Sleep and seizure risk in epilepsy: bed and wake times are more important than sleep duration.

Sleep duration, sleep deprivation and the sleep-wake cycle are thought to play an important role in the generation of epileptic activity and may also influence seizure risk. Hence, people diagnosed with epilepsy are commonly asked to maintain consistent sleep routines. However, emerging evidence paints a more nuanced picture of the relationship between seizures and sleep, with bidirectional effects between changes in sleep and seizure risk in addition to modulation by sleep stages and transitions between stages. We conducted a longitudinal study investigating sleep parameters and self-reported seizure occurrence in an ambulatory at-home setting using mobile and wearable monitoring. Sixty subjects wore a Fitbit smartwatch for at least 28 days while reporting their seizure activity in a mobile app. Multiple sleep features were investigated, including duration, oversleep and undersleep, and sleep onset and offset times. Sleep features in participants with epilepsy were compared to a large (n = 37 921) representative population of Fitbit users, each with 28 days of data. For participants with at least 10 seizure days (n = 34), sleep features were analysed for significant changes prior to seizure days. A total of 4956 reported seizures (mean = 83, standard deviation = 130) and 30 485 recorded sleep nights (mean = 508, standard deviation = 445) were included in the study. There was a trend for participants with epilepsy to sleep longer than the general population, although this difference was not significant. Just 5 of 34 participants showed a significant difference in sleep duration the night before seizure days compared to seizure-free days. However, 14 of 34 subjects showed significant differences between their sleep onset (bed) and/or offset (wake) times before seizure occurrence. In contrast to previous studies, the current study found undersleeping was associated with a marginal 2% decrease in seizure risk in the following 48 h (P < 0.01). Nocturnal seizures were associated with both significantly longer sleep durations and increased risk of a seizure occurring in the following 48 h. Overall, the presented results demonstrated that day-to-day changes in sleep duration had a minimal effect on reported seizures, while patient-specific changes in bed and wake times were more important for identifying seizure risk the following day. Nocturnal seizures were the only factor that significantly increased the risk of seizures in the following 48 h on a group level. Wearables can be used to identify these sleep-seizure relationships and guide clinical recommendations or improve seizure forecasting algorithms.

User experience of a seizure risk forecasting app: A mixed methods investigation.

OBJECTIVE: Over recent years, there has been a growing interest in exploring the utility of seizure risk forecasting, particularly how it could improve quality of life for people living with epilepsy. This study reports on user experiences and perspectives of a seizure risk forecaster app, as well as the potential impact on mood and adjustment to epilepsy. METHODS: Active app users were asked to complete a survey (baseline and 3-month follow-up) to assess perspectives on the forecast feature as well as mood and adjustment. Post-hoc, nine neutral forecast users (neither agreed nor disagreed it was useful) completed semi-structured interviews, to gain further insight into their perspectives of epilepsy management and seizure forecasting. Non-parametric statistical tests and inductive thematic analyses were used to analyse the quantitative and qualitative data, respectively. RESULTS: Surveys were completed by 111 users. Responders consisted of "app users" (n = 58), and "app and forecast users" (n = 53). Of the "app and forecast users", 40 % believed the forecast was accurate enough to be useful in monitoring for seizure risk, and 60 % adopted it for purposes like scheduling activities and helping mental state. Feeling more in control was the most common response to both high and low risk forecasted states. In-depth interviews revealed five broad themes, of which 'frustrations with lack of direction' (regarding their current epilepsy management approach), 'benefits of increased self-knowledge' and 'current and anticipated usefulness of forecasting' were the most common. SIGNIFICANCE: Preliminary results suggest that seizure risk forecasting can be a useful tool for people with epilepsy to make lifestyle changes, such as scheduling daily events, and experience greater feelings of control. These improvements may be attributed, at least partly, to the improvements in self-knowledge experienced through forecast use.

Anomalies in global network connectivity associated with early recovery from alcohol dependence: A network transcranial magnetic stimulation and electroencephalography study.

