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Изследване и анализиране предаването на данни в различни компютърни мрежи. Предаването и приемането на данни (или в по-широк план предаването на данни или цифровата комуникация) е предаването и приемането на данни (цифров бит поток или цифровизиран аналогов сигнал над комуникацията от точка до точка в комуникационния канал. Аналоговото предаване е метод за предаване на звук, данни, изображение, сигнал или видео информация с помощта на непрекъснат сигнал, който варира по амплитуда, фаза или някакво друго свойство пропорционално на параметрите на променливата. Модулацията на честотната лента и съответната демодулация (известна още като откриване) се извършва от хардуера на модема. Според най-често срещаното определение за цифров сигнал, както сигналите за базова лента, така и сигналите за честотната лента, представляващи бит потоците, се считат за цифрово предаване, докато алтернативно определение счита само сигнал за базова лента като цифров, така и предаването на цифрови данни в честотната лента като форма на цифрово преобразуване към цифрово - аналогово преобразуване. Research and analysis of data transmission in various computer networks. Data transmission and reception (or, more broadly, data transmission or digital communication) is the transmission and reception of data (digital bitstream or digitized analog signal over point-to-point communication in the communication channel. Analog transmission is a method of transmitting sound, data, image, signal or video information using a continuous signal that varies in amplitude, phase or some other property in proportion to the parameters of the variable. The messages are represented either by a sequence of pulses using a linear code (transmission in the main frequency band) or by a limited set of continuously changing signals (transmission in the frequency band) using the digital modulation method. Bandwidth modulation and corresponding demodulation (also known as detection) is performed by the modem hardware. According to the most common definition of a digital signal, both baseband and bandwidth signals representing bit streams are considered digital transmission, while an alternative definition considers only baseband signal as both digital and digital transmission, bandwidth data as a form of digital to digital conversion - analog conversion.

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors.

High impulsivity in children with attention deficit/hyperactivity disorder (ADHD) plays a key role in their vulnerability to substance abuse disorders (SUDs). Although impulsivity is increasingly recognized as a multidimensional construct, efforts to describe the contribution of different impulsivity aspects to the development of SUD have been hindered by conceptual and experimental inconsistencies. This review seeks to map potential trajectories from childhood ADHD to SUD by examining the hypothesized mediating role of three different impulsivity-related constructs: disinhibition, impulsive choice, and sensation seeking. Integration of data from developmental, cognitive, and neurophysiological research suggests that childhood ADHD and SUD are both associated with behavioural and neurophysiological deficits in all three impulsivity-related constructs. Examination of brain mechanisms related to the three impulsivity-related constructs indicates that ADHD share neurophysiological deficits with SUD, such as abnormal brain activity in areas involved in inhibition and complex cognitive-emotional processes. We conclude that different impulsivity constructs operate independently and interact with each other to affect adult risk taking behaviour and SUD in patients with childhood ADHD. This review highlights the current theoretical and methodological challenges in the study of impulsivity and discusses clinical implications and directions for future research.

Psychostimulants, such as methylphenidate, are thought to improve information processing in motivation-reward and attention-activation networks by enhancing the effects of more relevant signals and suppressing those of less relevant ones; however the nature of such reciprocal influences remains poorly understood. To explore this question, we tested the effect of methylphenidate on performance and associated brain activity in the Anticipation, Conflict, Reward (ACR) task. Sixteen healthy adult volunteers, ages 21-45, were scanned twice using functional magnetic resonance imaging (fMRI) as they performed the ACR task under placebo and methylphenidate conditions. A three-way repeated measures analysis of variance, with cue (reward vs. non-reward), target (congruent vs. incongruent) and medication condition (methylphenidate vs. placebo) as the factors, was used to analyze behaviors on the task. Blood oxygen level dependent (BOLD) signals, reflecting task-related neural activity, were evaluated using linear contrasts. Participants exhibited significantly greater accuracy in the methylphenidate condition than the placebo condition. Compared with placebo, the methylphenidate condition also was associated with lesser task-related activity in components of attention-activation systems irrespective of the reward cue, and less task-related activity in components of the reward-motivation system, particularly the insula, during reward trials irrespective of target difficulty. These results suggest that methylphenidate enhances task performance by improving efficiency of information processing in both reward-motivation and in attention-activation systems.

The paper analyses the devices used in developing the identity of the Russian communities in Bulgaria and in the UK. The corpus for the study comprises articles from the Bulgarian Dnevnik and the British The Independent.

Background: Widely used psychotropic medications for obsessive-compulsive disorder (OCD) may change the volumes of subcortical brain structures, and differently in children vs. adults. We measured subcortical volumes cross-sectionally in patients finely stratified for age taking various common classes of OCD drugs. Methods: The ENIGMA-OCD consortium sample (1081 medicated/1159 unmedicated OCD patients and 2057 healthy controls aged 6–65) was divided into six successive 6–10-year age-groups. Individual structural MRIs were parcellated automatically using FreeSurfer into 8 regions-of-interest (ROIs). ROI volumes were compared between unmedicated and medicated patients and controls, and between patients taking serotonin reuptake inhibitors (SRIs), tricyclics (TCs), antipsychotics (APs), or benzodiazepines (BZs) and unmedicated patients. Results: Compared to unmedicated patients, volumes of accumbens, caudate, and/or putamen were lower in children aged 6–13 and adults aged 50–65 with OCD taking SRIs (Cohen's d = −0.24 to −0.74). Volumes of putamen, pallidum (d = 0.18–0.40), and ventricles (d = 0.31–0.66) were greater in patients aged 20–29 receiving APs. Hippocampal volumes were smaller in patients aged 20 and older taking TCs and/or BZs (d = −0.27 to −1.31). Conclusions: Results suggest that TCs and BZs could potentially aggravate hippocampal atrophy of normal aging in older adults with OCD, whereas SRIs may reduce striatal volumes in young children and older adults. Similar to patients with psychotic disorders, OCD patients aged 20–29 may experience subcortical nuclear and ventricular hypertrophy in relation to APs. Although cross-sectional, present results suggest that commonly prescribed agents exert macroscopic effects on subcortical nuclei of unknown relation to therapeutic response.

ObjectiveNo diagnostic biomarkers are available for obsessive-compulsive disorder (OCD). Magnetic resonance imaging (MRI) studies have provided evidence for structural abnormalities in distinct brain regions, but effect sizes are small and have limited clinical relevance. To investigate whether individual patients can be distinguished from healthy controls, we performed multivariate analysis of structural neuroimaging data from the ENIGMA-OCD consortium.MethodWe included 46 data sets with neuroimaging and clinical data from adult (≥18 years) and pediatric (1images from 2,304 OCD patients and 2,068 healthy controls were analyzed using standardized processing to extract regional measures of cortical thickness, surface area and subcortical volume. Machine learning classification performance was tested using cross-validation, and possible effects of clinical variables were investigated by stratification.ResultsClassification performance for OCD versus controls using the complete sample with different classifiers and cross-validation strategies was poor (AUC—0.57 (standard deviation (SD)=0.02;Pcorr=0.19) to 0.62 (SD=0.03;Pcorrcorr>.99) to 0.54 (SD=0.08;Pcorr>.99)). In contrast, good classification performance (>0.8 AUC) was achieved within subgroups of patients split according to their medication status.ConclusionsParcellated structural MRI data do not enable good distinction between patients with OCD and controls. However, classifying subgroups of patients based on medication status enables good identification at the individual subject level. This underlines the need for longitudinal studies on the short- and long-term effects of medication on brain structure.