Classification Of Attention Deficit Hyperactivity Disorder Research Articles

An fMRI Sequence Representation Learning Framework for Attention Deficit Hyperactivity Disorder Classification

For attention deficit hyperactivity disorder (ADHD), a common neurological disease, accurate identification is the basis for treatment. In this paper, a novel end-to-end representation learning framework for ADHD classification of functional magnetic resonance imaging (fMRI) sequences is proposed. With such a framework, the complexity of the sequence representation learning neural network decreases, the overfitting problem of deep learning for small samples cases is solved effectively, and superior classification performance is achieved. Specifically, a data conversion module was designed to convert a two-dimensional sequence into a three-dimensional image, which expands the modeling area and greatly reduces the computational complexity. The transfer learning method was utilized to freeze or fine-tune the parameters of the pre-trained neural network to reduce the risk of overfitting in the cases with small samples. Hierarchical feature extraction can be performed automatically by combining the sequence representation learning modules with a weighted cross-entropy loss. Experiments were conducted both with individual imaging sites and combining them, and the results showed that the classification average accuracies with the proposed framework were 73.73% and 72.02%, respectively, which are much higher than those of the existing methods.

Use of machine learning on clinical questionnaires data to support the diagnostic classification of Attention DeficitHyperactivity Disorder: a personalized medicine approach

IntroductionAttention Deficit / Hyperactivity Disorder (ADHD) is a highly prevalent neurodevelopmental condition characterized by inattention, motor hyperactivity and impulsivity. ADHD cognitive and behavioral presentation is characterized by a high heterogeneity (APA, 2013). Indeed, a complex diagnostic process, that considers several validated tools, is, to date, necessary.ObjectivesThe main aim is to develop supervised machine learning (ML) algorithms that could be used to support the diagnostic process for ADHD, by identifying the most relevant features in discriminating between the presence or absence of the ADHD diagnosis in children.MethodsWe analyzed data from 342 children (Mean age: 8y 8m ± 1y; 61 F) referred for possible ADHD symptomatology. Assessments were performed by an expert clinician and through questionnaires: Social Responsiveness Scale (SRS), Child Behavior Checklist (CBCL), Conners Rating Scale for Parents (CPRS) and for Teachers (CTRS). Data were analyzed using a decision tree classifier and random forest algorithms.ResultsThe decision tree model performed an accuracy of 0.71. The random forest model that was identified as the best tested, performed an accuracy of 0.77 (Figure 1) and it identified as most informative parent- and teacher-rated DSM-oriented ADHD symptoms (Figure 2).Figure 1: Random forest confusion matrix and statistics.Figure 2: Ranking of variables importance.ConclusionsA random forest classifier could represent an effective algorithm to support the identification of ADHD children and to simplify the diagnostic process as an initial step. The use of supervised machine learning algorithms could be useful in helping the diagnostic process, highlighting the importance of a personalized medicine approach.DisclosureNo significant relationships.

Transdiagnostic and sex differences in cognitive profiles of autism spectrum disorder and attention-deficit/hyperactivity disorder.

An increasing number of studies have shown that autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) share symptoms and aetiologies. However, transdiagnostic comparisons between ASD and ADHD is complicated due to the sex differences within each condition. To clarify the similarities and differences in the cognitive functioning between ASD and ADHD, while considering potential sex differences, this study compared cognitive profiles assessed by the WAIS-III between the four groups created by orthogonally combining diagnosis and sex based on the data from 277 ASD males, 86 ASD females, 99 ADHD males and 64 ADHD females. The analysis revealed three major findings. First, performance IQ and perceptual organization index were higher in ADHD males than in ASD males and ADHD females. Second, Gaussian mixture model fitting revealed two clusters underlying the distribution of subindex scores. The percentage of being classified into the cluster that scored lower in all the subindices was higher in females than in males irrespective of diagnosis. Third, feature importance for classification of ASD and ADHD yielded by random forest classifier, a supervised machine learning algorithm, revealed that autism quotient was most informative feature in classifying ASD and ADHD in males, while the discrepancy between verbal and performance intelligence quotient was in females, indicating that the set of behavioral features contributing to classification differs between males and females. Thus, these findings indicate that sex as well as diagnosis is critical in determining the cognitive profiles of people with ASD and ADHD. LAY SUMMARY: The present study compared profiles of cognitive functions measured by Wechsler Adult Intelligence Scale between males and females with ASD and ADHD. The analyses revealed clear sex differences in cognitive functions in both ASD and ADHD and that the set of cognitive functions useful in classifying ASD and ADHD differed between males and females. Thus, biological sex seems to be a critical factor in determining the cognitive profiles of people with ASD and ADHD.

