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Selecting the right response to emotional stressors is vital for survival. From the psycho-spatial point of view, the perceived stressors can fall into two conditions: the stressors outside the confined safe zone or inside. Although this novel approach of categorizing the stressors distinguishes two anxiety-associated pathological conditions, agoraphobia and claustrophobia, the difference between the behavioral responses in these two conditions and the underlying brain circuits are not well understood. Here, using novel behavioral paradigms in mice, we found that outside- and inside-stressors elicit two distinct behaviors, push-bedding and escape-jumping, respectively. Next, in an attempt to identify the brain regions activated in both outside- and inside-stressor conditions, the whole-brain c-Fos staining led us to an unexpected brain region, dorsomedial hypothalamus (DMH). Using optogenetics, chemogenetics, and photometry recording, we discovered that glutamatergic projections from DMH to the periaqueductal gray drive the escape to the inside-stressor, while GABAergic projections, in particular from neurons expressing tachykinin 1, are essential for coping with the outside-stressor. We conclude that distinct populations within the DMH region control the response to psycho-spatially distinct stressors. This discovery shows how the stressors and the spatial information of the stressors integrate in the brain and it is the first to shed light on understanding the narrow border between the two space-associated stress disorders/phobias, i.e. agoraphobia and claustrophobia and the underlying brain mechanisms.

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.

Background 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. Objective 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. Methods 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. Results 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. Conclusion 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.

A brain circuit that drives and gates curiosity Curiosity is what drives organisms to investigate each other and their environment. It is considered by many to be as intrinsic as hunger and thirst, but the neurobiological mechanisms behind curiosity have remained elusive. In mice, Ahmadlou et al. found that a specific population of genetically identified γ-aminobutyric acid (GABA)—ergic neurons in a brain region called the zona incerta receive excitatory input in the form of novelty and/or arousal information from the prelimbic cortex, and these neurons send inhibitory projections to the periaqueductal gray region (see the Perspective by Farahbakhsh and Siciliano). This circuitry is necessary for the exploration of new objects and conspecifics. Science , this issue p. eabe9681 ; see also p. 684

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.

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.

Inhibition in the cerebral cortex is delivered by a variety of GABAergic interneurons. These cells have been categorized by their morphology, physiology, gene expression and connectivity. Many of these classes appear to be conserved across species, suggesting that the classes play specific functional roles in cortical processing. What these functions are, is still largely unknown. The largest group of interneurons in the upper layers of mouse primary visual cortex (V1) is formed by cells expressing the calcium-binding protein calretinin (CR). This heterogeneous class contains subsets of vasoactive intestinal polypeptide (VIP) interneurons and somatostatin (SOM) interneurons. Here we show, using in vivo two-photon calcium imaging in mice, that CR neurons can be sensitive to stimulus orientation, but that they are less selective on average than the overall neuronal population. Responses of CR neurons are suppressed by a surrounding stimulus, but less so than the overall population. In rats and primates, CR interneurons have been suggested to provide disinhibition, but we found that in mice their in vivo activation by optogenetics causes a net inhibition of cortical activity. Our results show that the average functional properties of CR interneurons are distinct from the averages of the parvalbumin, SOM and VIP interneuron populations.

We detect objects more readily if they differ from their surroundings in motion, color, or texture. This increased saliency is thought to be related to increased responses in the visual cortex. The superior colliculus is another brain area involved in vision and especially in directing gaze and attention. In this study, we show that differences in texture orientation also increase responses in the superficial layers of the superior colliculus that receive retinal and cortical input. We found that gratings evoke more neural response when surrounded by orthogonal gratings than when surrounded by parallel gratings, particularly in the awake mouse. This pop-out is not originating from the visual cortex, and silencing visual cortex increased the relative difference in response. A model shows that this can result from retinotopically matched excitation from visual cortex to the superior colliculus. We suggest that the perceptual saliency of a stimulus differing from its surround in a low-level feature like grating orientation could depend on visual processing in the superior colliculus.

Objective To explore how the global organization or topology of the functional brain connectivity (FBC) is affected in Down Syndrome (DS). Methods As the brain is a highly complex network including numerous nonlinearly interacted neuronal areas, the FBCs of typically developing (TD) children and DS patients were computed using a nonlinear synchronization method. Then the differences in global organization of the obtained FBCs of the two groups were analyzed, in all electroencephalogram (EEG) frequency bands, in the framework of Small-Worldness Network (a network with optimum balance between segregation and integration of information). Results The topology of the functional connectivity of DS patients is disrupted in the whole brain in alpha and theta bands, and especially in the left intra-hemispheric brain networks in upper alpha band. Conclusions The global organization of the DS brain does not resemble a Small-World network, but it works as a random network. Significance It is the first study on global organization of the FBC in DS.

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.

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.

