Sihyong J Kim, Onur Tanglay, Elizabeth H N Chong, Isabella M Young, Rannulu D Fonseka, Hugh Taylor, Peter Nicholas, Stephane Doyen, Michael E Sughrue
{"title":"多动症儿童在完成 Go/No-Go 任务时的功能连接:一项 fMRI 系统回顾和荟萃分析。","authors":"Sihyong J Kim, Onur Tanglay, Elizabeth H N Chong, Isabella M Young, Rannulu D Fonseka, Hugh Taylor, Peter Nicholas, Stephane Doyen, Michael E Sughrue","doi":"10.1515/tnsci-2022-0299","DOIUrl":null,"url":null,"abstract":"<p><p>Attention deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders diagnosed in childhood. Two common features of ADHD are impaired behavioural inhibition and sustained attention. The Go/No-Go experimental paradigm with concurrent functional magnetic resonance imaging (fMRI) scanning has previously revealed important neurobiological correlates of ADHD such as the supplementary motor area and the prefrontal cortex. The coordinate-based meta-analysis combined with quantitative techniques, such as activation likelihood estimate (ALE) generation, provides an unbiased and objective method of summarising these data to understand the brain network architecture and connectivity in ADHD children. Go/No-Go task-based fMRI studies involving children and adolescent subjects were selected. Coordinates indicating foci of activation were collected to generate ALEs using threshold values (voxel-level: <i>p</i> < 0.001; cluster-level: <i>p</i> < 0.05). ALEs were matched to one of seven canonical brain networks based on the cortical parcellation scheme derived from the Human Connectome Project. Fourteen studies involving 457 children met the eligibility criteria. No significant convergence of Go/No-Go related brain activation was found for ADHD groups. Three significant ALE clusters were detected for brain activation relating to controls or ADHD < controls. Significant clusters were related to specific areas of the default mode network (DMN). Network-based analysis revealed less extensive DMN, dorsal attention network, and limbic network activation in ADHD children compared to controls. The presence of significant ALE clusters may be due to reduced homogeneity in the selected sample demographic and experimental paradigm. Further investigations regarding hemispheric asymmetry in ADHD subjects would be beneficial.</p>","PeriodicalId":23227,"journal":{"name":"Translational Neuroscience","volume":"14 1","pages":"20220299"},"PeriodicalIF":1.8000,"publicationDate":"2023-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10896184/pdf/","citationCount":"0","resultStr":"{\"title\":\"Functional connectivity in ADHD children doing Go/No-Go tasks: An fMRI systematic review and meta-analysis.\",\"authors\":\"Sihyong J Kim, Onur Tanglay, Elizabeth H N Chong, Isabella M Young, Rannulu D Fonseka, Hugh Taylor, Peter Nicholas, Stephane Doyen, Michael E Sughrue\",\"doi\":\"10.1515/tnsci-2022-0299\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Attention deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders diagnosed in childhood. Two common features of ADHD are impaired behavioural inhibition and sustained attention. The Go/No-Go experimental paradigm with concurrent functional magnetic resonance imaging (fMRI) scanning has previously revealed important neurobiological correlates of ADHD such as the supplementary motor area and the prefrontal cortex. The coordinate-based meta-analysis combined with quantitative techniques, such as activation likelihood estimate (ALE) generation, provides an unbiased and objective method of summarising these data to understand the brain network architecture and connectivity in ADHD children. Go/No-Go task-based fMRI studies involving children and adolescent subjects were selected. Coordinates indicating foci of activation were collected to generate ALEs using threshold values (voxel-level: <i>p</i> < 0.001; cluster-level: <i>p</i> < 0.05). ALEs were matched to one of seven canonical brain networks based on the cortical parcellation scheme derived from the Human Connectome Project. Fourteen studies involving 457 children met the eligibility criteria. No significant convergence of Go/No-Go related brain activation was found for ADHD groups. Three significant ALE clusters were detected for brain activation relating to controls or ADHD < controls. Significant clusters were related to specific areas of the default mode network (DMN). Network-based analysis revealed less extensive DMN, dorsal attention network, and limbic network activation in ADHD children compared to controls. The presence of significant ALE clusters may be due to reduced homogeneity in the selected sample demographic and experimental paradigm. Further investigations regarding hemispheric asymmetry in ADHD subjects would be beneficial.</p>\",\"PeriodicalId\":23227,\"journal\":{\"name\":\"Translational Neuroscience\",\"volume\":\"14 1\",\"pages\":\"20220299\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2023-12-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10896184/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Translational Neuroscience\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1515/tnsci-2022-0299\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q4\",\"JCRName\":\"NEUROSCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Translational Neuroscience","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1515/tnsci-2022-0299","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/1/1 0:00:00","PubModel":"eCollection","JCR":"Q4","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
Functional connectivity in ADHD children doing Go/No-Go tasks: An fMRI systematic review and meta-analysis.
Attention deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders diagnosed in childhood. Two common features of ADHD are impaired behavioural inhibition and sustained attention. The Go/No-Go experimental paradigm with concurrent functional magnetic resonance imaging (fMRI) scanning has previously revealed important neurobiological correlates of ADHD such as the supplementary motor area and the prefrontal cortex. The coordinate-based meta-analysis combined with quantitative techniques, such as activation likelihood estimate (ALE) generation, provides an unbiased and objective method of summarising these data to understand the brain network architecture and connectivity in ADHD children. Go/No-Go task-based fMRI studies involving children and adolescent subjects were selected. Coordinates indicating foci of activation were collected to generate ALEs using threshold values (voxel-level: p < 0.001; cluster-level: p < 0.05). ALEs were matched to one of seven canonical brain networks based on the cortical parcellation scheme derived from the Human Connectome Project. Fourteen studies involving 457 children met the eligibility criteria. No significant convergence of Go/No-Go related brain activation was found for ADHD groups. Three significant ALE clusters were detected for brain activation relating to controls or ADHD < controls. Significant clusters were related to specific areas of the default mode network (DMN). Network-based analysis revealed less extensive DMN, dorsal attention network, and limbic network activation in ADHD children compared to controls. The presence of significant ALE clusters may be due to reduced homogeneity in the selected sample demographic and experimental paradigm. Further investigations regarding hemispheric asymmetry in ADHD subjects would be beneficial.
期刊介绍:
Translational Neuroscience provides a closer interaction between basic and clinical neuroscientists to expand understanding of brain structure, function and disease, and translate this knowledge into clinical applications and novel therapies of nervous system disorders.