Altered Periodic Dynamics in the Default Mode Network in Autism and Attention-Deficit/Hyperactivity Disorder

BackgroundAltered resting-state functional connectivity in the default mode network (DMN) is characteristic of both autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD). Standard analytical pipelines for resting-state functional connectivity focus on linear correlations in activation time courses between neural networks or regions of interest. These features may be insensitive to temporally lagged or nonlinear relationships. MethodsIn a twin cohort study comprising 292 children, including 52 with a diagnosis of ASD and 70 with a diagnosis of ADHD, we applied nonlinear analytical methods to characterize periodic dynamics in the DMN. Using recurrence quantification analysis and related methods, we measured the prevalence, duration, and complexity of periodic processes within and between DMN regions of interest. We constructed generalized estimating equations to compare these features between neurotypical children and children with ASD and/or ADHD while controlling for familial relationships, and we leveraged machine learning algorithms to construct models predictive of ASD or ADHD diagnosis. ResultsIn within-pair analyses of twins with discordant ASD diagnoses, we found that DMN signal dynamics were significantly different in dizygotic twins but not in monozygotic twins. Considering our full sample, we found that these patterns allowed a robust predictive classification of both ASD (81.0% accuracy; area under the curve = 0.85) and ADHD (82% accuracy; area under the curve = 0.87) cases. ConclusionsThese findings indicate that synchronized periodicity among regions comprising the DMN relates both to neurotypical function and to ASD and/or ADHD, and they suggest generally that a dynamical analysis of network interconnectivity may be a useful methodology for future neuroimaging studies.

ADHD classification using auto-encoding neural network and binary hypothesis testing

Attention Deficit Hyperactivity Disorder (ADHD) is a highly prevalent neurodevelopmental disease of school-age children. Early diagnosis is crucial for ADHD treatment, wherein its neurobiological diagnosis (or classification) is helpful and provides the objective evidence to clinicians. The existing ADHD classification methods suffer two problems, i.e., insufficient data and feature noise disturbance from other associated disorders. As an attempt to overcome these difficulties, a novel deep-learning classification architecture based on a binary hypothesis testing framework and a modified auto-encoding (AE) network is proposed in this paper. The binary hypothesis testing framework is introduced to cope with insufficient data of ADHD database. Brain functional connectivities (FCs) of test data (without seeing their labels) are incorporated during feature selection along with those of training data and affect the sequential deep learning procedure under binary hypotheses. On the other hand, the modified AE network is developed to capture more effective features from training data, such that the difference of inter- and intra-class variability scores between binary hypotheses can be enlarged and effectively alleviate the disturbance of feature noise. On the test of ADHD-200 database, our method significantly outperforms the existing classification methods. The average accuracy reaches 99.6% with the leave-one-out cross validation. Our method is also more robust and practically convenient for ADHD classification due to its uniform parameter setting across various datasets.

A Novel Knowledge Distillation-Based Feature Selection for the Classification of ADHD.