Methamphetamine (MA) is a drug which has got a considerable prevalence of abuse in the world. Therefore, it is of great importance to understand the deficits and problems that it makes in brain, structurally and functionally, in order to increase knowledge of people about it and help finding better ways of treatments. Neuroimaging techniques as the most powerful tools to study the brain functions and structures, in the recent decades have been used to find out the brain deficits caused by the MA abuse. Here we would have a short review on the neuroimaging findings in MA abusers and the children with prenatally exposure to MA, with the focus on electroencephalography (EEG), magnetic resonance imaging (MRI) and functional MRI studies.

Recently, the visibility graph (VG) algorithm was proposed for mapping a time series to a graph to study complexity and fractality of the time series through investigation of the complexity of its graph. The visibility graph algorithm converts a fractal time series to a scale-free graph. VG has been used for the investigation of fractality in the dynamic behavior of both artificial and natural complex systems. However, robustness and performance of the power of scale-freeness of VG (PSVG) as an effective method for measuring fractality has not been investigated. Since noise is unavoidable in real life time series, the robustness of a fractality measure is of paramount importance. To improve the accuracy and robustness of PSVG to noise for measurement of fractality of time series in biological time-series, an improved PSVG is presented in this paper. The proposed method is evaluated using two examples: a synthetic benchmark time series and a complicated real life Electroencephalograms (EEG)-based diagnostic problem, that is distinguishing autistic children from non-autistic children. It is shown that the proposed improved PSVG is less sensitive to noise and therefore more robust compared with PSVG. Further, it is shown that using improved PSVG in the wavelet-chaos neural network model of Adeli and c-workers in place of the Katz fractality dimension results in a more accurate diagnosis of autism, a complicated neurological and psychiatric disorder.

EEGs of the frontal brain of patients diagnosed with major depressive disorder (MDD) have been investigated in recent years using linear methods but not based on nonlinear methods. This paper presents an investigation of the frontal brain of MDD patients using the wavelet-chaos methodology and Katz's and Higuchi's fractal dimensions (KFD and HFD) as measures of nonlinearity and complexity. EEGs of the frontal brain of healthy adults and MDD patients are decomposed into 5 EEG sub-bands employing a wavelet filter bank, and the FDs of the band-limited as well as those of their 5 sub-bands are computed. Then, using the ANOVA statistical test, HFDs and KFDs of the left and right frontal lobes in EEG full-band and sub-bands of MDD and healthy groups are compared in order to discover the FDs showing the most meaningful differences between the two groups. Finally, the discovered FDs are used as input to a classifier, enhanced probabilistic neural network (EPNN), to discriminate the MDD from healthy EEGs. The results of HFD show higher complexity of left, right and overall frontal lobes of the brain of MDD compared with non-MDD in beta and gamma sub-bands. Moreover, it is observed that HFD of the beta band is more discriminative than HFD of the gamma band for discriminating MDD and non-MDD participants, while the KFD did not show any meaningful difference. A high accuracy of 91.3% is achieved for classification of MDD and non-MDD EEGs based on HFDs of left, right, and overall frontal brain beta sub-band. The findings of this research, however, should be considered tentative because of limited data available to the authors.

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 (12–15 Hz) with inhibiting theta activity (4–8 Hz) and beta2 activity (18–25 Hz). 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.

Synchronization is defined as interdependencies among coupled dynamic systems. In most coupled systems the intrinsic and internal variants, and the interdependencies among their subsystems are not accessible. Therefore, in order to quantify the interdependencies among the coupled systems, attempts have been made through measuring the synchronization between their outputs represented mostly as time series. In this paper a new method, called Visibility Graph Similarity (VGS), is presented as a method of measuring Generalized Synchronization. First, each time series is reconstructed as a trajectory in a state space. Next, a Distance Time Series (DTS) is created from a sequence of relative distances of the states to a reference state. Subsequently, a visibility graph (VG) is constructed using DTS. Then, a sequence of degrees of the VG, called Degree Sequence (DS), is obtained. Correlation of the DSs of two coupled systems is called VGS and is presented as a measurement of similarity of dynamics of the coupled systems. The synchronization measurement performance of the VGS is compared with synchronization likelihood (SL) and the classical cross correlation method using two identical and non-identical models of two coupled Henon map over the entire time domain. Also, it is compared with SL for tracing temporal synchronization using both models. It is shown that VGS provides a more accurate measure of the overall synchronization compared with SL. It is more reliable for measuring weak couplings compared with the cross correlation method. Moreover, VGS uses fewer parameters and detects the temporal synchronization sooner than the SL.

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

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.

