Microscale Neuronal Activity Collectively Drives Chaotic and Inflexible Dynamics at the Macroscale in Seizures.

Neuronal activity propagates through the network during seizures, engaging brain dynamics at multiple scales. Such propagating events can be described through the avalanches framework, which can relate spatiotemporal activity at the microscale with global network properties. Interestingly, propagating avalanches in healthy networks are indicative of critical dynamics, where the network is organized to a phase transition, which optimizes certain computational properties. Some have hypothesized that the pathologic brain dynamics of epileptic seizures are an emergent property of microscale neuronal networks collectively driving the brain away from criticality. Demonstrating this would provide a unifying mechanism linking microscale spatiotemporal activity with emergent brain dysfunction during seizures. Here, we investigated the effect of drug-induced seizures on critical avalanche dynamics, using in vivo whole-brain two-photon imaging of GCaMP6s larval zebrafish (males and females) at single neuron resolution. We demonstrate that single neuron activity across the whole brain exhibits a loss of critical statistics during seizures, suggesting that microscale activity collectively drives macroscale dynamics away from criticality. We also construct spiking network models at the scale of the larval zebrafish brain, to demonstrate that only densely connected networks can drive brain-wide seizure dynamics away from criticality. Importantly, such dense networks also disrupt the optimal computational capacities of critical networks, leading to chaotic dynamics, impaired network response properties and sticky states, thus helping to explain functional impairments during seizures. This study bridges the gap between microscale neuronal activity and emergent macroscale dynamics and cognitive dysfunction during seizures.SIGNIFICANCE STATEMENT Epileptic seizures are debilitating and impair normal brain function. It is unclear how the coordinated behavior of neurons collectively impairs brain function during seizures. To investigate this we perform fluorescence microscopy in larval zebrafish, which allows for the recording of whole-brain activity at single-neuron resolution. Using techniques from physics, we show that neuronal activity during seizures drives the brain away from criticality, a regime that enables both high and low activity states, into an inflexible regime that drives high activity states. Importantly, this change is caused by more connections in the network, which we show disrupts the ability of the brain to respond appropriately to its environment. Therefore, we identify key neuronal network mechanisms driving seizures and concurrent cognitive dysfunction.

Minimum clinical utility standards for wearable seizure detectors: A simulation study.

OBJECTIVE: Epilepsy management employs self-reported seizure diaries, despite evidence of seizure underreporting. Wearable and implantable seizure detection devices are now becoming more widely available. There are no clear guidelines about what levels of accuracy are sufficient. This study aimed to simulate clinical use cases and identify the necessary level of accuracy for each. METHODS: Using a realistic seizure simulator (CHOCOLATES), a ground truth was produced, which was then sampled to generate signals from simulated seizure detectors of various capabilities. Five use cases were evaluated: (1) randomized clinical trials (RCTs), (2) medication adjustment in clinic, (3) injury prevention, (4) sudden unexpected death in epilepsy (SUDEP) prevention, and (5) treatment of seizure clusters. We considered sensitivity (0%-100%), false alarm rate (FAR; 0-2/day), and device type (external wearable vs. implant) in each scenario. RESULTS: The RCT case was efficient for a wide range of wearable parameters, though implantable devices were preferred. Lower accuracy wearables resulted in subtle changes in the distribution of patients enrolled in RCTs, and therefore higher sensitivity and lower FAR values were preferred. In the clinic case, a wide range of sensitivity, FAR, and device type yielded similar results. For injury prevention, SUDEP prevention, and seizure cluster treatment, each scenario required high sensitivity and yet was minimally influenced by FAR. SIGNIFICANCE: The choice of use case is paramount in determining acceptable accuracy levels for a wearable seizure detection device. We offer simulation results for determining and verifying utility for specific use case and specific wearable parameters.

Global nonlinear approach for mapping parameters of neural mass models.

