Connecting the dots: Sensory cueing enhances functional connectivity between pre-motor and supplementary motor areas in Parkinson's disease.

People with Parkinson's disease often exhibit improvements in motor tasks when exposed to external sensory cues. While the effects of different types of sensory cues on motor functions in Parkinson's disease have been widely studied, the underlying neural mechanism of these effects and the potential of sensory cues to alter the motor cortical activity patterns and functional connectivity of cortical motor areas are still unclear. This study aims to compare changes in oxygenated haemoglobin, deoxygenated haemoglobin and correlations among different cortical regions of interest during wrist movement under different external stimulus conditions between people with Parkinson's disease and controls. Ten Parkinson's disease patients and 10 age- and sex-matched neurologically healthy individuals participated, performing repetitive wrist flexion and extension tasks under auditory and visual cues. Changes in oxygenated and deoxygenated haemoglobin in motor areas were measured using functional near-infrared spectroscopy, along with electromyograms from wrist muscles and wrist movement kinematics. The functional near-infrared spectroscopy data revealed significantly higher neural activity changes in the Parkinson's disease group's pre-motor area compared to controls (p = 0.006), and functional connectivity between the supplementary motor area and pre-motor area was also significantly higher in the Parkinson's disease group when external sensory cues were present (p = 0.016). These results indicate that external sensory cues' beneficial effects on motor tasks are linked to changes in the functional connectivity between motor areas responsible for planning and preparation of movements.

Functional near-infrared spectroscopy based blood pressure variations and hemodynamic activity of brain monitoring following postural changes: A systematic review.

Postural change from supine or sitting to standing up leads to displacement of 300 to 1000 mL of blood from the central parts of the body to the lower limb, which causes a decrease in venous return to the heart, hence decrease in cardiac output, causing a drop in blood pressure. This may lead to falling down, syncope, and in general reducing the quality of daily activities, especially in the elderly and anyone suffering from nervous system disorders such as Parkinson's or orthostatic hypotension (OH). Among different modalities to study brain function, functional near-infrared spectroscopy (fNIRS) is a neuroimaging method that optically measures the hemodynamic response in brain tissue. Concentration changes in oxygenated hemoglobin (HbO2) and deoxygenated hemoglobin (HHb) are associated with brain neural activity. fNIRS is significantly more tolerant to motion artifacts compared to fMRI, PET, and EEG. At the same time, it is portable, has a simple structure and usage, is safer, and much more economical. In this article, we systematically reviewed the literature to examine the history of using fNIRS in monitoring brain oxygenation changes caused by sudden changes in body position and its relationship with the blood pressure changes. First, the theory behind brain hemodynamics monitoring using fNIRS and its advantages and disadvantages are presented. Then, a study of blood pressure variations as a result of postural changes using fNIRS is described. It is observed that only 58 % of the references concluded a positive correlation between brain oxygenation changes and blood pressure changes. At the same time, 3 % showed a negative correlation, and 39 % did not show any correlation between them.

Quantitative Comparison of Analytical Solution and Finite Element Method for Investigation of Near-infrared Light Propagation in Brain Tissue Model.

Introduction: Functional near-infrared spectroscopy (fNIRS) is an imaging method in which a light source and detector are installed on the head; consequently, the re-emission of light from human skin contains information about cerebral hemodynamic alteration. The spatial probability distribution profile of photons penetrating tissue at a source spot, scattering into the tissue, and being released at an appropriate detector position, represents the spatial sensitivity. Methods: Modeling light propagation in a human head is essential for quantitative near-infrared spectroscopy and optical imaging. The specific form of the distribution of light is obtained using the theory of perturbation. An analytical solution of the perturbative diffusion equation (DE) and finite element method (FEM) in a Slab media (similar to the human head) makes it possible to study light propagation due to absorption and scattering of brain tissue. Results: The simulation result indicates that sensitivity is slowly decreasing in the deep area, and the sensitivity below the source and detector is the highest. The depth sensitivity and computation time of both analytical and FEM methods are compared. The simulation time of the analytical approach is four times larger than the FEM. Conclusion: In this paper, an analytical solution and the performance of FEM methods when applied to the diffusion equation for heterogeneous media with a single spherical defect are compared. The depth sensitivity along with the computation time of simulation has been investigated for both methods. For simple and Slab modes of the human brain, the analytical solution is the right candidate. Whenever the brain model is sophisticated, it is possible to use FEM methods, but it costs a higher computation time. Highlights: Analytical and finite element method (FEM) depth sensitivity are almost the same.FEM requires more computation time, but can handle complicated head models.The analytical method is proposed for the first step and simple head models. Plain Language Summary: The functional near-infrared spectroscopy (fNIRS) is a type of neuromonitoring that uses near-infrared light to measure brain activity indirectly and is similar to electroencephalography (EEG). A single-channel fNIRS system contains a near-infrared light source, which emits near-infrared light (NIR), and a detector is placed near the source. A light intensity change received by detectors indicates brain activity when NIR light penetrates into the gray matter. It is necessary to have a prior understanding of light penetration depth in order to measure brain activity more accurately. fNIRS can be better understood, optimized, and investigated through modeling light propagation in brain tissue. In order to study light in tissues, analytical and numerical methods can be used. In this work, we compared these two approaches quantitatively in a simple slab medium. We concluded that the numerical method takes too much time to calculate the results, but it can be applied to complicated head models. The results of these studies provide researchers with new insights into the modeling and simulation of fNIRS and diffuse optical tomography.

