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The present study suggests that psychological resilience may independently contribute to low emotional distress in cancer patients. The relationship between resilience and emotional distress was also significant in the subgroup of metastatic cancer patients. Psychosocial interventions to enhance resilience might provide useful approaches to overcome cancer-related emotional distress.

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Vagus nerve stimulation (VNS) therapy has shown antidepressant effects in open acute and long-term studies of treatment-resistant major depression. Mechanisms of action are not fully understood, although clinical data suggest slower onset therapeutic benefit than conventional psychotropic interventions. We set out to map brain systems activated by VNS and to identify serial brain functional correlates of antidepressant treatment and symptomatic response. Nine adults, satisfying DSM-IV criteria for unipolar or bipolar disorder, severe depressed type, were implanted with adjunctive VNS therapy (MRI-compatible technique) and enrolled in a 3-month, doubleblind, placebo-controlled, serial-interleaved VNS/functional MRI (fMRI) study and open 20-month follow-up. A multiple regression mixed model with blood oxygenation level dependent (BOLD) signal as the dependent variable revealed that over time, VNS therapy was associated with ventro-medial prefrontal cortex deactivation. Controlling for other variables, acute VNS produced greater right insula activation among the participants with a greater degree of depression. These results suggest that similar to other antidepressant treatments, BOLD deactivation in the ventro-medial prefrontal cortex correlates with the antidepressant response to VNS therapy. The increased acute VNS insula effects among actively depressed participants may also account for the lower dosing observed in VNS clinical trials of depression compared with epilepsy. Future interleaved VNS/fMRI studies to confirm these findings and further clarify the regional neurobiological effects of VNS.

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Objective: Although distinctive structural abnormalities occur in patients with schizophrenia, detecting schizophrenia with magnetic resonance imaging (MRI) remains challenging. This study aimed to detect schizophrenia in structural MRI data sets using a trained deep learning algorithm. Method: Five public MRI data sets (BrainGluSchi, COBRE, MCICShare, NMorphCH, and NUSDAST) from schizophrenia patients and normal subjects, for a total of 873 structural MRI data sets, were used to train a deep convolutional neural network. Results: The deep learning algorithm trained with structural MR images detected schizophrenia in randomly selected images with reliable performance (area under the receiver operating characteristic curve [AUC] of 0.96). The algorithm could also identify MR images from schizophrenia patients in a previously unencountered data set with an AUC of 0.71 to 0.90. The deep learning algorithm's classification performance degraded to an AUC of 0.71 when a new data set with younger patients and a shorter duration of illness than the training data sets was presented. The brain region contributing the most to the performance of the algorithm was the right temporal area, followed by the right parietal area. Semitrained clinical specialists hardly discriminated schizophrenia patients from healthy controls (AUC: 0.61) in the set of 100 randomly selected brain images. Conclusions: The deep learning algorithm showed good performance in detecting schizophrenia and identified relevant structural features from structural brain MRI data; it had an acceptable classification performance in a separate group of patients at an earlier stage of the disease. Deep learning can be used to delineate the structural characteristics of schizophrenia and to provide supplementary diagnostic information in clinical settings.

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