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Disruptions of axonal connectivity are thought to be a core pathophysiological feature of psychotic illness, but whether they are present early in the illness, prior to antipsychotic exposure, and whether they can predict clinical outcome remain unknown. We acquired diffusion-weighted magnetic resonance images to map structural connectivity between each pair of 319 parcellated brain regions in 61 antipsychotic-naïve individuals with first-episode psychosis (15–25 years, 46% female) and a demographically matched sample of 27 control participants. Clinical follow-up data were also acquired in patients 3 and 12 months after the scan. We used connectome-wide analyses to map disruptions of inter-regional pairwise connectivity and connectome-based predictive modeling to predict longitudinal change in symptoms and functioning. Individuals with first-episode psychosis showed disrupted connectivity in a brainwide network linking all brain regions compared with controls (familywise error–corrected p =.03). Baseline structural connectivity significantly predicted change in functioning over 12 months (r = 0.44, familywise error–corrected p =.041), such that lower connectivity within fronto-striato-thalamic systems predicted worse functional outcomes. Brainwide reductions of structural connectivity exist during the early stages of psychotic illness and cannot be attributed to antipsychotic medication. Moreover, baseline measures of structural connectivity can predict change in patient functional outcomes up to 1 year after engagement with treatment services. [ABSTRACT FROM AUTHOR]

• Developed an accurate prediction system for the progression of mild cognitive impairment to AD. • Used longitudinal structural MRI data without the segmentation of regions of interest. • Applied dictionary learning to exploit the nuances between mild cognitive impairment patients. Efficient prediction of the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) is important for the early intervention and management of AD. The aim of our study was to develop a longitudinal structural magnetic resonance imaging-based prediction system for MCI progression. A total of 164 MCI patients with longitudinal data were collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI). After preprocessing, a discriminative dictionary learning framework was applied to differentiate MCI patches, avoiding the segmentation of regions of interest. Then, the proportion of patches classified as more severe atrophy patches in a patient was calculated as his or her feature to be input into a simple support vector machine. Finally, a new subject was predicted with fourfold cross-validation (CV), and the area under the receiver operating characteristic curve (AUC) was determined. The average accuracy and AUC values after fourfold CV were 0.973 and 0.984, respectively. The effects of the data from one or two time points were also investigated. The proposed prediction system achieves desirable and reliable performance in predicting progression for MCI patients. Additionally, the prediction of MCI progression with longitudinal data was more effective and accurate. The developed scheme is expected to advance the clinical research and treatment of MCI patients. [ABSTRACT FROM AUTHOR]

• We propose a trustworthy adversarial learning metamorphosis framework that accounts for both the appearance and structural changes in infant brain MRI. • A spatial-frequency transfer block captures structural changes from multiple frequency bands with wavelet decomposition. • A quality-guided learning strategy is incorporated to improve predictions in challenging regions. • A multi-scale hybrid loss function is employed to improve prediction of textural details and anatomical boundaries. Missing scans are inevitable in longitudinal studies due to either subject dropouts or failed scans. In this paper, we propose a deep learning framework to predict missing scans from acquired scans, catering to longitudinal infant studies. Prediction of infant brain MRI is challenging owing to the rapid contrast and structural changes particularly during the first year of life. We introduce a trustworthy metamorphic generative adversarial network (MGAN) for translating infant brain MRI from one time point to another. MGAN has three key features: (i) Image translation leveraging spatial and frequency information for detail-preserving mapping; (ii) Quality-guided learning strategy that focuses attention on challenging regions. (iii) Multi-scale hybrid loss function that improves translation of image contents. Experimental results indicate that MGAN outperforms existing GANs by accurately predicting both tissue contrasts and anatomical details. [ABSTRACT FROM AUTHOR]

As connected autonomous vehicles (CAVs) enter the fleet, there will be a long period when these vehicles will have to interact with human drivers. One of the challenges for CAVs is that human drivers do not communicate their decisions well. Fortunately, the kinematic behavior of a human-driven vehicle may be a good predictor of driver intent within a short time frame. We analyzed the kinematic time series data (e.g., speed) for a set of drivers making left turns at intersections to predict whether the driver would stop before executing the turn. We used principal components analysis (PCA) to generate independent dimensions that explain the variation in vehicle speed before a turn. These dimensions remained relatively consistent throughout the maneuver, allowing us to compute independent scores on these dimensions for different time windows throughout the approach to the intersection. We then linked these PCA scores to whether a driver would stop before executing a left turn using the random intercept Bayesian additive regression trees. Five more road and observable vehicle characteristics were included to enhance prediction. Our model achieved an area under the receiver operating characteristic curve (AUC) of 0.84 at 94 m away from the center of an intersection and steadily increased to 0.90 by 46 m away from the center of an intersection. [ABSTRACT FROM AUTHOR]

Longitudinal predictors of dietary behavior change are important and in need of study. This secondary data analysis combined primary data across three randomized trials to examine transtheoretical model (TTM) and specific dietary predictors of successful dietary change at 12 and 24 months separately in treatment and control groups (N = 4178). The treatment group received three TTM-tailored print interventions over 12 months between 1995 and 2000. Chi-square and MANOVA analyses were used to examine baseline predictors of dietary outcome at 12 and 24 months. Last, a multivariable logistic regression was conducted with all baseline variables included. Across all analyses in both treatment and control groups, the most robust predictors of successful change were for TTM-tailored treatment group, preparation stage of change, and increased use of dietary behavior variables such as moderating fat intake, substitution of lower fat foods, and increasing intake of healthful foods. These results provide strong evidence for treatment, stage and behavioral dietary severity effects predicting dietary behavior change over time, and for targeting these variables with the strongest relationships to outcome in interventions, such as TTM-tailored dietary interventions. [ABSTRACT FROM AUTHOR]

Abstract: Longitudinal predictors of dietary behavior change are important and in need of study. This secondary data analysis combined primary data across three randomized trials to examine transtheoretical model (TTM) and specific dietary predictors of successful dietary change at 12 and 24months separately in treatment and control groups (N=4178). The treatment group received three TTM-tailored print interventions over 12months between 1995 and 2000. Chi-square and MANOVA analyses were used to examine baseline predictors of dietary outcome at 12 and 24months. Last, a multivariable logistic regression was conducted with all baseline variables included. Across all analyses in both treatment and control groups, the most robust predictors of successful change were for TTM-tailored treatment group, preparation stage of change, and increased use of dietary behavior variables such as moderating fat intake, substitution of lower fat foods, and increasing intake of healthful foods. These results provide strong evidence for treatment, stage and behavioral dietary severity effects predicting dietary behavior change over time, and for targeting these variables with the strongest relationships to outcome in interventions, such as TTM-tailored dietary interventions. [Copyright &y& Elsevier]

Can vision tests predict subsequent loss of acuity? The association between performance on several low contrast spatial vision measures, glare recovery, color discrimination, flicker sensitivity, stereopsis and ocular disease status at baseline and acuity loss 4.4 years later was examined in a large aged random sample with good initial acuity. In univariate analyses, several vision measures, retinal disease status and age were each significant predictors of subsequent acuity loss. In a multiple regression analysis, only low contrast spatial vision was a significant predictor, but the other vision measures, retinal disease status and age were not. For each doubling of low contrast spatial vision threshold at baseline, individuals were more than two times as likely to suffer subsequent significant visual acuity loss. Tests of low contrast spatial vision are strong predictors of significant subsequent visual acuity loss. These findings have implications for clinical trials, clinical management, and acceptance of these measures into clinical practice. [ABSTRACT FROM AUTHOR]