Taxonomy and utility in the diagnostic classification of mental disorders

The assessment of transdiagnostic dimensions of emotional disorders: Validation of the Multidimensional Emotional Disorders Inventory (MEDI) in adolescents with subthreshold anxiety and depression

BACKGROUND

The transdiagnostic approach to psychopathology has emerged as an alternative to traditional taxonomic approaches. The Multidimensional Emotional Disorders Inventory (MEDI) is a specifically designed self-report to measure the transdiagnostic dimensions proposed by Brown and Barlow (2009). This study aims to analyse the psychometric properties of the MEDI scores in adolescents with subthreshold anxiety and depression.

METHOD

The sample consisted of a total of 476 students. The mean age was 13.77 years (SD = 1.43) (range 10 to 18 years), 73.9 % were females. Several questionnaires assessing positive affect, negative affect, mental health difficulties, and quality of life were used.

RESULTS

The original 9-factor structure of the MEDI was confirmed with good fit indices. Satisfactory levels of internal consistency were observed in most of the MEDI scores using McDonald's Omega, ranging from 0.58 to 0.87. The MEDI dimensions were associated with psychopathology, positive affect, negative affect, and quality of life.

LIMITATIONS

Reliance on self-reported data, a cross-sectional design limiting temporal assessment, and a 73.9 % female gender imbalance.

CONCLUSION

The MEDI scores showed adequate psychometric properties among adolescents with subclinical emotional symptoms. The results found might have potential clinical implications for conceptualization, assessment, intervention, and prevention of emotional disorders at both clinical and research levels.

The harmonium model and its unified system view of psychopathology: a validation study by means of a convolutional neural network

The harmonium model (HM) is a recent conceptualization of the unifying view of psychopathology, namely the idea of a general mechanism underpinning all mental disorders (the p factor). According to HM, psychopathology consists of a low dimensional Phase Space of Meaning (PSM), where each dimension of meaning maps a component of the environmental variability. Accordingly, the lower thenumber of independent dimensions in the PSM, and hence its intrinsic complexity, the more limited the way of interpreting the environment. The current simulation study, based on a Convolutional Neural Network (CNN) framework, aims at validating the HM low-dimensionality hypothesis. CNN-based classifiers were employed to simulate normotypical and pathological cognitive processes. Results revealed that normotypical and pathological CNNs were different in terms of both classification performance and layer activation patterns. Using Principal Component Analysis to characterize the PSM associated with the two algorithms, we found that the performance of the normotypical CNN relies on a larger and more evenly distributed number of components, compared with the pathological one. This finding might be indicative of the fact that psychopathology can be modelled as a low-dimensional, poorly modulable PSM, which means the environment is detected through few components of meaning, preventing complex information patterns from being taken into account.