fine Contractions and Accurate Performance

It is widely recognized that continuous sensory feedback plays a crucial role in accurate motor control in everyday life. Feedback information is used to adapt force output and to correct errors. While primary motor cortex contralateral to the movement (cM1) plays a dominant role in this control, converging evidence supports the idea that ipsilateral primary motor cortex (iM1) also directly contributes to hand and finger movements. Similarly, when visual feedback is available, primary visual cortex (V1) and its interactions with the motor network also become important for accurate motor performance. To elucidate this issue, we performed and integrated behavioral and electroencephalography (EEG) measurements during isometric compression of a compliant rubber bulb, at 10% and 30% of maximum voluntary contraction, both with and without visual feedback. We used a semi-blind approach (functional source separation (FSS)) to identify separate functional sources of mu-frequency (8-13[Formula: see text]Hz) EEG responses in cM1, iM1 and V1. Here for the first time, we have used orthogonal FSS to extract multiple sources, by using the same functional constraint, providing the ability to extract different sources that oscillate in the same frequency range but that have different topographic distributions. We analyzed the single-trial timecourses of mu power event-related desynchronization (ERD) in these sources and linked them with force measurements to understand which aspects are most important for good task performance. Whilst the amplitude of mu power was not related to contraction force in any of the sources, it was able to provide information on performance quality. We observed stronger ERDs in both contralateral and ipsilateral motor sources during trials where contraction force was most consistently maintained. This effect was most prominent in the ipsilateral source, suggesting the importance of iM1 to accurate performance. This ERD effect was sustained throughout the duration of visual feedback trials, but only present at the start of no feedback trials, consistent with more variable performance in the absence of feedback. Overall, we found that the behavior of the ERD in iM1 was the most informative aspect concerning the accuracy of the contraction performance, and the ability to maintain a steady level of contraction. This new approach of using FSS to extract multiple orthogonal sources provides the ability to investigate both contralateral and ipsilateral nodes of the motor network without the need for additional information (e.g. electromyography). The enhanced signal-to-noise ratio provided by FSS opens the possibility of extracting complex EEG features on an individual trial basis, which is crucial for a more nuanced understanding of fine motor performance, as well as for applications in brain-computer interfacing.

Sleep disruption in children and adolescents with epilepsy: A systematic review and meta-analysis

This systematic review and meta-analysis aims to assess and quantify putative differences in sleep architecture, sleep efficiency, sleep timing and broadly-defined sleep difficulties between children with and without epilepsy. Databases were searched systematically, and studies identified in PubMed, EMBASE, PsychINFO and Medline. The meta-analysis included 19 studies comparing a total of 901 children with epilepsy to 1470 healthy children. Relative to healthy children, children with epilepsy experienced reduced sleep time, sleeping on average 34 mins less across self-report, actigraphy, 24-h video-EEG and polysomnography measures. They had more sleep difficulties specifically in the domains of night waking, parasomnias and sleep disordered breathing. The analysis also revealed a significantly increased percentage of N2 sleep and decreased sleep efficiency in children with epilepsy compared to healthy children. These results illustrate that children with epilepsy are vulnerable to more sleep difficulties compared to healthy children. This suggests that screening for sleep difficulties should be an integral part in a diagnosis of epilepsy to ensure that clinically relevant sleep difficulties are identified and treated. Such an approach may ultimately aid in the development of treatment strategies which can contribute to improvements in both developmental and diagnostic outcomes for children with epilepsy.

Neurobiological evidence of longer-term physical activity interventions on mental health outcomes and cognition in young people: A systematic review of randomised controlled trials

OBJECTIVE

To investigate putative neurobiological mechanisms that link longer-term physical activity interventions to mental health and cognitive outcomes using randomised controlled trials in children, adolescents and young adults.

DATA SOURCES

A range of medical and psychological science electronic databases were searched (MEDLINE, EMBASE, Scopus, Web of Science, PsychINFO).

REVIEW METHODS

Original research studies were selected, data were extracted and quality was appraised.

RESULTS

Sixteen primary papers were included, ranging from healthy and community samples to subclinical and clinical populations across a variety of age ranges and using different neurobiological measures (e.g. magnetic resonance imaging, electroencephalography, cortisol, brain-derived neurotropic factor).

DISCUSSION

The majority of studies report improvement in mental health and cognition outcomes following longer-term physical activity interventions which coincide with neurobiological alterations, especially neuroimaging alterations in activation and electrophysiological parameters in frontal areas. Future research should include measures of pre-existing fitness and target those who would benefit the most from this type of intervention (e.g. those with a lower level of fitness and at risk for or with mental health problems).