The superior colliculus is a layered structure important for body- and gaze-orienting responses. Its superficial layer is, next to the lateral geniculate nucleus, the second major target of retinal ganglion axons and is retinotopically organized. Here we show that in the mouse there is also a precise organization of orientation preference. In columns perpendicular to the tectal surface, neurons respond to the same visual location and prefer gratings of the same orientation. Calcium imaging and extracellular recording revealed that the preferred grating varies with retinotopic location, and is oriented parallel to the concentric circle around the centre of vision through the receptive field. This implies that not all orientations are equally represented across the visual field. This makes the superior colliculus different from visual cortex and unsuitable for translation-invariant object recognition and suggests that visual stimuli might have different behavioural consequences depending on their retinotopic location., The mammalian superior colliculus (SC) processes visual stimuli but little is known about the spatial organization of the response preferences for specific visual features. Here the authors show that the mouse SC contains a map for orientation preference such that preferred grating orientation is aligned to concentric circles around the centre of the visual field.

Transcranial electrical stimulation is a popular method to modulate brain activity by applying a low electric field to the skull. Despite this, its long-term effects are still ill understood. In this study, we applied anodal transcranial direct current stimulation to the visual cortex of mice while presenting visual stimuli. We used the genetically encoded calcium indicator GCaMP6 to measure the effect of current stimulation on the response in the visual cortex. We found a strong direct increase in the visual response when concurrently applying tDCS. We did not find a lasting effect of the stimulation during an imaging session. However, when we repeated tDCS for four sessions at two days intervals, we found an increase in the visual response in the visual cortex. This increased responses was not specific to the grating stimulus which was coupled to the anodal stimulation, but was also seen for an orthogonal grating presented without electrical stimulation. A similar increase was not found in mice repeatedly imaged without tDCS. We conclude that anodal tDCS of the visual cortex can cause LTP-like plasticity of visual responses.

Objectives The objective is to study the changes of brain activity in patients with mild cognitive impairment (MCI). Using magneto-encephalogram (MEG) signals, the authors investigate differences of complexity of functional connectivity network between MCI and normal elderly subjects during a working memory task. Methods MEGs are obtained from 18 right handed patients with MCI and 19 age-matched elderly participants without cognitive impairment used as the control group. The brain networks' complexities are measured by Graph Index Complexity ( C r ) and Efficiency Complexity ( C e ). Results The results obtained by both measurements show complexity of functional networks involved in the working memory function in MCI subjects is reduced at alpha and theta bands compared with subjects with control subjects, and at the theta band this reduction is more pronounced in the whole brain and intra left hemisphere. Conclusions C e would be a better measurement for showing the global differences between normal and MCI brains compared with C r . Significance The high accuracy of the classification shows C e at theta band can be used as an index for assessing deficits associated with working memory, a good biomarker for diagnosis of MCI.

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.

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.

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 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.

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.

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.

Synchronization as a measure of quantification of similarities in dynamic systems is an important concept in many scientific fields such as neuroscience. A new measure of synchronization, fuzzy SL, was used to investigate overall cortical functional connectivity during a cognitive task compared to resting conditions. High-resolution electroencephalogram (EEG) was recorded during eyes-open resting (EO1), continuous performance test (CPT), and eyes-open resting again (EO2) in 16 healthy children. Global fuzzy SL for delta and beta bands was calculated per condition. The opposite pattern was observed within conditions; delta increased but beta decreased from the EO1 to CPT condition. From the CPT to EO2, changes in the synchronization were also opposite. The findings might be interpreted as condition-specific modulation of functional connectivity of brain neuronal networks.

A new chaos-wavelet approach is presented for electroencephalogram (EEG)-based diagnosis of Alzheimer's disease (AD) employing a recently developed concept in graph theory, visibility graph (VG). The approach is based on the research ideology that nonlinear features may not reveal differences between AD and control group in the band-limited EEG, but may represent noticeable differences in certain sub-bands. Hence, complexity of EEGs is computed using the VGs of EEGs and EEG sub-bands produced by wavelet decomposition. Two methods are employed for computation of complexity of the VGs: one based on the power of scale-freeness of a graph structure and the other based on the maximum eigenvalue of the adjacency matrix of a graph. Analysis of variation is used for feature selection. Two classifiers are applied to the selected features to distinguish AD and control EEGs: a Radial Basis Function Neural Network (RBFNN) and a two-stage classifier consisting of Principal Component Analysis (PCA) and the RBFNN. After comprehensive statistical studies, effective classification features and mathematical markers were discovered. Finally, using the discovered features and a two-stage classifier (PCA-RBFNN), a high diagnostic accuracy of 97.7% was obtained.

In this paper a new Chaotic Neural Network (CNN) have been made. This network contains desired number of interacting units and each one has its own chaotic dynamic and strange attractor caused by creating convex hull among output units. Having a special interaction characteristic, the model is able to create enormous different chaotic behaviors. Lyapunov Exponent and phase space plane criteria have been used for demonstrating discrimination between behaviors. Making use of convex hull for trapping generated outputs of each unit in subsequent iteration, its folding characteristic and stretching property of logistic function, emerging of arbitrary number of various strange attractors have been accomplished. Therefore, based on desired criterion, this network is able to assign each strange attractor to each sensory input. In other words the network has the ability of being a candidate for modeling perception.