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Multivariate associations of ideal affect with clinical symptoms.

ABSTRACT: Prior research has indicated that ideal affect (i.e., the affective states that people value and would ideally like to experience) may be relevant to mental health outcomes. Studies to date, however, have not used comprehensive multivariate models that account for covariation among facets of ideal affect and incorporate multiple clinical outcomes. In the present studies, we used structural equation modeling to examine the multivariate effects of ideal affect on symptoms of depression, anxiety, and alcohol abuse in 2 moderately large samples of undergraduates (N = 293 and N = 146). Exploratory results of Study 1 indicated that valuation of high arousal positive affective states was significantly associated with lower depression symptoms but higher anxiety and alcohol abuse symptoms and that valuation of high arousal negative states was specifically associated with greater anxiety symptoms. These results were shown to be structurally invariant across samples and ethnicities in Study 2, which also found that ideal-actual affect discrepancies were significantly associated with symptoms of depression and anxiety. These findings support and extend the hypothesis that ideal affect is implicated in clinical outcomes by highlighting the importance of jointly considering multiple facets of ideal and actual affect as they relate to a range of clinical syndromes. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

SUBMITTER: Swerdlow BA

PROVIDER: S-EPMC6771288 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Multivariate associations of ideal affect with clinical symptoms.

Swerdlow Benjamin A BA Pearlstein Jennifer G JG Johnson Sheri L SL

Emotion (Washington, D.C.) 20180625 4

Prior research has indicated that ideal affect (i.e., the affective states that people value and would ideally like to experience) may be relevant to mental health outcomes. Studies to date, however, have not used comprehensive multivariate models that account for covariation among facets of ideal affect and incorporate multiple clinical outcomes. In the present studies, we used structural equation modeling to examine the multivariate effects of ideal affect on symptoms of depression, anxiety, a ...[more]

PMID: 29939058

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Project description:Importance:Alterations in multiple neuroimaging phenotypes have been reported in psychotic disorders. However, neuroimaging measures can be influenced by factors that are not directly related to psychosis and may confound the interpretation of case-control differences. Therefore, a detailed characterization of the contribution of these factors to neuroimaging phenotypes in psychosis is warranted. Objective:To quantify the association between neuroimaging measures and behavioral, health, and demographic variables in psychosis using an integrated multivariate approach. Design, Setting, and Participants:This imaging study was conducted at a university research hospital from June 26, 2014, to March 9, 2017. High-resolution multimodal magnetic resonance imaging data were obtained from 100 patients with schizophrenia, 40 patients with bipolar disorder, and 50 healthy volunteers; computed were cortical thickness, subcortical volumes, white matter fractional anisotropy, task-related brain activation (during working memory and emotional recognition), and resting-state functional connectivity. Ascertained in all participants were nonimaging measures pertaining to clinical features, cognition, substance use, psychological trauma, physical activity, and body mass index. The association between imaging and nonimaging measures was modeled using sparse canonical correlation analysis with robust reliability testing. Main Outcomes and Measures:Multivariate patterns of the association between nonimaging and neuroimaging measures in patients with psychosis and healthy volunteers. Results:The analyses were performed in 92 patients with schizophrenia (23 female [25.0%]; mean [SD] age, 27.0 [7.6] years), 37 patients with bipolar disorder (12 female [32.4%]; mean [SD] age, 27.5 [8.1] years), and 48 healthy volunteers (20 female [41.7%]; mean [SD] age, 29.8 [8.5] years). The imaging and nonimaging data sets showed significant covariation (r?=?0.63, P?<?.001), which was independent of diagnosis. Among the nonimaging variables examined, age (r?=?-0.53), IQ (r?=?0.36), and body mass index (r?=?-0.25) were associated with multiple imaging phenotypes; cannabis use (r?=?0.23) and other substance use (r?=?0.33) were associated with subcortical volumes, and alcohol use was associated with white matter integrity (r?=?-0.15). Within the multivariate models, positive symptoms retained associations with the global neuroimaging (r?=?-0.13), the cortical thickness (r?=?-0.22), and the task-related activation variates (r?=?-0.18); negative symptoms were mostly associated with measures of subcortical volume (r?=?0.23), and depression/anxiety was associated with measures of white matter integrity (r?=?0.12). Conclusions and Relevance:Multivariate analyses provide a more accurate characterization of the association between brain alterations and psychosis because they enable the modeling of other key factors that influence neuroimaging phenotypes.

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Multivariate associations of ideal affect with clinical symptoms.

Publications

Multivariate associations of ideal affect with clinical symptoms.

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