Although previous research in alcohol dependent populations identified alterations within local structures of the addiction 'reward' circuitry, there is limited research into global features of this network, especially in early recovery. Transcranial magnetic stimulation (TMS) is capable of non-invasively perturbing the brain network while electroencephalography (EEG) measures the network response. The current study is the first to apply a TMS inhibitory paradigm while utilising network science (graph theory) to quantify network anomalies associated with alcohol dependence. Eleven individuals with alcohol-dependence (ALD) in early recovery and 16 healthy controls (HC) were administered 75 single pulses and 75 paired-pulses (inhibitory paradigm) to both the left and right prefrontal cortex (PFC). For each participant, Pearson cross-correlation was applied to the EEG data and correlation matrices constructed. Global network measures (mean degree, clustering coefficient, local efficiency and global efficiency) were extracted for comparison between groups. Following administration of the inhibitory paired-pulse TMS to the left PFC, the ALD group exhibited altered mean degree, clustering coefficient, local efficiency and global efficiency compared to HC. Decreases in local efficiency increased the prediction of being in the ALD group, while all network metrics (following paired-pulse left TMS) were able to adequately discriminate between the groups. In the ALD group, reduced mean degree and global clustering was associated with increased severity of past alcohol use. Our study provides preliminary evidence of altered network topology in patients with alcohol dependence in early recovery. Network anomalies were predictive of high alcohol use and correlated with clinical features of alcohol dependence. Further research using this novel brain mapping technique may identify useful network biomarkers of alcohol dependence and recovery.

A quantitative physical model of the TMS-induced discharge artifacts in EEG.

The combination of Transcranial Magnetic Stimulation (TMS) with Electroencephalography (EEG) exposes the brain's global response to localized and abrupt stimulations. However, large electric artifacts are induced in the EEG by the TMS, obscuring crucial stages of the brain's response. Artifact removal is commonly performed by data processing techniques. However, an experimentally verified physical model for the origin and structure of the TMS-induced discharge artifacts, by which these methods can be justified or evaluated, is still lacking. We re-examine the known contribution of the skin in creating the artifacts, and outline a detailed model for the relaxation of the charge accumulated at the electrode-gel-skin interface due to the TMS pulse. We then experimentally validate implications set forth by the model. We find that the artifacts decay like a power law in time rather than the commonly assumed exponential. In fact, the skin creates a power-law decay of order 1 at each electrode, which is turned into a power law of order 2 by the reference electrode. We suggest an artifact removal method based on the model which can be applied from times after the pulse as short as 2 milliseconds onwards to expose the full EEG from the brain. The method can separate the capacitive discharge artifacts from those resulting from cranial muscle activation, demonstrating that the capacitive effect dominates at short times. Overall, our insight into the physical process allows us to accurately access TMS-evoked EEG responses that directly follow the TMS pulse, possibly opening new opportunities in TMS-EEG research.

Neuromodulation of Attentional Control in Major Depression: A Pilot DeepTMS Study.

While Major Depressive Disorder (MDD) is primarily characterized by mood disturbances, impaired attentional control is increasingly identified as a critical feature of depression. Deep transcranial magnetic stimulation (deepTMS), a noninvasive neuromodulatory technique, can modulate neural activity and induce neuroplasticity changes in brain regions recruited by attentional processes. This study examined whether acute and long-term high-frequency repetitive deepTMS to the dorsolateral prefrontal cortex (DLPFC) can attenuate attentional deficits associated with MDD. Twenty-one MDD patients and 26 matched control subjects (CS) were administered the Beck Depression Inventory and the Sustained Attention to Response Task (SART) at baseline. MDD patients were readministered the SART and depressive assessments following a single session (n = 21) and after 4 weeks (n = 13) of high-frequency (20 Hz) repetitive deepTMS applied to the DLPFC. To control for the practice effect, CS (n = 26) were readministered the SART a further two times. The MDD group exhibited deficits in sustained attention and cognitive inhibition. Both acute and long-term high-frequency repetitive frontal deepTMS ameliorated sustained attention deficits in the MDD group. Improvement after acute deepTMS was related to attentional recovery after long-term deepTMS. Longer-term improvement in sustained attention was not related to antidepressant effects of deepTMS treatment.

Phase-Amplitude Markers of Synchrony and Noise: A Resting-State and TMS-EEG Study of Schizophrenia.

The electroencephalogram (EEG) of schizophrenia patients is known to exhibit a reduction of signal-to-noise ratio and of phase locking, as well as a facilitation of excitability, in response to a variety of external stimuli. Here, we demonstrate these effects in transcranial magnetic stimulation (TMS)-evoked potentials and in the resting-state EEG. To ensure veracity, we used 3 weekly sessions and analyzed both resting-state and TMS-EEG data. For the TMS responses, our analysis verifies known results. For the resting state, we introduce the methodology of mean-normalized variation to the EEG analysis (quartile-based coefficient of variation), which allows for a comparison of narrow-band EEG amplitude fluctuations to narrow-band Gaussian noise. This reveals that amplitude fluctuations in the delta, alpha, and beta bands of healthy controls are different from those in schizophrenia patients, on time scales of tens of seconds. We conclude that the EEG-measured cortical activity patterns of schizophrenia patients are more similar to noise, both in alpha- and beta-resting state and in TMS responses. Our results suggest that the ability of neuronal populations to form stable, locally, and temporally correlated activity is reduced in schizophrenia, a conclusion, that is, in accord with previous experiments on TMS-EEG and on resting-state EEG.

