As traditional handwriting is progressively being replaced by digital devices, it is essential to investigate the implications for the human brain. Brain electrical activity was recorded in 36 university students as they were handwriting visually presented words using a digital pen and typewriting the words on a keyboard. Connectivity analyses were performed on EEG data recorded with a 256-channel sensor array. When writing by hand, brain connectivity patterns were far more elaborate than when typewriting on a keyboard, as shown by widespread theta/alpha connectivity coherence patterns between network hubs and nodes in parietal and central brain regions. Existing literature indicates that connectivity patterns in these brain areas and at such frequencies are crucial for memory formation and for encoding new information and, therefore, are beneficial for learning. Our findings suggest that the spatiotemporal pattern from visual and proprioceptive information obtained through the precisely controlled hand movements when using a pen, contribute extensively to the brain\'s connectivity patterns that promote learning. We urge that children, from an early age, must be exposed to handwriting activities in school to establish the neuronal connectivity patterns that provide the brain with optimal conditions for learning. Although it is vital to maintain handwriting practice at school, it is also important to keep up with continuously developing technological advances. Therefore, both teachers and students should be aware of which practice has the best learning effect in what context, for example when taking lecture notes or when writing an essay.

The aim of this study was to explore functional network age-related changes and sex-related differences during the early lifespan with a high-density resting state electroencephalography (rs-EEG).
We analyzed two data sets of high-density rs-EEG in healthy children and adolescents. We recorded a 64-channel EEG and calculated functional connectomes in 27 participants aged 5-18 years. To validate our results, we used publicly available data and calculated functional connectomes in another 86 participants aged 6-18 years from a 128-channel rs-EEG. We were primarily interested in alpha frequency band, but we also analyzed theta and beta frequency bands.
We observed age-related increase of characteristic path, clustering coefficient and interhemispheric strength in the alpha frequency band of both data sets and in the beta frequency band of the larger validation data set. Age-related increase of global efficiency was seen in the theta band of the validation data set and in the alpha band of the test data set. Increase in small worldness was observed only in the alpha frequency band of the test data set. We also observed an increase of individual peak alpha frequency with age in both data sets. Sex-related differences were only observed in the beta frequency band of the larger validation data set, with females having higher values than same aged males.
Functional brain networks show indices of higher segregation, but also increasing global integration with maturation. Age-related changes are most prominent in the alpha frequency band.
To the best of our knowledge, our study was the first to analyze maturation related changes and sex-related differences of functional brain networks with a high-density EEG and to compare functional connectomes generated from two diverse high-density EEG data sets. Understanding the age-related changes and sex-related differences of functional brain networks in healthy children and adolescents is crucial for identifying network abnormalities in different neurologic and psychiatric conditions, with the aim to identify possible markers for prognosis and treatment.

The dysfunctional patterns of microstates dynamics in obsessive-compulsive disorder (OCD) remain uncertain. Using high-density electrical neuroimaging (EEG) at rest, we explored microstates deterioration in OCD and whether abnormal microstates patterns are associated with a dysregulation of the resting-state networks interplay. We used EEG microstates analyses, TESS method for sources reconstruction, and General Linear Models to test for the effect of disease severity on neural responses. OCD patients exhibited an increased contribution and decreased duration of microstates C and D, respectively. Activity was decreased in the Salience Network (SN), associated with microstate C, but increased in the Default Mode Network (DMN) and Executive Control Network (ECN), respectively, associated with microstates E and D. The hyperactivity of the right angular gyrus in the ECN correlated with the symptoms severity. The imbalance between microstates C and D invalidates the hypothesis that this electrophysiological pattern is specific to psychosis. Demonstrating that the SN-ECN dysregulation manifests as abnormalities in microstates C and D, we confirm that the SN deterioration in OCD is accompanied by a failure of the DMN to deactivate and aberrant compensatory activation mechanisms in the ECN. These abnormalities explain typical OCD clinical features but also detachment from reality, shared with psychosis.

We investigated brain cortical activity alterations, using a resting-state 256-channel high-density EEG (hd-EEG), in Alzheimer\'s (AD) and Parkinson\'s (PD) disease subjects with mild cognitive impairment (MCI) and correlations between quantitative spectral EEG parameters and the global cognitive status assessed by Montreal Cognitive Assessment (MoCA) score.
Fifteen AD-MCI, eleven PD-MCI and ten age-matched healthy-controls (HC) underwent hd-EEG recordings and neuropsychological assessment. Cerebrospinal fluid biomarker analysis was performed to obtain well-characterized groups. EEG spectral features were extracted and the differences between the three groups, as well as correlations with MoCA, were investigated.
The results showed significantly lower alpha2 power and alpha2/alpha1 ratio in both AD-MCI and PD-MCI compared to controls. The significantly higher theta and lower beta power and alpha/theta ratio were observed in PD-MCI compared to AD-MCI and HC. MoCA score correlated inversely with theta power and directly with alpha2 and beta powers, as well as with alpha2/alpha1 and alpha/theta ratios.
This study highlighted significant differences in EEG patterns in AD-MCI and PD-MCI patients and remarked the role of EEG parameters as possible surrogate markers of cognitive status in both neurodegenerative diseases.
In addition to well-established biomarkers, our findings could support early detection of cognitive dysfunction in neurodegenerative disorders and could help to monitor disease progression and therapeutic responses.