Orchestration of saccadic eye-movements by brain rhythms in macaque Frontal Eye Field.

Visual perception has been suggested to operate on temporal 'chunks' of sensory input, rather than on a continuous stream of visual information. Saccadic eye movements impose a natural rhythm on the sensory input, as periods of steady fixation between these rapid eye movements provide distinct temporal segments of information. Ideally, the timing of saccades should be precisely locked to the brain's rhythms of information processing. Here, we investigated such locking of saccades to rhythmic neural activity in rhesus monkeys performing a visual foraging task. We found that saccades are phase-locked to local field potential oscillations (especially, 9-22 Hz) in the Frontal Eye Field, with the phase of oscillations predictive of the saccade onset as early as 100 ms prior to these movements. Our data also indicate a functional role of this phase-locking in determining the direction of saccades. These findings show a tight-and likely important-link between oscillatory brain activity and rhythmic behavior that imposes a rhythmic temporal structure on sensory input, such as saccadic eye movements.

EEG-based classification of individuals with neuropsychiatric disorders using deep neural networks: A systematic review of current status and future directions.

The use of deep neural networks for electroencephalogram (EEG) classification has rapidly progressed and gained popularity in recent years, but automatic feature extraction from EEG signals remains a challenging task. The classification of neuropsychiatric disorders demands the extraction of neuro-markers for use in automated EEG classification. Numerous advanced deep learning algorithms can be used for this purpose. In this article, we present a comprehensive review of the main factors and parameters that affect the performance of deep neural networks in classifying different neuropsychiatric disorders using EEG signals. We also analyze the EEG features used for improving classification performance. Our analysis includes 82 scientific journal papers that applied deep neural networks for subject-wise classification based on EEG signals. We extracted information on the EEG dataset and types of disorders, deep neural network structures, performance, and hyperparameters. The results show that most studies have focused on clinical classification, achieving an average accuracy of 91.83 ± 7.34, with convolutional neural networks (CNNs) being the most frequently used network architecture and resting-state EEG signals being the most commonly used data type. Additionally, the review reveals that depression (N = 18), Alzheimer's (N = 11), and schizophrenia (N = 11) were studied more frequently than other types of neuropsychiatric disorders. Our review provides insight into the performance of deep neural networks in EEG classification and highlights the importance of EEG feature extraction in improving classification accuracy. By identifying the main factors and parameters that affect deep neural network performance in EEG classification, our review can guide future research in this area. We hope that our findings will encourage further exploration of deep learning methods for EEG classification and contribute to the development of more accurate and effective methods for diagnosing and monitoring neuropsychiatric disorders using EEG signals.

Extracting a Novel Emotional EEG Topographic Map Based on a Stacked Autoencoder Network.