The interactive effects of test-retest and methylphenidate administration on cognitive performance in youth with ADHD: A double-blind placebo-controlled crossover study.

Studies have shown that Methylphenidate (MPH) affects cognitive performance on the neuropsychological tests and clinical symptoms of individuals diagnosed with attention deficit/hyperactivity disorder (ADHD). This study investigated the acute effects of MPH on neuropsychological tests to explore the interaction between MPH and test-retest effects. Twenty youths with ADHD were tested before and after MPH intake in a double-blind placebo-controlled crossover design and compared to twenty matched controls. Participants were tested on a range of standardized tasks including sustained attention to response, N-Back, and Word/Color Stroop. Identical tasks were administered twice each testing day, before and 1 hour after MPH/Placebo administration. Healthy controls were tested similarly with no intervention. Decreases in response time (RT) variability across tasks and in commission errors were found in ADHD after MPH. Conversely, a significant increase in RT variability and increase in omission errors were observed after the placebo. In the control group, RT variability and omission errors increased whereas commission errors decreased, suggesting fatigue and practice effects, respectively. Test-retest reliability was higher in controls than ADHD. It is suggested that cognitive tests are sensitive objective measures for the assessment of responses to MPH in ADHD but are also affected by repetition and fatigue.

Electroencephalography Functional Networks Reveal Global Effects of Methylphenidate in Youth with Attention Deficit/Hyperactivity Disorder.

Methylphenidate (MPH) is the leading drug for treatment of attention deficit/hyperactivity disorder (ADHD), yet its underlying neuronal mechanisms are still unclear. Here, we use a dynamical brain networks approach to explore the effects of cognitive effort and MPH on ADHD subjects. Electroencephalography data were recorded from 19 ADHD subjects and 18 controls during a Go/No-Go Task. ADHD subjects completed the task twice a day over 2 days. The second session was administered post-ingestion of placebo/MPH (alternately). Controls performed two tasks in 1 day. The data were divided into 300 ms windows from -300 pre-stimulus until 1200 ms post-stimulus. Brain networks were constructed per subject and window, from which network metrics were extracted and compared across the experimental conditions. We identified an immediate shift of global connectivity and of network segregation after the stimulus for both groups, followed by a gradual return to baseline. Decreased global connectivity was found to be 400-700 ms post-stimulus in ADHD compared with controls, and it was normalized post-MPH. An increase of the networks' segregation occurred post-placebo at 100-400 and 400-700 ms post-stimulus, yet it was inhibited post-MPH. These global alterations resulted mainly from changes in task-relevant frontal and parietal regions. The networks of medicated ADHD subjects and controls exhibited a more significant and lasting change, relative to baseline, compared with those of nonmedicated ADHD. These results suggest impaired network flexibility in ADHD, corrected by MPH.

Effects of methylphenidate on the ERP amplitude in youth with ADHD: A double-blind placebo-controlled cross-over EEG study.

Methylphenidate (MPH) is a first line drug for attention-deficit/hyperactivity disorder (ADHD), yet the neuronal mechanisms underlying the condition and the treatment are still not fully understood. Previous EEG studies on the effect of MPH in ADHD found changes in evoked response potential (ERP) components that were inconsistent between studies. These inconsistencies highlight the need for a well-designed study which includes multiple baseline sessions and controls for possible fatigue, learning effects and between-days variability. To this end, we employ a double-blind placebo-controlled cross-over study and explore the effect of MPH on the ERP response of subjects with ADHD during a Go/No-Go cognitive task. Our ERP analysis revealed significant differences in ADHD subjects between the placebo and MPH conditions in the frontal-parietal region at 250ms-400ms post stimulus (P3). Additionally, a decrease in the late 650ms-800ms ERP component (LC) is observed in frontal electrodes of ADHD subjects compared to controls. The standard deviation of response time of ADHD subjects was significantly smaller in the MPH condition compared to placebo and correlated with the increased P3 ERP response in the frontoparietal electrodes. We suggest that mental fatigue plays a role in the decrease of the P3 response in the placebo condition compared to pre-placebo, a phenomenon that is significant in ADHD subjects but not in controls, and which is interestingly rectified by MPH.

Altered Brain Network Dynamics in Schizophrenia: A Cognitive Electroencephalography Study.

