Stable Connectivity Research Articles

U2RV: UAV‐assisted reactive routing protocol for VANETs

SummaryWhen it comes to keeping the data routing robust and effective in vehicular ad hoc networks (VANETs), stable and durable connectivity constitutes the keystone to ensure successful point‐to‐point communication. Since VANETs can comprise all kinds of mobile vehicles moving and changing direction frequently, this may result in frequent link failures and network partitions. Moreover, when VANETs are deployed in a city environment, another problem arises, that is, the existing obstructions (eg, buildings, trees, and hoppers) preventing the line‐of‐sight between vehicles, thus degrading wireless transmissions. Therefore, it is more complicated to design a routing technique that adapts to frequent changes in the topology. In order to settle all these problems, in this work, we design a flooding scheme that automatically reacts at each topology variation while overcoming the present obstacles while exchanging data in ad hoc mode with drones that are commonly called unmanned aerial vehicles (UAVs). Also, the aim of this work is to explore well‐regulated routing paths providing a long lifetime connectivity based on the amount of traffic and the expiration time of each discovered path. A set of experiments is carried out using a simulation, and the outcomes are confronted with similar protocols based on a couple of metrics. The results clearly show that the assistance of UAVs to vehicles is capable of providing high delivery ratios and low delivery delays while efficiently extending the network connectivity.

Functional connectivity between memory and reward centers across task and rest track memory sensitivity to reward.

External motivation, such as a promise of future monetary reward for remembering an event, can affect which events are remembered. Reward-based memory modulation is thought to result from encoding and post-encoding interactions between dopaminergic midbrain, signaling reward, and hippocampus and parahippocampal cortex, supporting episodic memory. We asked whether hippocampal and parahippocampal interactions with other reward-related regions are related to reward modulation of memory and whether such relationships are stable over time. Individuals' memory sensitivity to reward was measured using a monetary incentive encoding task in which a cue indicated potential monetary reward (penny, dime, or dollar) for remembering an upcoming object pair. Functional connectivity between memory and reward regions was measured before, during, and following the task. Reward-related regions of interest were generated using a meta-analysis of existing studies on reward and included ventral striatum, medial and orbital prefrontal cortices and anterior cingulate cortex, in addition to midbrain. The results showed that connectivity between memory and reward regions tracked individual differences in reward modulation of memory, irrespective of when connectivity was measured. Connectivity patterns of anterior cingulate, orbitofrontal cortex, and ventral striatum covaried together and tracked behavior most strongly. These findings implicate a broader set of reward regions in reward modulation of memory than considered previously and provide new evidence that stable connectivity patterns between memory and reward centers relate to individual differences in how reward impacts memory.

An Efficient Message Dissemination Scheme for Minimizing Delivery Delay in Delay Tolerant Networks

Delay tolerant networks (DTNs) are a kind of sparse and highly mobile wireless networks, where no stable connectivity guarantee can be assumed. Most DTN users have several points of interest (PoIs), and they enjoy disseminating messages to the other users of the same PoI through WiFi. In DTNs, some time-sensitive messages (disaster warnings, search notices, etc.) need to be rapidly propagated among specific users or areas. Therefore, finding a path from the source to the destination with the shortest delay is the key problem. Taking the dissemination cost into consideration, we propose an efficient message dissemination strategy for minimizing delivery delay (MDMD) in DTNs, which first defines the user’s activeness according to the transiting habit among different PoIs. Furthermore, depending on the activeness, an optimal user in each PoI is selected to constitute the path with the shortest delay. Finally, the MDMD with inactive state (on the way between PoIs) is further proposed to enhance the applicability. Simulation results show that, compared with other dissemination strategies, MDMD achieves the lowest average delay, and the comparable average hopcounts, on the premise that the delivery ratio is guaranteed to be 100% by the sufficient simulation time.

