Biases in Interpretation and Memory in Generalized Social Phobia

Two experiments examined the link between interpretation and memory in individuals diagnosed with Generalized Social Phobia (GSP). In Experiment 1, GSP and control participants generated continuations for nonsocial and ambiguous social scenarios. GSP participants produced more socially anxious and negative continuations for the social scenarios than did the controls. On the subsequent test of recalling the social scenarios, intrusion errors that shared meaning with the original continuations were made more frequently by the GSP group, producing false recall with emotionally negative features. To examine whether nonanxious individuals would also produce such errors if given emotional interpretations, in Experiment 2 the authors asked university students to read the scenarios plus endings produced by GSP participants in Experiment 1. The students either constructed vivid mental images of themselves as the main characters or thought about whether the endings provided closure. Low-anxious students in the closure condition produced fewer ending-based intrusions in recalling the social scenarios than did students in the other 3 conditions. Results illustrate the importance of examining the nature of source-monitoring errors in investigations of memory biases in social anxiety

Training Forgetting of Negative Material in Depression

In this study, the authors investigated whether training participants to use cognitive strategies can aid forgetting in depression. Participants diagnosed with major depressive disorder (MDD) and never-depressed participants learned to associate neutral cue words with a positive or negative target word and were then instructed not to think about the negative targets when shown their cues. The authors compared 3 different conditions: an unaided condition, a positive-substitute condition, and a negative-substitute condition. In the substitute conditions, participants were instructed to use new targets to keep from thinking about the original targets. After the training phase, participants were instructed to recall all targets when presented with the cues. MDD participants, in contrast with control participants, did not exhibit forgetting of negative words in the unaided condition. In both the negative and positive substitute conditions, however, MDD participants showed successful forgetting of negative words and a clear practice effect. In contrast, negative substitute words did not aid forgetting by the control participants. These findings suggest that training depressed individuals to use cognitive strategies can increase forgetting of negative words

Remembering the Good, Forgetting the Bad: Intentional Forgetting of Emotional Material in Depression

The authors examined intentional forgetting of negative material in depression. Participants were instructed to not think about emotional nouns that they had learned to associate with a neutral cue word. The authors provided participants with multiple occasions to suppress the unwanted words. Overall, depressed participants successfully forgot negative words. Moreover, the authors obtained a clear practice effect. However, forgetting came at a cost: Compared with the nondepressed participants and with the depressed participants who were instructed to forget positive words, depressed participants who were instructed to forget negative words showed significantly worse recall of the baseline words. These results indicate that training depressed individuals in intentional forgetting could prove to be an effective strategy to counteract automatic ruminative tendencies and mood-congruent biases

DenseNet and Support Vector Machine classifications of major depressive disorder using vertex-wise cortical features

Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. In this study, we used globally representative data from the ENIGMA-MDD working group containing an extensive sample of people with MDD (N=2,772) and HC (N=4,240), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. As we analyzed a multi-site sample, we additionally applied the ComBat harmonization tool to remove potential nuisance effects of site. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between MDD and HC. Our results support the notion that MDD classification on this combination of features and classifiers is unfeasible

Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects

Subcortical volumes across the lifespan: Data from 18,605 healthy individuals aged 3–90 years

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta‐Analysis (ENIGMA) Consortium to examine age‐related trajectories inferred from cross‐sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3–90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter‐individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age‐related morphometric patterns

Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3-90 years

Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large‐scale studies. In response, we used cross‐sectional data from 17,075 individuals aged 3–90 years from the Enhancing Neuroimaging Genetics through Meta‐Analysis (ENIGMA) Consortium to infer age‐related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta‐analysis and one‐way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes