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According to influential theories about mood, exposure to environments characterized by specific patterns of punishments and rewards could shape mood response to future stimuli. This raises the intriguing possibility that mood could be trained by exposure to controlled environments. The aim of the present study is to investigate experimental settings that increase resilience of mood to negative stimuli. We developed a new task where participants register their mood when rewards are added or subtracted from their score. We examined sixteen experimental environments with different mood-reward relationships. In an exploratory experiment, we identified environments that produce the greatest improvements in mood resilience, as measured by decreased sensitivity to loss. In a next step, we replicated our results around mood training and tested mood’s resilience over time in a novel sample. We found that training effects are detectable and remain significant after multiple task rounds (approximately two minutes). These findings are a first step in our effort to better understand the mechanisms behind mood training and its potential clinical utility.

Suicide deaths and suicidal thoughts and behaviors are considered a public health emergency, yet their underpinnings in the brain remain elusive. The authors examined the classification accuracy of individual, environmental, and clinical characteristics, as well as multimodal brain imaging correlates, of suicidal thoughts and behaviors in a U.S. population-based sample of school-age children.Children ages 9-10 years (N=7,994) from a population-based sample from the Adolescent Brain Cognitive Development study were assessed for lifetime suicidal thoughts and behaviors. After quality control procedures, structural MRI (N=6,238), resting-state functional MRI (N=4,134), and task-based functional MRI (range, N=4,075-4,608) were examined. Differences with Welch's t test and equivalence tests, with observed effect sizes (Cohen's d) and their 90% confidence intervals|0.15|, were examined. Classification accuracy was examined with area under precision-recall curves (AUPRCs).Among the 7,994 unrelated children (females, N=3,757, 47.0%), those with lifetime suicidal thoughts and behaviors based on child (N=684, 8.6%), caregiver (N=654, 8.2%), and concordant (N=198, 2.5%) reports had higher levels of social adversity and psychopathology, among themselves and their caregivers, compared with never-suicidal children (N=6,854, 85.7%). Only one imaging test survived statistical correction: caregiver-reported suicidal thoughts and behaviors were associated with a thinner left bank of the superior temporal sulcus. On the basis of the prespecified bounds of |0.15|, approximately 48% of the group mean differences for child-reported suicidal thoughts and behaviors comparisons and approximately 22% for caregiver-reported suicidal thoughts and behaviors comparisons were considered equivalent. All observed effect sizes were relatively small (d≤|0.30|), and both non-imaging and imaging correlates had low classification accuracy (AUPRC ≤0.10).Commonly applied neuroimaging measures did not reveal a discrete brain signature related to suicidal thoughts and behaviors in youths. Improved approaches to the neurobiology of suicide are critically needed.

SynopsisThe MRIQC Web-API is a resource for scientists to train new automatic quality classifiers. The MRIQC Web-API has collected more than 30K sets of image quality measures automatically extracted from BOLD and T1-weighted scans using MRIQC. MRIQC is an automated MRI Quality Control tool, and here we present an extension to crowdsource these quality metrics along with anonymized metadata and manual quality ratings. This new resource will allow a better understanding of the normative values and distributions of these quality metrics, help determine the relationships between image quality and metadata such as acquisition parameters and finally, provide a cost-effective, easy way to annotate the quality of a large number of cross-site MR scans.

Quality control of MR images is essential for excluding problematic acquisitions and avoiding bias in subsequent image processing and analysis. However, the visual inspection of individual images is time-consuming and limited by both intra- and inter-rater variance. The difficulty of visual inspection scales with study size and with the heterogeneity of multi-site data. Here, we describe a tool for the automated assessment of T1-weighted MR images of the brain - MRIQC. MRIQC calculates a set of quality measures from each image and uses them as features in a binary (include/exclude) classifier. The classifier was designed to ensure generalization to new samples acquired in different centers and using different scanning parameters from our training dataset. To achieve that goal, the classifier was trained on the Autism Brain Imaging Data Exchange (ABIDE) dataset (N=1102), acquired at 17 locations with heterogeneous scanning parameters. We selected random forests from a set of models and pre-processing options using nested cross-validation on the ABIDE dataset. We report a performance of ∼89% accuracy of the best model evaluated with nested cross-validation. The best performing classifier was then evaluated on a held-out (unseen) dataset, unrelated to ABIDE and labeled by a different expert, yielding ∼73% accuracy. The MRIQC software package and the trained classifier are released as an open source project, so that individual researchers and large consortia can readily curate their data regardless the size of their databases. Robust QC is crucial to identify early structured imaging artifacts in ongoing acquisition efforts, and helps detect individual substandard images that may bias downstream analyses.