Attention Deficit Hyperactivity Disorder (ADHD) is a brain disorder with characteristics such as lack of concentration, excessive fidgeting, outbursts of emotions, lack of patience, difficulty in organizing tasks, increased forgetfulness, and interrupting conversation, and it is affecting millions of people worldwide. There is, until now, not a gold standard test using which an ADHD expert can differentiate between an individual with ADHD and a healthy subject, making accurate diagnosis of ADHD a challenging task. We are proposing a Knowledge Distillation-based approach to search for discriminating features between the ADHD and healthy subjects. Learned embeddings from a large neural network, trained on the functional connectivity features, were fed to one hidden layer Autoencoder for reproduction of the embeddings using the same connectivity features. Finally, a forward feature selection algorithm was used to select a combination of most discriminating features between the ADHD and the Healthy Controls. We achieved promising classification results for each of the five individual sites. A combined accuracy of 81% in KKI, 60% Peking, 56% in NYU, 64% NI, and 56% OHSU and individual site wise accuracy of 72% in KKI, 60% Peking, 73% in NYU, 70% NI, and 71% OHSU were obtained using our extracted features. Our results also outperformed state-of-the-art methods in literature which validates the efficacy of our proposed approach.

Deep learning model reveals potential risk genes for ADHD, especially Ephrin receptor gene EPHA5.

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder. Although genome-wide association studies (GWAS) identify the risk ADHD-associated variants and genes with significant P-values, they may neglect the combined effect of multiple variants with insignificant P-values. Here, we proposed a convolutional neural network (CNN) to classify 1033 individuals diagnosed with ADHD from 950 healthy controls according to their genomic data. The model takes the single nucleotide polymorphism (SNP) loci of P-values n}{}le{1times 10^{-3}}, i.e. 764 loci, as inputs, and achieved an accuracy of 0.9018, AUC of 0.9570, sensitivity of 0.8980 and specificity of 0.9055. By incorporating the saliency analysis for the deep learning network, a total of 96 candidate genes were found, of which 14 genes have been reported in previous ADHD-related studies. Furthermore, joint Gene Ontology enrichment and expression Quantitative Trait Loci analysis identified a potential risk gene for ADHD, EPHA5 with a variant of rs4860671. Overall, our CNN deep learning model exhibited a high accuracy for ADHD classification and demonstrated that the deep learning model could capture variants’ combining effect with insignificant P-value, while GWAS fails. To our best knowledge, our model is the first deep learning method for the classification of ADHD with SNPs data.

Neuropsychiatric disease classification using functional connectomics - results of the connectomics in neuroimaging transfer learning challenge.

Large, open-source datasets, such as the Human Connectome Project and the Autism Brain Imaging Data Exchange, have spurred the development of new and increasingly powerful machine learning approaches for brain connectomics. However, one key question remains: are we capturing biologically relevant and generalizable information about the brain, or are we simply overfitting to the data? To answer this, we organized a scientific challenge, the Connectomics in NeuroImaging Transfer Learning Challenge (CNI-TLC), held in conjunction with MICCAI 2019. CNI-TLC included two classification tasks: (1) diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) within a pre-adolescent cohort; and (2) transference of the ADHD model to a related cohort of Autism Spectrum Disorder (ASD) patients with an ADHD comorbidity. In total, 240 resting-state fMRI (rsfMRI) time series averaged according to three standard parcellation atlases, along with clinical diagnosis, were released for training and validation (120 neurotypical controls and 120 ADHD). We also provided Challenge participants with demographic information of age, sex, IQ, and handedness. The second set of 100 subjects (50 neurotypical controls, 25 ADHD, and 25 ASD with ADHD comorbidity) was used for testing. Classification methodologies were submitted in a standardized format as containerized Docker images through ChRIS, an open-source image analysis platform. Utilizing an inclusive approach, we ranked the methods based on 16 metrics: accuracy, area under the curve, F1-score, false discovery rate, false negative rate, false omission rate, false positive rate, geometric mean, informedness, markedness, Matthew's correlation coefficient, negative predictive value, optimized precision, precision, sensitivity, and specificity. The final rank was calculated using the rank product for each participant across all measures. Furthermore, we assessed the calibration curves of each methodology. Five participants submitted their method for evaluation, with one outperforming all other methods in both ADHD and ASD classification. However, further improvements are still needed to reach the clinical translation of functional connectomics. We have kept the CNI-TLC open as a publicly available resource for developing and validating new classification methodologies in the field of connectomics.

