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Empirically supported interventions in psychological disorders should provide (1) evidence supporting the underlying psychological mechanisms of psychopathology to target in the intervention and (2) evidence supporting the efficacy of the intervention. However, research has been dedicated in a greater extent to efficacy than to the acquisition of empirical support for the theoretical basis of therapies. Research Domain Criteria (RDoC) emerges as a new framework to provide empirically based theories about psychological mechanisms that may be targeted in intervention and tested for its efficacy. The current review aims to demonstrate the possible applications of RDoC to design empirically supported interventions for psychological disorders. Two RDoC-inspired interventions are reviewed, and the RDoC framework is broadly explored in terms of its contributions and limitations. From preliminary evidence, RDoC offers many avenues for improving evidence-based interventions in psychology, but some limitations must be anticipated to increase the RDoC applicability to naturalistic settings.

RESUMEN

Abstract Empirically supported interventions in psychological disorders should provide (1) evidence supporting the underlying psychological mechanisms of psychopathology to target in the intervention and (2) evidence supporting the efficacy of the intervention. However, research has been dedicated in a greater extent to efficacy than to the acquisition of empirical support for the theoretical basis of therapies. Research Domain Criteria (RDoC) emerges as a new framework to provide empirically based theories about psychological mechanisms that may be targeted in intervention and tested for its efficacy. The current review aims to demonstrate the possible applications of RDoC to design empirically supported interventions for psychological disorders. Two RDoC-inspired interventions are reviewed, and the RDoC framework is broadly explored in terms of its contributions and limitations. From preliminary evidence, RDoC offers many avenues for improving evidence-based interventions in psychology, but some limitations must be anticipated to increase the RDoC applicability to naturalistic settings. (AU)

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In response to the challenges set forth by the CEGS N-GRID 2016 Shared Task in Clinical Natural Language Processing, we describe a framework to automatically classify initial psychiatric evaluation records to one of four positive valence system severities: absent, mild, moderate, or severe. We used a dataset provided by the event organizers to develop a framework comprised of natural language processing (NLP) modules and 3 predictive models (two decision tree models and one Bayesian network model) used in the competition. We also developed two additional predictive models for comparison purpose. To evaluate our framework, we employed a blind test dataset provided by the 2016 CEGS N-GRID. The predictive scores, measured by the macro averaged-inverse normalized mean absolute error score, from the two decision trees and Naïve Bayes models were 82.56%, 82.18%, and 80.56%, respectively. The proposed framework in this paper can potentially be applied to other predictive tasks for processing initial psychiatric evaluation records, such as predicting 30-day psychiatric readmissions.

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OBJECTIVE: This paper aims to analyse in a philosophically informed way the recent National Institute of Mental Health proposal for the Research Domain Criteria (RDoC) framework. CONCLUSION: Current classification systems have helped unify psychiatry and the conditions that it is most concerned with. However, by relying too much on syndromes and symptoms, they too often do not define stable constructs. As a result, inclusions and removals from the manuals are not always backed by sound reasons. The RDoC framework is an important move towards ameliorating matters. This paper argues that it improves the current situation by re-referencing constructs to physical properties (biomarkers for disorders, for example), by allowing theoretical levels within the framework, and by treating psychiatry as a special case of the cognitive sciences.

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OBJECTIVE: The National Institute of Mental Health has initiated the Research Domain Criteria (RDoC) project. Instead of using disorder categories as the basis for grouping individuals, the RDoC suggests finding relevant dimensions that can cut across traditional disorders. Our aim was to use the RDoC's framework to study patterns of attention deficit based on results of Conners' Continuous Performance Test (CPT II) in youths diagnosed with bipolar disorder (BD), attention-deficit/hyperactivity disorder (ADHD), BD+ADHD and controls. METHOD: Eighteen healthy controls, 23 patients with ADHD, 10 with BD and 33 BD+ADHD aged 12-17 years old were assessed. Pattern recognition was used to partition subjects into clusters based simultaneously on their performance in all CPT II variables. A Fisher's linear discriminant analysis was used to build a classifier. RESULTS: Using cluster analysis, the entire sample set was best clustered into two new groups, A and B, independently of the original diagnoses. ADHD and BD+ADHD were divided almost 50% in each subgroup, and there was an agglomeration of controls and BD in group B. Group A presented a greater impairment with higher means in all CPT II variables and lower Children's Global Assessment Scale. We found a high cross-validated classification accuracy for groups A and B: 95.2%. Variability of response time was the strongest CPT II measure in the discriminative pattern between groups A and B. CONCLUSION: Our classificatory exercise supports the concept behind new approaches, such as the RDoC framework, for child and adolescent psychiatry. Our approach was able to define clinical subgroups that could be used in future pathophysiological and treatment studies.

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