Emotion recognition based on brain signals has increasingly become attractive to evaluate human's internal emotional states. Conventional emotion recognition studies focus on developing machine learning and classifiers. However, most of these methods do not provide information on the involvement of different areas of the brain in emotions. Brain mapping is considered as one of the most distinguishing methods of showing the involvement of different areas of the brain in performing an activity. Most mapping techniques rely on projection and visualization of only one of the electroencephalogram (EEG) subband features onto brain regions. The present study aims to develop a new EEG-based brain mapping, which combines several features to provide more complete and useful information on a single map instead of common maps. In this study, the optimal combination of EEG features for each channel was extracted using a stacked autoencoder (SAE) network and visualizing a topographic map. Based on the research hypothesis, autoencoders can extract optimal features for quantitative EEG (QEEG) brain mapping. The DEAP EEG database was employed to extract topographic maps. The accuracy of image classifiers using the convolutional neural network (CNN) was used as a criterion for evaluating the distinction of the obtained maps from a stacked autoencoder topographic map (SAETM) method for different emotions. The average classification accuracy was obtained 0.8173 and 0.8037 in the valence and arousal dimensions, respectively. The extracted maps were also ranked by a team of experts compared to common maps. The results of quantitative and qualitative evaluation showed that the obtained map by SAETM has more information than conventional maps.

A Low-Complexity and High-Resolution Beamformer for Portable Medical Ultrasound Imaging.

So far, researchers have proposed various methods to improve the quality of medical ultrasound imaging. However, in portable medical ultrasound imaging systems, features, such as low cost and low power consumption for battery longevity, are very important. Hence, most of the proposed algorithms have not been proper substitutes for the delay and sum (DAS) algorithm in portable clinical applications due to their high computational complexity and cost. In this article, a new algorithm is presented concentrating on reducing the computational complexity based on a technique that separates the signal from the correlated interferences to overcome the negative characteristics, particularly for portable applications such as high price, high power consumption, and off-axis clutters in the azimuth direction. Also, the proposed algorithm yields a higher contrast compared to that of the DAS algorithm while achieving a similar computation complexity order of O ( n ) similar to the DAS algorithm. Furthermore, the performed simulations confirm that the proposed method is able to achieve a better resolution almost twice as that of the filtered delay multiply and sum (F-DMAS) algorithm with the same sidelobe level.

Dynamic causal modeling of evoked responses during emergency braking: an ERP study.

Describing a neural activity map based on observed responses in emergency situations, especially during driving, is a challenging issue that would help design driver-assistant devices and a better understanding of the brain. This study aimed to investigate which regions were involved during emergency braking, measuring the interactions and strength of the connections and describing coupling among these brain regions by dynamic causal modeling (DCM) parameters that we extracted from event-related potential signals, which were then estimated based on emergency braking data with visual stimulation. The data were reanalyzed from a simulator study, which was designed to create emergency situations for participants during a simple driving task. The experimental protocol includes driving a virtual reality car, and the subjects were exposed to emergency situations in a simulator system, while electroencephalogram, electro-oculogram, and electromyogram signals were recorded. In this research, locations of active brain regions in montreal neurological institute coordinates from event-related responses were identified using multiple sparse priors method, in which sensor space was allocated to resource space. Source localization results revealed nine active regions. After applying DCM on data, a proposed model during emergency braking for all people was obtained. The braking response time was defined based on the first noticeable (above noise-level) braking pedal deflection after an induced braking maneuver. The result revealed a significant difference in response time between subjects who have the lateral connection between visual cortex, visual processing, and detecting objects areas have shorter response time (p-value = 0.05) than the subjects who do not have such connections.

Classification of Breast Cancer Lesions in Ultrasound Images by Using Attention Layer and Loss Ensemble in Deep Convolutional Neural Networks.

The reliable classification of benign and malignant lesions in breast ultrasound images can provide an effective and relatively low-cost method for the early diagnosis of breast cancer. The accuracy of the diagnosis is, however, highly dependent on the quality of the ultrasound systems and the experience of the users (radiologists). The use of deep convolutional neural network approaches has provided solutions for the efficient analysis of breast ultrasound images. In this study, we propose a new framework for the classification of breast cancer lesions with an attention module in a modified VGG16 architecture. The adopted attention mechanism enhances the feature discrimination between the background and targeted lesions in ultrasound. We also propose a new ensembled loss function, which is a combination of binary cross-entropy and the logarithm of the hyperbolic cosine loss, to improve the model discrepancy between classified lesions and their labels. This combined loss function optimizes the network more quickly. The proposed model outperformed other modified VGG16 architectures, with an accuracy of 93%, and also, the results are competitive with those of other state-of-the-art frameworks for the classification of breast cancer lesions. Our experimental results show that the choice of loss function is highly important and plays a key role in breast lesion classification tasks. Additionally, by adding an attention block, we could improve the performance of the model.