Neural mass models (NMMs) are important for helping us interpret observations of brain dynamics. They provide a means to understand data in terms of mechanisms such as synaptic interactions between excitatory and inhibitory neuronal populations. To interpret data using NMMs we need to quantitatively compare the output of NMMs with data, and thereby find parameter values for which the model can produce the observed dynamics. Mapping dynamics to NMM parameter values in this way has the potential to improve our understanding of the brain in health and disease. Though abstract, NMMs still comprise of many parameters that are difficult to constrain a priori. This makes it challenging to explore the dynamics of NMMs and elucidate regions of parameter space in which their dynamics best approximate data. Existing approaches to overcome this challenge use a combination of linearising models, constraining the values they can take and exploring restricted subspaces by fixing the values of many parameters a priori. As such, we have little knowledge of the extent to which different regions of parameter space of NMMs can yield dynamics that approximate data, how nonlinearities in models can affect parameter mapping or how best to quantify similarities between model output and data. These issues need to be addressed in order to fully understand the potential and limitations of NMMs, and to aid the development of new models of brain dynamics in the future. To begin to overcome these issues, we present a global nonlinear approach to recovering parameters of NMMs from data. We use global optimisation to explore all parameters of nonlinear NMMs simultaneously, in a minimally constrained way. We do this using multi-objective optimisation (multi-objective evolutionary algorithm, MOEA) so that multiple data features can be quantified. In particular, we use the weighted horizontal visibility graph (wHVG), which is a flexible framework for quantifying different aspects of time series, by converting them into networks. We study EEG alpha activity recorded during the eyes closed resting state from 20 healthy individuals and demonstrate that the MOEA performs favourably compared to single objective approaches. The addition of the wHVG objective allows us to better constrain the model output, which leads to the recovered parameter values being restricted to smaller regions of parameter space, thus improving the practical identifiability of the model. We then use the MOEA to study differences in the alpha rhythm observed in EEG recorded from 20 people with epilepsy. We find that a small number of parameters can explain this difference and that, counterintuitively, the mean excitatory synaptic gain parameter is reduced in people with epilepsy compared to control. In addition, we propose that the MOEA could be used to mine for the presence of pathological rhythms, and demonstrate the application of this to epileptiform spike-wave discharges.

Functional abilities, respiratory and cardiac function in a large cohort of adults with Duchenne muscular dystrophy treated with glucocorticoids.

BACKGROUND AND PURPOSE: The transition to adult services, and subsequent glucocorticoid management, is critical in adults with Duchenne muscular dystrophy. This study aims (1) to describe treatment, functional abilities, respiratory and cardiac status during transition to adulthood and adult stages; and (2) to explore the association between glucocorticoid treatment after loss of ambulation (LOA) and late-stage clinical outcomes. METHODS: This was a retrospective single-centre study on individuals with Duchenne muscular dystrophy (≥16 years old) between 1986 and 2022. Logistic regression, Cox proportional hazards models and survival analyses were conducted utilizing data from clinical records. RESULTS: In all, 112 individuals were included. Mean age was 23.4 ± 5.2 years and mean follow-up was 18.5 ± 5.5 years. At last assessment, 47.2% were on glucocorticoids; the mean dose of prednisone was 0.38 ± 0.13 mg/kg/day and of deflazacort 0.43 ± 0.16 mg/kg/day. At age 16 years, motor function limitations included using a manual wheelchair (89.7%), standing (87.9%), transferring from a wheelchair (86.2%) and turning in bed (53.4%); 77.5% had a peak cough flow <270 L/min, 53.3% a forced vital capacity percentage of predicted <50% and 40.3% a left ventricular ejection fraction <50%. Glucocorticoids after LOA reduced the risk and delayed the time to difficulties balancing in the wheelchair, loss of hand to mouth function, forced vital capacity percentage of predicted <30% and forced vital capacity <1 L and were associated with lower frequency of left ventricular ejection fraction <50%, without differences between prednisone and deflazacort. Glucocorticoid dose did not differ by functional, respiratory or cardiac status. CONCLUSION: Glucocorticoids after LOA preserve late-stage functional abilities, respiratory and cardiac function. It is suggested using functional abilities, respiratory and cardiac status at transition stages for adult services planning.

Mental health and neurodevelopmental patient-reported outcome measures (PROMs) for children and young people with epilepsy: A systematic review.

