[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124418-en":3,"doc-seo-124418-105":30,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124418,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",7,"Healthcare","ADHD DIAGNOSIS BASED ON ACTION CHARACTERISTICS RECORDED IN VIDEOS USING MACHINE LEARNING","Rising demand for ADHD diagnosis and treatment is creating service capacity constraints and delays. This study introduces a video-based approach that captures action characteristics and applies machine learning to support ADHD diagnosis. The work focuses on translating behavioral signals from recorded video into learnable features, enabling group-level discrimination and providing a potential pathway to more timely and scalable diagnostic support. It aligns automated sensing with clinical decision needs while motivating further validation.","Abstracts Neuroscience Applied 2 (2023) 102439  \nReceptor System. Front Pharmacol. 13, 856672. [2] Leconte, C., Patricia Sales, A., Bergoin, E., Beray-Berthat, V., Noble, F., Mongeau, R. 2020. Traumatic-like fear memory recall causes persistent morphine seeking behavior in mice. European Neuropsychopharmacol. 40(1), S3-S4. [3] Daumas, S., Betourne, A., Halley, H., Wolfer, D.P., Lipp, H-P., Lassalle, J-M., Francs, B. 2007. Transient activation of the CA3 Kappa opioid system in the dorsal hippocampus modulates complex memory processing in mice. Neurobiol Learn Mem. 88(1), 94-103.  \nNo conflict of interest  \ndoi: [https://doi.org/10.1016/j.nsa.2023.102833](https://doi.org/10.1016/j.nsa.2023.102833)  \n[P.0026](P.0026)  \nNEUROSCIENCE APPLIED 2 (2023) 102439 102834  \nGROUP-BASED SURFACE STRUCTURAL COVARIANCE ANALYSIS OF ADHD IN THE ENIGMA DATASET  \nO. Grimm 1, Z. Mossawaty 2, E.A.G. Please change 3, B. Franke 4, M. Hoogman 5.  \n1 Goethe-University-Frankfurt, University Clinic- Departement of PsychiatryPsychotherapy and Psychosomatics, Frankfurt Am Main, Germany; 2 Goethe University, University Clinic- Departement Psychiatry- Psychosomatics and Psychotherapy, Frankfurt, Germany; 3 Please change, Please change, Please change, Germany; 4 Radboud University, Radboud Medical centre, Nijmegen, Netherlands;  \n5 Radboud University, Radboud University Medical Centre Nijmegen, Nijmegen, Netherlands  \nAttention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that has been linked to delayed brain maturation, particularly in early ADHD cases. Structural Magnetic Resonance Imaging (MRI) can provide an understanding of how cortical thickness and surface area behave in relation to ADHD diagnosis. However, developmental patterns are not easily understood using region-of-interest analyses. A recent study of the ENIGMA consortium demonstrated lower surface area in children [1]. The technique of structural covariance can be used to understand how different brain regions are related to each other in terms of morphological properties at the group level [2]. We conducted a cortical structural covariance analysis of the ADHD ENIGMA mega-analysis dataset, which consisted of over 3000 MRI datasets in both children and adults, with more than 1500 ADHD cases. To investigate the alterationsin the architecture of structural networks in LLD compared with controls, we applied graph theoretical methods using the GAT [3], which integrates the Brain Connectivity Toolbox [4] for the calculation and statistical comparisons of network measures. Specifically, networks were constructed for the ADHD and control group for both children as well as adults separately using the structural covariance approach). The nodes in the network correspond to the cortical FreeSurfer parcellations. Consistent with previous studies, linear regression was performed at each ROI to remove the effects ofcovariates, including age, site, and mean intracranial volume The resulting residuals of this regression are then substituted for the unadjusted cortical measures at each ROI. Comparison between correlation matrices from the parcellations were done for the ADHD and the control groups and comparison was done with a permutation-based statistic. The results showed that there was a higher cumulative degree distribution in ADHD compared to healthy controls (HC) in terms of surface-based structural covariance. At a network matrix density between 0.25 and 0.3, the characteristic path length was also higher in ADHD. When the researchers looked at regional hubs driving these effects, they found that the normalized degree was significantly higher in ADHD (p¼0.001), whereas the rostral Anterior Cingulate Cortex (ACC) was significantly better connected in HC (p¼0.036). These effects were not found in adults, and to a lesser degree in the analysis of cortical thickness covariance.  \nIn summary, the results suggest that surface structural covariance is higher in children below the age of 16 ","cbCaiq1D9bWjalh7","https://ap.wps.com/l/cbCaiq1D9bWjalh7","pdf",82667,1,2,"English","en",105,"# Introduction\n## Video-based action feature modeling\n## Machine learning for ADHD diagnosis","[{\"question\":\"为什么需要一种新的ADHD诊断方法？\",\"answer\":\"对ADHD诊断与治疗的需求显著增长，而现有服务难以及时满足需求，导致诊断延迟。\"},{\"question\":\"这项研究如何利用视频数据支持ADHD诊断？\",\"answer\":\"研究通过记录视频中的动作特征，将这些可观测的行为信息转化为可用于学习的特征输入。\"},{\"question\":\"机器学习在该方法中起什么作用？\",\"answer\":\"机器学习用于从视频动作特征中学习与ADHD相关的判别模式，从而实现对诊断支持的自动化。\"}]","ADHD DIAGNOSIS BASED ON ACTION CHARACTERISTICS RECORDED IN VIDEOS USING MACHINE LEARNING | PDF",1785822162,5,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":28},"adhd-diagnosis-based-on-action-characteristics-recorded-in-videos-using-machine-learning","",{"@graph":36,"@context":84},[37,53,67],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":21},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/healthcare/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/adhd-diagnosis-based-on-action-characteristics-recorded-in-videos-using-machine-learning/124418/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"为什么需要一种新的ADHD诊断方法？","Question",{"text":74,"@type":75},"对ADHD诊断与治疗的需求显著增长，而现有服务难以及时满足需求，导致诊断延迟。","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"这项研究如何利用视频数据支持ADHD诊断？",{"text":79,"@type":75},"研究通过记录视频中的动作特征，将这些可观测的行为信息转化为可用于学习的特征输入。",{"name":81,"@type":72,"acceptedAnswer":82},"机器学习在该方法中起什么作用？",{"text":83,"@type":75},"机器学习用于从视频动作特征中学习与ADHD相关的判别模式，从而实现对诊断支持的自动化。","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,108,113,116,121,126,129,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":105,"show_sort_weight":106,"slug":107},"Comic",60,"comic",{"id":109,"doc_module":4,"doc_module_name":46,"category_name":110,"show_sort_weight":111,"slug":112},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":114,"slug":115},40,"healthcare",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},8,"Research & Report",30,"research-report",{"id":122,"doc_module":4,"doc_module_name":46,"category_name":123,"show_sort_weight":124,"slug":125},9,"Religion & Spirituality",20,"religion-spirituality",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":124,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":29,"slug":136},19,"General","general"]