Characterisation of Haemodynamic Activity in Resting State Networks by Fractal Analysis

Intrinsic brain activity is organized into large-scale networks displaying specific structural-functional architecture, known as resting-state networks (RSNs). RSNs reflect complex neurophysiological processes and interactions, and have a central role in distinct sensory and cognitive functions, making it crucial to understand and quantify their anatomical and functional properties. Fractal dimension (FD) provides a parsimonious way of summarizing self-similarity over different spatial and temporal scales but despite its suitability for functional magnetic resonance imaging (fMRI) signal analysis its ability to characterize and investigate RSNs is poorly understood. We used FD in a large sample of healthy participants to differentiate fMRI RSNs and examine how the FD property of RSNs is linked with their functional roles. We identified two clusters of RSNs, one mainly consisting of sensory networks (C1, including auditory, sensorimotor and visual networks) and the other more related to higher cognitive (HCN) functions (C2, including dorsal default mode network and fronto-parietal networks). These clusters were defined in a completely data-driven manner using hierarchical clustering, suggesting that quantification of Blood Oxygen Level Dependent (BOLD) signal complexity with FD is able to characterize meaningful physiological and functional variability. Understanding the mechanisms underlying functional RSNs, and developing tools to study their signal properties, is essential for assessing specific brain alterations and FD could potentially be used for the early detection and treatment of neurological disorders.

Human Circadian Phenotyping and Diurnal Performance Testing in the Real World

In our continuously developing 'around the clock' society, there is a need to increase our understanding of how changes in biology, physiology and psychology influence our health and performance. Embedded within this challenge, is the increasing need to account for individual differences in sleep and circadian rhythms, as well as to explore the impact of time of day on performance in the real world. There are a number of ways to measure sleep and circadian rhythms from subjective questionnaire-based methods to objective sleep/wake monitoring, actigraphy and analysis of biological samples. This paper proposes a protocol that combines multiple techniques to categorize individuals into Early, Intermediate or Late circadian phenotype groups (ECPs/ICPs/LCPs) and recommends how to conduct diurnal performance testing in the field. Representative results show large differences in rest-activity patterns derived from actigraphy, circadian phase (dim light melatonin onset and peak time of cortisol awakening response) between circadian phenotypes. In addition, significant differences in diurnal performance rhythms between ECPs and LCPs emphasizes the need to account for circadian phenotype. In summary, despite the difficulties in controlling influencing factors, this protocol allows a real-world assessment of the impact of circadian phenotype on performance. This paper presents a simple method to assess circadian phenotype in the field and supports the need to consider time of day when designing performance studies.

Late Diagnosis of Narcolepsy With Cataplexy: A Novel Case of Cataplectic Facies Presenting in an Elderly Woman

None

Cataplectic facies is an unusual feature described in children with narcolepsy and cataplexy. The typical manifestations of cataplectic facies consist of repetitive mouth opening, tongue protrusion, and ptosis. An interesting observation is that the usual emotional triggers associated with cataplexy such as laughter and joking are not always present, thus hampering diagnosis of the underlying syndrome. Cataplectic facies is thought to be a phenomenon observed in the early stages of narcolepsy type 1 and is thought to disappear by the time the patient reaches puberty. We present a unique case of an elderly woman with narcolepsy type 1 demonstrating cataplectic facies. The novel circumstances of this case highlight that facial cataplexy can present later in life, in contrast with previous descriptions that report resolution of cataplectic facies before puberty. Wider recognition of these features throughout the life course may aid in accurate diagnosis and thereby ensure swift access to appropriate treatment.

Resetting the late timing of 'night owls' has a positive impact on mental health and performance

BACKGROUND

There is conflict between living according to our endogenous biological rhythms and our external environment, with disruptions resulting in negative consequences to health and performance. This is often documented in shift work and jet lag, but 'societal norms' (eg, typical working hours) can create profound issues for 'night owls', people whose internal biological timing predisposes them to follow an unusually late sleep-wake cycle. Night owls have also been associated with health issues, mood disturbances, poorer performance and increased mortality rates.

METHODS

This study used a randomized control trial design aimed to shift the late timing of night owls to an earlier time (phase advance), using non-pharmacological, practical interventions in a real-world setting. These interventions targeted light exposure (through earlier wake up/sleep times), fixed meals times, caffeine intake and exercise.

RESULTS

Overall, participants demonstrated a significant advance of ∼2 h in sleep/wake timings as measured by actigraphy and circadian phase markers (dim light melatonin onset and peak time of the cortisol awakening response), whilst having no adverse effect on sleep duration. Notably, the phase advance was accompanied by significant improvements to self-reported depression and stress, as well as improved cognitive (reaction time) and physical (grip strength) performance measures during the typical 'suboptimal' morning hours.

CONCLUSIONS

Our findings propose a novel strategy for shifting clock timing towards a pattern that is more aligned to societal demands that could significantly improve elements of performance, mental health and sleep timing in the real world.

Circadian phenotype impacts the brain's resting-state functional connectivity, attentional performance, and sleepiness

INTRODUCTION

Functional connectivity (FC) of the human brain's intrinsically connected networks underpins cognitive functioning and disruptions of FC are associated with sleep and neurological disorders. However, there is limited research on the impact of circadian phenotype and time of day on FC.

STUDY OBJECTIVES

The aim of this study was to investigate resting-state FC of the default mode network (DMN) in Early and Late circadian phenotypes over a socially constrained day.