Hybrid scheme to enable DTN routing protocols to efficiently exploit stable MANET contacts

Disaster communication is still challenging due to the unpredictable nature and high variance in possible scenarios. Hybrid networking solutions that integrate principles from mobile ad hoc networks (MANETs) and delay tolerant networks (DTN) have been suggested to guarantee a certain robustness for the communication service in case of partial or complete failure of infrastructure. Real-time communication remains however challenging and the goal of any system is to minimize the message delay. One common approach is to provide better connectivity by including additional nodes and exploit the resulting contacts as efficiently as possible. Well-known DTN protocols are however not able to guarantee that, because they are unaware of connections that last for a relatively long time and thus provide stable connectivity. This results from general design assumptions of the DTN protocols and is crucial for the performance of hybrid MANET-DTN solutions. In this paper, we provide a review to this situation and suggest a hybrid solution concept based on layer 3 service discovery and a contact-aware utility scoring mechanism for DTN protocols and implement our concept as an example in one DTN protocol. Using simulations, we are able to show that this combination of mechanisms is able to provide better overall performance in the presence of long-lasting stable contacts.

Content sharing with mobility in an infrastructure-less environment

Current networking technologies are ill-suited for content sharing in emerging military and first-responder networks where fixed infrastructures and stable connectivity cannot be assumed. New paradigms, in particular content-based networking, is proving to be a viable solution for operation in mobile infrastructure-less environments where intermittent and disrupted connectivity is normal. This paper presents CASCADE, a content-centric networking architecture that facilitates generation and dissemination of content in challenging “edge” environments. CASCADE is implemented in Android and has been subjected to thorough performance evaluation using both ad hoc Wi-Fi technology as well as military tactical radios. This paper discusses architectural approaches and decisions, and offers insights and lessons learned over the course of the project.

Principal States of Dynamic Functional Connectivity Reveal the Link Between Resting-State and Task-State Brain: An fMRI Study.

Task-related reorganization of functional connectivity (FC) has been widely investigated. Under classic static FC analysis, brain networks under task and rest have been demonstrated a general similarity. However, brain activity and cognitive process are believed to be dynamic and adaptive. Since static FC inherently ignores the distinct temporal patterns between rest and task, dynamic FC may be more a suitable technique to characterize the brain's dynamic and adaptive activities. In this study, we adopted [Formula: see text]-means clustering to investigate task-related spatiotemporal reorganization of dynamic brain networks and hypothesized that dynamic FC would be able to reveal the link between resting-state and task-state brain organization, including broadly similar spatial patterns but distinct temporal patterns. In order to test this hypothesis, this study examined the dynamic FC in default-mode network (DMN) and motor-related network (MN) using Blood-Oxygenation-Level-Dependent (BOLD)-fMRI data from 26 healthy subjects during rest (REST) and a hand closing-and-opening (HCO) task. Two principal FC states in REST and one principal FC state in HCO were identified. The first principal FC state in REST was found similar to that in HCO, which appeared to represent intrinsic network architecture and validated the broadly similar spatial patterns between REST and HCO. However, the second FC principal state in REST with much shorter "dwell time" implied the transient functional relationship between DMN and MN during REST. In addition, a more frequent shifting between two principal FC states indicated that brain network dynamically maintained a "default mode" in the motor system during REST, whereas the presence of a single principal FC state and reduced FC variability implied a more temporally stable connectivity during HCO, validating the distinct temporal patterns between REST and HCO. Our results further demonstrated that dynamic FC analysis could offer unique insights in understanding how the brain reorganizes itself during rest and task states, and the ways in which the brain adaptively responds to the cognitive requirements of tasks.

Localization of Epileptogenic Zone With the Correction of Pathological Networks.

Patients with focal drug-resistant epilepsy are potential candidates for surgery. Stereo-electroencephalograph (SEEG) is often considered as the “gold standard” to identify the epileptogenic zone (EZ) that accounts for the onset and propagation of epileptiform discharges. However, visual analysis of SEEG still prevails in clinical practice. In addition, epilepsy is increasingly understood to be the result of network disorder, but the specific organization of the epileptic network is still unclear. Therefore, it is necessary to quantitatively localize the EZ and investigate the nature of epileptogenic networks. In this study, intracranial recordings from 10 patients were analyzed through adaptive directed transfer function, and the out-degree of effective network was selected as the principal indicator to localize the epileptogenic area. Furthermore, a coupled neuronal population model was used to qualitatively simulate electrical activity in the brain. By removing individual populations, virtual surgery adjusting the network organization could be performed. Results suggested that the accuracy and detection rate of the EZ localization were 82.86 and 85.29%, respectively. In addition, the same stage shared a relatively stable connectivity pattern, while the patterns changed with transition to different processes. Meanwhile, eight cases of simulations indicated that networks in the ictal stage were more likely to generate rhythmic spikes. This indicated the existence of epileptogenic networks, which could enhance local excitability and facilitate synchronization. The removal of the EZ could correct these pathological networks and reduce the amount of spikes by at least 75%. This might be one reason why accurate resection could reduce or even suppress seizures. This study provides novel insights into epilepsy and surgical treatments from the network perspective.