FMRI Feature Extraction Model for ADHD Classification Using Convolutional Neural Network

Biomedical intelligence provides a predictive mechanism for the automatic diagnosis of diseases and disorders. With the advancements of computational biology, neuroimaging techniques have been used extensively in clinical data analysis. Attention deficit hyperactivity disorder (ADHD) is a psychiatric disorder, with the symptomology of inattention, impulsivity, and hyperactivity, in which early diagnosis is crucial to prevent unwelcome outcomes. This study addresses ADHD identification using functional magnetic resonance imaging (fMRI) data for the resting state brain by evaluating multiple feature extraction methods. The features of seed-based correlation (SBC), fractional amplitude of low-frequency fluctuation (fALFF), and regional homogeneity (ReHo) are comparatively applied to obtain the specificity and sensitivity. This helps to determine the best features for ADHD classification using convolutional neural networks (CNN). The methodology using fALFF and ReHo resulted in an accuracy of 67%, while SBC gained an accuracy between 84% and 86% and sensitivity between 65% and 75%.

Deep Spatio-Temporal Representation and Ensemble Classification for Attention Deficit/Hyperactivity Disorder.

Attention deficit/Hyperactivity disorder (ADHD) is a complex, universal and heterogeneous neurodevelopmental disease. The traditional diagnosis of ADHD relies on the long-term analysis of complex information such as clinical data (electroencephalogram, etc.), patients' behavior and psychological tests by professional doctors. In recent years, functional magnetic resonance imaging (fMRI) has been developing rapidly and is widely employed in the study of brain cognition due to its non-invasive and non-radiation characteristics. We propose an algorithm based on convolutional denoising autoencoder (CDAE) and adaptive boosting decision trees (AdaDT) to improve the results of ADHD classification. Firstly, combining the advantages of convolutional neural networks (CNNs) and the denoising autoencoder (DAE), we developed a convolutional denoising autoencoder to extract the spatial features of fMRI data and obtain spatial features sorted by time. Then, AdaDT was exploited to classify the features extracted by CDAE. Finally, we validate the algorithm on the ADHD-200 test dataset. The experimental results show that our method offers improved classification compared with state-of-the-art methods in terms of the average accuracy of each individual site and all sites, meanwhile, our algorithm can maintain a certain balance between specificity and sensitivity.

Participant-experimenter rapport in experimental settings: A test case of executive functions among children with ADHD.

There is a growing interest in the effects of social engagement on cognition, yet, research on the effects of social engagement with the experimenter in empirical contexts has been sparse. During an experiment, the experimenter and participant form a dyad, establishing a certain level of rapport-a sense of a positive and congruent relationship. This rapport is thought to promote performance by providing a comfortable testing environment, thereby reducing resource demand, and enhancing participant engagement and willingness to exert effort to perform. The current study sought to better understand the role of rapport by examining the effects of perceived rapport on effortful control, that is, inhibition and shifting, in an experimental setting among children with and without attention-deficit/hyperactivity disorder (ADHD). Forty-nine children (9 to 12 years old) were divided into two groups based on ADHD classification (i.e., typically developing children, n = 27; children with ADHD, n = 22). Participants completed the day/night Stroop task and the Wisconsin Card Sorting Task following a short rapport-building conversation with the experimenter. Later, both participant and experimenter filled the CHARM questionnaire reporting the rapport constructed during the experiment. Results show moderating effects of ADHD on the relationship between perceived rapport quality and congruency, and participant's executive functions performance. Specifically, children with ADHD showed higher susceptibility to rapport quality and were impervious to the effects of rapport congruency. Results highlight the importance of rapport with the experimenter in experimental research and suggest incorporating considerations concerning rapport, both in designing the experimental paradigm as well as an independent factor affecting task performance and outcome. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Classification of ADHD with fMRI data and multi-objective optimization