This study investigated evoked and oscillatory brain activity in response to forward visual motion at three different ecologically valid speeds, simulated through an optic flow pattern consisting of a virtual road with moving poles at either side of it. Participants were prelocomotor infants at 4-5 months, crawling infants at 9-11 months, primary school children at 6 years, adolescents at 12 years, and young adults. N2 latencies for motion decreased significantly with age from around 400 ms in prelocomotor infants to 325 ms in crawling infants, and from 300 and 275 ms in 6- and 12-year-olds, respectively, to 250 ms in adults. Infants at 4-5 months displayed the longest latencies and appeared unable to differentiate between motion speeds. In contrast, crawling infants at 9-11 months and 6-year-old children differentiated between low, medium and high speeds, with shortest latency for low speed. Adolescents and adults displayed similar short latencies for the three motion speeds, indicating that they perceived them as equally easy to detect. Time-frequency analyses indicated that with increasing age, participants showed a progression from low- to high-frequency desynchronized oscillatory brain activity in response to visual motion. The developmental differences in motion speed perception are interpreted in terms of a combination of neurobiological development and increased experience with self-produced locomotion. Our findings suggest that motion speed perception is not fully developed until adolescence, which has implications for children\'s road traffic safety.

Background: Slow-wave activity (SWA) during non-rapid eye movement (NREM) sleep reflects synaptic potentiation during preceding wakefulness. Epileptic activity may induce increases in state-dependent SWA in human brains, therefore, localization of SWA may prove useful in the presurgical workup of epileptic patients. We analyzed high-density electroencephalography (HDEEG) data across vigilance states from a reflex epilepsy patient with a clearly localizable ictal symptomatogenic zone to provide a proof-of-concept for the testability of this hypothesis. Methods: Overnight HDEEG recordings were obtained in the patient during REM sleep, NREM sleep, wakefulness, and during a right facial motor seizure then compared to 10 controls. After preprocessing, SWA (i.e., delta power; 1-4 Hz) was calculated at each channel. Scalp level and source reconstruction analyses were computed. We assessed for statistical differences in maximum SWA between the patient and controls within REM sleep, NREM sleep, wakefulness, and seizure. Then, we completed an identical statistical comparison after first subtracting intrasubject REM sleep SWA from that of NREM sleep, wakefulness, and seizure SWA. Results: The topographical analysis revealed greater left hemispheric SWA in the patient vs. controls in all vigilance states except REM sleep (which showed a right hemispheric maximum). Source space analysis revealed increased SWA in the left inferior frontal cortex during NREM sleep and wakefulness. Ictal data displayed poor source-space localization. Comparing each state to REM sleep enhanced localization accuracy; the most clearly localizing results were observed when subtracting REM sleep from wakefulness. Conclusion: State-dependent SWA during NREM sleep and wakefulness may help to identify aspects of the potential epileptogenic zone. Future work in larger cohorts may assess the clinical value of sleep SWA to help presurgical planning.

Insomnia is a highly prevalent and costly condition that is associated with increased health risks and healthcare utilisation. Anecdotally, cannabis use is frequently reported by consumers to promote sleep. However, there is limited research on the effects of cannabis on sleep and daytime function in people with insomnia disorder using objective measures. This proof-of-concept study will evaluate the effects of a single dose of an oral cannabis-based medicine on sleep and daytime function in participants with chronic insomnia disorder.
A randomised, crossover, placebo-controlled, single-dose study design will be used to test the safety and efficacy of an oral oil solution (\'ETC120\') containing 10 mg Δ9-tetrahydrocannabinol (THC) and 200 mg cannabidiol (CBD) in 20 participants diagnosed with chronic insomnia disorder. Participants aged 35-60 years will be recruited over an 18-month period commencing August 2019. Each participant will receive both the active drug and matched placebo, in a counterbalanced order, during two overnight study assessment visits, with at least a 1-week washout period between each visit. The primary outcomes are total sleep time and wake after sleep onset assessed via polysomnography. In addition, 256-channel high-density electroencephalography and source modelling using structural brain MRI will be used to comprehensively examine brain activation during sleep and wake periods on ETC120 versus placebo. Next-day cognitive function, alertness and simulated driving performance will also be investigated.
Ethics approval was received from Bellberry Human Research Ethics Committee (2018-04-284). The findings will be disseminated in a peer-reviewed open-access journal and at academic conferences.
ANZCTRN12619000714189.

