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Transcranial electrical stimulation (tES) is a popular method to modulate brain activity by sending a weak electric current through the head. Despite its popularity, long-term effects are poorly understood. We wanted to test if anodal tES immediately changes cerebral responses to visual stimuli, and if repeated sessions of tES produce plasticity in these responses. We applied repeated anodal tES, like transcranial direct current stimulation (tDCS), but pulsed (8 s on, 10 s off), to the visual cortex of mice while visually presenting gratings. We measured the responses to these visual stimuli in the visual cortex using the genetically encoded calcium indicator GCaMP3. We found an increase in the visual response when concurrently applying tES on the bone without skin (epicranially). This increase was only transient when tES was applied through the skin (transcutaneous). There was no immediate after-effect of tES. However, repeated transcutaneous tES for four sessions at two-day intervals increased the visual response in the visual cortex. This increase was not specific to the grating stimulus coupled to tES and also occurred for an orthogonal grating presented in the same sessions but without concurrent tES. No increase was found in mice that received no tES. Our study provides evidence that tES induces long-term changes in the mouse brain. Results in mice do not directly translate to humans, because of differences in stimulation protocols and the way current translates to electric field strength in vastly different heads. • Concurrent anodal tES increases calcium response in the mouse visual cortex. • Anodal tES over the visual cortex enhances visual responses over multiple sessions. • The increase in visual response is not specific to the visual stimulus coupled to the tES. [ABSTRACT FROM AUTHOR]

In recent years complexity of the brain structure in healthy and disordered subjects has been studied increasingly. But to the best of the authors’ knowledge, researchers so far have investigated the structural complexity only in the context of two restricted networks known as Small-World and Scale-free networks; whereas other aspects of the structural complexity of brain activities may be affected by aging and neurodegenerative disorders such as the Alzheimer’s disease and autism spectrum disorder. In this study, two general complexity metrics of graphs, Graph Index Complexity and Offdiagonal Complexity are proposed as general measures of complexity, not restricted to SWN only. They are adopted to measure the structural complexity of the weighted graphs instead of the common binary graphs. Fuzzy Synchronization Likelihood is applied to the EEGs and their sub-bands, as a functional connectivity metric of the brain, to construct the functional connectivity graphs. Two applications are used to evaluate the efficacy of the complexity measures: diagnosis of autism and aging, both based on EEG. It was discovered that the Graph Index Complexity of gamma band is discriminative in distinguishing autistic children from non-autistic children. Also, Offdiagonal Complexity of theta band in young subjects was observed to be significantly different than old subjects. This study shows that changes in the structure of functional connectivity of brain in disorders and different healthy states can be revealed by unrestricted metrics of graph complexity. While the applications presented in this paper are based on EEG, the approach is general and can be used with other modalities such as fMRI, MEG, etc. Further, it can be used to study every other neurological and psychiatric disorder. [ABSTRACT FROM AUTHOR]

Highlights: [•] We use magneto-encephalograms in patients with mild cognitive impairment (MCI). [•] We investigate complexity of functional connectivity network of MCI patients using two different measures: Graph Index Complexity and Efficiency Complexity. [•] Efficiency Complexity is superior to Graph Index Complexity and its value at theta band can be used for diagnosis of MCI. [Copyright &y& Elsevier]

Highlights: [•] The first study on global organization of the functional brain connectivity (FBC) in DS. [•] FBC of the DS and normal children are topologically different in theta and alpha bands. [•] The topological abnormalities are primarily in upper alpha within left hemisphere. [ABSTRACT FROM AUTHOR]

Abstract: Objective: This study aimed to determine effects of chronic methamphetamine (MA) abuse on global organization of the functional brain connectivity. Methods: Eyes-closed resting-state EEGs of 36 MA abusers and 36 age-matched healthy subjects were recorded using a 32-channel system. The EEGs (1–60Hz), after removing artifacts, were decomposed into the conventional EEG bands. Using visibility graph similarity (VGS) and coherence methods, the VGS and coherence matrices in each EEG band were constructed. Then the Small-World Network properties, clustering coefficient (C), mean path length (L) and C/L, of the VGS and coherence matrices, were computed in all EEG bands. Then using the Mann–Whitney test and an artificial neural network the differences of C, L and C/L between the two groups were evaluated. Results: The MA abusers showed higher C, lower L and higher C/L at the gamma band (p-value <0.005). An accuracy of 82.8% in discriminating the two groups was obtained by the classifier. Conclusions: The topology of the functional brain connectivity is disrupted in MA abusers, as depicted by deviation from Small-Worldness in the gamma band. Significance: This is the first but quasi-experimental study showing disrupted topology of the functional brain networks in MA abusers. [Copyright &y& Elsevier]

This study was carried out to determine linear and nonlinear changes of brain dynamics and their relationships with the motor dysfunctions in CP children. For this purpose power of EEG frequency bands (as a linear analysis) and EEG fractality (as a nonlinear analysis) were computed in eyes-closed resting state and statistically compared between 26 CP and 26 normal children. Based on these characteristics accuracy of the classification between the two groups was obtained by enhanced probabilistic neural network (EPNN). Severity of gross motor and manual disabilities was determined by standard systems and the relation between the deficient brain dynamics and severity of the motor dysfunctions was obtained by Pearson''s correlation coefficient. A definitely higher delta and lower theta and alpha powers, and higher EEG complexity in CP patients. As such a high accuracy of 94.8% in distinguishing the two groups was obtained. Moreover significant positive correlations were found between beta power and severity of manual disabilities and gross motor dysfunctions in the boys with CP. It is concluded that the obtained brain dynamics’ characteristics are useful in diagnosis of CP. Furthermore severity of the motor dysfunctions in boys with CP could be evaluated by the beta activity. [Copyright &y& Elsevier]

