Similarity based classification of ADHD using Singular Value Decomposition

Attention deficit hyperactivity disorder (ADHD) is one of the most common brain disorders among children. This disorder is considered as a big threat for public health and causes attention, focus and organizing difficulties for children and even adults. Since the cause of ADHD is not known yet, data mining algorithms are being used to help discover patterns which discriminate healthy from ADHD subjects. Numerous efforts are underway with the goal of developing classification tools for ADHD diagnosis based on functional and structural magnetic resonance imaging data of the brain. In this paper, we used Eros, which is a technique for computing similarity between two multivariate time series along with k-Nearest-Neighbor classifier, to classify healthy vs ADHD children. We designed a model selection scheme called J-Eros which is able to pick the optimum value of k for k-Nearest-Neighbor from the training data. We applied this technique to the public data provided by ADHD-200 Consortium competition and our results show that J-Eros is capable of discriminating healthy from ADHD children such that we outperformed the best results reported by ADHD-200 competition more than 20 percent for two datasets

Adaptive Functioning and Time Processing in Children with Tourette Syndrome

Overview This thesis focuses on behavioural outcomes in children with Tourette syndrome (TS). Part I is a systematic review of the literature on aggression in TS. The aim of the review is to better understand aggression in TS and specifically to determine the impact of ADHD symptoms on aggressive behaviour in TS. The impact of these findings will support families and clinicians in knowing the risk factors, and potentially best treatment approaches, for behaviours that challenge in TS. In Part II, adaptive functioning, or the ability to apply one’s cognitive abilities to achieve day-to-day tasks, is examined in children with TS. Time processing, or the ability to process time intervals, is increasingly becoming known as one of the brains most important basic functions and has been shown to be impaired in multiple neurodevelopmental conditions. An experimental time measure is used in this study to investigate the impact of time processing on adaptive functioning outcome in TS. This was a joint project with DClinPsy Trainee, LH. In the final portion of this thesis, Part III, the research process is critically appraised, and challenges that arose in the process are highlighted. This includes a reflection on broader themes relating to working with children with TS, application of the study findings, and future directions for research

Gender Differences in Cognitive Distortions in Adults with ADHD

Research has established that men and women with ADHD often manifest varying symptom constellations and are typically referred at different ages for initial evaluation. However, there is a dearth of research into how such gender differences may impact the manifestation of various psychological processes, such as cognitive distortions, even though the latter may explain up to half the variance in many clinical syndromes and personality disorders. The primary objective of this study was to identify whether there is a significant difference in the frequency of cognitive distortions between men and women with ADHD. The secondary objective was to determine the relationship between executive-functioning deficits, severity of functional impairment, and gender in relation to the frequency of cognitive distortions. Data were collected from an archival data set from an outpatient university-based adult ADHD specialty clinic in a large northeastern city. Participants were adults diagnosed with ADHD by a comprehensive evaluation including the Revised NEO Personality Inventory, Inventory of Cognitive Distortions, Barkley Deficits in Executive Functioning Scale, Barkley Functional Impairment Scale, Beck Depression Inventory-II, and Penn State Worry Questionnaire. A multiple logistic regression indicated that gender was not predicted by Big Five personality factors of neuroticism, agreeableness, and conscientiousness; depression; or cognitive distortions in adults with ADHD. However, a hierarchical multiple regression indicated a statistically significant, positive linear relationship between depressed mood, conscientiousness, and functional impairment on the one hand and frequency of cognitive distortions on the other hand. Implications for assessment and treatment of adult ADHD are discussed

Neural correlates of post-traumatic brain injury (TBI) attention deficits in children

Traumatic brain injury (TBI) in children is a major public health concern worldwide. Attention deficits are among the most common neurocognitive and behavioral consequences in children post-TBI which have significant negative impacts on their educational and social outcomes and compromise the quality of their lives. However, there is a paucity of evidence to guide the optimal treatment strategies of attention deficit related symptoms in children post-TBI due to the lack of understanding regarding its neurobiological substrate. Thus, it is critical to understand the neural mechanisms associated with TBI-induced attention deficits in children so that more refined and tailored strategies can be developed for diagnoses and long-term treatments and interventions. This dissertation is the first study to investigate neurobiological substrates associated with post-TBI attention deficits in children using both anatomical and functional neuroimaging data. The goals of this project are to discover the quantitatively measurable markers utilizing diffusion tensor imaging (DTI), structural magnetic resonance imaging (MRI), and functional MRI (fMRI) techniques, and to further identify the most robust neuroimaging features in predicting severe post-TBI attention deficits in children, by utilizing machine learning and deep learning techniques. A total of 53 children with TBI and 55 controls from age 9 to 17 are recruited. The results show that the systems-level topological properties in left frontal regions, parietal regions, and medial occipitotemporal regions in structural and functional brain network are significantly associated with inattentive and/or hyperactive/impulsive symptoms in children post-TBI. Semi-supervised deep learning modeling further confirms the significant contributions of these brain features in the prediction of elevated attention deficits in children post-TBI. The findings of this project provide valuable foundations for future research on developing neural markers for TBI-induced attention deficits in children, which may significantly assist the development of more effective and individualized diagnostic and treatment strategies

