Comparison of Machine Learning predictive methods to diagnose the Attention Deficit/Hyperactivity Disorder levels using SPECT

Η Διαταραχή Ελλειμματικής Προσοχής-Υπερκινητικότητα (ΔΕΠΥ), γνωστή και με τον αγγλικό όρο Attention Deficit Hyperactivity Disorder (ADHD), είναι μια νευροαναπτυξιακή διαταραχή που ορίζεται από ένα εύρος βασικών χαρακτηριστικών. Ερευνητές καταλήγουν στο συμπέρασμα ότι η ΔΕΠΥ έχει πολλές διαφορετικές κλινικές εικόνες, ωστόσο υπάρχουν κάποια βασικά διαγνωστικά κριτήρια. Η ΔΕΠΥ αποδίδεται κυρίως σε γενετικούς παράγοντες και σε ένα μικρό ποσοστό σε πρώιμους περιβαλλοντικούς. Τα άτομα με ΔΕΠΥ εμφανίζουν προβλήματα συμπεριφοράς και δυσκολεύονται στις διαπροσωπικές τους σχέσεις. Πολύ συχνά παρατηρείται συνύπαρξη της ΔΕΠΥ με άλλες διαταραχές, όπως για παράδειγμα ψυχιατρικές. Στα διάφορα αναπτυξιακά στάδια η συννοσηρότητα μεταβάλλεται κατά την πάροδο του χρόνου. Σημαντικός παράγοντας για την ανάπτυξη συμπτωμάτων ΔΕΠΥ είναι τα ελλείμματα στις επιτελικές λειτουργίες( Executive Functions – Efs). Tα άτομα με ΔΕΠΥ είναι ευάλωτα στην παραβατικότητα. Είναι αξιοσημείωτο ότι τα άτομα με ΔΕΠΥ παρουσιάζουν διαταραχές στον ύπνο, όπως αϋπνία, αναπνευστική διαταραχή και διάφορα άλλα σύνδρομα. Για τη βελτίωση του ύπνου συνίστανται παρεμβάσεις με χρήση φαρμάκων καθώς και με συμπεριφορικές μεθόδους. Τα παιδιά με ΔΕΠΥ αντιμετωπίζουν μαθησιακές δυσκολίες, αυξημένο άγχος, και δυσκολεύονται στην αλληλεπίδραση με τους συμμαθητές και δασκάλους τους. Η ΔΕΠΥ στην παιδική ηλικία έχει σημαντικό αντίκτυπο στους γονείς, οι οποίοι καλούνται να αντιμετωπίσουν αυξημένες προκλήσεις σε σχέση με τον γονεϊκό τους ρόλο.

Attention deficit hyperactivity disorder (ADHD) is associated with driving deficits. Visual standards for driving define minimum qualifications for safe driving, including acuity and field of vision, but they do not consider the ability to explore the environment efficiently by shifting the gaze, which is a critical element of safe driving. To examine visual exploration during simulated driving in adolescents with and without ADHD. Adolescents with and without ADHD drove a driving simulator for approximately 10 min while their gaze was monitored. They then completed a battery of questionnaires. University lab. Participants with (n = 16) and without (n = 15) ADHD were included. Participants had a history of neurological disorders other than ADHD and normal or corrected-to-normal vision. Control participants reported not having a diagnosis of ADHD. Participants with ADHD had been previously diagnosed by a qualified professional. We compared the following measures between ADHD and non-ADHD groups: dashboard dwell times, fixation variance, entropy, and fixation duration. Findings showed that participants with ADHD were more restricted in their patterns of exploration than control group participants. They spent considerably more time gazing at the dashboard and had longer periods of fixation with lower variability and randomness. The results support the hypothesis that adolescents with ADHD engage in less active exploration during simulated driving. This study raises concerns regarding the driving competence of people with ADHD and opens up new directions for potential training programs that focus on exploratory gaze control.

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.

Attention deficit hyperactivity disorder (ADHD) is a common psychological disorder for a broad range of ages. Child and adolescent ADHD patients show different behavior patterns. The differences between child and adolescent ADHD patients have not been fully explored in terms of brain connectivity. In this study, we explored the differences of connectivity patterns between child and adolescent ADHD patients using resting-state functional magnetic resonance imaging (rs-fMRI) of 52 ADHD patients (26 children and 26 adolescents). Default mode network and frontoparietal network showed significant group-wise connectivity pattern differences between child and adolescent ADHD patients. The results of our study might suggest potential imaging biomarkers for further ADHD related studies.

