OBJECTIVE: To date, the affective temperamental characteristics of adults with attention-deficit hyperactivity disorder (ADHD) have not been studied. The aim of this study is to explore those temperamental characteristics for adults diagnosed with ADHD as measured by the TEMPS-A and then to compare those results with results for individuals diagnosed with bipolar disorder (BD) and with healthy controls. METHODS: Forty adults with ADHD, 40 patients with BD, and 40 healthy controls were enrolled in this study. The groups were matched by age and gender. All patients were assessed using the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID I), the Hamilton Depression Rating Scale, the Hamilton Anxiety Rating Scale, the Young Mania Rating Scale and the Wender Utah Rating Scale. Subjects' temperamental characteristics were examined using the Turkish version of the Temperament Evaluation of Memphis, Pisa, Paris and San Diego-auto questionnaire (TEMPS-A). RESULTS: Ten subjects (25%) in the ADHD group and 15 subjects (30%) in the bipolar group had at least one dominant temperament. There was no identifiable dominant temperament in the control group. Compared to the control group, the ADHD group scored higher than other groups on all domains of the TEMPS-A: depressive cyclothymic, irritable and anxious. However, the hyperthymic domain was not higher for this group. Adults with ADHD scored higher on the irritable temperament scale as compared to the BD group. The ADHD and BD groups had similar mean scores for each of the other four temperaments. CONCLUSION: The adults diagnosed with ADHD in this study had different temperamental characteristics from the control group, and these temperamental characteristics were similar to those of the bipolar patients. Recognizing the role of temperamental characteristics in adults with ADHD may increase our understanding of ADHD.
Adult ; Anxiety ; Attention Deficit Disorder with Hyperactivity ; Axis, Cervical Vertebra ; Bipolar Disorder ; Depression ; Diagnostic and Statistical Manual of Mental Disorders ; Humans ; Paris ; Surveys and Questionnaires ; Temperament ; Utah

Deep learning (DL) algorithms have achieved important successes in data analysis tasks, thanks to their capability of revealing complex patterns in data. With the advance of new sensors, data storage, and processing hardware, DL algorithms start dominating various fields including neuropsychiatry. There are many types of DL algorithms for different data types from survey data to functional magnetic resonance imaging scans. Because of limitations in diagnosing, estimating prognosis and treatment response of neuropsychiatric disorders; DL algorithms are becoming promising approaches. In this review, we aim to summarize the most common DL algorithms and their applications in neuropsychiatry and also provide an overview to guide the researchers in choosing the proper DL architecture for their research.

Deep learning (DL) algorithms have achieved important successes in data analysis tasks, thanks to their capability of revealing complex patterns in data. With the advance of new sensors, data storage, and processing hardware, DL algorithms start dominating various fields including neuropsychiatry. There are many types of DL algorithms for different data types from survey data to functional magnetic resonance imaging scans. Because of limitations in diagnosing, estimating prognosis and treatment response of neuropsychiatric disorders; DL algorithms are becoming promising approaches. In this review, we aim to summarize the most common DL algorithms and their applications in neuropsychiatry and also provide an overview to guide the researchers in choosing the proper DL architecture for their research.