Cognitive Internet of Vehicles

To fully realize future autonomous driving scenarios, Internet of Vehicles (IoV) has attracted wide attention from both academia and industry. However, suitable cost and stable connectivity cannot be strongly guaranteed by existing architectures such as cellular networks, vehicular ad hoc networks, etc. With the prosperous development of artificial intelligence, cloud/edge computing and 5G network slicing, a more intelligent vehicular network is under deliberation. In this paper, an innovative paradigm called Cognitive Internet of Vehicles (CIoV) is proposed to help address the aforementioned challenge. Different from existing works, which mainly focus on communication technologies, CIoV enhances transportation safety and network security by mining effective information from both physical and network data space. We first present an overview of CIoV including its evolution, related technologies, and architecture. Then we highlight crucial cognitive design issues from three perspectives, namely, intra-vehicle network, inter-vehicle network and beyond-vehicle network. Simulations are then conducted to prove the effect of CIoV and finally some open issues will be further discussed. Our study explores this novel architecture of CIoV, as well as research opportunities in vehicular network.

Perceptions, preferences of scholarly publishing in Open Access journals

PurposeThe purpose of the study is to know the various factors librarians consider while selecting Open Access (OA) journal for publication and to identify the challenges librarians face with OA journal publishing.Design/methodology/approachOnline questionnaire was designed to collect data from 402 academic librarians in 87 universities in Africa using SurveyMonkey software.FindingsThe study found that academic librarians are aware of the “green” and “gold” routes but not familiar with the “diamond” route. The study revealed that a large number of the academic librarians have published only one paper in OA journals, followed by those who have not published any paper in OA journals. The study also revealed that reputation of journal and impact factor of journal were seen as very important among the factors that inform them of choosing OA. The majority of the respondents agreed that author fees, and lack of stable internet connectivity are the major barriers to publishing in OA journals.Practical implicationsThe study recommends that academic libraries in institutions of higher learning in Africa should organize seminars periodically on the need for their librarians to research and publish in OA journals.Social implicationsIdentifying factors involved in author decisions to publish in OA journals will help illuminate issues that may encourage or discourage author support of OA publishing models. Further understanding of these issues can assist the efforts to improve author perceptions of and confidence in OA publications.Originality/valueIt is believed that this study of African librarians publishing in OA journals is the first study in the region.

P242 Dynamic time warping distance based connectivity: A new method for resting-state FMRI functional connectivity analysis

Objectives Traditionally resting-state networks are analysed with methods implying zero-lag linear dependence between brain regions, i.e. functional connectivity strength between voxel pairs is characterized by the correlation-coefficient of the two measured signal. It is known that the shape and timing of hemodynamic response function differs between brain regions and this introduces artefacts in linear measures. Methods We proposed Dynamic Time Warping (DTW) distance to be used as an alternative similarity-measure between BOLD signals. Our method was validated in a longitudinal single-subject study where seed-based and whole-brain functional connectivity was calculated based on both DTW similarity and correlation. DTW connectivity’s sensitivity was assessed in a classification task, where a preprocessed public dataset was used: 126 subjects’ resting-state data and phenotypic information, including diagnosis for ADHD. Results The results of the single-subject measurements revealed that DTW similarity-based connectivity map calculation is more stable in multiple measurements than a correlation-based paradigm, as DTW-based connectivity strengths are similarly stable within and between sessions, while correlation yields larger variations between sessions. Furthermore, the stability of the DTW-based connectivity patterns result in significantly higher classification performance than the same classifiers trained on correlation-based features of connectivity. Discussion As DTW handles non-stationery processes, it results in stable connectivity patterns in multiple measurements, while its sensitivity for group differences is higher than correlation’s as the classification study shows. Conclusion The results demonstrate that DTW similarity is indeed an applicable and advantageous tool of resting-state functional connectivity analysis. Significance DTW-based connectivity can be efficiently used in longitudinal studies and in connectome-based classification tasks.