BACKGROUND: Alterations in the dynamic coordination of widespread brain networks are proposed to underlie cognitive symptoms of schizophrenia. However, there is limited understanding of the temporal evolution of these networks and how they relate to cognitive impairment. The current study was designed to explore dynamic patterns of network connectivity underlying cognitive features of schizophrenia. METHODS: In total, 21 inpatients with schizophrenia and 28 healthy control participants completed a cognitive task while electroencephalography data were simultaneously acquired. For each participant, Pearson cross-correlation was applied to electroencephalography data to construct correlation matrices that represent the static network (averaged over 1200 ms) and dynamic network (1200 ms divided into four windows of 300 ms) in response to cognitive stimuli. Global and regional network measures were extracted for comparison between groups. RESULTS: Dynamic network analysis identified increased global efficiency; decreased clustering (globally and locally); reduced strength (weighted connectivity) around the frontal, parietal, and sensory-motor areas; and increased strength around the occipital lobes (a peripheral hub) in patients with schizophrenia. Regional network measures also correlated with clinical features of schizophrenia. Network differences were prominent 900 ms following the cognitive stimuli before returning to levels comparable to those of healthy control participants. CONCLUSIONS: Patients with schizophrenia exhibited altered dynamic patterns of network connectivity across both global and regional measures. These network differences were time sensitive and may reflect abnormalities in the flexibility of the network that underlies aspects of cognitive function. Further research into network dynamics is critical to better understanding cognitive features of schizophrenia and identification of network biomarkers to improve diagnosis and treatment models.

Cortical inhibition within motor and frontal regions in alcohol dependence post-detoxification: A pilot TMS-EEG study.

OBJECTIVES: Preclinical studies suggest that cortical alterations within the prefrontal cortex (PFC) are critical to the pathophysiology of alcohol dependence. Combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG) allows direct assessment of cortical excitability and inhibition within the PFC of human subjects. We report the first application of TMS-EEG to measure these indices within the PFC of alcohol-dependent (ALD) patients post-detoxification. METHODS: Cortical inhibition was assessed in 12 ALD patients and 14 healthy controls through single and paired-pulse TMS paradigms. Long-interval cortical inhibition indexed cortical inhibition in the PFC. In the motor cortex (MC), short- interval intracortical inhibition and cortical silent period determined inhibition, while intracortical facilitation measured facilitation, resting and active motor threshold indexed cortical excitability. RESULTS: ALD patients demonstrated altered cortical inhibition across the bilateral frontal cortices relative to controls. There was evidence of altered cortical excitability in ALD patients; however, no significant differences in MC inhibition. CONCLUSIONS: Our study provides first direct evidence of reduced cortical inhibition in the PFC of ALD patients post-detoxification. Altered cortical excitability in the MC may reflect hyper-excitability within the cortex associated with chronic alcohol consumption. These findings provide initial neurophysiological evidence of disrupted cortical excitability within the PFC of ALD patients.

Neurocognitive deficits, craving, and abstinence among alcohol-dependent individuals following detoxification.

Alcohol dependence, a chronic relapsing disorder, is characterized by an impaired ability to regulate compulsive urges to consume alcohol. Very few empirical studies have examined the presence of these executive deficits, how they relate to craving, and the enduring nature of these deficits during abstinence. As such, the current study aimed to characterize these cognitive deficits within a sample of 24 alcohol-dependent participants post-detoxification and 23 non-alcohol-dependent participants. Participants were administered the Sustained Attention to Response Task to measure response inhibition and sustained attention and the Random Number Generation Task to examine executive deficits. Correlations between cognitive performance and clinical measures of alcohol dependence were examined. As predicted, the alcohol-dependent group exhibited poorer performance across the domains of response inhibition, executive function, and attentional control. Cognitive performance was related to clinical measures of craving and years of alcohol consumption, whereas the duration of abstinence was not associated with improved cognitive performance. These findings highlight the need for therapeutic strategies to target these enduring neurocognitive deficits in improving the treatment of alcohol dependence.

Addiction.

Drug and alcohol addiction is a debilitating disorder characterized by persistent drug-seeking behaviors despite negative physiological, medical, or social consequences. Neurobiological models of addiction propose that the reinforcing effects of addictive drugs are associated with altered neurotransmission within the reward 'mesocorticolimbic' circuitry in the brain. Immense efforts are therefore designed to target the mesocorticolimbic circuitry in attenuating drug dependence and addiction-related behaviors. Yet, to date, most addiction treatments have demonstrated only limited success in reducing addiction-related behaviors. Accumulating and compelling evidence suggests that novel nonsurgical brain stimulation techniques, such as transcranial magnetic stimulation and transcranial direct current stimulation, could serve as promising tools for indexing altered neurotransmission associated with repetitive drug use, and moreover, may hold therapeutic potential for the treatment of drug dependence and addiction-related behaviors. This chapter reviews and discusses the current and potential applications of such techniques in the study and treatment of addiction; we focus on a number of common drugs of abuse, including nicotine, alcohol, cocaine, cannabis, and ecstasy.