Inviting an author to review:
Find an author and click ‘Invite to review selected article’ near their name.
Search for authorsSearch for similar articles
3
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Advances in Clustering and Classification of Tic Disorders: A Systematic Review

      review-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Purpose

          Tic disorders (TD) are common neurodevelopmental disorders characterized by heterogeneous tic symptoms in children, making diagnostic classification difficult. This complexity requires accurate subtyping using data-driven computational methods to identify patterns within clinical data. This systematic review primarily summarizes the current evidence for the classification of TD using a data-driven approach.

          Patients and Methods

          We conducted a systematic literature search on PubMed and Web of Science up to December 2023 and identified 16 publications analyzing 14 unique samples, totaling approximately 6000 subjects.

          Results

          Nine studies classified different subtypes of TD based on symptoms and behavior. Seven studies identified novel factor structures based on TD and its complex comorbidity patterns. Seven studies highlighted associations between TD symptom patterns and genetics, reflecting the diversity of underlying genetic mechanisms underlying TD.

          Conclusion

          This systematic review reveals significant variability in research on the classification of TD, which limits the application of findings for accurate diagnosis and guiding treatment strategies in pediatric psychiatry. Further research incorporating multidimensional information (such as genetic, neuroimaging, and environmental and social factors) is essential to improve the understanding of TD subtypes.

          Related collections

          Most cited references64

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          The PRISMA 2020 statement: an updated guideline for reporting systematic reviews

          The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions

            Non-randomised studies of the effects of interventions are critical to many areas of healthcare evaluation, but their results may be biased. It is therefore important to understand and appraise their strengths and weaknesses. We developed ROBINS-I (“Risk Of Bias In Non-randomised Studies - of Interventions”), a new tool for evaluating risk of bias in estimates of the comparative effectiveness (harm or benefit) of interventions from studies that did not use randomisation to allocate units (individuals or clusters of individuals) to comparison groups. The tool will be particularly useful to those undertaking systematic reviews that include non-randomised studies.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review.

              Standard univariate analysis of neuroimaging data has revealed a host of neuroanatomical and functional differences between healthy individuals and patients suffering a wide range of neurological and psychiatric disorders. Significant only at group level however these findings have had limited clinical translation, and recent attention has turned toward alternative forms of analysis, including Support-Vector-Machine (SVM). A type of machine learning, SVM allows categorisation of an individual's previously unseen data into a predefined group using a classification algorithm, developed on a training data set. In recent years, SVM has been successfully applied in the context of disease diagnosis, transition prediction and treatment prognosis, using both structural and functional neuroimaging data. Here we provide a brief overview of the method and review those studies that applied it to the investigation of Alzheimer's disease, schizophrenia, major depression, bipolar disorder, presymptomatic Huntington's disease, Parkinson's disease and autistic spectrum disorder. We conclude by discussing the main theoretical and practical challenges associated with the implementation of this method into the clinic and possible future directions. Copyright © 2012 Elsevier Ltd. All rights reserved.
                Bookmark

                Author and article information

                Journal
                Neuropsychiatr Dis Treat
                Neuropsychiatr Dis Treat
                ndt
                Neuropsychiatric Disease and Treatment
                Dove
                1176-6328
                1178-2021
                30 December 2024
                2024
                : 20
                : 2663-2677
                Affiliations
                [1 ]Department of Psychiatry, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health , Beijing, 100045, People’s Republic of China
                [2 ]Laboratory for Clinical Medicine, Capital Medical University , Beijing, 100045, People’s Republic of China
                Author notes
                Correspondence: Tianyuan Lei; Yonghua Cui, Email tianyuanlei@bch.com.cn; cuiyonghua@bch.com.cn
                Article
                499080
                10.2147/NDT.S499080
                11697672
                39758558
                43297b28-8773-4bf4-b1c7-22386dca4f2d
                © 2024 Yang et al.

                This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License ( http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms ( https://www.dovepress.com/terms.php).

                History
                : 03 October 2024
                : 08 December 2024
                Page count
                Figures: 1, Tables: 2, References: 64, Pages: 15
                Funding
                Funded by: National Natural Science Foundation of China, open-funder-registry 10.13039/501100001809;
                Funded by: Beijing High level Public Health Technology Talent Construction;
                Funded by: Beijing Science and Technology Commission;
                The study was supported by the grant from National Natural Science Foundation of China (NSFC) under Grant No. 82171538, the Beijing High level Public Health Technology Talent Construction Project No. 2022-2-007, Joint Basic-Clinical Laboratory of Pediatric Epilepsy and Cognitive Developmental, 3-1-013-03 and Beijing Science and Technology Commission: AI+ Health Collaborative Innovation Cultivation, No. Z221100003522017.
                Categories
                Review

                Neurology
                tic disorders,tourette syndrome,subtype classification,cluster analysis
                Neurology
                tic disorders, tourette syndrome, subtype classification, cluster analysis

                Comments

                Comment on this article