The minimum resting-state fNIRS imaging duration for accurate and stable mapping of brain connectivity network in children

Resting-state functional near-infrared spectroscopy (fNIRS) is a potential technique for the study of brain functional connectivity (FC) and networks in children. However, the necessary fNIRS scanning duration required to map accurate and stable functional brain connectivity and graph theory metrics in the resting-state brain activity remains largely unknown. Here, we acquired resting-state fNIRS imaging data from 53 healthy children to provide the first empirical evidence for the minimum imaging time required to obtain accurate and stable FC and graph theory metrics of brain network activity (e.g., nodal efficiency and network global and local efficiency). Our results showed that FC was accurately and stably achieved after 7.0-min fNIRS imaging duration, whereas the necessary scanning time for accurate and stable network measures was a minimum of 2.5 min at low network thresholds. These quantitative results provide direct evidence for the choice of the resting-state fNIRS imaging time in children in brain FC and network topology study. The current study also demonstrates that these methods are feasible and cost-effective in the application of time-constrained infants and critically ill children.

Contextual and Developmental Differences in the Neural Architecture of Cognitive Control.

Because both development and context impact functional brain architecture, the neural connectivity signature of a cognitive or affective predisposition may similarly vary across different ages and circumstances. To test this hypothesis, we investigated the effects of age and cognitive versus social-affective context on the stable and time-varying neural architecture of inhibition, the putative core cognitive control component, in a subsample (N = 359, 22-36 years, 174 men) of the Human Connectome Project. Among younger individuals, a neural signature of superior inhibition emerged in both stable and dynamic connectivity analyses. Dynamically, a context-free signature emerged as stronger segregation of internal cognition (default mode) and environmentally driven control (salience, cingulo-opercular) systems. A dynamic social-affective context-specific signature was observed most clearly in the visual system. Stable connectivity analyses revealed both context-free (greater default mode segregation) and context-specific (greater frontoparietal segregation for higher cognitive load; greater attentional and environmentally driven control system segregation for greater reward value) signatures of inhibition. Superior inhibition in more mature adulthood was typified by reduced segregation in the default network with increasing reward value and increased ventral attention but reduced cingulo-opercular and subcortical system segregation with increasing cognitive load. Failure to evidence this neural profile after the age of 30 predicted poorer life functioning. Our results suggest that distinguishable neural mechanisms underlie individual differences in cognitive control during different young adult stages and across tasks, thereby underscoring the importance of better understanding the interplay among dispositional, developmental, and contextual factors in shaping adaptive versus maladaptive patterns of thought and behavior.SIGNIFICANCE STATEMENT The brain's functional architecture changes across different contexts and life stages. To test whether the neural signature of a trait similarly varies, we investigated cognitive versus social-affective context effects on the stable and time-varying neural architecture of inhibition during a period of neurobehavioral fine-tuning (age 22-36 years). Younger individuals with superior inhibition showed distinguishable context-free and context-specific neural profiles, evidenced in both static and dynamic connectivity analyses. More mature individuals with superior inhibition evidenced only context-specific profiles, revealed in the static connectivity patterns linked to increased reward or cognitive load. Delayed expression of this profile predicted poorer life functioning. Our results underscore the importance of understanding the interplay among dispositional, developmental, and contextual factors in shaping behavior.

Influence of demographic factors on knowledge sharing among researchers in selected research institutes in Ibadan, Nigeria

Knowledge sharing is an effective means to provide information for organizational growth. The study investigated the influence of demographic factors on knowledge sharing among researchers in selected research institutes in Oyo state, Nigeria. The study adopted survey research method. Total enumeration technique was used to cover all the 440 researchers in the four selected research institutes in Ibadan, Oyo State in Nigeria. Five (5) research questions guided the study and three hypotheses were tested at 0.05 level of significance. Data were analysed using descriptive statistics. The findings of the study reveals that, there were significant relationship between knowledge sharing and gender (r=0.1; p<0.5) and rank of researcher (r=0.1; p<0.5). Knowledge sharing correlated with each of researcher demographic factors: age, gender, marital status, religion, and educational level. The study recommends that necessary encouragement by institutions should be given to the researchers to improve their knowledge sharing behavior. In addition, adequate training and re-training should be provided as well as stable Internet connectivity. Keywords: knowledge sharing, demographic factors, research institutes, researchers

Network anomaly detection based on probabilistic analysis

In this paper, we provide a detection technology for a common type of network intrusion (traffic flood attack) using an anomaly data detection method based on probabilistic model analysis. Victim’s computers under attack show various symptoms such as degradation of TCP throughput, increase of CPU usage, increase of RTT (Round Trip Time), frequent disconnection to the web sites, and etc. These symptoms can be used as components to comprise k-dimensional feature space of multivariate normal distribution where an anomaly detection method can be applied for the detection of the attack. These features are in general correlated one another. In other words, most of these symptoms are caused by the attack, so they are highly correlated. Thus we choose only a few of these features for the anomaly detection in multivariate normal distribution. We study this technology for those IoT networks prepared to provide u-health services in the future, where stable and consistent network connectivity is extremely important because the connectivity is highly related to the loss of human lives eventually.

