15Background: In major depressive disorder (MDD), it is unclear to what extent structural brain changes are associated with depressive episodes or represent part of the mechanism by which the risk for illness is mediated. The aim of this study was to investigate whether structural abnormalities are related to risk for the development of MDD. Methods: We compared healthy controls with a positive family history for MDD (HC-FHP), healthy controls with no family history of any psychiatric disease (HC-FHN) and patients with MDD. Groups were age-and sex-matched. We analyzed data from high-resolution magnetic resonance imaging using voxel-based morphometry. We performed small volume corrections for our regions of interest (hippocampus, dorsolateral [DLPFC] and dorsomedial prefrontal cortex [DMPFC], anterior cingulate cortex [ACC] and basal ganglia) using a family-wise error correction (p < 0.05) to control for multiple comparisons. Results: There were 30 participants in the HC-FHP group, 64 in the HC-FHN group and 33 patients with MDD. The HC-FHP group had smaller right hippocampal and DLPFC grey matter volumes compared with the HC-FHN group, and even smaller right hippocampal volumes compared with patients with MDD. In addition, the HC-FHP group exhibited smaller white matter volumes in the DLPFC and left putamen but also greater volumes in 2 areas of the DMPFC compared with the HC-FHN group. Patients with MDD exhibited smaller volumes in the ACC, DMPFC, DLPFC and the basal ganglia compared with healthy controls. Limitations: The retrospective identification of family history might result in a bias toward unidentified participants in the control group at risk for MDD, diminishing the effect size. Conclusion: Volume reductions in the hippocampus and DLPFC might be associated with a greater risk for MDD. The HC-FHP group had smaller hippocampal volumes compared with patients with MDD, which is suggestive for neuroplastic effects of treatment. The HC-FHP group had not yet experienced a depressive episode and therefore might have been resilient and might have had some protective strategies. Whether resilience is associated with the larger white matter volumes in the DMPFC (e.g., owing to compensatory, neuroplastic remodelling mechanisms) needs to be confirmed in future studies.

The processing of emotional facial expression is a major part of social communication and understanding. In addition to explicit processing, facial expressions are also processed rapidly and automatically in the absence of explicit awareness. We investigated 12 healthy subjects by presenting them with an implicit and explicit emotional paradigm. The subjects reacted significantly faster in implicit than in explicit trials but did not differ in their error ratio. For the implicit condition increased signals were observed in particular in the thalami, the hippocampi, the frontal inferior gyri and the right middle temporal region. The analysis of the explicit condition showed increased blood-oxygen-level-dependent signals especially in the caudate nucleus, the cingulum and the right prefrontal cortex. The direct comparison of these 2 different processes revealed increased activity for explicit trials in the inferior, superior and middle frontal gyri, the middle cingulum and left parietal regions. Additional signal increases were detected in occipital regions, the cerebellum, and the right angular and lingual gyrus. Our data partially confirm the hypothesis of different neural substrates for the processing of implicit and explicit emotional stimuli.

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IntroductionDifferent electrophysiological indices have been investigated to identify diagnostic and prognostic markers of schizophrenia (SCZ). However, these indices have limited use in clinical practice, since both specificity and association with illness outcome remain unclear. In recent years, machine learning techniques, through the combination of multidimensional data, have been used to better characterize SCZ and to predict illness course.ObjectivesThe aim of the present study is to identify multimodal electrophysiological biomarkers that could be used in clinical practice in order to improve precision in diagnosis and prognosis of SCZ.MethodsIllness-related and functioning-related variables were measured at baseline in 113 subjects with SCZ and 57 healthy controls (HC), and after four-year follow-up in 61 SCZ. EEGs were recorded at baseline in resting-state condition and during two auditory tasks (MMN-P3a and N100-P3b). Through a Linear Support Vector Machine, using EEG data as predictors, four models were generated in order to classify SCZ and HC. Then, we combined unimodal classifiers’ scores through a stacking procedure. Pearson’s correlations between classifiers score with illness-related and functioning-related variables, at baseline and follow-up, were performed.ResultsEach EEG model produced significant classification (p < 0.05). Global classifier discriminated SCZ from HC with accuracy of 75.4% (p < 0.01). A significant correlation (r=0.40, p=0.002) between the global classifier scores with negative symptoms at follow-up was found. Within negative symptoms, blunted affect showed the strongest correlation.ConclusionsAbnormalities in electrophysiological indices might be considered trait markers of schizophrenia. Our results suggest that multimodal electrophysiological markers might have prognostic value for negative symptoms.