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Experience-dependent plasticity in the adult visual system is generally thought of as a cortical process. However, several recent studies have shown that perceptual learning or monocular deprivation can also induce plasticity in the adult dorsolateral geniculate nucleus (dLGN) of the thalamus. How plasticity in the thalamus and cortex interact in the adult visual system is ill-understood. To assess the influence of thalamic plasticity on plasticity in primary visual cortex (V1), we made use of our previous finding that during the critical period ocular dominance (OD) plasticity occurs in dLGN and requires thalamic synaptic inhibition. Using multielectrode recordings we find that this is also true in adult mice, and that in the absence of thalamic inhibition and plasticity, OD plasticity in adult V1 is absent. To study the influence of V1 on thalamic plasticity, we silenced V1 and show that during the critical period, but not in adulthood, the OD shift in dLGN is partially caused by feedback from V1. We conclude that during adulthood the thalamus plays an unexpectedly dominant role in experience-dependent plasticity in V1. Our findings highlight the importance of considering the thalamus as a potential source of plasticity in learning events that are typically thought of as cortical processes. [ABSTRACT FROM AUTHOR]

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. (Contains 4 figures and 4 tables.)

The perception of speed is influenced by visual contrast. In primary visual cortex (V1), an early stage in the visual perception pathway, the neural tuning to speed is directly related to the neural tuning to temporal frequency of stimulus changes. The influence of contrast on speed perception can be caused by the joint dependency of neural responses in V1 on temporal frequency and contrast. Here, we investigated how tuning to contrast and temporal frequency in V1 of anesthetized mice are related. We found that temporal frequency tuning is contrast-dependent. V1 was more responsive at lower temporal frequencies than the dLGN, consistent with previous work at high contrast. The temporal frequency tuning moves toward higher temporal frequencies with increasing contrast. The low half-maximum temporal frequency does not change with contrast. The Heeger divisive normalization equation provides a good fit to many response characteristics in V1, but does not fit the dependency of temporal frequency and contrast with set of parameters for all temporal frequencies. Different mechanisms for normalization in the visual cortex may predict different relationships between temporal frequency and contrast non-linearity. Our data could help to make a model selection. [ABSTRACT FROM AUTHOR]

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]

To the best knowledge of the authors there is no study on nonlinear brain dynamics of down syndrome (DS) patients, whereas brain is a highly complex and nonlinear system. In this study, fractal dimension of EEG, as a key characteristic of brain dynamics, showing irregularity and complexity of brain dynamics, was used for evaluation of the dynamical changes in the DS brain. The results showed higher fractality of the DS brain in almost all regions compared to the normal brain, which indicates less centrality and higher irregular or random functioning of the DS brain regions. Also, laterality analysis of the frontal lobe showed that the normal brain had a right frontal laterality of complexity whereas the DS brain had an inverse pattern (left frontal laterality). Furthermore, the high accuracy of 95.8 % obtained by enhanced probabilistic neural network classifier showed the potential of nonlinear dynamic analysis of the brain for diagnosis of DS patients. Moreover, the results showed that the higher EEG fractality in DS is associated with the higher fractality in the low frequencies (delta and theta), in broad regions of the brain, and the high frequencies (beta and gamma), majorly in the frontal regions. [ABSTRACT FROM AUTHOR]

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]

In recent years the Probabilistic Neural Network (PPN) has been used in a large number of applications due to its simplicity and efficiency. PNN assigns the test data to the class with maximum likelihood compared with other classes. Likelihood of the test data to each training data is computed in the pattern layer through a kernel density estimation using a simple Bayesian rule. The kernel is usually a standard probability distribution function such as a Gaussian function. A spread parameter is used as a global parameter which determines the width of the kernel. The Bayesian rule in the pattern layer estimates the conditional probability of each class given an input vector without considering any probable local densities or heterogeneity in the training data. In this paper, an enhanced and generalized PNN (EPNN) is presented using local decision circles (LDCs) to overcome the aforementioned shortcoming and improve its robustness to noise in the data. Local decision circles enable EPNN to incorporate local information and non-homogeneity existing in the training population. The circle has a radius which limits the contribution of the local decision. In the conventional PNN the spread parameter can be optimized for maximum classification accuracy. In the proposed EPNN two parameters, the spread parameter and the radius of local decision circles, are optimized to maximize the performance of the model. Accuracy and robustness of EPNN are compared with PNN using three different benchmark classification problems, iris data, diabetic data, and breast cancer data, and five different ratios of training data to testing data: 90:10, 80:20, 70:30, 60:40, and 50:50. EPNN provided the most accurate results consistently for all ratios. Robustness of PNN and EPNN is investigated using different values of signal to noise ratio (SNR). Accuracy of EPNN is consistently higher than accuracy of PNN at different levels of SNR and for all ratios of training data to testing data. [ABSTRACT FROM AUTHOR]