Attention deficit hyperactivity disorder (ADHD) is the most commonly diagnosed neurobehavioural disorder in childhood, affecting over 5% of children worldwide. As well as the core symptoms of inattention, hyperactivity and impulsivity, patients often exhibit learning difficulties and impairment in social functioning. The frequency of referral is higher for boys than for girls (about 2:1), and girls are generally older at the time of referral.Pharmacological therapy is considered the first-line treatment for patients with severe ADHD and severe impairment. Stimulant medications are licensed in the UK for the management of ADHD in school-age children and young people, and are effective in controlling ADHD symptoms.While immediate-release preparations of methylphenidate (MPH) have proven effective in the treatment of ADHD, there are a number of problems associated with their use, most notably compliance, stigma and medication diversion. Modified release preparations are now available that overcome the need for multiple daily dosing, and which offer different MPH release profiles, thereby enabling the physician to match the medication to the patient's particular requirements.This review describes the diagnosis, referral and treatment pathways for patients with ADHD in the UK and the practical considerations when initiating pharmacological treatment. The clinical experience of treating ADHD with a modified-release MPH preparation (Equasym XL®) is illustrated with case studies.

BackgroundEffective and accurate diagnosis of attention-deficit/hyperactivity disorder (ADHD) is currently of significant interest. ADHD has been associated with multiple cortical features from structural MRI data. However, most existing learning algorithms for ADHD identification contain obvious defects, such as time-consuming training, parameters selection, etc. The aims of this study were as follows: (1) Propose an ADHD classification model using the extreme learning machine (ELM) algorithm for automatic, efficient and objective clinical ADHD diagnosis. (2) Assess the computational efficiency and the effect of sample size on both ELM and support vector machine (SVM) methods and analyze which brain segments are involved in ADHD.MethodsHigh-resolution three-dimensional MR images were acquired from 55 ADHD subjects and 55 healthy controls. Multiple brain measures (cortical thickness, etc.) were calculated using a fully automated procedure in the FreeSurfer software package. In total, 340 cortical features were automatically extracted from 68 brain segments with 5 basic cortical features. F-score and SFS methods were adopted to select the optimal features for ADHD classification. Both ELM and SVM were evaluated for classification accuracy using leave-one-out cross-validation.ResultsWe achieved ADHD prediction accuracies of 90.18% for ELM using eleven combined features, 84.73% for SVM-Linear and 86.55% for SVM-RBF. Our results show that ELM has better computational efficiency and is more robust as sample size changes than is SVM for ADHD classification. The most pronounced differences between ADHD and healthy subjects were observed in the frontal lobe, temporal lobe, occipital lobe and insular.ConclusionOur ELM-based algorithm for ADHD diagnosis performs considerably better than the traditional SVM algorithm. This result suggests that ELM may be used for the clinical diagnosis of ADHD and the investigation of different brain diseases.

Low spatial resolution and the related partial volume effects limit the diagnostic potential of brain single photon emission computed tomography (SPECT) imaging. As a possible remedy for this problem we propose a technique for the fusion of SPECT and MR images, which requires for a given patient the SPECT data and the T1-weighted MR image. Basically, after the reconstruction and coregistration steps, the high-frequency part of the MR, which would be unrecoverable by the set SPECT acquisition system + reconstruction algorithm, is extracted and added to the SPECT image. The tuning of the weight of the MR on the resulting fused image can be performed very quickly, any iterative reconstruction algorithm can be used and, in the case that the SPECT projections are not available, the proposed technique can also be applied directly to the SPECT image, provided that the performance of the scanner is known. The procedure has the potential of increasing the diagnostic value of a SPECT image. Even in the locations of SPECT-MR mismatch it does not significantly affect quantitation over regions of interest (ROIs) whose dimensions are decidedly larger than the SPECT resolution distance. On the other hand, appreciable corrections for partial volume effects are expected in the locations where the contrast in the structural MR matches the corresponding contrast in functional activity.

This article presents a method based on support vector machines (SVMs) and the Osuna–Platt algorithm used to diagnose the ischemic heart disease. It also includes the necessary concepts from the optimization theory which make it possible to formulate the problem for support vectors and the Osuna–Platt algorithm applied. Next, a principal component analysis (PCA) algorithm used is also presented. Heart images acquired using Single Photon Emission Computed Tomography (SPECT) have been used in the experimental part. Results of classifying cardiac SPECT images using SVM, PCA and neural networks are compared here with those obtained using another method of machine learning – CLIP3 – a combination of the decision tree algorithm and the rule induction algorithm. Tests against an SPECT image database have shown that SVMs are generally more accurate and specific, while the PCA algorithm is the most sensitive for all data sets analysed in this research project.

Multimodality nuclear medicine imaging in three-dimensional radiation treatment planning for lung cancer: challenges and prospects

