Neuroscience
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Neurofeedback training is a form of brain training in which information about a neural measure is fed back to the trainee who is instructed to increase or decrease the value of that particular measure. This paper focuses on electroencephalography (EEG) neurofeedback in which the neural measures of interest are the brain oscillations. To date, the neural mechanisms that underlie successful neurofeedback training are still unexplained. ⋯ The simulation successfully learns to increase its peak alpha frequency and demonstrates the influence of threshold setting - the threshold that determines whether positive or negative feedback is provided. Analyses of the model suggest that neurofeedback can be likened to a search process that uses importance sampling to estimate the posterior probability distribution over striatal representational space, with each representation being associated with a distribution of values of the target EEG band. The model provides an important proof of concept to address pertinent methodological questions about how to understand and improve EEG neurofeedback success.
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Drug addiction is a major health problem worldwide. Recent neuroimaging studies have shed light into the underlying mechanisms of drug addiction as well as its consequences to the human brain. The most vulnerable, to heroin addiction, brain regions have been reported to be specific prefrontal, parietal, occipital, and temporal regions, as well as, some subcortical regions. ⋯ In the current manuscript, we comprehensively review and discuss existing resting-state neuroimaging findings classified into three overlapping and interconnected groups: functional connectivity alterations, structural deficits and abnormal topological properties. Moreover, behavioral traits of heroin-addicted individuals as well as the limitations of the currently available studies are also reviewed. Finally, in need of a contemporary therapy a multimodal therapeutic approach is suggested using classical treatment practices along with current neurotechonologies, such as neurofeedback and goal-oriented video-games.
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There is a growing body of evidence pointing at several types of motor abnormalities found in attention-deficit/hyperactivity disorder (ADHD). In this article we review findings stemming from different paradigms, and suggest an interweaving approach to the different stages involved in the motor regulation process. We start by reviewing various aspects of motor abnormalities found in ADHD and related brain mechanisms. ⋯ Additionally we discuss EMG-Biofeedback interventions targeted at feedback on motor activity. Further we review physical activity and motor interventions aimed at improving motor difficulties, associated with ADHD. These kinds of interventions are shown to be helpful not only in aspects of physical ability, but also in enhancing cognition and executive functioning.
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Despite evidence that Sensorimotor Rhythm (SMR) and beta1 neurofeedback have distinct cognitive enhancement effects, it remains unclear whether their amplitudes can be independently enhanced. Furthermore, demands for top-down attention control, postural restraint and maintenance of cognitive set processes, all requiring low-beta frequencies, might masquerade as learning and confound interpretation. The feasibility of selectively enhancing SMR and beta1 amplitudes was investigated with the addition of a random frequency control condition that also requires the potentially confounding cognitive processes. ⋯ Interestingly, SMR and beta1 amplitude increased across sessions in the three groups suggesting unspecific effects of neurofeedback in the low beta frequency band. Moreover, there was no clear evidence of frequency specificity associated with either SMR or beta1 training. Some methodological limitations may underpin the divergent results with previous studies.
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It is recognized that lower electroencephalography (EEG) frequencies correspond to distributed brain activity over larger spatial regions than higher frequencies and are associated with coordination. In motor processes it has been suggested that this is not always the case. Our objective was to explore this contradiction. ⋯ Comparing the models we observed lower CPL for both rhythms, lower CC in alpha and higher CC in beta when the number of ROIs increased. Also, denser networks with higher SW were correlated with higher number of ROIs. We propose a non-exclusive model where alpha rhythm uses greater wiring costs to engage in local information progression while beta rhythm coordinates the neurophysiological processes in sensorimotor tasks.