The influences of individual musical practice and the same practice supplemented with biofeedback using electrophysiological markers for optimum music-performing activity were studied in 39 music students. Traditional technical practice produced increases in integral EMG power and decreases in alpha activity in most of the students with initially low maximum alpha activity peak frequencies. Similar practice but combined with individual sessions of alpha-EEG/EMG biofeedback were accompanied by increases in the frequency, bandwidth, and activation responses of EEG alpha rhythms in all subjects, along with decreases in EEG integral power. The efficacy of training with biofeedback and the ability to experience psychomotor learning depended on the initial individual characteristics of EEG alpha activity.

Modern society is characterized by the steadily increasing demand and desire for wakefulness at all hours of the day. Research in the fields of sleep physiology and chronophysiology can offer instruments enabling the identification of people with and without advantageous traits, such as little need for sleep, decreased sleepiness associated with sleep loss, and rapid adaptation to changes in the workrest schedule. These instruments can also help to quantify the severity of sleep-wake disorders (i.e., excessive daytime sleepiness) and the effects of their treatment. However, inter-individual differences in sleep need, vulnerability to sleep loss, and circadian adaptation remain scientifically understudied and are rarely theoretically and practically considered [19]. At present, it remains largely unknown what may underlie and predict sleep-wake related traits, what relationship these traits may have to each other, and what functional significance may be associated with these traits [20].Van Dongen and Dinges [19] emphasized the importance of three independent parameters for any model aimed at predicting reduced alertness and performance in the conditions of sleep loss and sleep disruption. These include (1) the timing and/or rate of circadian adjustment (i.e., circadian phase), (2) the amount of sleep required per day (i.e., sleep need), and (3) the rate of impairment per hour of sleep loss (i.e., vulnerability to sleep deprivation). There is solid evidence for substantial and clearly distinguishable interindividual variation in each of these three parameters. The differences between individuals are often characterized by within-individual stability (i.e., replicability) and robustness (i.e., insensitivity to experimental manipulation). These features suggest that the inter-individual differences represent systematic trait-like variability (see [18,20] for details). Understanding the basis of this variability may yield new insight into sleep-wake regulation and sleep-wake pathology [20].Agreement exists with respect to the markers of the individual circadian phase (i.e., it can be determined by tracing daily variations in core body temperature, secretion of melatonin and cortisol), but, to date, there is no consensus on the biological markers of sleep need and vulnerability to impairment from lack of sleep. However, it is possible to identify these markers by quantification of the response of brain wave activity to sleep deprivation. The analysis of changes in spectral characteristics of the electroencephalogram (EEG) is regarded as the physiological gold standard for identifying shifts along the alertness-drowsiness continuum [1,2,9,17]. It was demonstrated that sleep loss selectively modulates the spectral power densities of the EEG signal recorded either during prolonged wakefulness [3,4] or during subsequent recovery sleep [4,5,12]. Moreover, the results of numerous studies indicate that the changes in oscillatory brain activity within certain frequency ranges can provide reliable indexes of the reduced alertn...

Network mechanisms of depression development and especially of improvement from nonpharmacological treatment remain understudied. The current study is aimed at examining brain networks functional connectivity in depressed patients and its dynamics in nonpharmacological treatment. Resting state fMRI data of 21 healthy adults and 51 patients with mild or moderate depression were analyzed with spatial independent component analysis; then, correlations between time series of the components were calculated and compared between-group (study 1). Baseline and repeated-measure data of 14 treated (psychotherapy or fMRI neurofeedback) and 15 untreated depressed participants were similarly analyzed and correlated with changes in depression scores (study 2). Aside from diverse findings, studies 1 and 2 both revealed changes in within-default mode network (DMN) and DMN to executive control network (ECN) connections. Connectivity in one pair, initially lower in depression, decreased in no treatment group and was inversely correlated with Montgomery-Asberg depression score change in treatment group. Weak baseline connectivity in this pair also predicted improvement on Montgomery-Asberg scale in both treatment and no treatment groups. Coupling of another pair, initially stronger in depression, increased in therapy though was unrelated to improvement. The results demonstrate possible role of within-DMN and DMN-ECN functional connectivity in depression treatment and suggest that neural mechanisms of nonpharmacological treatment action may be unrelated to normalization of initially disrupted connectivity.

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