Theory of Mind: Development, Neurobiology, Related Fields and Neurodevelopmental Disorders

Theory of mind is a social cognition skills demonstrated its importance in the last forty years with psychiatric clinical trials. Theory of mind is seen as an effective and necessary skill in the social function-ing of human who is a social creature as the ability to recognize the mental states and emotions of others. In the first six years of life, theory of mind has been associated with many fields. Findings related to many neurobiological bases, such as limbic-paralimbic structures, prefrontal cortex, which start with mirror neurons, help this sense of meaning. Areas associated with theory of mind development provide better understanding of theory of mind skills and deficits, the first psychopathology studies have been carried out in children with autism, and the studies about theory of mind skills in the diagnosis of neurodevelopmental disorders are becoming more and more interesting. In this review, theory of mind development, neurobiological basis and related areas will be explained and the relation of theory of mind with psychopathology will be examined

Child and adolescent psychiatry training in Europe: differences and challenges in harmonization

Objective To investigate the current situation of Child and Adolescent Psychiatry (CAP) training in European countries. In addition, current agenda of different organizations in order to harmonize the training across Europe are reported. Method In order to collect data for this descriptive documentation on CAP training in European countries, we have communicated with "European Union of Medical Specialists Section on Child and Adolescent Psychiatry" (UEMS-CAP) and "The European Federation of Psychiatric Trainees" (EFPT) representatives of each country via e-mail. In addition, we used UEMS and EFPT annual forum minutes and web sites of national CAP societies to validate the data. Result The structure of CAP training has many differences between 34 European countries. For instance, in 32.4% of the countries, CAP is not a specialty in its own right but is mostly linked to general psychiatry. After medical school, the minimum training duration to become a CAP specialist ranges between 12 and 96 (mean: 59.71 +/- 17.1) months. While, a trainee should pass an examination to begin CAP training in 37.9% of the countries, 64.7% have an examination to graduate and both is the case in 29.7% of the countries. Conclusion By the year 2006, European countries still have large differences in the structure of CAP training. It is assumed that the same holds true for content of training, which was not the focus of this documentation. UEMS-CAP, ESCAP (European Society of Child and Adolescent Psychiatry) and EFPT are the major bodies that have to manage the harmonization of CAP training across Europe. The obligatory conditions of a fruitful training, the high quality of teachers and teaching facilities, essential ingredients of an efficient training programme, are prone to be determined by evidence-based evaluations in the process of harmonization

Psychiatric comorbidity distribution and diversities in children and adolescents with attention deficit/hyperactivity disorder: a study from Turkey

Objective: We aimed to determine distribution and diversities of psychiatric comorbidities in children and adolescents with attention deficit/hyperactivity disorder (ADHD) in terms of age groups, sex, and ADHD subtype

Use of machine learning methods in prediction of short-term outcome in autism spectrum disorders

OBJECTIVE Studies show partial improvements in some core symptoms of Autism Spectrum Disorders (ASD) in time. However, the predictive factors (e.g. pretreatment IQ, comorbid psychiatric disorders, adaptive, and language skills, etc.) for a better the outcome was not studied with machine learning methods. We aimed to examine the predictors of outcome with machine learning methods, which are novel computational methods including statistical estimation, information theories and mathematical learning automatically discovering useful patterns in large amounts of data. METHOD The study the group comprised 433 children (mean age: 72.3 ± 45.9 months) with ASD diagnosis. The ASD symptoms were assessed by the Autism Behavior Checklist, Aberrant Behavior Checklist, Clinical Global Impression scales at baseline (T0) and 12th (T1), 24th (T2), and 36th (T3) months. We tested the performance of for machine learning algorithms (Naive Bayes, Generalized Linear Model, Logistic Regression, Decision Tree) on our data, including the 254 items in the baseline forms. Patients with ≤2 CGI points in ASD symptoms at in 36 months were accepted as the group who has “better outcome” as the prediction class. RESULTS The significant proportion of the cases showed significant improvement in ASD symptoms (39.7% in T1, 60.7% in T2; 77.8% in T3). Our machine learning model in T3 showed that diagnosis group affected the prognosis. In the autism group, older father and mother age; in PDD-NOS group, MR comorbidity, less birth weight and older age at diagnosis have a worse outcome. In Asperger’s Disorder age at diagnosis, age at first evaluation and developmental cornerstones has affected prognosis. CONCLUSION In accordance with other studies we found early age diagnosis, early start rehabilitation, the severity of ASD symptoms at baseline assessment predicted outcome. Also, we found comorbid psychiatric diagnoses are affecting the outcome of ASD symptoms in clinical observation. The machine learning models reveal several others are more significant (e.g. parental age, birth weight, sociodemographic variables, etc.) in terms of prognostic information and also planning treatment of children with ASD