METHODS

Thirty-eight healthy individuals (14 male, 22.7 ± 4.2 years) categorized as Early (n = 16) or Late (n = 22) using the Munich ChronoType Questionnaire took part. Following a 2-week baseline of actigraphy coupled with saliva samples for melatonin and cortisol rhythms, participants underwent testing at 14:00 hours, 20:00 hours, and 08:00 hours the following morning. Testing consisted of resting-state functional magnetic resonance imaging (fMRI), a structural T1 scan, attentional cognitive performance tasks, and self-reported daytime sleepiness. Seed-based FC analysis from the medial prefrontal and posterior cingulate cortices of the DMN was performed, compared between groups and linked with behavioral data.

RESULTS

Fundamental differences in the DMN were observed between Early and Late circadian phenotypes. Resting-state FC of the DMN predicted individual differences in attention and subjective ratings of sleepiness.

CONCLUSION

Differences in FC of the DMN may underlie the compromised attentional performance and increased sleepiness commonly associated with Late types when they conform to a societally constrained day that does not match their intrinsic circadian phenotype.

Objective and subjective measures of prior sleep-wake behavior predict functional connectivity in the default mode network during NREM sleep

INTRODUCTION

Prior sleep behavior has been shown to correlate with waking resting-state functional connectivity (FC) in the default mode network (DMN). However, the impact of sleep history on FC during sleep has not been investigated. The aim of this study was to establish whether there is an association between intersubject variability in habitual sleep behaviors and the strength of FC within the regions of the DMN during non-rapid eye movement (NREM) sleep.

METHODS

Wrist actigraphy and sleep questionnaires were used as objective and subjective measures of habitual sleep behavior, and EEG-functional MRI during NREM sleep was used to quantify sleep.

RESULTS

There was a significant, regionally specific association between the interindividual variability in objective (total sleep time on the night before scanning) and subjective (Insomnia Severity Index) measures of prior sleep-wake behavior and the strength of DMN FC during subsequent wakefulness and NREM sleep. In several cases, FC was related to sleep measures independently of sleep stage, suggesting that previous sleep history effects sleep FC globally across the stages.

CONCLUSIONS

This work highlights the need to consider a subject's prior sleep history in studies utilizing FC analysis during wakefulness and sleep, and indicates the complexity of the impact of sleep on the brain both in the short and long term.

Thalamic functional connectivity and its association with behavioral performance in older age

Introduction

Despite the thalamus' dense connectivity with both cortical and subcortical structures, few studies have specifically investigated how thalamic connectivity changes with age and how such changes are associated with behavior. This study investigated the effect of age on thalamo-cortical and thalamo-hippocampal functional connectivity (FC) and the association between thalamic FC and visual-spatial memory and reaction time (RT) performance in older adults.

Methods

Resting-state functional magnetic resonance images were obtained from younger (n = 20) and older (n = 20) adults. A seed-based approach was used to assess the FC between the thalamus and (1) sensory resting-state networks; (2) the hippocampus. Participants also completed visual-spatial memory and RT tasks, from the Cambridge Neuropsychological Test Automated Battery (CANTAB).

Results

Older adults exhibited a loss of specificity in the FC between sensory thalamic subregions and corresponding sensory cortex. Greater thalamo-motor FC in older adults was associated with faster RTs. Furthermore, older adults exhibited greater thalamo-hippocampal FC compared to younger adults, which was greatest for those with the poorest visual-spatial memory performance.

Conclusion

Although older adults exhibited poorer visual-spatial memory and slower reaction times compared to younger adults, "good" and "poorer" older performers exhibited different patterns of thalamo-cortical and thalamo-hippocampal FC. These results highlight the potential role of thalamic connectivity in supporting reaction times and memory in aging. Furthermore, these results highlight the importance of including the thalamus in studies of aging to fully understand how brain changes with age may be associated with behavior.

Sleep onset uncovers thalamic abnormalities in patients with idiopathic generalised epilepsy

The thalamus is crucial for sleep regulation and the pathophysiology of idiopathic generalised epilepsy (IGE), and may serve as the underlying basis for the links between the two. We investigated this using EEG-fMRI and a specific emphasis on the role and functional connectivity (FC) of the thalamus. We defined three types of thalamic FC: thalamocortical, inter-hemispheric thalamic, and intra-hemispheric thalamic. Patients and controls differed in all three measures, and during wakefulness and sleep, indicating disorder-dependent and state-dependent modification of thalamic FC. Inter-hemispheric thalamic FC differed between patients and controls in somatosensory regions during wakefulness, and occipital regions during sleep. Intra-hemispheric thalamic FC was significantly higher in patients than controls following sleep onset, and disorder-dependent alterations to FC were seen in several thalamic regions always involving somatomotor and occipital regions. As interactions between thalamic sub-regions are indirect and mediated by the inhibitory thalamic reticular nucleus (TRN), the results suggest abnormal TRN function in patients with IGE, with a regional distribution which could suggest a link with the thalamocortical networks involved in the generation of alpha rhythms. Intra-thalamic FC could be a more widely applicable marker beyond patients with IGE.