Background and objectiveDataset imbalance is an important problem in neuroimaging. Imbalanced datasets would cause the performance degradation of a classifier by utilizing imbalanced learning, which tends to overfocus on the majority class. In this paper, we consider an imbalanced neuroimaging classification problem, namely, classification of attention deficit hyperactivity disorder (ADHD) using resting-state functional magnetic resonance imaging. MethodsWe propose a multi-objective classification scheme based on support vector machine (SVM). Our scheme addresses the imbalanced dataset problem by using a three objective SVM model with the positive and negative empirical errors being handled explicitly and separately. Moreover, an interactive multi-objective method incorporating the decision maker's preference is adopted, thus a preferred subset of pareto optimal classifiers for decision making can be obtained. ResultsThe proposed scheme is assessed on five datasets from the ADHD- 200 consortium. Numerical results show that the proposed multi-objective scheme considerably outperforms some traditional classification methods in the literature. ConclusionThe proposed multi-objective classification scheme avoids hyper-parameter selection, it effectively addresses dataset imbalanced problem from algorithm level. The scheme can not only be used in the diagnosis of ADHD but also in the diagnosis of other diseases, such as Alzheimer and Autism etc.

ADHD classification based on a multi-objective support vector machine

Attention deficit hyperactivity disorder (ADHD) is one of the most common mental disorders during childhood, which lasts until adulthood in most cases. In recent years, ADHD classification based on functional magnetic resonance imaging (fMRI) data has become a research hotspot. Most existing classification algorithms reported in the literature assume that samples are balanced; however, ADHD data sets are usually imbalanced. Imbalanced data sets can cause the performance degradation of a classifier by imbalanced learning, which tends to overfocus on the majority class. In this study, we considered an imbalanced neuroimaging classification problem: classification of ADHD using resting state fMRI. We used the functional connection matrix of fMRI as the classification feature and proposed a multi-objective data classification scheme based on a support vector machine (SVM) to aid the diagnosis of ADHD. In this scheme, the imbalanced data classification problem is formulated as an SVM model with three objectives: maximizing the margin, minimizing the sum of positive errors, and minimizing the sum of negative errors. Accordingly, the positive and negative sample empirical errors can be separately handled. Then, the model is solved by a multi-objective optimization method, i.e., normal boundary intersection method. A set of representative classifiers are computed for selection by decision makers. The proposed scheme was tested and evaluated on five data sets from the ADHD-200 consortium and compared with traditional classification methods. Experimental results show that the proposed three-objective SVM classification scheme is better than traditional classification methods reported in the literature. It can effectively address the data imbalance problem from the algorithm level. This scheme can be used in the diagnosis of ADHD as well as other diseases, such as Alzheimer’s and Autism.

Recent advances in understanding of attention deficit hyperactivity disorder (ADHD): how genetics are shaping our conceptualization of this disorder.

Attention deficit hyperactivity disorder (ADHD) is a clinically defined disorder, and inattention and hyperactivity/impulsivity are its main symptom domains. The presentation, lifelong continuation and treatment response of ADHD symptoms, however, is highly heterogeneous. To better define, diagnose, treat and prevent ADHD, it is essential that we understand the biological processes underlying all of these elements. In this review, given the high heritability of ADHD, we discuss how and why genetics can foster such progress. We examine what genetics have taught us so far with regard to ADHD definition, classification, clinical presentation, diagnosis and treatment. Finally, we offer a prospect of what genetic studies on ADHD may bring in the future.

DeepFMRI: End-to-end deep learning for functional connectivity and classification of ADHD using fMRI