Background: Atypical neural processing of social visual information contributes to impaired social cognition in autism spectrum disorder. However, evidence for early developmental alterations in neural processing of social contingencies is scarce. Most studies in the literature have been conducted in older children and adults. Here, we aimed to investigate alterations in neural processing of social visual information in children with autism spectrum disorder compared to age-matched typically developing peers. Methods: We used a combination of 129-channel electroencephalography and high-resolution eye-tracking to study differences in the neural processing of dynamic cartoons containing human-like social interactions between 14 male children with autism spectrum disorder and 14 typically developing male children, aged 2-5 years. Using a microstate approach, we identified four prototypical maps in both groups and compared the temporal characteristics and inverse solutions (activation of neural sources) of these maps between groups. Results: Inverse solutions of the group maps that were most dominant during free viewing of the dynamic cartoons indicated decreased prefrontal and cingulate activation, impaired activation of the premotor cortex, and increased activation of parietal, temporal, occipital, and cerebellar regions in children with autism spectrum disorder compared to their typically developing peers. Conclusions: Our findings suggest that impairments in brain regions involved in processing social contingencies embedded in dynamic cartoons are present from an early age in autism spectrum disorder. To the best of our knowledge, this is the first study to investigate neural processing of social interactions of children with autism spectrum disorder using dynamic semi-naturalistic stimuli.

The neural principles behind semantic category representation are still under debate. Dominant theories mostly focus on distinguishing concrete from abstract concepts but, in such theories, divisions into categories of concrete concepts are more developed than for their abstract counterparts. An encompassing theory on semantic category representation could be within reach when charting the semantic attributes that are capable of describing both concept types. A good candidate are the three semantic dimensions defined by Osgood (potency, valence, arousal). However, to show to what extent they affect semantic processing, specific neuroimaging tools are required. Electroencephalography (EEG) is on par with the temporal resolution of cognitive behavior and source reconstruction. Using high-density set-ups, it is able to yield a spatial resolution in the scale of millimeters, sufficient to identify anatomical brain parcellations that could differentially contribute to semantic category representation. Cognitive neuroscientists traditionally focus on scalp domain analysis and turn to source reconstruction when an effect in the scalp domain has been detected. Traditional methods will potentially miss out on the fine-grained effects of semantic features as they are possibly obscured by the mixing of source activity due to volume conduction. For this reason, we have developed a mass-univariate analysis in the source domain using a mixed linear effect model. Our analyses reveal distinct networks of sources for different semantic features that are active during different stages of lexico-semantic processing of single words. With our method we identified differences in the spatio-temporal activation patterns of abstract and concrete words, high and low potency words, high and low valence words, and high and low arousal words, and in this way shed light on how word categories are represented in the brain.

Dreaming can occur in both rapid eye movement (REM) and non-REM (NREM) sleep. We recently showed that in both REM and NREM sleep, dreaming is associated with local decreases in slow wave activity (SWA) in posterior brain regions. To expand these findings, here we asked how specific features of slow waves and spindles, the hallmarks of NREM sleep, relate to dream experiences. Fourteen healthy human subjects (10 females) underwent nocturnal high-density EEG recordings combined with a serial awakening paradigm. Reports of dreaming, compared with reports of no experience, were preceded by fewer, smaller, and shallower slow waves, and faster spindles, especially in central and posterior cortical areas. We also identified a minority of very steep and large slow waves in frontal regions, which occurred on a background of reduced SWA and were associated with high-frequency power increases (local \"microarousals\") heralding the successful recall of dream content. These results suggest that the capacity of the brain to generate experiences during sleep is reduced in the presence of neuronal off-states in posterior and central brain regions, and that dream recall may be facilitated by the intermittent activation of arousal systems during NREM sleep.SIGNIFICANCE STATEMENT By combining high-density EEG recordings with a serial awakening paradigm in healthy subjects, we show that dreaming in non-rapid eye movement sleep occurs when slow waves in central and posterior regions are sparse, small, and shallow. We also identified a small subset of very large and steep frontal slow waves that are associated with high-frequency activity increases (local \"microarousals\") heralding successful recall of dream content. These results provide noninvasive measures that could represent a useful tool to infer the state of consciousness during sleep.