Children and young people with epilepsy are at higher risk of mental health disorders and atypical neurodevelopmental outcomes compared to the general population. It is essential to detect such comorbidities early in children with epilepsy and provide appropriate interventions, to improve clinical outcomes. We aimed to identify and evaluate the measurement properties of Patient-Reported Outcome Measures (PROMs) that have been validated specifically to measure mental health and neurodevelopmental outcomes in children and/or young people with epilepsy. We searched Embase, Medline, and PsycINFO in May 2023 for relevant studies. Mental health was defined as psychological symptoms (e.g., anxiety, depression, psychosis) and/or behavioural difficulties (e.g., conduct disorders). Neurodevelopmental outcomes included neurodevelopmental disorder traits such as attention-deficit hyperactivity disorder (ADHD) and autistic spectrum disorders. We assessed methodological quality using Consensus-based Standards for the selection of health Measurement Instruments (COSMIN) guidance. Twelve papers were identified that psychometrically evaluated 13 relevant PROMs (two epilepsy-specific, eleven generic). The appraisal of the PROMs was limited by the availability of only one or two published articles for each, and incomplete psychometric evaluations in some cases. The tool demonstrating the strongest evidence was The Neurological Disorders Depression Inventory-Epilepsy for Youth. The ADHD Rating Scale-IV and The Paediatric Symptom Checklist -17 demonstrated good evidence in favour of at least two measurement properties. This review identified only a small number of mental health and neurodevelopmental PROMs evaluated specifically in paediatric epilepsy. There is a need for further validation of mental health and neurodevelopmental PROMs in children with epilepsy.

Feasibility and acceptability of an ultra-long-term at-home EEG monitoring system (EEG@HOME) for people with epilepsy.

BACKGROUND: Recent technological advancements offer new ways to monitor and manage epilepsy. The adoption of these devices in routine clinical practice will strongly depend on patient acceptability and usability, with their perspectives being crucial. Previous studies provided feedback from patients, but few explored the experience of them using independently multiple devices independently at home. PURPOSE: The study, assessed through a mixed methods design, the direct experiences of people with epilepsy independently using a non-invasive monitoring system (EEG@HOME) for an extended duration of 6 months, at home. We aimed to investigate factors affecting engagement, gather qualitative insights, and provide recommendations for future home epilepsy monitoring systems. MATERIALS AND METHODS: Adults with epilepsy independently were trained to use a wearable dry EEG system, a wrist-worn device, and a smartphone app for seizure tracking and behaviour monitoring for 6 months at home. Monthly acceptability questionnaires (PSSUQ, SUS) and semi-structured interviews were conducted to explore participant experience. Adherence with the procedure, acceptability scores and systematic thematic analysis of the interviews, focusing on the experience with the procedure, motivation and benefits and opinion about the procedure were assessed. RESULTS: Twelve people with epilepsy took part into the study for an average of 193.8 days (range 61 to 312) with a likelihood of using the system at six months of 83 %. The e-diary and the smartwatch were highly acceptable and preferred to a wearable EEG system (PSSUQ score of 1.9, 1.9, 2.4). Participants showed an acceptable level of adherence with all solutions (Average usage of 63 %, 66 %, 92 %) reporting more difficulties using the EEG twice a day and remembering to complete the daily behavioural questionnaires. Clear information and training, continuous remote support, perceived direct and indirect benefits and the possibility to have a flexible, tailored to daily routine monitoring were defined as key factors to ensure compliance with long-term monitoring systems. CONCLUSIONS: EEG@HOME study demonstrated people with epilepsy' interest and ability in active health monitoring using new technologies. Remote training and support enable independent home use of new non-invasive technologies, but to ensure long term acceptability and usability systems will require to be integrated into patients' routines, include healthcare providers, and offer continuous support and personalized feedback.

Warm early Mars surface enabled by high-altitude water ice clouds.

Despite receiving just 30% of the Earth's present-day insolation, Mars had water lakes and rivers early in the planet's history, due to an unknown warming mechanism. A possible explanation for the >102-y-long lake-forming climates is warming by water ice clouds. However, this suggested cloud greenhouse explanation has proved difficult to replicate and has been argued to require unrealistically optically thick clouds at high altitudes. Here, we use a global climate model (GCM) to show that a cloud greenhouse can warm a Mars-like planet to global average annual-mean temperature ([Formula: see text]) ∼265 K, which is warm enough for low-latitude lakes, and stay warm for centuries or longer, but only if the planet has spatially patchy surface water sources. Warm, stable climates involve surface ice (and low clouds) only at locations much colder than the average surface temperature. At locations horizontally distant from these surface cold traps, clouds are found only at high altitudes, which maximizes warming. Radiatively significant clouds persist because ice particles sublimate as they fall, moistening the subcloud layer so that modest updrafts can sustain relatively large amounts of cloud. The resulting climates are arid (area-averaged surface relative humidity ∼25%). In a warm, arid climate, lakes could be fed by groundwater upwelling, or by melting of ice following a cold-to-warm transition. Our results are consistent with the warm and arid climate favored by interpretation of geologic data, and support the cloud greenhouse hypothesis.