A robust time delay estimation method for ultrasonic echo signals and elastography.

Modern medicine cannot ignore the significance of elastography in diagnosis and treatment plans. Despite improvements in accuracy and spatial resolution of elastograms, robustness against noise remains a neglected attribute. A method that can perform in a satisfactory manner under noisy conditions may prove useful for various elastography methods. Here, we propose a method based on eigenvalue decomposition (EVD). In this method, the estimated time delay is defined as the index of the maximum element in the eigenvector that corresponds to the minimum eigenvalue in the covariance matrix of the received signal. Moreover, the implementation of the least-squares (LS) solution and the lower-upper (LU) decomposition contributes to improving the speed of computation and the accuracy of the estimation under low signal-to-noise ratio (SNR) conditions. To assess the performance of the proposed algorithm, it is evaluated along with generalized cross-correlation (GCC) and EVD. The simulation results clearly confirm that the jitter variance achieved in the proposed algorithm outperforms GCC and EVD in the proximity of the Cramer-Rau lower band. Moreover, our algorithm provides satisfactory performance in terms of variance and bias against sub-sample delay at low SNRS. According to the experimental results, the calculated values of the elastographic signal-to-noise ratio (SNRe) and the elastographic contrast-to-noise ratio (CNRe) of the proposed algorithm were 16.7 and 20.09, respectively, clearly better than the values of the other two methods. Furthermore, the proposed algorithm offers less execution time (about 30% of GCC), with a computational complexity equal to GCC and better than EVD.

The 2017 and 2018 Iranian Brain-Computer Interface Competitions.

This article summarizes the first and second Iranian brain-computer interface competitions held in 2017 and 2018 by the National Brain Mapping Lab. Two 64-channel electroencephalography (EEG) datasets were contributed, including motor imagery as well as motor execution by three limbs. The competitors were asked to classify the type of motor imagination or execution based on EEG signals in the first competition and the type of executed motion as well as the movement onset in the second competition. Here, we provide an overview of the datasets, the tasks, the evaluation criteria, and the methods proposed by the top-ranked teams. We also report the results achieved with the submitted algorithms and discuss the organizational strategies for future campaigns.

Performance assessment of high-density diffuse optical topography regarding source-detector array topology.

Recent advances in optical neuroimaging systems as a functional interface enhance our understanding of neuronal activity in the brain. High density diffuse optical topography (HD-DOT) uses multi-distance overlapped channels to improve the spatial resolution of images comparable to functional magnetic resonance imaging (fMRI). The topology of the source and detector (SD) array directly impacts the quality of the hemodynamic reconstruction in HD-DOT imaging modality. In this work, the effect of different SD configurations on the quality of cerebral hemodynamic recovery is investigated by presenting a simulation setup based on the analytical approach. Given that the SD arrangement determines the elements of the Jacobian matrix, we conclude that the more individual components in this matrix, the better the retrieval quality. The results demonstrate that the multi-distance multi-directional (MDMD) arrangement produces more unique elements in the Jacobian array. Consequently, the inverse problem can accurately retrieve the brain activity of diffuse optical topography data.

IQ estimation by means of EEG-fNIRS recordings during a logical-mathematical intelligence test.

Intelligence differences of individuals are attributed to the structural and functional differences of the brain. Neural processing operations of the human brain vary according to the difficulty level of the problem and the intelligence level of individuals. In this study, we used a bimodal system consisting of functional Near-Infrared Spectroscopy (fNIRS) and Electroencephalogram (EEG) to investigate these inter-individual differences. A continuous wave 32-channel fNIRS from OxyMonfNIRS device (Artinis) and 19-channel EEG from (g.tec's company) were utilized to study the oxygenation procedure as well as the electrical activity of the brain when doing the problems of Raven's Progressive Matrix (RPM) intelligence test. We used this information to estimate the Intelligence Quotient (IQ) of the individual without performing a complete logical-mathematical intelligence test in a long-time period and examining the answers of people to the questions. After EEG preprocessing, different features including Higuchi's fractal dimension, Shannon entropy values from wavelet transform coefficients, and average power of frequency sub-bands were extracted. Clean fNIRS signals were also used to compute features such as slope, mean, variance, kurtosis, skewness, and peak. Then dimension reduction algorithms such as Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) were applied to select an effective feature set from fNIRS and EEG in order to improve the IQ estimation process. We utilized two regression methods, i.e., Linear Regression (LR) and Support Vector Regression (SVR), to extract optimum models for the IQ determination. The best regression models based on fNIRS-EEG and fNIRS presented 3.093% and 3.690% relative error for 11 subjects, respectively.