Neurobiological markers for remission and persistence of childhood attention-deficit/hyperactivity disorder

Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent neurodevelopmental disorders in children. Symptoms of childhood ADHD persist into adulthood in around 65% of patients, which elevates the risk for a number of adverse outcomes, resulting in substantial individual and societal burden. A neurodevelopmental double dissociation model is proposed based on existing studies in which the early onset of childhood ADHD is suggested to associate with dysfunctional subcortical structures that remain static throughout the lifetime; while diminution of symptoms over development could link to optimal development of prefrontal cortex. Current existing studies only assess basic measures including regional brain activation and connectivity, which have limited capacity to characterize the functional brain as a high performance parallel information processing system, the field lacks systems-level investigations of the structural and functional patterns that significantly contribute to the symptom remission and persistence in adults with childhood ADHD. Furthermore, traditional statistical methods estimate group differences only within a voxel or region of interest (ROI) at a time without having the capacity to explore how ROIs interact in linear and/or non-linear ways, as they quickly become overburdened when attempting to combine predictors and their interactions from high-dimensional imaging data set. This dissertation is the first study to apply ensemble learning techniques (ELT) in multimodal neuroimaging features from a sample of adults with childhood ADHD and controls, who have been clinically followed up since childhood. A total of 36 adult probands who were diagnosed with ADHD combined-type during childhood and 36 matched normal controls (NCs) are involved in this dissertation research. Thirty-six adult probands are further split into 18 remitters (ADHD-R) and 18 persisters (ADHD-P) based on the symptoms in their adulthood from DSM-IV ADHD criteria. Cued attention task-based fMRI, structural MRI, and diffusion tensor imaging data from each individual are analyzed. The high-dimensional neuroimaging features, including pair-wise regional connectivity and global/nodal topological properties of the functional brain network for cue-evoked attention process, regional cortical thickness and surface area, subcortical volume, volume and fractional anisotropy of major white matter fiber tract for each subject are calculated. In addition, all the currently available optimization strategies for ensemble learning techniques (i.e., voting, bagging, boosting and stacking techniques) are tested in a pool of semi-final classification results generated by seven basic classifiers, including K-Nearest Neighbors, support vector machine (SVM), logistic regression, Naïve Bayes, linear discriminant analysis, random forest, and multilayer perceptron. As hypothesized, results indicate that the features of nodal efficiency in right inferior frontal gyrus, right middle frontal (MFG)-inferior parietal (IPL) functional connectivity, and right amygdala volume significantly contributed to accurate discrimination between ADHD probands and controls; higher nodal efficiency of right MFG greatly contributed to inattentive and hyperactive/impulsive symptom remission, while higher right MFG-IPL functional connectivity strongly linked to symptom persistence in adults with childhood ADHD. The utilization of ELTs indicates that the bagging-based ELT with the base model of SVM achieves the best results, with the most significant improvement of the area under the receiver of operating characteristic curve (0.89 for ADHD probands vs. NCs, and 0.9 for ADHD-P vs. ADHD-R). The outcomes of this dissertation research have considerable value for the development of novel interventions that target mechanisms associated with recovery

THE VALIDITY OF SELF- VERSUS OTHER REPORTS OF ADHD SYMPTOMS IN COLLEGE STUDENTS: COGNITIVE AND ACADEMIC ACHIEVEMENT OUTCOMES