Abstract: Investigating gender differences of the brain is of both scientific and clinical importance, as understanding such differences may be helpful for improving gender specific treatments of neuropsychiatric disorders. As brain is a highly complex system, it is crucial to investigate its activity in terms of nonlinear dynamics. However, there are few studies that investigated gender differences based on dynamical characteristics of the brain. Fractal dimension (FD) is a key characteristic of the brain dynamics which indicates the level of complexity on which the neuronal regions function or interact and quantifies the associated brain processes on a scale ranging from fully deterministic to fully random. This study investigates the gender differences of brain dynamics, comparing fractal dimension of scalp EEGs (in eyes-closed resting state) of 34 female and 34 male healthy adults. The results showed significantly greater FDs in females compared to males in all brain regions except in lateral and occipital lobes. This indicates a higher complexity of the brain dynamics in females relative to males. The high accuracies of 87.8% and 93.1% obtained by logistic regression and enhanced probabilistic neural network, respectively, in discriminating between the gender groups based on the FDs also confirmed the great gender differences of complexity of brain activities. The results showed that delta, alpha, and beta bands are the frequency bands that contribute most to the gender differences in brain complexity. Furthermore, the lateralization analysis showed the leftward lateralization of complexity in females is greater than in males. [Copyright &y& Elsevier]

Abstract: This paper presents a methodology for investigation of functional connectivity in patients with autism spectrum disorder (ASD) using Fuzzy Synchronization Likelihood (Fuzzy SL). Fuzzy SLs between and within brain regions are calculated in all EEG sub-bands produced by the wavelet decomposition as well as in the full-band EEG. Then, discriminative Fuzzy SLs between and within different regions and different EEG sub-bands or full-band EEG for distinguishing autistic children from healthy control children are determined based on Analysis of Variation (ANOVA). Finally, the selected features are used as input to an Enhanced Probabilistic Neural Network classifier to make an accurate diagnosis of ASD based on the detected differences in the regional functional connectivity of autistic and healthy EEGs. The methodology is validated using EEG data obtained from 9 autistic and 9 healthy children. The ANOVA test showed high ability of the regional Fuzzy SLs in low frequency bands, delta and theta, as well as alpha band for discriminating the two groups. A high classification accuracy of 95.5% was achieved for distinguishing autistic EEGs from healthy EEGs. It is concluded that the methodology presented in this paper can be used as an effective tool for diagnosis of the autism. Further, the regional Fuzzy SLs discovered in this research can be used as reliable markers in neurofeedback treatments to improve neuronal plasticity and connectivity in autistic patients. [Copyright &y& Elsevier]

Abstract: Neurofeedback training is increasingly used for ADHD treatment. However some ADHD patients are not treated through the long-time neurofeedback trainings with common protocols. In this paper a new graph theoretical approach is presented for EEG-based prediction of ADHD patients’ responses to a common neurofeedback training: rewarding SMR activity (12–15Hz) with inhibiting theta activity (4–8Hz) and beta2 activity (18–25Hz). Eyes closed EEGs of two groups before and after neurofeedback training were studied: ADHD patients with (15 children) and without (15 children) positive response to neurofeedback training. Employing a recent method to measure synchronization, fuzzy synchronization likelihood, functional connectivity graphs of the patients’ brains were constructed in the full-band EEGs and 6 common EEG sub-bands produced by wavelet decomposition. Then, efficiencies of the brain networks in synchronizability and high speed information transmission were computed based on mean path length of the graphs, before and after neurofeedback training. The results were analyzed by ANOVA and showed synchronizability of the neocortex activity network at beta band in ADHDs with positive response is obviously less than that of ADHDs resistant to neurofeedback therapy, before treatment. The accuracy of linear discriminant analysis (LDA) in distinguishing these patients based on this feature is so high (84.2%) that this feature can be considered as reliable characteristics for prediction of responses of ADHDs to the neurofeedback trainings. Also difference between flexibility of the neocortex in beta band before and after treatment is obviously larger in the ADHDs with positive response in comparison to those with negative response which may be a neurophysiologic reason for dissatisfaction of the last group to the neurofeedback therapy. [Copyright &y& Elsevier]

Many organisms rely on a capacity to rapidly replicate, disperse, and evolve when faced with uncertainty and novelty. But mammals do not evolve and replicate quickly. They rely on a sophisticated nervous system to generate predictions and select responses when confronted with these challenges. An important component of their behavioral repertoire is the adaptive context-dependent seeking or avoiding of perceptually novel objects, even when their values have not yet been learned. Here, we outline recent cross-species breakthroughs that shed light on how the zona incerta (ZI), a relatively evolutionarily conserved brain area, supports novelty-seeking and novelty-related investigations. We then conjecture how the architecture of the ZI's anatomical connectivity – the wide-ranging top-down cortical inputs to the ZI, and its specifically strong outputs to both the brainstem action controllers and to brain areas involved in action value learning – place the ZI in a unique role at the intersection of cognitive control and learning. [ABSTRACT FROM AUTHOR]