Predicting 'Brainage' in the Developmental Period using Structural MRI, Morphometric Similarity, and Machine Learning.

Brain development is regularly studied using structural MRI. Recently, studies have used a combination of statistical learning and large-scale imaging databases of healthy-children to predict an individual's age from structural MRI. This data-driven, 'brainage' typically differs from the subjects chronological age, with this difference a potential measure of individual difference. Few studies have leveraged higher-order or connectomic representations of structural MRI data for this brainage approach. We leveraged morphometric similarity as a network-level approach to structural MRI to generate predictive models of age. We benchmarked these novel brain-age approaches using morphometric similarity against more typical, single feature (i.e. cortical thickness) approaches. We showed that these novel methods did not outperform cortical thickness or cortical volume measures. All models were significantly biased by age, but robust to motion confounds. The main results show that, whilst morphometric similarity mapping may be a novel way to leverage additional information from a T1-weighted structural MRI beyond individual features, in the context of a brain-age framework, morphometric similarity does not explain more variance than individual structural features. Morphometric similarity as a network-level approach to structural MRI may be poorly positioned to study individual differences in brain development in healthy individuals.

Visual illusions in young people reporting psychotic-like experiences.

BACKGROUND AND OBJECTIVES: A disruption in the co-ordination of bottom-up and top-down processing is thought to underlie anomalous perceptual experiences in psychosis. Visual illusions represent a valuable methodology in exploring this disruption. Here, we examined visual illusions in a group of young people having psychotic-like experiences. We also examined the relationship between illusion susceptibility and appraisal of psychotic-like experiences as well as depression, anxiety and stress levels. METHOD: 25 young people reporting psychotic-like experiences and 53 healthy participants performed an adjustment task that measured susceptibility to a battery of 13 visual illusions. Levels of depression, anxiety and stress were quantified in both groups. The clinical group also completed measures examining frequency, appraisals and emotional responses to psychotic-like experiences. RESULTS: A general increase of illusion susceptibility was found in the clinical group compared to the control group. However, when depression, anxiety and stress levels were controlled for, this difference disappeared. Stress turned out to be the best predictor of illusion susceptibility in the clinical group, whereas anomalous experiences, depression and anxiety were unrelated to overall illusion strength. LIMITATIONS: This study is limited to young participants reporting significant mental health difficulties and psychotic-like experiences. Findings should be replicated in an Ultra High Risk (prodromal) group. CONCLUSIONS: Increased levels of stress explained the enhanced vulnerability to illusions in the clinical group. This increased susceptibility suggests a perceptual style that relies too heavily on prior expectations at the expense of the true sensory evidence, potentially leading to an altered perceptual experience of the world.