Dataset Information

Measuring strengths and weaknesses in dimensional psychiatry.

ABSTRACT:

Background

The Extended Strengths and Weaknesses Assessment of Normal Behavior (E-SWAN) reconceptualizes each diagnostic criterion for selected DSM-5 disorders as a behavior, which can range from high (strengths) to low (weaknesses). Initial development focused on Panic Disorder, Social Anxiety, Major Depression, and Disruptive Mood Dysregulation Disorder.

Methods

Data were collected from 523 participants (ages 6-17). Parents completed each of the four E-SWAN scales and traditional unidirectional scales addressing the same disorders. Distributional properties, Item Response Theory Analysis (IRT), and Receiver Operating Characteristic (ROC) curves were used to assess and compare the performance of E-SWAN and traditional scales.

Results

In contrast to the traditional scales, which exhibited truncated distributions, all four E-SWAN scales had symmetric distributions. IRT analyses indicate the E-SWAN subscales provided reliable information about respondents throughout the population distribution; traditional scales only provided reliable information about respondents at the high end of the distribution. Predictive value for DSM-5 diagnoses was comparable to prior scales.

Conclusions

E-SWAN bidirectional scales can capture the full spectrum of the population distribution of behavior underlying DSM disorders. The additional information provided can better inform examination of inter-individual variation in population studies, as well as facilitate the identification of factors related to resiliency in clinical samples.

SUBMITTER: Alexander LM

PROVIDER: S-EPMC6916607 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Publications

Measuring strengths and weaknesses in dimensional psychiatry.

Alexander Lindsay M LM Salum Giovanni A GA Swanson James M JM Milham Michael P MP

Journal of child psychology and psychiatry, and allied disciplines 20190818 1

<h4>Background</h4>The Extended Strengths and Weaknesses Assessment of Normal Behavior (E-SWAN) reconceptualizes each diagnostic criterion for selected DSM-5 disorders as a behavior, which can range from high (strengths) to low (weaknesses). Initial development focused on Panic Disorder, Social Anxiety, Major Depression, and Disruptive Mood Dysregulation Disorder.<h4>Methods</h4>Data were collected from 523 participants (ages 6-17). Parents completed each of the four E-SWAN scales and traditiona ...[more]

PMID: 31423596

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Project description:BackgroundAlternative splicing contributes significantly to the complexity of the human transcriptome and proteome. Computational prediction of alternative splice isoforms are usually based on EST sequences that also allow to approximate the expression pattern of the related transcripts. However, the limited number of tissues represented in the EST data as well as the different cDNA construction protocols may influence the predictive capacity of ESTs to unravel tissue-specifically expressed transcripts.MethodsWe predict tissue and tumor specific splice isoforms based on the genomic mapping (SpliceNest) of the EST consensus sequences and library annotation provided in the GeneNest database. We further ascertain the potentially rare tissue specific transcripts as the ones represented only by ESTs derived from normalized libraries. A subset of the predicted tissue and tumor specific isoforms are then validated via RT-PCR experiments over a spectrum of 40 tissue types.ResultsOur strategy revealed 427 genes with at least one tissue specific transcript as well as 1120 genes showing tumor specific isoforms. While our experimental evaluation of computationally predicted tissue-specific isoforms revealed a high success rate in confirming the expression of these isoforms in the respective tissue, the strategy frequently failed to detect the expected restricted expression pattern. The analysis of putative lowly expressed transcripts using normalized cDNA libraries suggests that our ability to detect tissue-specific isoforms strongly depends on the expression level of the respective transcript as well as on the sensitivity of the experimental methods. Especially splice isoforms predicted to be disease-specific tend to represent transcripts that are expressed in a set of healthy tissues rather than novel isoforms.ConclusionsWe propose to combine the computational prediction of alternative splice isoforms with experimental validation for efficient delineation of an accurate set of tissue-specific transcripts.

Dataset Information

Measuring strengths and weaknesses in dimensional psychiatry.

Background

Methods

Results

Conclusions

Publications

Measuring strengths and weaknesses in dimensional psychiatry.

Similar Datasets

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