Jeggan Tiego, Antonio Verdejo-Garcia, Alexandra Anderson, Julia Koutoulogenis, Mark A. Bellgrove
{"title":"认知去抑制机制解释了成人注意力缺陷多动障碍特征的个体差异。","authors":"Jeggan Tiego, Antonio Verdejo-Garcia, Alexandra Anderson, Julia Koutoulogenis, Mark A. Bellgrove","doi":"10.1016/j.cortex.2023.06.013","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><p>Attention deficit hyperactivity disorder (ADHD) in adults is strongly associated with psychiatric comorbidity and functional impairment. Here, we aimed to use a newly developed online cognitive battery with strong psychometric properties for measuring individual differences in three cognitive mechanisms proposed to underlie ADHD traits in adults: 1) attentional control – the ability to mobilize cognitive resources to stop a prepotent motor response; 2) information sampling/gathering – adequate sampling of information in a stimulus detection task prior to making a decision; and 3) shifting - the ability to adapt behavior in response to positive and negative contingencies.</p></div><div><h3>Methods</h3><p>This cross-sectional and correlational study recruited 650 adults (330 males) aged 18–69 years (<em>M</em> = 33.06; <em>MD</em> = 31.00; <em>SD</em> = 10.50), with previously diagnosed ADHD (<em>n</em> = 329) and those from the general community without a history of ADHD (<em>n</em> = 321). Self-report measures of ADHD traits (i.e., inattention/disorganization, impulsivity, hyperactivity) and the cognitive battery were completed online.</p></div><div><h3>Results</h3><p>Latent class analysis, exploratory structural equation modeling and factor mixture modeling revealed self-reported ADHD traits formed a unidimensional and approximately normally distributed phenotype. Bayesian structural equation modeling demonstrated that all three mechanisms measured by the cognitive battery, explained unique, incremental variance in ADHD traits, with a total of 15.9% explained in the ADHD trait factor.</p></div><div><h3>Conclusions</h3><p>Attentional control and shifting, as well as the less researched cognitive process of information gathering, explain individual difference variance in self-reported ADHD traits with potential to yield genetic and neurobiological insights into adult ADHD.</p></div>","PeriodicalId":10758,"journal":{"name":"Cortex","volume":"167 ","pages":"Pages 178-196"},"PeriodicalIF":3.2000,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Mechanisms of cognitive disinhibition explain individual differences in adult attention deficit hyperactivity disorder traits\",\"authors\":\"Jeggan Tiego, Antonio Verdejo-Garcia, Alexandra Anderson, Julia Koutoulogenis, Mark A. Bellgrove\",\"doi\":\"10.1016/j.cortex.2023.06.013\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><p>Attention deficit hyperactivity disorder (ADHD) in adults is strongly associated with psychiatric comorbidity and functional impairment. Here, we aimed to use a newly developed online cognitive battery with strong psychometric properties for measuring individual differences in three cognitive mechanisms proposed to underlie ADHD traits in adults: 1) attentional control – the ability to mobilize cognitive resources to stop a prepotent motor response; 2) information sampling/gathering – adequate sampling of information in a stimulus detection task prior to making a decision; and 3) shifting - the ability to adapt behavior in response to positive and negative contingencies.</p></div><div><h3>Methods</h3><p>This cross-sectional and correlational study recruited 650 adults (330 males) aged 18–69 years (<em>M</em> = 33.06; <em>MD</em> = 31.00; <em>SD</em> = 10.50), with previously diagnosed ADHD (<em>n</em> = 329) and those from the general community without a history of ADHD (<em>n</em> = 321). Self-report measures of ADHD traits (i.e., inattention/disorganization, impulsivity, hyperactivity) and the cognitive battery were completed online.</p></div><div><h3>Results</h3><p>Latent class analysis, exploratory structural equation modeling and factor mixture modeling revealed self-reported ADHD traits formed a unidimensional and approximately normally distributed phenotype. Bayesian structural equation modeling demonstrated that all three mechanisms measured by the cognitive battery, explained unique, incremental variance in ADHD traits, with a total of 15.9% explained in the ADHD trait factor.</p></div><div><h3>Conclusions</h3><p>Attentional control and shifting, as well as the less researched cognitive process of information gathering, explain individual difference variance in self-reported ADHD traits with potential to yield genetic and neurobiological insights into adult ADHD.</p></div>\",\"PeriodicalId\":10758,\"journal\":{\"name\":\"Cortex\",\"volume\":\"167 \",\"pages\":\"Pages 178-196\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2023-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cortex\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0010945223001739\",\"RegionNum\":2,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BEHAVIORAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cortex","FirstCategoryId":"102","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0010945223001739","RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BEHAVIORAL SCIENCES","Score":null,"Total":0}
Mechanisms of cognitive disinhibition explain individual differences in adult attention deficit hyperactivity disorder traits
Background
Attention deficit hyperactivity disorder (ADHD) in adults is strongly associated with psychiatric comorbidity and functional impairment. Here, we aimed to use a newly developed online cognitive battery with strong psychometric properties for measuring individual differences in three cognitive mechanisms proposed to underlie ADHD traits in adults: 1) attentional control – the ability to mobilize cognitive resources to stop a prepotent motor response; 2) information sampling/gathering – adequate sampling of information in a stimulus detection task prior to making a decision; and 3) shifting - the ability to adapt behavior in response to positive and negative contingencies.
Methods
This cross-sectional and correlational study recruited 650 adults (330 males) aged 18–69 years (M = 33.06; MD = 31.00; SD = 10.50), with previously diagnosed ADHD (n = 329) and those from the general community without a history of ADHD (n = 321). Self-report measures of ADHD traits (i.e., inattention/disorganization, impulsivity, hyperactivity) and the cognitive battery were completed online.
Results
Latent class analysis, exploratory structural equation modeling and factor mixture modeling revealed self-reported ADHD traits formed a unidimensional and approximately normally distributed phenotype. Bayesian structural equation modeling demonstrated that all three mechanisms measured by the cognitive battery, explained unique, incremental variance in ADHD traits, with a total of 15.9% explained in the ADHD trait factor.
Conclusions
Attentional control and shifting, as well as the less researched cognitive process of information gathering, explain individual difference variance in self-reported ADHD traits with potential to yield genetic and neurobiological insights into adult ADHD.
期刊介绍:
CORTEX is an international journal devoted to the study of cognition and of the relationship between the nervous system and mental processes, particularly as these are reflected in the behaviour of patients with acquired brain lesions, normal volunteers, children with typical and atypical development, and in the activation of brain regions and systems as recorded by functional neuroimaging techniques. It was founded in 1964 by Ennio De Renzi.