Inhibition and attention deficit hyperactivity disorder.

J Abnorm Child Psychol

University of Miami, Coral Gables, Florida 33124, USA.

Published: February 1997

This paper updates the author's earlier hypothesis that Attention Deficit Hyperactivity Disorder (ADHD) reflects underactivity in Gray's Behavioral Inhibition System. Five areas of research are reviewed: (1) studies using the stop-signal task, (2) studies of errors of commission, (3) a study of inhibition indexed by eye movements, (4) a neuroimaging study of the corpus callosum, and (5) a study on the prediction of response to methylphenidate. Data from the many different dependent variables in these studies are interpreted as supporting disinhibition as a core deficit in ADHD.

Download full-text PDF

Source
http://dx.doi.org/10.1023/a:1025799122529DOI Listing

Publication Analysis

Top Keywords

attention deficit
8
deficit hyperactivity
8
hyperactivity disorder
8
inhibition attention
4
disorder paper
4
paper updates
4
updates author's
4
author's earlier
4
earlier hypothesis
4
hypothesis attention
4

Similar Publications

Biomarkers and Social Determinants in atherosclerotic Arterial Diseases: A Scoping Review.

Ann Vasc Surg

January 2025

Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100, Catanzaro, Italy; Interuniversity Center of Phlebolymphology (CIFL), "Magna Graecia" University, 88100 Catanzaro, Italy. Electronic address:

Background: Arterial diseases like coronary artery disease, carotid stenosis, peripheral artery disease, and abdominal aortic aneurysm have high morbidity and mortality, making them key research areas. Their multifactorial nature complicates patient treatment and prevention. Biomarkers offer insights into the biochemical and molecular processes, while social factors also significantly impact patients' health and quality of life.

View Article and Find Full Text PDF

Introduction: Diagnostic evaluations for attention-deficit/hyperactivity disorder (ADHD) are becoming increasingly complicated by the number of adults who fabricate or exaggerate symptoms. Novel methods are needed to improve the assessment process required to detect these noncredible symptoms. The present study investigated whether unsupervised machine learning (ML) could serve as one such method, and detect noncredible symptom reporting in adults undergoing ADHD evaluations.

View Article and Find Full Text PDF

Purpose: Attention-deficit hyperactivity disorder (ADHD) is a common neurodevelopmental condition that affects approximately 5% of the pediatric population, with increased prevalence among those with type 1 diabetes (T1D). Reports suggest that unrecognized and untreated ADHD impairs T1D control and that ADHD may be underdiagnosed in the Polish population. The International Society for Pediatric and Adolescent Diabetes recommends neurodevelopmental assessments in children with T1D, but specific guidelines on procedures and implementation are lacking.

View Article and Find Full Text PDF

Background/objectives: Although ADHD in adults has become visible and inclusive in recent years in diagnostic manuals, research is still limited regarding the long-term outcomes of patients with this disorder. The main objective of this research was to address the many facets of predictor variables in girls with ADHD facing unplanned pregnancies at young ages in order to improve the management of pre-, peri-, and postnatal complications that may occur, as well as for early psychiatric diagnosis and effective intervention.

Methods: PubMed and Web of Science Databases were used to perform literature research, and a total of 27 records were selected and used for data extraction.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!