Learning stable and predictive network-based patterns of schizophrenia and its clinical symptoms

Schizophrenia is often associated with disrupted brain connectivity. However, identifying specific neuroimaging-based patterns pathognomonic for schizophrenia and related symptom severity remains a challenging open problem requiring large-scale data-driven analyses emphasizing not only statistical significance but also stability across multiple datasets, contexts and cohorts. Accurate prediction on previously unseen subjects, or generalization, is also essential for any useful biomarker of schizophrenia. In order to build a predictive model based on functional network feature patterns, we studied whole-brain fMRI functional networks, both at the voxel level and lower-resolution supervoxel level. Targeting Auditory Oddball task data on the FBIRN fMRI dataset (n = 95), we considered node-degree and link-weight network features and evaluated stability and generalization accuracy of statistically significant feature sets in discriminating patients vs. controls. We also applied sparse multivariate regression (elastic net) to whole-brain functional connectivity features, for the first time, to derive stable predictive features for symptom severity. Whole-brain link-weight features achieved 74% accuracy in identifying patients and were more stable than voxel-wise node-degrees. Link-weight features predicted severity of several negative and positive symptom scales, including inattentiveness and bizarre behavior. The most-significant, stable and discriminative functional connectivity changes involved increased correlations between thalamus and primary motor/primary sensory cortex, and between precuneus (BA7) and thalamus, putamen, and Brodmann areas BA9 and BA44. Precuneus, along with BA6 and primary sensory cortex, was also involved in predicting severity of several symptoms. Overall, the proposed multi-step methodology may help identify more reliable multivariate patterns allowing for accurate prediction of schizophrenia and its symptoms severity.

Resting State fMRI Functional Connectivity Analysis Using Dynamic Time Warping.

Traditional resting-state network concept is based on calculating linear dependence of spontaneous low frequency fluctuations of the BOLD signals of different brain areas, which assumes temporally stable zero-lag synchrony across regions. However, growing amount of experimental findings suggest that functional connectivity exhibits dynamic changes and a complex time-lag structure, which cannot be captured by the static zero-lag correlation analysis. Here we propose a new approach applying Dynamic Time Warping (DTW) distance to evaluate functional connectivity strength that accounts for non-stationarity and phase-lags between the observed signals. Using simulated fMRI data we found that DTW captures dynamic interactions and it is less sensitive to linearly combined global noise in the data as compared to traditional correlation analysis. We tested our method using resting-state fMRI data from repeated measurements of an individual subject and showed that DTW analysis results in more stable connectivity patterns by reducing the within-subject variability and increasing robustness for preprocessing strategies. Classification results on a public dataset revealed a superior sensitivity of the DTW analysis to group differences by showing that DTW based classifiers outperform the zero-lag correlation and maximal lag cross-correlation based classifiers significantly. Our findings suggest that analysing resting-state functional connectivity using DTW provides an efficient new way for characterizing functional networks.

Increasing Competition in the Telecommunications Sector of the Philippines

In the advent of broadband technology, the telecommunications sector plays a more important role in the development of the economy. Despite the need for cheaper, faster, and more stable internet connectivity, the Philippines currently lags behind its ASEAN neighbors with an average broadband speed of 3.5 Megabits per second (Mbps) at a relatively high subscription price at USD 24 per Mbps. With the subpar quality of service provided by the incumbent operators, the sector still manages to be one of the most profitable industries in the country, which the proponents hypothesize to be due to the lack of players in the market; eventually raising concern over the state of consumer welfare.Given these issues, this paper seeks to understand how and why the industry reached its current state through an in-depth industry analysis: evaluating the industry’s value chain, market structure, market performance, and possible anti-competitive behavior. With this examination, the paper looks at possible solutions to increase competition in the industry, of which we recommend regulatory local loop unbundling to open the doors to open network access as well as updating regulations and penalties to ensure industry player’s compliance to anti-competitive laws.