Changes in the heartbeat-evoked potential are associated with functional seizures.

BACKGROUND: Patients with functional seizures (FS) can experience dissociation (depersonalisation) before their seizures. Depersonalisation reflects disembodiment, which may be related to changes in interoceptive processing. The heartbeat-evoked potential (HEP) is an electroencephalogram (EEG) marker of interoceptive processing. AIM: To assess whether alterations in interoceptive processing indexed by HEP occur prior to FS and compare this with epileptic seizures (ES). METHODS: HEP amplitudes were calculated from EEG during video-EEG monitoring in 25 patients with FS and 19 patients with ES, and were compared between interictal and preictal states. HEP amplitude difference was calculated as preictal HEP amplitude minus interictal HEP amplitude. A receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of HEP amplitude difference in discriminating FS from ES. RESULTS: The FS group demonstrated a significant reduction in HEP amplitude between interictal and preictal states at F8 (effect size rB=0.612, false discovery rate (FDR)-corrected q=0.030) and C4 (rB=0.600, FDR-corrected q=0.035). No differences in HEP amplitude were found between states in the ES group. Between diagnostic groups, HEP amplitude difference differed between the FS and ES groups at F8 (rB=0.423, FDR-corrected q=0.085) and C4 (rB=0.457, FDR-corrected q=0.085). Using HEP amplitude difference at frontal and central electrodes plus sex, we found that the ROC curve demonstrated an area under the curve of 0.893, with sensitivity=0.840 and specificity=0.842. CONCLUSION: Our data support the notion that aberrant interoception occurs prior to FS. Changes in HEP amplitude may reflect a neurophysiological biomarker of FS and may have diagnostic utility in differentiating FS and ES.

Seizure forecasting: Where do we stand?

A lot of mileage has been made recently on the long and winding road toward seizure forecasting. Here we briefly review some selected milestones passed along the way, which were discussed at the International Conference for Technology and Analysis of Seizures-ICTALS 2022-convened at the University of Bern, Switzerland. Major impetus was gained recently from wearable and implantable devices that record not only electroencephalography, but also data on motor behavior, acoustic signals, and various signals of the autonomic nervous system. This multimodal monitoring can be performed for ultralong timescales covering months or years. Accordingly, features and metrics extracted from these data now assess seizure dynamics with a greater degree of completeness. Most prominently, this has allowed the confirmation of the long-suspected cyclical nature of interictal epileptiform activity, seizure risk, and seizures. The timescales cover daily, multi-day, and yearly cycles. Progress has also been fueled by approaches originating from the interdisciplinary field of network science. Considering epilepsy as a large-scale network disorder yielded novel perspectives on the pre-ictal dynamics of the evolving epileptic brain. In addition to discrete predictions that a seizure will take place in a specified prediction horizon, the community broadened the scope to probabilistic forecasts of a seizure risk evolving continuously in time. This shift of gears triggered the incorporation of additional metrics to quantify the performance of forecasting algorithms, which should be compared to the chance performance of constrained stochastic null models. An imminent task of utmost importance is to find optimal ways to communicate the output of seizure-forecasting algorithms to patients, caretakers, and clinicians, so that they can have socioeconomic impact and improve patients' well-being.

Perceived seizure risk in epilepsy: Chronic electronic surveys with and without concurrent electroencephalography.

