Repeated measurement of implicit self-associations in clinical depression: Psychometric, neural, and computational properties.

Abstract

Implicit self-associations are theorized to be rigidly and excessively negative in affective disorders like depression. Such information processing patterns may be useful as an approach to parsing heterogeneous etiologies, substrates, and treatment outcomes within the broad syndrome of depression. However, there is a lack of sufficient data on the psychometric, neural, and computational substrates of Implicit Association Test (IAT) performance in patient populations. In a treatment-seeking, clinically depressed sample (n ϭ 122), we administered five variants of the IAT-a dominant paradigm used in hundreds of studies of implicit cognition to date-at two repeated sessions (outside and inside a functional MRI scanner). We examined reliability, clinical correlates, and neural and computational substrates of IAT performance. IAT scores showed adequate (.67-.81) split-half reliability and convergent validity with one another and with relevant explicit symptom measures. Test-retest correlations (in vs. outside the functional MRI scanner) were present but modest (.15-.55). In depressed patients, on average, negative implicit self-representations appeared to be weaker or less efficiently processed relative to positive self-representations; elicited greater recruitment of frontoparietal task network regions; and, according to computational modeling of trial-by-trial data, were driven primarily by differential efficiency of information accumulation for negative and positive attributes. Greater degree of discrepancy between implicit and explicit self-worth predicted depression severity. Overall, these IATs show potential utility in understanding heterogeneous substrates of depression but leave substantial room for improvement. The observed clinical, neural, and computational correlates of implicit self-associations offer novel insights into a simple computer-administered task in a clinical population and point toward heterogeneous depression mechanisms and treatment targets.

Applying the questionnaire-based Implicit Association Test to measure depression

“…Among them, the classical Implicit Association Test (IAT; Greenwald et al, 1998) is surely the most used and tested. Interestingly for the present research, various studies (e.g., Dentale et al, 2016, Price et al, 2021 applied the classical IAT experimental paradigm to measure depression, providing the first evidence for its reliability and validity. The depression-IAT is a computer-based task designed to measure automatic associations between two opposing target categories (i.e., self vs. others) and two opposing attribute categories (e.g., depression vs. wellness).…”

“…These correlations are partially in line with some evidences found on the convergent validity of the classical depression IAT (Creemers et al, 2013;Van Tuijl et al, 2018), even if disagreement is present in literature. Price et al (2021), for instance, found no significant correlations between the classical depression IAT and traditional self-report measures of depression. Moreover, the Depression-qIAT showed significant negative correlations of small/moderate size with self-report measures of self-esteem, satisfaction with life, positive oriented thinking, and positive affect, supporting its criterion validity.…”

No evidence that implicit identification with mental illness predicts recovery

“…Although the results suggest that implicit identification is not predictive of recovery, it may be that implicit attitudes are predictive of recovery for some people. For example, the discrepancy between implicit and explicit self‐worth has been shown to be associated with depressive symptoms (Kim & Moore, 2019; Price, Panny, Degutis, & Griffo, 2020). That is, in some studies, it appears that the mismatch between implicit and explicit self‐views is driving symptom severity (Price et al., 2020).…”

“…For example, the discrepancy between implicit and explicit self‐worth has been shown to be associated with depressive symptoms (Kim & Moore, 2019; Price, Panny, Degutis, & Griffo, 2020). That is, in some studies, it appears that the mismatch between implicit and explicit self‐views is driving symptom severity (Price et al., 2020). Of course, our research is examining identification with mental illness, not self‐worth.…”

Cytokine and Reward Circuitry Relationships in Treatment-Resistant Depression

“…A 3T Siemens PRISMA scanner (Siemens Healthineers) was used to obtain BOLD fMRI data through use of Human Connectome Project sequences (multiband factor = 8, repetition time = 800 ms, echo time = 37, fractional anisotropy = 52°, field of view = 200 × 200, 72 slices, 2 mm isotropic voxels). Standard preprocessing steps were applied in Analysis of Functional NeuroImages (AFNI) consistent with the afni_proc.py pipeline, as described in previous publications ( 36 ). Briefly, preprocessing steps included slice timing correction, motion correction, spatial distortion correction, cross-registration to a magnetization prepared rapid acquisition gradient-echo structural scan, warping to the Montreal Neurological Institute-27 template, and smoothing (6 mm full width at half maximum).…”