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Sleep onset modifies thalamic FC in generalised epilepsy differently to controls.

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Differences are regionally specific.

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Regions connected to somatomotor/occipital cortices are consistently affected.

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Intra-thalamic FC may be a surrogate marker of thalamic reticular nucleus function.

Dynamic spatiotemporal variability of alpha-BOLD relationships during the resting-state and task-evoked responses

Accurate characterization of the spatiotemporal relationship between two of the most prominent neuroimaging measures of neuronal activity, the 8-13Hz, occipito-parietal EEG alpha oscillation and the BOLD fMRI signal, must encompass the intrinsically dynamic nature of both alpha power and brain function. Here, during the eyes-open resting state, we use a 16s sliding-window analysis and demonstrate that the mean spatial network of dynamic alpha-BOLD correlations is highly comparable to the static network calculated over six minutes. However, alpha-BOLD correlations showed substantial spatiotemporal variability within-subjects and passed through many different configurations such that the static network was fully represented in only ~10% of 16s epochs, with visual and parietal regions (coherent on average) often opposingly correlated with each other or with alpha. We find that the common assumption of static-alpha BOLD correlations greatly oversimplifies temporal variation in brain network dynamics. Fluctuations in alpha-BOLD coupling significantly depended upon the instantaneous amplitude of alpha power, and primary and lateral visual areas were most strongly negatively correlated with alpha during different alpha power states, possibly suggesting the action of multiple alpha mechanisms. Dynamic alpha-BOLD correlations could not be explained by eye-blinks/movements, head motion or non-neuronal physiological variability. Individual's mean alpha power and frequency were found to contribute to between-subject variability in alpha-BOLD correlations. Additionally, application to a visual stimulation dataset showed that dynamic alpha-BOLD correlations provided functional information pertaining to the brain's response to stimulation by exhibiting spatiotemporal fluctuations related to variability in the trial-by-trial BOLD response magnitude. Significantly weaker visual alpha-BOLD correlations were found both preceding and following small amplitude BOLD response trials compared to large response trials.

Habitual sleep durations and subjective sleep quality predict white matter differences in the human brain

Self-imposed short sleep durations are increasingly commonplace in society, and have considerable health and performance implications for individuals. Reduced sleep duration over multiple nights has similar behavioural effects to those observed following acute total sleep deprivation, suggesting that lack of sleep affects brain function cumulatively. A link between habitual sleep patterns and functional connectivity has previously been observed, and the effect of sleep duration on the brain's intrinsic functional architecture may provide a link between sleep status and cognition. However, it is currently not known whether differences in habitual sleep patterns across individuals are related to changes in the brain's white matter, which underlies structural connectivity. In the present study we use diffusion–weighted imaging and a group comparison application of tract based spatial statistics (TBSS) to investigate changes to fractional anisotropy (FA) and mean diffusivity (MD) in relation to sleep duration and quality, hypothesising that white matter metrics would be positively associated with sleep duration and quality. Diffusion weighted imaging data was acquired from a final cohort of 33 (23–29 years, 10 female, mean 25.4 years) participants. Sleep patterns were assessed for a 14 day period using wrist actigraphs and sleep diaries, and subjective sleep quality with the Pittsburgh Sleep Quality Index (PSQI). Median splits based on total sleep time and PSQI were used to create groups of shorter/longer and poorer/better sleepers, whose imaging data was compared using TBSS followed by post-hoc correlation analysis in regions identified as significantly different between the groups. There were significant positive correlations between sleep duration and FA in the left orbito-frontal region and the right superior corona radiata, and significant negative correlations between sleep duration and MD in right orbito-frontal white matter and the right inferior longitudinal fasciculus. Improved sleep quality was positively correlated with FA in left caudate nucleus, white matter tracts to the left orbito-frontal region, the left anterior cingulum bundle and the white matter tracts associated with the right operculum and insula, and negatively correlated with MD in left orbito-frontal white matter and the left anterior cingulum bundle. Our findings suggest that reduced cumulative total sleep time (cTST) and poorer subjective sleep quality are associated with subtle white matter micro-architectural changes. The regions we identified as being related to habitual sleep patterns were restricted to the frontal and temporal lobes, and the functions they support are consistent with those which have previously been demonstrated as being affected by short sleep durations (e.g., attention, cognitive control, memory). Examining how inter-individual differences in brain structure are related to habitual sleep patterns could help to shed light on the mechanisms by which sleep habits are associated with brain function, behaviour and cognition, as well as potentially the networks and systems responsible for variations in sleep patterns themselves.

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Habitual sleep duration and quality are associated with changes to DTI metrics.

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Shorter duration and poorer quality link with reduced FA and increased MD.

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Changes are specific to frontal and temporal regions.

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Changes to brain structure could underlie behavioral effects of sleep.