The article provides information on study which revealed a cell type-specific cortico-subcortical brain circuit of the curiosity and novelty-seeking behavior. Topics discussed include challenges of studying novelty seeking and distinguishing it from eating and hunting in nonarticulating animals, and analysis of the transitions within action sequences in object free-access double-choice (FADC) and social interaction tests.

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]

Experience-dependent plasticity in the adult visual system is generally thought of as a cortical process. However, several recent studies have shown that perceptual learning or monocular deprivation can also induce plasticity in the adult dorsolateral geniculate nucleus (dLGN) of the thalamus. How plasticity in the thalamus and cortex interact in the adult visual system is ill-understood. To assess the influence of thalamic plasticity on plasticity in primary visual cortex (V1), we made use of our previous finding that during the critical period ocular dominance (OD) plasticity occurs in dLGN and requires thalamic synaptic inhibition. Using multielectrode recordings we find that this is also true in adult mice, and that in the absence of thalamic inhibition and plasticity, OD plasticity in adult V1 is absent. To study the influence of V1 on thalamic plasticity, we silenced V1 and show that during the critical period, but not in adulthood, the OD shift in dLGN is partially caused by feedback from V1. We conclude that during adulthood the thalamus plays an unexpectedly dominant role in experience-dependent plasticity in V1. Our findings highlight the importance of considering the thalamus as a potential source of plasticity in learning events that are typically thought of as cortical processes., Competing Interests: YQ, MA, SS, PN, Ld, JH, CL No competing interests declared, (© 2023, Qin et al.)

A new nonlinear technique for analysis of brain dynamics called spatiotemporal analysis of relative convergence (STARC) of electroencephalograms (EEGs) is introduced, based on the relative convergence of EEGs of different loci. This technique shows how many times EEGs of each loci pair converge together, which in turn is used as an indicator to determine the different neuronal regions involved in performing the same task. A higher STARC value indicates that more regions are recruited to perform the same task. The STARC methodology was used to reveal sex difference pathophysiology and brain dynamics, using EEG data from 11 male and 11 female adults with major depressive disorder (MDD). The results show significant differences in relative convergences of EEGs of intraleft temporal and frontoleft temporal lobes at δ band, between male and female patients.

This article presents a new methodology for investigation of the organization of the overall and hemispheric brain network of patients with attention-deficit hyperactivity disorder (ADHD) using theoretical analysis of a weighted graph with the goal of discovering how the brain topology is affected in such patients. The synchronization measure used is the nonlinear fuzzy synchronization likelihood (FSL) developed by the authors recently. Recent evidence indicates a normal neocortex has a small-world (SW) network with a balance between local structure and global structure characteristics. Such a network results in optimal balance between segregation and integration which is essential for high synchronizabilty and fast information transmission in a complex network. The SW network is characterized by the coexistence of dense clustering of connections (C) and short path lengths (L) among the network units. The results of investigation of C show the local structure of functional left-hemisphere brain networks of ADHD diverges from that of non-ADHD which is recognizable in the delta electroencephalograph (EEG) sub-band. Also, the results of investigation for L show the global structure of functional left-hemisphere brain networks of ADHD diverges from that of non-ADHD which is observable in the delta EEG sub-band. It is concluded that the changes in left-hemisphere brain's structure of ADHD from that of the non-ADHD are so much that L and C can distinguish the ADHD brain from the non-ADHD brain in the delta EEG sub-band.