Despite the abundance of studies investigating Attention-Deficit Hyperactivity Disorder in children and adults, little research has focused on ADHD and comorbid learning disabilities (LD) in college students. The dearth of research in this population is becoming increasingly important given that ADHD and LD are the two most commonly reported and diagnosed disabilities in higher academic institutions (National Center for Education Statistics, 2011). As a result, clinicians\u27 are continually faced with the difficult task of determining sensitive and valid assessment measures to use with this population. While some overlap exists between college students and younger and older counterparts, research has shown that this subpopulation represents a distinct subgroup of young adults further complicating diagnostic decisions and, ultimately, subsequent accommodation and intervention recommendations based on assessment results (Frazier, Youngstrom, Glutting, & Watkins, 2007). Little is known about the degree of concordance between self- and other reports of ADHD symptoms, both of which are commonly used in diagnostic decisions. This study was designed to investigate the relationship between self- and other reports of childhood/current ADHD symptoms and neurocognitive and academic achievement performance. Data for this study is based on students at-risk for learning and/or attention disorders that sought a comprehensive psychological evaluation at the University of Georgia Regents\u27 Center for Learning Disorders. The sample (N = 347) was comprised of three groups: (1) ADHD; (2) LD; and, (3) ADHD+LD. Participants were classified into three groups based on the results of the evaluation process and clinical diagnoses. Assessments utilized in this study included criterion- and norm-referenced ADHD measures, academic achievement, IQ, verbal memory, working memory, and processing speed tests. Results suggest that the relationship between self- and other ratings is strongest within scales regardless of time (childhood, current) or type of informant (self, parent). ADHD behaviors, as rated by self- and other report, were weakly correlated with neurocognitive measures and moderately associated with academic achievement test. Measures most sensitive to group differences were academic achievement tests; by in large, neurocognitive tests did not differentiate groups. Implications for future research are discussed

The Efficacy of Biofeedback and Its Use Towards ADHD

Attention deficit hyperactivity disorder (ADHD) is a psychopathology commonly characterized by general inattentiveness and/or a lack of impulse control resulting in hyperactive tendencies. ADHD is estimated to cost the United States roughly $266 billion every year. ADHD is currently treated via medications, cognitive behavioral therapy, or more recently, neurofeedback. Neurofeedback – and biofeedback in general – is the process of providing a patient with information about autonomic bodily functions so that they may control said autonomic function. In the case of ADHD, neurofeedback focuses on reinforcing the behaviors and sensations associated with attentiveness. Currently however, neurofeedback systems are large and require a patient to travel to a clinic. Furthermore, the current offering of portable neuro/biofeedback devices do not have the technological capabilities to provide effective neurofeedback therapy. Current wearable tech devices – such as the Apple Watch and Samsung Gear – possess the technological capabilities to measure important bodily functions, and provide appropriate biofeedback therapy while remaining discrete and most importantly, portable

What do ADHD neuroimaging studies reveal for teachers, teacher educators and inclusive education?

Background: Ongoing debate about Attention Deficit Hyperactivity Disorder (ADHD) has not resolved ambivalent teacher beliefs about ADHD. This is an important matter since teachers’ beliefs influence their pedagogy, classroom management, and their referral procedures for formal diagnoses of ADHD. They therefore must be provided with up-to-date professional learning about ADHD. Objective: To synthesise neuroimaging studies, which examined differences in brain organisation and function in those with ADHD compared to matched unaffected controls. The overarching goal was to enhance teachers’ understanding of ADHD by providing synthesised research findings around the neurological basis of ADHD. Method: The PRISMA method was used to search the Medline, PsycINFO, Web of Science and Scopus databases to complete a systematic review of peer-reviewed research that compared individuals with ADHD with matched controls published between 2010 and December 2015. Results: The identification and analyses of 174 MRI and fMRI relevant studies across a sample of over 24,000 showed that there are significant differences in neural anatomy and processing in ADHD compared to unaffected matched controls. Conclusions: Compelling evidence shows ADHD is a neurodevelopmental disability, not a socially determined set of behaviours. Results point to an urgent need for teacher professional learning and systematic up-to-date preservice teacher education along with inclusive education policy reform

Insight into ADHD diagnosis with deep learning on Actimetry: Quantitative interpretation of occlusion maps in age and gender subgroups

Producción CientíficaAttention Deficit/Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder in childhood that often persists into adulthood. Objectively diagnosing ADHD can be challenging due to the reliance on subjective questionnaires in clinical assessment. Fortunately, recent advancements in artificial intelligence (AI) have shown promise in providing objective diagnoses through the analysis of medical images or activity recordings. These AI-based techniques have demonstrated accurate ADHD diagnosis; however, the growing complexity of deep learning models has introduced a lack of interpretability. These models often function as black boxes, unable to offer meaningful insights into the data patterns that characterize ADHD.Agencia Estatal de Investigación (grants PID2020-115339RB-I00, TED2021-130090B-I00 and TED2021-131536B-I00)EU Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie grant agreement (101008297)Company ESAOTE Ltd (grant 18IQBM