Natural visibility graphs for diagnosing attention deficit hyperactivity disorder (ADHD)

“NOTICE: this is the author’s version of a work that was accepted for publication in Electronic Notes in Discrete Mathematics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Electronic Notes in Discrete Mathematics, [Volume 54, October 2016, Pages 337-342] DOI 10.1016/j.endm.2016.09.058 ¨Reaction times are described as a measure of perception, decision making, and other cognitive processes. For each individual, they usually follow an ex-gaussian distribution. However, this approach omits relationships between consecutive answers to tasks geared to evaluate attention. We show how natural visibility graphs (NVG s) can provide a further insight for analyzing these times and in the prediction of attention deficit hyperactivity disorder (ADHD) among young students.Mira-Iglesias, A.; Conejero, JA.; Navarro-Pardo, E. (2016). Natural visibility graphs for diagnosing attention deficit hyperactivity disorder (ADHD). Electronic Notes in Discrete Mathematics. 54:337-342. doi:10.1016/j.endm.2016.09.058S3373425

The Rise of Autism

"This innovative book addresses the question of why increasing numbers of people are being diagnosed with autism since the 1990s. Providing an engaging account of competing and widely debated explanations, it investigates how these have led to differing interpretations of the same data. Crucially, the author argues that the increased use of autism diagnosis is due to medicalisation across the life course, whilst holding open the possibility that the rise may also be partly accounted for by modern-day environmental exposures, again, across the life course. A further focus of the book is not on whether autism itself is valid as a diagnostic category, but whether and how it is useful as a diagnostic category, and how the utility of the diagnosis has contributed to the rise. This serves to move beyond the question of whether diagnoses are 'real' or social constructions, and instead asks: who do diagnoses serve to benefit, and at what cost do they come? The book will appeal to clinicians and health professionals, as well as medical researchers, who are interested in a review of the data which demonstrates the rising use of autism as a diagnosis, and an analysis of the reasons why this has occurred. Providing theory through which to interpret the expanding application of the diagnosis and the broadening of autism as a concept, it will also be of interest to scholars and students of sociology, philosophy, psychiatry, psychology, social work, disability studies and childhood studies.

Scalable Machine Learning Methods for Massive Biomedical Data Analysis.

Modern data acquisition techniques have enabled biomedical researchers to collect and analyze datasets of substantial size and complexity. The massive size of these datasets allows us to comprehensively study the biological system of interest at an unprecedented level of detail, which may lead to the discovery of clinically relevant biomarkers. Nonetheless, the dimensionality of these datasets presents critical computational and statistical challenges, as traditional statistical methods break down when the number of predictors dominates the number of observations, a setting frequently encountered in biomedical data analysis. This difficulty is compounded by the fact that biological data tend to be noisy and often possess complex correlation patterns among the predictors. The central goal of this dissertation is to develop a computationally tractable machine learning framework that allows us to extract scientifically meaningful information from these massive and highly complex biomedical datasets. We motivate the scope of our study by considering two important problems with clinical relevance: (1) uncertainty analysis for biomedical image registration, and (2) psychiatric disease prediction based on functional connectomes, which are high dimensional correlation maps generated from resting state functional MRI.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111354/1/takanori_1.pd

Makeover nation: the United States of reinvention

(print) vii, 209 p. : ill. ; 23 cmThe psy-function : making over minds -- Ritalin : making over youth -- Metrosexuality : making over menItem embargoed for five year

Advances in Autism Research

This book represents one of the most up-to-date collections of articles on clinical practice and research in the field of Autism Spectrum Disorders (ASD). The scholars who contributed to this book are experts in their field, carrying out cutting edge research in prestigious institutes worldwide (e.g., Harvard Medical School, University of California, MIND Institute, King’s College, Karolinska Institute, and many others). The book addressed many topics, including (1) The COVID-19 pandemic; (2) Epidemiology and prevalence; (3) Screening and early behavioral markers; (4) Diagnostic and phenotypic profile; (5) Treatment and intervention; (6) Etiopathogenesis (biomarkers, biology, and genetic, epigenetic, and risk factors); (7) Comorbidity; (8) Adulthood; and (9) Broader Autism Phenotype (BAP). This book testifies to the complexity of performing research in the field of ASD. The published contributions underline areas of progress and ongoing challenges in which more certain data is expected in the coming years. It would be desirable that experts, clinicians, researchers, and trainees could have the opportunity to read this updated text describing the challenging heterogeneity of Autism Spectrum Disorder