OBJECTIVE: Previous studies suggested that patients with epilepsy might be able to forecast their own seizures. This study aimed to assess the relationships between premonitory symptoms, perceived seizure risk, and future and recent self-reported and electroencephalographically (EEG)-confirmed seizures in ambulatory patients with epilepsy in their natural home environments. METHODS: Long-term e-surveys were collected from patients with and without concurrent EEG recordings. Information obtained from the e-surveys included medication adherence, sleep quality, mood, stress, perceived seizure risk, and seizure occurrences preceding the survey. EEG seizures were identified. Univariate and multivariate generalized linear mixed-effect regression models were used to estimate odds ratios (ORs) for the assessment of the relationships. Results were compared with the seizure forecasting classifiers and device forecasting literature using a mathematical formula converting OR to equivalent area under the curve (AUC). RESULTS: Fifty-four subjects returned 10 269 e-survey entries, with four subjects acquiring concurrent EEG recordings. Univariate analysis revealed that increased stress (OR = 2.01, 95% confidence interval [CI] = 1.12-3.61, AUC = .61, p = .02) was associated with increased relative odds of future self-reported seizures. Multivariate analysis showed that previous self-reported seizures (OR = 5.37, 95% CI = 3.53-8.16, AUC = .76, p < .001) were most strongly associated with future self-reported seizures, and high perceived seizure risk (OR = 3.34, 95% CI = 1.87-5.95, AUC = .69, p < .001) remained significant when prior self-reported seizures were added to the model. No correlation with medication adherence was found. No significant association was found between e-survey responses and subsequent EEG seizures. SIGNIFICANCE: Our results suggest that patients may tend to self-forecast seizures that occur in sequential groupings and that low mood and increased stress may be the result of previous seizures rather than independent premonitory symptoms. Patients in the small cohort with concurrent EEG showed no ability to self-predict EEG seizures. The conversion from OR to AUC values facilitates direct comparison of performance between survey and device studies involving survey premonition and forecasting.

Seizure occurrence is linked to multiday cycles in diverse physiological signals.

OBJECTIVE: The factors that influence seizure timing are poorly understood, and seizure unpredictability remains a major cause of disability. Work in chronobiology has shown that cyclical physiological phenomena are ubiquitous, with daily and multiday cycles evident in immune, endocrine, metabolic, neurological, and cardiovascular function. Additionally, work with chronic brain recordings has identified that seizure risk is linked to daily and multiday cycles in brain activity. Here, we provide the first characterization of the relationships between the cyclical modulation of a diverse set of physiological signals, brain activity, and seizure timing. METHODS: In this cohort study, 14 subjects underwent chronic ambulatory monitoring with a multimodal wrist-worn sensor (recording heart rate, accelerometry, electrodermal activity, and temperature) and an implanted responsive neurostimulation system (recording interictal epileptiform abnormalities and electrographic seizures). Wavelet and filter-Hilbert spectral analyses characterized circadian and multiday cycles in brain and wearable recordings. Circular statistics assessed electrographic seizure timing and cycles in physiology. RESULTS: Ten subjects met inclusion criteria. The mean recording duration was 232 days. Seven subjects had reliable electroencephalographic seizure detections (mean = 76 seizures). Multiday cycles were present in all wearable device signals across all subjects. Seizure timing was phase locked to multiday cycles in five (temperature), four (heart rate, phasic electrodermal activity), and three (accelerometry, heart rate variability, tonic electrodermal activity) subjects. Notably, after regression of behavioral covariates from heart rate, six of seven subjects had seizure phase locking to the residual heart rate signal. SIGNIFICANCE: Seizure timing is associated with daily and multiday cycles in multiple physiological processes. Chronic multimodal wearable device recordings can situate rare paroxysmal events, like seizures, within a broader chronobiology context of the individual. Wearable devices may advance the understanding of factors that influence seizure risk and enable personalized time-varying approaches to epilepsy care.

Use of a gene expression signature to identify trimetazidine for repurposing to treat bipolar depression.

OBJECTIVES: The aim of this study was to repurpose a drug for the treatment of bipolar depression. METHODS: A gene expression signature representing the overall transcriptomic effects of a cocktail of drugs widely prescribed to treat bipolar disorder was generated using human neuronal-like (NT2-N) cells. A compound library of 960 approved, off-patent drugs were then screened to identify those drugs that affect transcription most similar to the effects of the bipolar depression drug cocktail. For mechanistic studies, peripheral blood mononuclear cells were obtained from a healthy subject and reprogrammed into induced pluripotent stem cells, which were then differentiated into co-cultured neurons and astrocytes. Efficacy studies were conducted in two animal models of depressive-like behaviours (Flinders Sensitive Line rats and social isolation with chronic restraint stress rats). RESULTS: The screen identified trimetazidine as a potential drug for repurposing. Trimetazidine alters metabolic processes to increase ATP production, which is thought to be deficient in bipolar depression. We showed that trimetazidine increased mitochondrial respiration in cultured human neuronal-like cells. Transcriptomic analysis in induced pluripotent stem cell-derived neuron/astrocyte co-cultures suggested additional mechanisms of action via the focal adhesion and MAPK signalling pathways. In two different rodent models of depressive-like behaviours, trimetazidine exhibited antidepressant-like activity with reduced anhedonia and reduced immobility in the forced swim test. CONCLUSION: Collectively our data support the repurposing of trimetazidine for the treatment of bipolar depression.