BackgroundResting state fMRI has emerged as a popular neuroimaging method for automated recognition and classification of brain disorders. Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common brain disorders affecting young children, yet its underlying mechanism is not completely understood and its diagnosis is mainly dependent on behaviour analysis. New methodIn this paper, we propose an end-to-end deep learning architecture to diagnose ADHD. Our aim is to (1) automatically classify a subject as ADHD or healthy control, and (2) demonstrate the importance of functional connectivity to increase classification accuracy and provide interpretable results. The proposed method, called DeepFMRI, is comprised of three sequential networks, namely (1) a feature extractor, (2) a functional connectivity network, and (3) a classification network. The model takes fMRI pre-processed time-series signals as input and outputs a diagnosis, and is trained end-to-end using back-propagation. ResultsExperimental results on the publicly available ADHD-200 dataset demonstrate that this innovative method outperforms previous state-of-the-art. Different imaging sites contributed the data to the ADHD-200 dataset. For the New York University imaging site, our proposed method was able to achieve classification accuracy of 73.1% (specificity 91.6%, sensitivity 65.5%). Comparison with existing methodsIn this work, we propose a novel end-to-end deep learning method incorporating functional connectivity for the classification of ADHD. To the best of our knowledge, this has not been explored by existing studies. ConclusionsThe results suggest that the proposed end-to-end deep learning architecture achieves better performance as compared to the other state-of-the-art methods. The findings suggest that the frontal lobe contains the most discriminative power towards the classification of ADHD.

EEG spectral power, but not theta/beta ratio, is a neuromarker for adult ADHD.

Adults with attention-deficit/hyperactivity disorder (ADHD) have been described as having altered resting-state electroencephalographic (EEG) spectral power and theta/beta ratio (TBR). However, a recent review (Pulini et al. 2018) identified methodological errors in neuroimaging, including EEG, ADHD classification studies. Therefore, the specific EEG neuromarkers of adult ADHD remain to be identified, as do the EEG characteristics that mediate between genes and behaviour (mediational endophenotypes). Resting-state eyes-open and eyes-closed EEG was measured from 38 adults with ADHD, 45 first-degree relatives of people with ADHD and 51 unrelated controls. A machine learning classification analysis using penalized logistic regression (Elastic Net) examined if EEG spectral power (1-45Hz) and TBR could classify participants into ADHD, first-degree relatives and/or control groups. Random-label permutation was used to quantify any bias in the analysis. Eyes-open absolute and relative EEG power distinguished ADHD from control participants (area under receiver operating characteristic=0.71-0.77). The best predictors of ADHD status were increased power in delta, theta and low-alpha over centro-parietal regions, and in frontal low-beta and parietal mid-beta. TBR did not successfully classify ADHD status. Elevated eyes-open power in delta, theta, low-alpha and low-beta distinguished first-degree relatives from controls (area under receiver operating characteristic=0.68-0.72), suggesting that these features may be a mediational endophenotype for adult ADHD. Resting-state EEG spectral power may be a neuromarker and mediational endophenotype of adult ADHD. These results did not support TBR as a diagnostic neuromarker for ADHD. It is possible that TBR is a characteristic of childhood ADHD.

ADHD classification by dual subspace learning using resting-state functional connectivity.

As one of the most common neurobehavioral diseases in school-age children, Attention Deficit Hyperactivity Disorder (ADHD) has been increasingly studied in recent years. But it is still a challenge problem to accurately identify ADHD patients from healthy persons. To address this issue, we propose a dual subspace classification algorithm by using individual resting-state Functional Connectivity (FC). In detail, two subspaces respectively containing ADHD and healthy control features, called as dual subspaces, are learned with several subspace measures, wherein a modified graph embedding measure is employed to enhance the intra-class relationship of these features. Therefore, given a subject (used as test data) with its FCs, the basic classification principle is to compare its projected component energy of FCs on each subspace and then predict the ADHD or control label according to the subspace with larger energy. However, this principle in practice works with low efficiency, since the dual subspaces are unstably obtained from ADHD databases of small size. Thereby, we present an ADHD classification framework by a binary hypothesis testing of test data. Here, the FCs of test data with its ADHD or control label hypothesis are employed in the discriminative FC selection of training data to promote the stability of dual subspaces. For each hypothesis, the dual subspaces are learned from the selected FCs of training data. The total projected energy of these FCs is also calculated on the subspaces. Sequentially, the energy comparison is carried out under the binary hypotheses. The ADHD or control label is finally predicted for test data with the hypothesis of larger total energy. In the experiments on ADHD-200 dataset, our method achieves a significant classification performance compared with several state-of-the-art machine learning and deep learning methods, where our accuracy is about 90 % for most of ADHD databases in the leave-one-out cross-validation test.