Gender Specific Re-organization of Resting-State Networks in Older Age

Advancing age is commonly associated with changes in both brain structure and function. Recently, the suggestion that alterations in brain connectivity may drive disruption in cognitive abilities with age has been investigated. However, the interaction between the effects of age and gender on the re-organization of resting-state networks is not fully understood. This study sought to investigate the effect of both age and gender on intra- and inter-network functional connectivity (FC) and the extent to which resting-state network (RSN) node definition may alter with older age. We obtained resting-state functional magnetic resonance images from younger (n = 20) and older (n = 20) adults and assessed the FC of three main cortical networks: default mode (DMN), dorsal attention (DAN), and saliency (SN). Older adults exhibited reduced DMN intra-network FC and increased inter-network FC between the anterior cingulate cortex (ACC) and nodes of the DAN, in comparison to younger participants. Furthermore, this increase in ACC-DAN inter-network FC with age was driven largely by male participants. However, further analyses suggested that the spatial location of ACC, bilateral anterior insula and orbitofrontal cortex RSN nodes changed with older age and that age-related gender differences in FC may reflect spatial re-organization rather than increases or decreases in FC strength alone. These differences in both the FC and spatial distribution of RSNs between younger and older adults provide evidence of re-organization of fundamental brain networks with age, which is modulated by gender. These results highlight the need to further investigate changes in both intra- and inter-network FC with age, whilst also exploring the modifying effect of gender. They also emphasize the difficulties in directly comparing the FC of RSN nodes between groups and suggest that caution should be taken when using the same RSN node definitions for different age or patient groups to investigate FC.

Global signal modulation of single-trial fMRI response variability: Effect on positive vs negative BOLD response relationship

In functional magnetic resonance imaging (fMRI), the relationship between positive BOLD responses (PBRs) and negative BOLD responses (NBRs) to stimulation is potentially informative about the balance of excitatory and inhibitory brain responses in sensory cortex. In this study, we performed three separate experiments delivering visual, motor or somatosensory stimulation unilaterally, to one side of the sensory field, to induce PBR and NBR in opposite brain hemispheres. We then assessed the relationship between the evoked amplitudes of contralateral PBR and ipsilateral NBR at the level of both single-trial and average responses. We measure single-trial PBR and NBR peak amplitudes from individual time-courses, and show that they were positively correlated in all experiments. In contrast, in the average response across trials the absolute magnitudes of both PBR and NBR increased with increasing stimulus intensity, resulting in a negative correlation between mean response amplitudes. Subsequent analysis showed that the amplitude of single-trial PBR was positively correlated with the BOLD response across all grey-matter voxels and was not specifically related to the ipsilateral sensory cortical response. We demonstrate that the global component of this single-trial response modulation could be fully explained by voxel-wise vascular reactivity, the BOLD signal standard deviation measured in a separate resting-state scan (resting state fluctuation amplitude, RSFA). However, bilateral positive correlation between PBR and NBR regions remained. We further report that modulations in the global brain fMRI signal cannot fully account for this positive PBR-NBR coupling and conclude that the local sensory network response reflects a combination of superimposed vascular and neuronal signals. More detailed quantification of physiological and noise contributions to the BOLD signal is required to fully understand the trial-by-trial PBR and NBR relationship compared with that of average responses.

Variability in Cumulative Habitual Sleep Duration Predicts Waking Functional Connectivity

STUDY OBJECTIVES

We examined whether interindividual differences in habitual sleep patterns, quantified as the cumulative habitual total sleep time (cTST) over a 2-w period, were reflected in waking measurements of intranetwork and internetwork functional connectivity (FC) between major nodes of three intrinsically connected networks (ICNs): default mode network (DMN), salience network (SN), and central executive network (CEN).

METHODS

Resting state functional magnetic resonance imaging (fMRI) study using seed-based FC analysis combined with 14-d wrist actigraphy, sleep diaries, and subjective questionnaires (N = 33 healthy adults, mean age 34.3, standard deviation ± 11.6 y). Data were statistically analyzed using multiple linear regression. Fourteen consecutive days of wrist actigraphy in participant's home environment and fMRI scanning on day 14 at the Birmingham University Imaging Centre. Seed-based FC analysis on ICNs from resting-state fMRI data and multiple linear regression analysis performed for each ICN seed and target. cTST was used to predict FC (controlling for age).

RESULTS

cTST was specific predictor of intranetwork FC when the mesial prefrontal cortex (MPFC) region of the DMN was used as a seed for FC, with a positive correlation between FC and cTST observed. No significant relationship between FC and cTST was seen for any pair of nodes not including the MPFC. Internetwork FC between the DMN (MPFC) and SN (right anterior insula) was also predicted by cTST, with a negative correlation observed between FC and cTST.

CONCLUSIONS

This study improves understanding of the relationship between intranetwork and internetwork functional connectivity of intrinsically connected networks (ICNs) in relation to habitual sleep quality and duration. The cumulative amount of sleep that participants achieved over a 14-d period was significantly predictive of intranetwork and inter-network functional connectivity of ICNs, an observation that may underlie the link between sleep status and cognitive 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.