to assess diagnostic capabilities of single-photon emission computed tomography (SPECT) in the detection of obstructive coronary artery (CA) lesions, depending on the meeting appropriate use criteria. We used in this retrospective analysis data from 107 patients with previously diagnosed ischemic heart disease (IHD) or in need to exclude it, who were hospitalized in inpatient departments of the Research Institute for Complex Issues of Cardiovascular Diseases in the period from 2012 to 2015. All patients underwent coronary angiography (CAG) and SPECT (the time interval between the studies did not exceed 3 months) for detection of hemodynamically significant CA stenoses. Patients were distributed into two groups according SPECT imaging appropriateness score: group 1-88 patients with score 7-9 (in whom SPECT imaging was appropriate), group 2-19 patients with score 1-6 (in whom SPECT imaging was uncertain, possibly appropriate, or inappropriate. Clinical signs and symptoms of angina pectoris were predominantly found in group 1 patients (p=0.499). Asymptomatic patients were more likely to be found in group 2 (p<0.001). Group 1 patients commonly had high pretest probability (PTP) (over 90 %, p<0.001), whereas group 2 patients commonly had low PTP (5-10 %, p<0.001). Mean PTP was 77 and 58 % in groups 1 and 2, respectively (p=0.003). According to positive SPECT imaging, significant CA lesions were more often found in group 1 compared to group 2 (31.8 and 10.5 %, respectively, p=0.060). Two- and three-vessel disease prevailed in group 1 (25 % and 14.7 %) according the analysis of prevalence and location of hemodynamically significant CA lesions, although the data did not reach statistical significance (p=0.057 and p=0.073). Stenoses >70 % were more commonly detected in group 1, compared to group 2: in anterior descending artery 52.3 vs. 5.3 % (p<0.001), circumflex artery 35.2 vs. 10.5 %; (p=0.034), right coronary artery 34.1 vs. 10.5 % (p=0.041). The sensitivity in both groups was rather low (40 % vs. 25 %), whereas specificity was 83 % in group 1 and 93 % in group 2. According to clinical examination, patients with IHD and indications for SPECT imaging more often had obstructive CA lesions (63.6 %), than patients with questionable or no indications (21.1 %). However, rate of positive findings during stress tests with SPECT imaging was low in both groups and did not differ significantly (p=0.06). Despite high specificity of SPECT imaging, its sensitivity was low in both groups.

ObjectivesTo analyse the differences in the clinical features and characteristics of 123I-labelled 2β-carbomethoxy-3β-(4-iodophenyl)-N-(3-fluoropropyl) nortropane (123I-FP-CIT) single photon emission CT (SPECT) imaging among patients with vascular parkinsonism (VP) and Parkinson's disease...

Background Lower parental education is linked to higher incidence of attention-deficit hyperactivity disorder (ADHD) diagnosis in offspring, as is having ADHD in family, but these associations are rarely studied together. We examined whether parental education predicts offspring ADHD diagnosis differently for children and adolescents with or without ADHD family members. Methods We used Finnish total population register-based data on individuals born between 1994-2003 with non-migrant background. We followed 228,247 boys and 217,866 girls for first ADHD diagnosis, measured from health care or medication registers, in childhood (4-12) and adolescence (13-17), and identified ADHD for biological parents or full siblings. We used Poisson regression to examine differences in diagnoses separately by offspring sex and age group. Results Preliminary results show that boys are more often diagnosed for ADHD in childhood and girls in adolescence, but incidence is higher for boys in both age groups. Among boys and girls without ADHD family members, lower parental education is associated with higher incidence in both childhood and adolescence. Among those with ADHD family members, lower parental education predicts higher incidence in childhood, but in adolescence the differences by parental education even out for boys, and for girls, higher parental education predicts higher incidence. Conclusions While lower parental education predicts higher ADHD diagnosis incidence in general, for adolescents in ADHD families, higher parental education seems to increase the likelihood of getting diagnosed -particularly for girls. This suggests that adolescents in ADHD families with lower parental education may remain without ADHD diagnosis if they are not diagnosed as children. Support measures should be developed to ensure equal access to diagnosis and care irrespective of age, gender and family background. Key messages • Lower parental education predicts higher ADHD diagnosis incidence in general. Yet, adolescents in ADHD families with lower educated parents may remain underdiagnosed. • Support measures should be developed to guarantee equal access to ADHD diagnosis and care regardless of family background.

Correlation of vitamin D to attention deficit hyperactivty disorder

Attention-deficit hyperactivity disorder (ADHD) in adults has been associated to a higher probability of problems with justice. Studies in prison populations disclosed higher prevalence of this disturbance. ADHD association with personality disorders and higher substance abuse could partially help in the explanation of this phenomenon. Based in a literature review, the authors present a reflection on the possible implications of the ADHD diagnosis to Forensic Medicine. After a brief review of the clinical aspects, epidemiology, etiology and nosology of ADHD, its repercussions in the sphere of justice are analysed, namely the prevalence of associated criminality, risk factors and the co-morbidity with personality disorders and substance abuse. These elements are used as a base to the discussion of the results and to a subsequent reflection on the question of (in)imputability related with this disorder.

Attention Deficit/Hyperactivity Disorder (ADHD) is one of the most common childhood psychological disorders which are characterized by hyperactive/impulsive and inattentive symptoms1. Mindfulness training is one of the emerging intervention programs for the treatment of ADHD. Current study aimed at examining the influence of mindfulness training on ADHD. 10 adolescents were recruited based on inclusion and exclusion criteria using purposive sampling method. The study included experimental single group Pre-Post design and intervention was based on mindfulness training consisting of 10 sessions. The participants were assessed with ADHD Rating Scale-V, at before and after the intervention. Descriptive statistics and Wilcoxon signed ranks test were used. Significant reduction in ADHD symptoms was observed after the intervention.