Normalization and deep learning based attention deficit hyperactivity disorder classification

Attention Deficit Hyperactivity Disorder (ADHD) is one of the major mental-health disorders worldwide. ADHD is typically characterized by impaired executive function, impulsivity, hyperactivity and with respect to these behavioral symptoms, diagnosis of ADHD is performed. These symptoms are obviously seen at in early stage. Serious impairments and substantial burdens are induced for society as well as to families. However, for ADHD, there is no diagnostic laboratory in current scenario. Psychological tests like Brown Attention Deficit Disorder Scale (BADDS), Conners Parent Rating Scale and ADHD Rating Scale (ADHD-RS) are carried out for ADHD diagnosis. Tedious and complex clinical analysis are needed in this testing and this makes low efficiency of the diagnostic process. A traditional diagnosis technique of ADHD produces degraded results. So, enhanced extreme learning machine is incorporated with existing techniques for avoiding the issues of performance degradation. There is a need to enhance the classifier performance further and there is a chance for unwanted noise in input samples, which may degrade the performance of classifier. For avoiding these issues, an enhanced and automated ADHS diagnosis technique is proposed. First stage is pre-processing, and it is carried out based on min max normalization and feature extraction is a next stage, which is carried out through Fast Independent Component Analysis and third stage is a Deep Extreme Learning Machine (DELM) based ADHD identification and classification. Extreme Learning Machine with Kernel (KELM) and Multilayer Extreme Learning Machine (MLELM) algorithm are combined in this method and it is termed as deep extreme learning machine (DELM). Collection of neuro images are used for quantitative and qualitative analysis and with respect to f-measure, recall, precision and accuracy, robustness of proposed technique is demonstrated.

High-Accuracy Classification of Attention Deficit Hyperactivity Disorder With l 2,1-Norm Linear Discriminant Analysis and Binary Hypothesis Testing

Attention Deficit Hyperactivity Disorder (ADHD) is a high incidence of neurobehavioral disease in school-age children. Its neurobiological diagnosis (or classification) is meaningful for clinicians to give proper treatment for ADHD patients. The existing ADHD classification methods suffer from two problems, i.e., insufficient data and noise disturbance. In this paper, a high-accuracy classification method is proposed by using brain Functional Connectivity (FC) as ADHD features, where an ${l_{2,1}}$ -norm Linear Discriminant Analysis (LDA) model and a binary hypothesis testing framework are effectively employed. In detail, we introduce a binary hypothesis testing framework to cope with insufficient data of ADHD database. The FCs of test data (without seeing its label) are used for training and thus affect the subspace learning of training data under binary hypotheses. On other hand, the ${l_{2,1}}$ -norm LDA model generates a subspace to represent ADHD features, aiming to overcome noise disturbance. By robustly learning ADHD features, the subspace energy difference between binary hypotheses becomes more discriminative. Thereby, the true hypothesis can be rightly estimated with its larger subspace energy, which provides reliable evidence to predict the label of test data. By the test on ADHD-200 database, it shows that our method outperforms other state-of-the-art methods with the significant average accuracy of 97.6%. Moreover, the corresponding result analysis with ADHD symptom score and the explanation of discriminative FCs between ADHD and healthy control groups are given, which further verifies the validity of our classification method.

Recent advances in understanding of attention deficit hyperactivity disorder (ADHD): how genetics are shaping our conceptualization of this disorder

Attention deficit hyperactivity disorder (ADHD) is a clinically defined disorder, and inattention and hyperactivity/impulsivity are its main symptom domains. The presentation, lifelong continuation and treatment response of ADHD symptoms, however, is highly heterogeneous. To better define, diagnose, treat and prevent ADHD, it is essential that we understand the biological processes underlying all of these elements. In this review, given the high heritability of ADHD, we discuss how and why genetics can foster such progress. We examine what genetics have taught us so far with regard to ADHD definition, classification, clinical presentation, diagnosis and treatment. Finally, we offer a prospect of what genetic studies on ADHD may bring in the future.