Early haemodynamic changes observed in patients with epilepsy, in a visual experiment and in simulations

OBJECTIVE

The objective of this study was to investigate whether previously reported early blood oxygen level dependent (BOLD) changes in epilepsy could occur as a result of the modelling techniques rather than physiological changes.

METHODS

EEG-fMRI data were analysed from seven patients with focal epilepsy, six control subjects undergoing a visual experiment, in addition to simulations. In six separate analyses the event timing was shifted by either -9,-6,-3,+3,+6 or +9 s relative to the onset of the interictal epileptiform discharge (IED) or stimulus.

RESULTS

The visual dataset and simulations demonstrated an overlap between modelled haemodynamic response function (HRF) at event onset and at ± 3 s relative to onset, which diminished at ± 6s. Pre-spike analysis at -6s improved concordance with the assumed IED generating lobe relative to the standard HRF in 43% of patients.

CONCLUSION

The visual and simulated dataset findings indicate a form of "temporal bleeding", an overlap between the modelled HRF at time 0 and at ± 3s which attenuated at ± 6s. Pre-spike analysis at -6s may improve concordance.

SIGNIFICANCE

This form of analysis should be performed at 6s prior to onset of IED to minimise temporal bleeding effect. The results support the presence of relevant BOLD responses occurring prior to IEDs.

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.

Evidence that the negative BOLD response is neuronal in origin: a simultaneous EEG-BOLD-CBF study in humans

Unambiguous interpretation of changes in the BOLD signal is challenging because of the complex neurovascular coupling that translates changes in neuronal activity into the subsequent haemodynamic response. In particular, the neurophysiological origin of the negative BOLD response (NBR) remains incompletely understood. Here, we simultaneously recorded BOLD, EEG and cerebral blood flow (CBF) responses to 10 s blocks of unilateral median nerve stimulation (MNS) in order to interrogate the NBR. Both negative BOLD and negative CBF responses to MNS were observed in the same region of the ipsilateral primary sensorimotor cortex (S1/M1) and calculations showed that MNS induced a decrease in the cerebral metabolic rate of oxygen consumption (CMRO2) in this NBR region. The ∆CMRO2/∆CBF coupling ratio (n) was found to be significantly larger in this ipsilateral S1/M1 region (n=0.91±0.04, M=10.45%) than in the contralateral S1/M1 (n=0.65±0.03, M=10.45%) region that exhibited a positive BOLD response (PBR) and positive CBF response, and a consequent increase in CMRO2 during MNS. The fMRI response amplitude in ipsilateral S1/M1 was negatively correlated with both the power of the 8-13 Hz EEG mu oscillation and somatosensory evoked potential amplitude. Blocks in which the largest magnitude of negative BOLD and CBF responses occurred therefore showed greatest mu power, an electrophysiological index of cortical inhibition, and largest somatosensory evoked potentials. Taken together, our results suggest that a neuronal mechanism underlies the NBR, but that the NBR may originate from a different neurovascular coupling mechanism to the PBR, suggesting that caution should be taken in assuming the NBR simply represents the neurophysiological inverse of the PBR.

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.

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.

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.

EEG-fMRI based information theoretic characterization of the human perceptual decision system

The modern metaphor of the brain is that of a dynamic information processing device. In the current study we investigate how a core cognitive network of the human brain, the perceptual decision system, can be characterized regarding its spatiotemporal representation of task-relevant information. We capitalize on a recently developed information theoretic framework for the analysis of simultaneously acquired electroencephalography (EEG) and functional magnetic resonance imaging data (fMRI) (Ostwald et al. (2010), NeuroImage 49: 498-516). We show how this framework naturally extends from previous validations in the sensory to the cognitive domain and how it enables the economic description of neural spatiotemporal information encoding. Specifically, based on simultaneous EEG-fMRI data features from n = 13 observers performing a visual perceptual decision task, we demonstrate how the information theoretic framework is able to reproduce earlier findings on the neurobiological underpinnings of perceptual decisions from the response signal features' marginal distributions. Furthermore, using the joint EEG-fMRI feature distribution, we provide novel evidence for a highly distributed and dynamic encoding of task-relevant information in the human brain.

Preictal changes in cerebral haemodynamics: review of findings and insights from intracerebral EEG

The possibility of recording changes in brain signals occurring before epileptic seizures is of considerable interest, both as markers for seizure anticipation and as a window into the mechanisms of seizure generation. Several studies have reported preictal changes on electrophysiological traces. More recently, observations have been made of changes occurring on haemodynamic signals before interictal events or before seizures, often without concurrent changes observed on electrophysiology. We present here a critical review of these findings, in optical imaging, SPECT and fMRI, followed by a discussion based on data from intracerebral EEG.

Brain mechanisms of altered consciousness in focal seizures

Consciousness is a central concept in epileptology, relevant to the understanding of both focal and generalized seizures. Within focal seizures, impairment of consciousness has long been considered as the main criterion differentiating complex partial seizures (CPS) from simple partial seizures With the development of improved tools for investigating human brain function, new insights into the brain mechanisms of altered consciousness in CPS have become available. This paper reviews the existing literature on how the currently available methods can be used to address the fundamental issue of how CPS alter consciousness.

