Pharmacotherapeutic challenges in treatment of a child with "the triad" of obsessive compulsive disorder, attention-deficit/hyperactivity disorder and Tourette's disorder

Scoping Review of Multidisciplinary Care in Tourette Syndrome

Objective

To review current multidisciplinary care practices in patients with Tourette syndrome (TS).

Background

Individuals with TS can have multiple symptoms and comorbidities and require treatment to encompass all of their needs. A multidisciplinary research or care model approaches the situation/problem from all sides and uses multiple perspectives.

Methods

A database search of Medline (using Pubmed), PsychINFO, and Scopus was performed using keywords related to multidisciplinary care and TS. The authors then screened the results for relevant information using a standardized extraction form to collect data. Next, relevant codes from text analysis were extracted with a final list agreed on with author consensus. Finally, we inferred common themes.

Results

The search revealed 2304 citations, and 87 were selected for full-text analysis. One additional article was identified by manual search. Thirty-one citations were deemed relevant. Multidisciplinary team members typically included a psychiatrist or child psychiatrist, a neurologist or child neurologist, and a psychologist or therapist at the core. Four primary benefits were associated with multidisciplinary care: establishing the diagnosis, managing the complexity of TS and its associated comorbidities, averting complications, and evaluating advanced therapies. Limitations include possible poor team dynamics and rigidity in the approach leading to an algorithmic treatment plan.

Conclusions

A multidisciplinary care model for TS is the preferred model advocated by patients, physicians, and organizations. This scoping review reveals that the impetus for multidisciplinary care rests on four primary benefits, but there is a lack of empirical evidence for defining and evaluating its use.

Neurodevelopmental heterogeneity and computational approaches for understanding autism

In recent years, the emerging field of computational psychiatry has impelled the use of machine learning models as a means to further understand the pathogenesis of multiple clinical disorders. In this paper, we discuss how autism spectrum disorder (ASD) was and continues to be diagnosed in the context of its complex neurodevelopmental heterogeneity. We review machine learning approaches to streamline ASD's diagnostic methods, to discern similarities and differences from comorbid diagnoses, and to follow developmentally variable outcomes. Both supervised machine learning models for classification outcome and unsupervised approaches to identify new dimensions and subgroups are discussed. We provide an illustrative example of how computational analytic methods and a longitudinal design can improve our inferential ability to detect early dysfunctional behaviors that may or may not reach threshold levels for formal diagnoses. Specifically, an unsupervised machine learning approach of anomaly detection is used to illustrate how community samples may be utilized to investigate early autism risk, multidimensional features, and outcome variables. Because ASD symptoms and challenges are not static within individuals across development, computational approaches present a promising method to elucidate subgroups of etiological contributions to phenotype, alternative developmental courses, interactions with biomedical comorbidities, and to predict potential responses to therapeutic interventions.