Subgenual activation and the finger of blame: individual differences and depression vulnerability

Self-blame-selective hyper-connectivity between anterior temporal and subgenual cortices predicts prognosis in major depressive disorder

BACKGROUND

Self-blame-related fMRI measures were shown to predict subsequent recurrence in remitted major depressive disorder (MDD). Their role in current MDD, however, is unknown. We hypothesised that these neural signatures reflect a highly recurrent but remitting course of MDD and therefore predict favourable outcomes over a four-month follow-up period in current MDD.

METHODS

Forty-five participants with current MDD and non-responders to at least two serotonergic antidepressants, were encouraged to optimise their medication and followed up after receiving four months of primary care treatment-as-usual. Prior to their medication review, participants completed an fMRI paradigm in which they viewed self- and other-blame emotion-evoking statements. Thirty-nine participants met pre-defined fMRI data minimum quality thresholds. Psychophysiological interaction analysis was used to determine baseline connectivity of the right superior anterior temporal lobe (RSATL), with an a priori BA25 region-of-interest for self-blaming vs other-blaming emotions, using Quick Inventory of Depressive Symptomatology (16-item) percentage change as a covariate.

RESULTS

We corroborated our pre-registered hypothesis that a favourable clinical outcome was associated with higher self-blame-selective RSATL-BA25 connectivity (Family-Wise Error-corrected p <.05 over the a priori BA25 region-of-interest; rs(34) = -0.47, p =.005). This generalised to the sample including participants with suboptimal fMRI quality (rs(39) = -0.32, p =.05).

CONCLUSIONS

This study shows that neural signatures of overgeneralised self-blame are relevant for prognostic stratification of current treatment-resistant MDD. Future studies need to confirm whether this neural signature indeed represents a trait-like feature of a fully remitting subtype of MDD, or whether it is also modulated by depressive state and related to treatment effects.

AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale

BACKGROUND

Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states.

METHODS

We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants.

RESULTS

We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites.

CONCLUSION

We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project.

The Psychopathology of Worthlessness in Depression

BACKGROUND

Despite common dissatisfaction with the syndromic heterogeneity of major depression, investigations into its symptom structure are scarce. Self-worthlessness/inadequacy is a distinctive and consistent symptom of major depression across cultures.

AIMS

We investigated whether self-worthlessness is associated with self-blaming attribution-related symptoms or is instead an expression of reduced positive feelings overall, as would be implied by reduced positive affect accounts of depression.

METHODS

44,161 undergraduate students in Study 1, and 215 patients with current Major Depressive Disorder (MDD) and 237 age-matched healthy control participants in Study 2 completed the well-validated Symptom Check List-90. Depression-relevant items were used to construct regularized partial correlation networks with bootstrap estimates of network parameter variability.

RESULTS

Worthlessness co-occurred more strongly with other symptoms linked to self-blaming attributions (hopelessness, and self-blame), displaying a combined edge weight with these symptoms which was significantly stronger than the edge weight representing its connection with reduced positive emotion symptoms (such as reduced pleasure/interest/motivation, difference in edge weight sum in Study 1 = 2.95, in Study 2 = 1.64; 95% confidence intervals: Study 1: 2.6-3.4; Study 2: 0.02-3.5; Bonferroni-corrected p < 0.05).

CONCLUSIONS

This confirms the prediction of the revised learned helplessness model that worthlessness is most strongly linked to hopelessness and self-blame. In contrast, we did not find a strong and direct link between anhedonia items and a reduction in self-worth in either study. This supports worthlessness as a primary symptom rather than resulting from reduced positive affect.

Neurocognitive Measures of Self-blame and Risk Prediction Models of Recurrence in Major Depressive Disorder

BACKGROUND

Overgeneralized self-blaming emotions, such as self-disgust, are core symptoms of major depressive disorder and prompt specific actions (i.e., action tendencies), which are more functionally relevant than the emotions themselves. We have recently shown, using a novel cognitive task, that when feeling self-blaming emotions, maladaptive action tendencies (feeling like hiding and feeling like creating a distance from oneself) and an overgeneralized perception of control are characteristic of major depressive disorder, even after remission of symptoms. Here, we probed the potential of this cognitive signature, and its combination with previously employed functional magnetic resonance imaging (fMRI) measures, to predict individual recurrence risk. For this purpose, we developed a user-friendly hybrid machine/statistical learning tool, which we make freely available.

METHODS

A total of 52 medication-free patients with remitted major depressive disorder, who had completed the action tendencies task and our self-blame fMRI task at baseline, were followed up clinically over 14 months to determine recurrence. Prospective prediction models included baseline maladaptive self-blame-related action tendencies and anterior temporal fMRI connectivity patterns across a set of frontolimbic a priori regions of interest, as well as including established clinical and standard psychological predictors. Prediction models used elastic net regularized logistic regression with nested 10-fold cross-validation.

RESULTS

Cross-validated discrimination was highly promising (area under the receiver-operating characteristic curve ≥ 0.86), and positive predictive values over 80% were achieved when including fMRI in multimodal models, but only up to 71% (area under the receiver-operating characteristic curve ≤ 0.74) when solely relying on cognitive and clinical measures.

CONCLUSIONS

This study shows the high potential of multimodal signatures of self-blaming biases to predict recurrence risk at an individual level and calls for external validation in an independent sample.

Internal reliability of blame-related functional MRI measures in major depressive disorder

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Self-blame-related fMRI measures were previously validated in depressive disorders.

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Reproducibility and internal consistency as a measure of reliability were examined.

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Whilst simple fMRI measures exhibited fair reliability, complex measures did not.

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Yet, complex measures showed reproducible clinical validity at the group level.

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Connectivity measures, that balance reliability and validity better, are needed.

Background

Methods

Results

Conclusions