A prospective 5-year longitudinal study detects neurocognitive and imaging correlates of seizure remission in self-limiting Rolandic epilepsy.

OBJECTIVE: Self-limiting Rolandic epilepsy (RE) is the most common epilepsy in school-age children. Seizures are generally infrequent, but cognitive, language, and motor coordination problems can significantly impact the child's life. To better understand brain structure and function changes in RE, we longitudinally assessed neurocognition, cortical thickness, and subcortical volumes. METHODS: At baseline, we recruited 30 participants diagnosed with RE and 24-healthy controls and followed up for 4.94 ± 0.8 years when the participants with RE were in seizure remission. Measures included were as follows: T1-weighted magnetic resonance brain imaging (MRI) with FreeSurfer analysis and detailed neuropsychological assessments. MRI and neuropsychological data were compared between baseline and follow-up in seizure remission. RESULTS: Longitudinal MRI revealed excess cortical thinning in the left-orbitofrontal (p = 0.0001) and pre-central gyrus (p = 0.044). There is a significant association (p = 0.003) between a reduction in cortical thickness in the left-orbitofrontal cluster and improved processing of filtered words. Longitudinal neuropsychology revealed significant improvements in the symptoms of developmental coordination disorder (DCD, p = 0.005) in seizure remission. CONCLUSIONS: There is evidence for altered development of neocortical regions between active seizure state and seizure remission in RE within two clusters maximal in the left-orbitofrontal and pre-central gyrus. There is significant evidence for improvement in motor coordination between active seizures and seizure remission and suggestive evidence for a decline in fluid intelligence and gains in auditory processing.

Metformin is Protective Against the Development of Mood Disorders.

INTRODUCTION: Mood disorders are a major cause of disability, and current treatment options are inadequate for reducing the burden on a global scale. The aim of this project was to identify drugs suitable for repurposing to treat mood disorders. METHODS: This mixed-method study utilized gene expression signature technology and pharmacoepidemiology to investigate drugs that may be suitable for repurposing to treat mood disorders. RESULTS: The transcriptional effects of a combination of drugs commonly used to treat mood disorders included regulation of the steroid and terpenoid backbone biosynthesis pathways, suggesting a mechanism involving cholesterol biosynthesis, and effects on the thyroid hormone signaling pathway. Connectivity Map analysis highlighted metformin, an FDA-approved treatment for type 2 diabetes, as a drug having global transcriptional effects similar to the mood disorder drug combination investigated. In a retrospective cohort study, we found evidence that metformin is protective against the onset of mood disorders. DISCUSSION: These results provide proof-of-principle of combining gene expression signature technology with pharmacoepidemiology to identify potential novel drugs for treating mood disorders. Importantly, metformin may have utility in the treatment of mood disorders, warranting future randomized controlled trials to test its efficacy.

Long non-coding RNA Tug1 modulates mitochondrial and myogenic responses to exercise in skeletal muscle.