The relationship between the visual evoked potential and the gamma band investigated by blind and semi-blind methods

Gamma Band Activity (GBA) is increasingly studied for its relation with attention, change detection, maintenance of working memory and the processing of sensory stimuli. Activity around the gamma range has also been linked with early visual processing, although the relationship between this activity and the low frequency visual evoked potential (VEP) remains unclear. This study examined the ability of blind and semi-blind source separation techniques to extract sources specifically related to the VEP and GBA in order to shed light on the relationship between them. Blind (Independent Component Analysis—ICA) and semi-Blind (Functional Source Separation—FSS) methods were applied to dense array EEG data recorded during checkerboard stimulation. FSS was performed with both temporal and spectral constraints to identify specifically the generators of the main peak of the VEP (P100) and of the GBA. Source localisation and time-frequency analyses were then used to investigate the properties and co-dependencies between VEP/P100 and GBA. Analysis of the VEP extracted using the different methods demonstrated very similar morphology and localisation of the generators. Single trial time frequency analysis showed higher GBA when a larger amplitude VEP/P100 occurred. Further examination indicated that the evoked (phase-locked) component of the GBA was more related to the P100, whilst the induced component correlated with the VEP as a whole. The results suggest that the VEP and GBA may be generated by the same neuronal populations, and implicate this relationship as a potential mediator of the correlation between the VEP and the Blood Oxygenation Level Dependent (BOLD) effect measured with fMRI.

Voxel-wise information theoretic EEG-fMRI feature integration

We have recently proposed the evaluation of a set of information theoretic quantities (ITQs) for the integration of simultaneously acquired EEG-fMRI data (Ostwald, D., Porcaro, C., Bagshaw, A.P., 2010. An information theoretic approach to EEG-fMRI integration of visually evoked responses. Neuroimage. 49, 498-516). In our previous experimental evaluation of the information theoretic framework, we defined the data subsets from which to calculate the ITQs using a priori constraints. In the case of EEG, this meant that data were extracted from a single electrode, while for fMRI the analysed data came from voxels contained within a sphere surrounding the most responsive voxel of visual cortex. While this approach was a natural starting point for the evaluation of the framework in the application to combined EEG-fMRI data sets, a more principled approach to data selection is desirable. Here, we propose to combine standard fMRI data pre-processing and low-resolution electromagnetic tomography (LORETA) for the evaluation of ITQs across the entire three-dimensional brain space. We apply the proposed method to a simultaneous EEG-fMRI data set acquired during checkerboard stimulation and assess the topographical informativeness of EEG (time and frequency domain) and fMRI features with respect to the stimulus and each other. The resulting information theoretic effect size maps are supplemented with a statistical evaluation based on Gaussian null model simulations using a false-discovery rate procedure. Given the contamination of EEG recordings by artefacts induced by the MR scanning environment we further assessed the influence of different advanced EEG pre-processing methods (independent component analysis and functional source separation) on the information topography. The results of this analysis provide evidence for the topographically focussed informativeness of both EEG and fMRI features with respect to the stimulus, but for the current feature selection do not detect EEG-fMRI activity dependence. More advanced EEG data pre-processing rendered the feature distributions more stimulus-informative, but did not alter the EEG-fMRI activity and conditional dependencies.

Functional connectivity of the posteromedial cortex

As different areas within the PMC have different connectivity patterns with various cortical and subcortical regions, we hypothesized that distinct functional modules may be present within the PMC. Because the PMC appears to be the most active region during resting state, it has been postulated to play a fundamental role in the control of baseline brain functioning within the default mode network (DMN). Therefore one goal of this study was to explore which components of the PMC are specifically involved in the DMN. In a sample of seventeen healthy volunteers, we performed an unsupervised voxelwise ROI-based clustering based on resting state functional connectivity. Our results showed four clusters with different network connectivity. Each cluster showed positive and negative correlations with cortical regions involved in the DMN. Progressive shifts in PMC functional connectivity emerged from anterior to posterior and from dorsal to ventral ROIs. Ventral posterior portions of PMC were found to be part of a network implicated in the visuo-spatial guidance of movements, whereas dorsal anterior portions of PMC were interlinked with areas involved in attentional control. Ventral retrosplenial PMC selectively correlated with a network showing considerable overlap with the DMN, indicating that it makes essential contributions in self-referential processing, including autobiographical memory processing. Finally, ventral posterior PMC was shown to be functionally connected with a visual network.

The paper represents the first attempt to provide a systematic, unsupervised, voxelwise clustering of the human posteromedial cortex (PMC), using resting-state functional connectivity data. Moreover, a ROI-based parcellation was used to confirm the results.