Emotion recognition through EEG phase space dynamics and Dempster-Shafer theory.

Emotions play an important role in our life. Emotion recognition which is considered a subset of brain computer interface (BCI), has drawn a great deal of attention during recent years. Researchers from different fields have tried to classify emotions through physiological signals. Nonlinear analysis has been reported to be successful and effective in emotion classification due to the nonlinear and non-stationary behavior of biological signals. In this study, phase space reconstruction and Poincare planes are employed to describe the dynamics of electroencephalogram (EEG) in emotional states. EEG signals are taken from a reliable database and phase space is reconstructed. A new transformation is introduced in order to quantify the phase space. Dynamic characteristics of the new space are considered as features. Most significant features are selected and samples are classified into four groups including high arousal - high valence (HAHV), low arousal - high valence (LAHV), high arousal - low valence (HALV) and low arousal - low valence (LALV). Classification accuracy was about 90% on average. Results suggest that the proposed method is successful and classification performance is good in comparison with most studies in this field. Brain activity is also reported with respect to investigating brain function during emotion elicitation. We managed to introduce a new way to analyze EEG phase space. The proposed method is applied in a real world and challenging application (i.e. emotion classification). Not only does the proposed method describe EEG changes due to different emotional states but also it is able to represent new characteristics of complex systems. The suggested approach paves the way for researchers to analyze and understand more about chaotic signals and systems.

Stress assessment by means of heart rate derived from functional near-infrared spectroscopy.

Many studies have been carried out in order to detect and quantify the level of mental stress by means of different physiological signals. From the physiological point of view, stress promptly affects brain and cardiac function; therefore, stress can be assessed by analyzing the brain- and heart-related signals more efficiently. Signals produced by functional near-infrared spectroscopy (fNIRS) of the brain together with the heart rate (HR) are employed to assess the stress induced by the Montreal Imaging Stress Task. Two different versions of the HR are used in this study. The first one is the commonly used HR derived from the electrocardiogram (ECG) and is considered as the reference HR (RHR). The other is the HR computed from the fNIRS signal (EHR) by means of an effective combinational algorithm. fNIRS and ECG signals were simultaneously recorded from 10 volunteers, and EHR and RHR are derived from them, respectively. Our results showed a high degree of agreement [r > 0.9, BAR (Bland Altman ratio) <5 % ] between the two HR. A principal component analysis/support vector machine-based algorithm for stress classification is developed and applied to the three measurements of fNIRS, EHR, and RHR and a classification accuracy of 78.8%, 94.6%, and 62.2% were obtained for the three measurements, respectively. From these observations, it can be concluded that the EHR carries more useful information with regards to the mental stress than the RHR and fNIRS signals. Therefore, EHR can be used alone or in combination with the fNIRS signal for a more accurate and real-time stress detection and classification.

Global optimal constrained ICA and its application in extraction of movement related cortical potentials from single-trial EEG signals.

BACKGROUND AND OBJECTIVE: The constrained ICA (cICA) is a recent approach which can extract the desired source signal by using prior information. cICA employs gradient-based algorithms to optimize non convex objective functions and therefore global optimum solution is not guaranteed. In this study, we propose the Global optimal constrained ICA (GocICA) algorithm for solving the conventional cICA problems. Due to the importance of movement related cortical potentials (MRCPs) for neurorehabilitation and developing a suitable mechanism for detection of movement intention, single-trial MRCP extraction is presented as an application of GocICA. METHODS: In order to evaluate the performance of the proposed technique, two kinds of datasets including simulated and real EEG data have been utilized in this paper. The GocICA method has been implemented based on the most popular meta-heuristic optimization algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Charged System Search (CSS) where the results have been compared with those of conventional cICA and two ICA-based methods (JADE and Infomax). RESULTS: It was found that GocICA enhanced the extracted MRCP from multi-channel EEG better than both conventional cICA and ICA-based methods and also outperformed them in single-trial MRCP detection with higher true positive rates (TPRs) and lower false positive rates (FPRs). Moreover, CSS-cICA resulted in the greatest TPR (91.2232 ±â€¯3.4708) and the lowest FPR (8.7465 ±â€¯3.7705) for single-trial MRCP detection from real EEG data and the greatest signal-to-noise ratio (SNR) (39.2818) and the lowest mean square error (MSE) and individual performance index (IPI) (41.8230 and 0.0012, respectively) for single-trial MRCP extraction from simulated EEG data. CONCLUSIONS: These results confirm the superiority of GocICA with respect to conventional cICA that is due to the ability of meta-heuristic optimization algorithms to escape from local optimal point. As such, GocICA is a promising new algorithm for single-trial MRCP detection which can be used for detecting other types of event related cortical potentials (ERPs) such as P300 and also for EEG artifact removal.