BACKGROUND: Mitochondria have an essential role in regulating metabolism and integrate environmental and physiological signals to affect processes such as cellular bioenergetics and response to stress. In the metabolically active skeletal muscle, mitochondrial biogenesis is one important component contributing to a broad set of mitochondrial adaptations occurring in response to signals, which converge on the biogenesis transcriptional regulator peroxisome proliferator-activated receptor coactivator 1-alpha (PGC-1α), and is central to the beneficial effects of exercise in skeletal muscle. We investigated the role of long non-coding RNA (lncRNA) taurine-upregulated gene 1 (TUG1), which interacts with PGC-1α in regulating transcriptional responses to exercise in skeletal muscle. RESULTS: In human skeletal muscle, TUG1 gene expression was upregulated post-exercise and was also positively correlated with the increase in PGC-1α gene expression (PPARGC1A). Tug1 knockdown (KD) in differentiating mouse myotubes led to decreased Ppargc1a gene expression, impaired mitochondrial respiration and morphology, and enhanced myosin heavy chain slow isoform protein expression. In response to a Ca2+-mediated stimulus, Tug1 KD prevented an increase in Ppargc1a expression. RNA sequencing revealed that Tug1 KD impacted mitochondrial Ca2+ transport genes and several downstream PGC-1α targets. Finally, Tug1 KD modulated the expression of ~300 genes that were upregulated in response to an in vitro model of exercise in myotubes, including genes involved in regulating myogenesis. CONCLUSIONS: We found that TUG1 is upregulated in human skeletal muscle after a single session of exercise, and mechanistically, Tug1 regulates transcriptional networks associated with mitochondrial calcium handling, muscle differentiation and myogenesis. These data demonstrate that lncRNA Tug1 exerts regulation over fundamental aspects of skeletal muscle biology and response to exercise stimuli.

Brain transcriptome analysis reveals gene expression differences associated with dispersal behaviour between range-front and range-core populations of invasive cane toads in Australia.

Understanding the mechanisms allowing invasive species to adapt to novel environments is a challenge in invasion biology. Many invaders demonstrate rapid evolution of behavioural traits involved in range expansion such as locomotor activity, exploration and risk-taking. However, the molecular mechanisms that underpin these changes are poorly understood. In 86 years, invasive cane toads (Rhinella marina) in Australia have drastically expanded their geographic range westward from coastal Queensland to Western Australia. During their range expansion, toads have undergone extensive phenotypic changes, particularly in behaviours that enhance the toads' dispersal ability. Common-garden experiments have shown that some changes in behavioural traits related to dispersal are heritable. At the molecular level, it is currently unknown whether these changes in dispersal-related behaviour are underlain by small or large differences in gene expression, nor is known the biological function of genes showing differential expression. Here, we used RNA-seq to gain a better understanding of the molecular mechanisms underlying dispersal-related behavioural changes. We compared the brain transcriptomes of toads from the Hawai'ian source population, as well as three distinct populations from across the Australian invasive range. We found markedly different gene expression profiles between the source population and Australian toads. By contrast, toads from across the Australian invasive range had very similar transcriptomic profiles. Yet, key genes with functions putatively related to dispersal behaviour showed differential expression between populations located at each end of the invasive range. These genes could play an important role in the behavioural changes characteristic of range expansion in Australian cane toads.

Captivity induces large and population-dependent brain transcriptomic changes in wild-caught cane toads (Rhinella marina).

Gene expression levels are key molecular phenotypes at the interplay between genotype and environment. Mounting evidence suggests that short-term changes in environmental conditions, such as those encountered in captivity, can substantially affect gene expression levels. Yet, the exact magnitude of this effect, how general it is, and whether it results in parallel changes across populations are not well understood. Here, we take advantage of the well-studied cane toad, Rhinella marina, to examine the effect of short-term captivity on brain gene expression levels, and determine whether effects of captivity differ between long-colonized and vanguard populations of the cane toad's Australian invasion range. We compared the transcriptomes of wild-caught toads immediately assayed with those from toads captured from the same populations but maintained in captivity for seven months. We found large differences in gene expression levels between captive and wild-caught toads from the same population, with an over-representation of processes related to behaviour and the response to stress. Captivity had a much larger effect on both gene expression levels and gene expression variability in toads from vanguard populations compared to toads from long-colonized areas, potentially indicating an increased plasticity in toads at the leading edge of the invasion. Overall, our findings indicate that short-term captivity can induce large and population-specific transcriptomic changes, which has significant implications for studies comparing phenotypic traits of wild-caught organisms from different populations that have been held in captivity.

Noninvasive mobile EEG as a tool for seizure monitoring and management: A systematic review.