An information theoretic approach to EEG-fMRI integration of visually evoked responses

The integration of signals from electro-encephalography (EEG) and functional magnetic resonance imaging (fMRI), acquired simultaneously from the same observer, holds great potential for the elucidation of the neurobiological underpinnings of human brain function. However, the most appropriate way in which to combine the data in order to achieve this goal is not clear. Here, we apply a novel route to the integration of simultaneously acquired multimodal brain imaging data. We adopt a theoretical framework developed in the study of neuronal population codes which explicitly takes into account the experimentally observed stimulus-response signal probability distributions using the concept of mutual information. We study the implications of this framework using simulated data sets generated from a set of linear Gaussian models, and apply the framework to EEG-fMRI data acquired during checkerboard stimulation of low and high contrast. We focus our evaluation on single-trial time-domain signal features from both modalities and provide evidence for the informativeness of a subset of these features with respect to the stimulus and each other. Specifically, the framework was able to identify the contrast dependency of the haemodynamic response and the P100 peak of the visual evoked potential, and showed that combining EEG and fMRI time-domain features by quantifying the information in their joint distribution was more informative than treating each one in isolation. In addition, the effect of different pre-processing strategies for EEG-fMRI data can be assessed quantitatively, indicating the improvements to be gained by more advanced methods. We conclude that the information theoretic framework is a promising methodology to quantify the relative importance of different response features in neural coding and neurovascular coupling, as well as the success of data pre-processing strategies.

The neural correlates of altered consciousness during epileptic seizures

Epileptic seizures are characterized by a multifaceted spectrum of alterations in the general level of awareness and/or the subjective contents of consciousness. Complete loss of consciousness occurs when epileptic activity involves both cortical and subcortical structures, as in generalized seizures. On the other hand, simple partial seizures can spare both the level and contents of consciousness. Finally, complex partial seizures associated with medial temporal lobe discharges can selectively impair the patient's subjective experiences with variable degrees of responsiveness. The differences in ictal semiology between patients with epilepsy offer unique avenues for understanding the relationship between pathological brain function and altered conscious states.

Effect of sleep stage on interictal high-frequency oscillations recorded from depth macroelectrodes in patients with focal epilepsy

PURPOSE

To investigate the effect of sleep stage on the properties of high-frequency oscillations (HFOs) recorded from depth macroelectrodes in patients with focal epilepsy.

METHODS

Ten-minute epochs of wakefulness (W), stage 1-2 non-REM (N1-N2), stage 3 non-REM (N3) and REM sleep (R) were identified from stereo-electroencephalography (SEEG) data recorded at 2 kHz in nine patients. Rates of spikes, ripples (>80 Hz), and fast ripples (>250 Hz) were calculated, as were HFO durations, degree of spike-HFO overlap, HFO rates inside and outside of spikes, and inside and outside of the seizure-onset zone (SOZ).

RESULTS

Ripples were observed in nine patients and fast ripples in eight. Spike rate was highest in N1-N2 in 5 of 9 patients, and in N3 in 4 of 9 patients, whereas ripple rate was highest in N1-N2 in 4 of 9 patients, in N3 in 4 of 9 patients, and in W in 1 of 9 patients. Fast ripple rate was highest in N1-N2 in 4 of 8 patients, and in N3 in 4 of 8 patients. HFO properties changed significantly with sleep stage, although the absolute effects were small. The difference in HFO rates inside and outside of the SOZ was highly significant (p < 0.000001) in all stages except for R and, for fast ripples, only marginally significant (p = 0.018) in W.

CONCLUSIONS

Rates of HFOs recorded from depth macroelectrodes are highest in non-REM sleep. HFO properties were similar in stages N1-N2 and N3, suggesting that accurate sleep staging is not necessary. The spatial specificity of HFO, particularly fast ripples, was affected by sleep stage, suggesting that recordings excluding REM sleep and wakefulness provide a more reliable indicator of the SOZ.

Scanning strategies for simultaneous EEG–fMRI evoked potential studies at 3 T

There are two basic strategies for applying simultaneous EEG-fMRI: either the fMRI data are acquired continuously, or the stimulus is presented during a brief gap in scanning when the EEG data is clear of gradient artefact. The former has the advantage that the protocol for the fMRI data acquisition is not affected by the presence of EEG. This study investigated the effect of these different strategies and the subsequent ballistocardiogram artefact removal methods (Average Artefact Subtraction (AAS) and Optimal Basis Set (OBS)) on EEG data quality recorded in response to a visual stimulus. Continuous scanning generally resulted in VEPs that were no worse, and in some cases were better, than those measured during a gap in scanning. The AAS and OBS methods lead to comparable results at the level of the grand average visual evoked potential (VEP), although when examined at the level of the single trial the OBS method was more effective. The spectral quality of the data was similar across scanning protocols, as demonstrated by the proportion of spectral power in each frequency band, although there was an effect of the artefact removal method on the overall spectral power. Some differences in the VEPs were also noted when a TR of 1.5 s was used relative to a TR of 3 s. The results indicate improved EEG quality when fMRI scanning is continuous and BCG artefacts are removed using the OBS method, confirming that EEG can be added to an fMRI experiment with minimal change to the experimental protocol.