A novel approach to emotion recognition using local subset feature selection and modified Dempster-Shafer theory.

BACKGROUND: Emotion recognition is an increasingly important field of research in brain computer interactions. INTRODUCTION: With the advance of technology, automatic emotion recognition systems no longer seem far-fetched. Be that as it may, detecting neural correlates of emotion has remained a substantial bottleneck. Settling this issue will be a breakthrough of significance in the literature. METHODS: The current study aims to identify the correlations between different emotions and brain regions with the help of suitable electrodes. Initially, independent component analysis algorithm is employed to remove artifacts and extract the independent components. The informative channels are then selected based on the thresholded average activity value for obtained components. Afterwards, effective features are extracted from selected channels common between all emotion classes. Features are reduced using the local subset feature selection method and then fed to a new classification model using modified Dempster-Shafer theory of evidence. RESULTS: The presented method is employed to DEAP dataset and the results are compared to those of previous studies, which highlights the significant ability of this method to recognize emotions through electroencephalography, by the accuracy of about 91%. Finally, the obtained results are discussed and new aspects are introduced. CONCLUSIONS: The present study addresses the long-standing challenge of finding neural correlates between human emotions and the activated brain regions. Also, we managed to solve uncertainty problem in emotion classification which is one of the most challenging issues in this field. The proposed method could be employed in other practical applications in future.

Automatic anesthesia depth staging using entropy measures and relative power of electroencephalogram frequency bands.

Many of the surgeries performed under general anesthesia are aided by electroencephalogram (EEG) monitoring. With increased focus on detecting the anesthesia states of patients in the course of surgery, more attention has been paid to analyzing the power spectra and entropy measures of EEG signal during anesthesia. In this paper, by using the relative power of EEG frequency bands and the EEG entropy measures, a new method for detecting the depth of anesthesia states has been presented based on the least squares support vector machine (LS-SVM) classifiers. EEG signals were recorded from 20 patients before, during and after general anesthesia in the operating room at a sampling rate of 200 Hz. Then, 12 features were extracted from each EEG segment, 10 s in length, which are used for anesthesia state monitoring. No significant difference was observed (p > 0.05) between these features and the bispectral index (BIS), which is the commonly used measure of anesthetic effect. The used LS-SVM classifier based method is able to identify the anesthesia states with an accuracy of 80% with reference to the BIS index. Since the underlying equation of the utilized LS-SVM is linear, the computational time of the algorithm is not significant and therefore it can be used for online application in operation rooms.

Modeling the connections of brain regions in children with autism using cellular neural networks and electroencephalography analysis.

The brain connections in the different regions demonstrate the characteristics of brain activities. In addition, in various conditions and with neuropsychological disorders, the brain has special patterns in different regions. This paper presents a model to show and compare the connection patterns in different brain regions of children with autism (53 boys and 36 girls) and control children (61 boys and 33 girls). The model is designed by cellular neural networks and it uses the proper features of electroencephalography. The results show that there are significant differences and abnormalities in the left hemisphere, (p < 0.05) at the electrodes AF3, F3, P7, T7, and O1 in the children with autism compared with the control group. Also, the evaluation of the obtained connections values between brain regions demonstrated that there are more abnormalities in the connectivity of frontal and parietal lobes and the relations of the neighboring regions in children with autism. It is observed that the proposed model is able to distinguish the autistic children from the control subjects with an accuracy rate of 95.1% based on the obtained values of CNN using the SVM method.