In the last two decades new noninvasive mobile electroencephalography (EEG) solutions have been developed to overcome limitations of conventional clinical EEG and to improve monitoring of patients with long-term conditions. Despite the availability of mobile innovations, their adoption is still very limited. The aim of this study is to review the current state-of-the-art and highlight the main advantages of adopting noninvasive mobile EEG solutions in clinical trials and research studies of people with epilepsy or suspected seizures. Device characteristics are described, and their evaluation is presented. Two authors independently performed a literature review in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A combination of different digital libraries was used (Embase, MEDLINE, Global Health, PsycINFO and https://clinicaltrials.gov/). Twenty-three full-text, six conference abstracts, and eight webpages were included, where a total of 14 noninvasive mobile solutions were identified. Published studies demonstrated at different levels how EEG recorded via mobile EEG can be used for visual detection of EEG abnormalities and for the application of automatic-detection algorithms with acceptable specificity and sensitivity. When the quality of the signal was compared with scalp EEG, many similarities were found in the background activities and power spectrum. Several studies indicated that the experience of patients and health care providers using mobile EEG was positive in different settings. Ongoing trials are focused mostly on improving seizure-detection accuracy and also on testing and assessing feasibility and acceptability of noninvasive devices in the hospital and at home. This review supports the potential clinical value of noninvasive mobile EEG systems and their advantages in terms of time, technical support, cost, usability, and reliability when applied to seizure detection and management. On the other hand, the limitations of the studies confirmed that future research is needed to provide more evidence regarding feasibility and acceptability in different settings, as well as the data quality and detection accuracy of new noninvasive mobile EEG solutions.

Diagnostic yield and limitations of in-hospital documentation in patients with epilepsy.

OBJECTIVE: To determine the diagnostic yield of in-hospital video-electroencephalography (EEG) monitoring to document seizures in patients with epilepsy. METHODS: Retrospective analysis of electronic seizure documentation at the University Hospital Freiburg (UKF) and at King's College London (KCL). Statistical assessment of the role of the duration of monitoring, and subanalyses on presurgical patient groups and patients undergoing reduction of antiseizure medication. RESULTS: Of more than 4800 patients with epilepsy undergoing in-hospital recordings at the two institutions since 2005, seizures with documented for 43% (KCL) and 73% (UKF).. Duration of monitoring was highly significantly associated with seizure recordings (p < .0001), and presurgical patients as well as patients with drug reduction had a significantly higher diagnostic yield (p < .0001). Recordings with a duration of >5 days lead to additional new seizure documentation in only less than 10% of patients. SIGNIFICANCE: There is a need for the development of new ambulatory monitoring strategies to document seizures for diagnostic and monitoring purposes for a relevant subgroup of patients with epilepsy in whom in-hospital monitoring fails to document seizures.

Seizure forecasting using minimally invasive, ultra-long-term subcutaneous EEG: Generalizable cross-patient models.

This study describes a generalized cross-patient seizure-forecasting approach using recurrent neural networks with ultra-long-term subcutaneous EEG (sqEEG) recordings. Data from six patients diagnosed with refractory epilepsy and monitored with an sqEEG device were used to develop a generalized algorithm for seizure forecasting using long short-term memory (LSTM) deep-learning classifiers. Electrographic seizures were identified by a board-certified epileptologist. One-minute data segments were labeled as preictal or interictal based on their relationship to confirmed seizures. Data were separated into training and testing data sets, and to compensate for the unbalanced data ratio in training, noise-added copies of preictal data segments were generated to expand the training data set. The mean and standard deviation (SD) of the training data were used to normalize all data, preserving the pseudo-prospective nature of the analysis. Different architecture classifiers were trained and tested using a leave-one-patient-out cross-validation method, and the area under the receiver-operating characteristic (ROC) curve (AUC) was used to evaluate the performance classifiers. The importance of each input signal was evaluated using a leave-one-signal-out method with repeated training and testing for each classifier. Cross-patient classifiers achieved performance significantly better than chance in four of the six patients and an overall mean AUC of 0.602 ± 0.126 (mean ± SD). A time in warning of 37.386% ± 5.006% (mean ± std) and sensitivity of 0.691 ± 0.068 (mean ± std) were observed for patients with better than chance results. Analysis of input channels showed a significant contribution (p < .05) by the Fourier transform of signals channels to overall classifier performance. The relative contribution of input signals varied among patients and architectures, suggesting that the inclusion of all signals contributes to robustness in a cross-patient classifier. These early results show that it is possible to forecast seizures training with data from different patients using two-channel ultra-long-term sqEEG.