Synchronization as a measure of quantification of similarities in dynamic systems is an important concept in many scientific fields such as nonlinear science, neuroscience, cardiology, ecology, and economics. When interdependencies and connections of coupled dynamic systems are not directly accessible and measurable such as those of the neurons of the brain, quantification of similarities between their time series outputs is the best possible way to detect the existent interdependencies among them. In recent years, Synchronization Likelihood (SL) has been used as one of the most suitable algorithms in highly nonlinear and non-stationary systems. In this method, the likelihood of patterns is measured statistically, and then it is determined which patterns of the time series are similar to each other considering a threshold. But the degree of similarities is not considered in the decision. In this paper, a new measure of synchronization, fuzzy SL, is presented using the theory of fuzzy logic and Gaussian membership functions. The new fuzzy SL is compared with the conventional SL using both a standard problem from the chaos literature and a complicated real life neurological diagnostic problem, that is, the EEG-based diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD). The results of ANOVA analysis indicate the interdependencies measured by the fuzzy SL are more reliable than the conventional SL for discriminating ADHD patients from healthy individuals.

Recently the senior author and his associates developed a spatiotemporal wavelet-chaos methodology for the analysis of electroencephalograms (EEGs) and their subbands for discovering potential markers of abnormality in Alzheimer disease (AD). In this study, fractal dimension (FD) is used for the evaluation of the dynamical changes in the AD brain. The approach presented in this study is based on the research ideology that nonlinear features, such as FD, may not show significant differences between the AD and the control groups in the band-limited EEG, but may manifest in certain subbands. First, 2 different FD algorithms for computing the fractality of EEGs are investigated and their efficacy for yielding potential mathematical markers of AD is compared. They are Katz FD (KFD) and Higuchi FD. Significant features in different loci and different EEG subbands or band-limited EEG for discrimination of the AD and the control groups are determined by analysis of variation. The most discriminative FD and the corresponding loci and EEG subbands for discriminating between AD and healthy EEGs are discovered. As KFD of all loci in the β subband showed very high ability (P value <0.001) in discriminating between the groups, all KFDs are abstracted in 1 global KFD by averaging across loci in each of the 2 eyes-closed and eyes-open conditions. This leads to a more robust classification in terms of common variation of electrode positions than a classification based on separate KFDs of certain loci. Finally, based on the 2 global features separately and together, linear discriminant analysis is used to classify EEGs of AD and elderly normal patients. A high accuracy of 99.3% was obtained for the diagnosis of the AD based on the global KFD in the β-band of the eyes-closed condition with a sensitivity of 100% and a specificity of 97.8%., (Copyright © 2011 by Lippincott Williams & Wilkins)

A method is presented for investigation of EEG of children with autistic spectrum disorder using complexity and chaos theory with the goal of discovering a nonlinear feature space. Fractal Dimension is proposed for investigation of complexity and dynamical changes in autistic spectrum disorder in brain. Two methods are investigated for computation of fractal dimension: Higuchi's Fractal Dimension and Katz's Fractal Dimension. A wavelet-chaos-neural network methodology is presented for automated EEG-based diagnosis of autistic spectrum disorder. The model is tested on a database of eyes-closed EEG data obtained from two groups: nine autistic spectrum disorder children, 6 to 13 years old, and eight non-autistic spectrum disorder children, 7 to 13 years old. Using a radial basis function classifier, an accuracy of 90% was achieved based on the most significant features discovered via analysis of variation statistical test, which are three Katz's Fractal Dimensions in delta (of loci Fp2 and C3) and gamma (of locus T6) EEG sub-bands with P < 0.001.

A multi-paradigm methodology is presented for electroencephalogram (EEG) based diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) through adroit integration of nonlinear science; wavelets, a signal processing technique; and neural networks, a pattern recognition technique. The selected nonlinear features are generalized synchronizations known as synchronization likelihoods (SL), both among all electrodes and among electrode pairs. The methodology consists of three parts: first detecting the more synchronized loci (group 1) and loci with more discriminative deficit connections (group 2). Using SLs among all electrodes, discriminative SLs in certain sub-bands are extracted. In part two, SLs are computed, not among all electrodes, but between loci of group 1 and loci of group 2 in all sub-bands and the band-limited EEG. This part leads to more accurate detection of deficit connections, and not just deficit areas, but more discriminative SLs in sub-bands with finer resolutions. In part three, a classification technique, radial basis function neural network, is used to distinguish ADHD from normal subjects. The methodology was applied to EEG data obtained from 47 ADHD and 7 control individuals with eyes closed. The Radial Basis Function (RBF) neural network classifier yielded a high accuracy of 95.6% for diagnosis of the ADHD in the feature space discovered in this research with a variance of 0.7%.