STATUS ON USAGE OF ELECTRONIC-RESOURCES BY STUDENTS AT THE COLLEGE OF BUSINESS EDUCATION

This paper assesses the status on the usage of electronic resources by students at the College of Business Education (CBE). Specific objectives were to find out the factors that promote students to use internet services, to find out the extent to which CBE students are accessing electronic resources, to identify challenges that CBE students are facing in the process of accessing electronic resources. The study was carried out at CBE main campus library in Dar Es Salaam. Simple random sampling was used to select 100 respondents. The study employed case study design and used a multmethods. Data were collected by using questionnaires, interviews and personal observations. The findings reveal the lack of awareness of the educational eâ€resources offered at the institution, students not using the academic and learning resources. Students were facing several challenges such as, the lack of connected computers, lack of searching skills, tendency of frequent closing computer labs. Finally, the study recommends improving the computer labs’ environments, guaranteeing a stable wireless connectivity, frequent computer repairs and other related ICT equipment and finally the College has to ensure the reliable  standby generators.

Quantifying functional connectivity in multi-subject fMRI data using component models.

Functional magnetic resonance imaging (fMRI) is increasingly used to characterize functional connectivity between brain regions. Given the vast number of between-voxel interactions in high-dimensional fMRI data, it is an ongoing challenge to detect stable and generalizable functional connectivity in the brain among groups of subjects. Component models can be used to define subspace representations of functional connectivity that are more interpretable. It is, however, unclear which component model provides the optimal representation of functional networks for multi-subject fMRI datasets. A flexible cross-validation approach that assesses the ability of the models to predict voxel-wise covariance in new data, using three different measures of generalization was proposed. This framework is used to compare a range of component models with varying degrees of flexibility in their representation of functional connectivity, evaluated on both simulated and experimental resting-state fMRI data. It was demonstrated that highly flexible subject-specific component subspaces, as well as very constrained average models, are poor predictors of whole-brain functional connectivity, whereas the best-generalizing models account for subject variability within a common spatial subspace. Within this set of models, spatial Independent Component Analysis (sICA) on concatenated data provides more interpretable brain patterns, whereas a consistent-covariance model that accounts for subject-specific network scaling (PARAFAC2) provides greater stability in functional connectivity relationships between components and their spatial representations. The proposed evaluation framework is a promising quantitative approach to evaluating component models, and reveals important differences between subspace models in terms of predictability, robustness, characterization of subject variability, and interpretability of the model parameters. Hum Brain Mapp 38:882-899, 2017. © 2016 Wiley Periodicals, Inc.

P.7.a.003 - Subcortical brain volume development over age in autism spectrum disorder: results from the ENIGMA-ASD working group

This paper presents a comprehensive and practical review of autism spectrum disorder (ASD) classification using several traditional machine learning and deep learning methods on data from the Autism Brain Imaging Data Exchange (ABIDE) repository. The objective of this study was to investigate different brain networks and determine their functional connectivity to distinguish between subjects with ASD and those considered typically developing (TD). In the experiments of this paper, functional connectivity was used as a classification feature for 871 resting-state functional magnetic resonance imaging (rs-fMRI) samples collected from the ABIDE repository. The methodology and results of this paper have three main parts.First, we reviewed eight different brain parcellation techniques used for ASD classification from structural, functional, and data-driven perspectives to identify the most promising brain atlas. Second, we evaluate the stability and efficiency of the correlation, partial correlation, and tangent space functional connectivity metrics, and identify the most stable functional connectivity metric. Third, we compared four different supervised learning models used in the ASD classification domain and evaluated the learning performance of each model. In summary, our experimental results show that Bootstrap Analysis of Stable Clusters (BASC) provides the most predictive power for ASD classification, while the correlation metric is the most stable candidate among those models considered. Furthermore, by comparing different classifiers, we conclude that among all the experimentally compared classifiers in this paper, the kernel support vector machine (kSVM) is the optimal classifier for classifying ABIDE fMRI data. The highest sensitivity 64.57% is identified in Table 7. This result was produced using the correlation metric with functional atlas BASC444 and RBF kernel SVM. The corresponding specificity is 73.61%, and the accuracy is 69.43%. Overall, this is the optimal result.