Utilizing the Glucose and Insulin Response Shape of an Oral Glucose Tolerance Test to Predict Dysglycemia in Children with Overweight and Obesity, Ages 8-18 Years

Common dysglycemia measurements including fasting plasma glucose (FPG), oral glucose tolerance test (OGTT)-derived 2 h plasma glucose, and hemoglobin A1c (HbA1c) have limitations for children. Dynamic OGTT glucose and insulin responses may better reflect underlying physiology. This analysis assessed glucose and insulin curve shapes utilizing classifications-biphasic, monophasic, or monotonically increasing-and functional principal components (FPCs) to predict future dysglycemia. The prospective cohort included 671 participants with no previous diabetes diagnosis (BMI percentile ≥ 85th, 8-18 years old); 193 returned for follow-up (median 14.5 months). Blood was collected every 30 min during the 2 h OGTT. Functional data analysis was performed on curves summarizing glucose and insulin responses. FPCs described variation in curve height (FPC1), time of peak (FPC2), and oscillation (FPC3). At baseline, both glucose and insulin FPC1 were significantly correlated with BMI percentile (Spearman correlation r = 0.22 and 0.48), triglycerides (r = 0.30 and 0.39), and HbA1c (r = 0.25 and 0.17). In longitudinal logistic regression analyses, glucose and insulin FPCs predicted future dysglycemia (AUC = 0.80) better than shape classifications (AUC = 0.69), HbA1c (AUC = 0.72), or FPG (AUC = 0.50). Further research should evaluate the utility of FPCs to predict metabolic diseases.

Multimodal Genotype and Phenotype Data Integration to Improve Partial Data-Based Longitudinal Prediction

Multimodal data analysis has attracted ever-increasing attention in computational biology and bioinformatics community recently. However, existing multimodal learning approaches need all data modalities available at both training and prediction stages, thus they cannot be applied to many real-world biomedical applications, which often have a missing modality problem as the collection of all modalities is prohibitively costly. Meanwhile, two diagnosis-related pieces of information are of main interest during the examination of a subject regarding a chronic disease (with longitudinal progression): their current status (diagnosis) and how it will change before next visit (longitudinal outcome). Correct responses to these queries can identify susceptible individuals and provide the means of early interventions for them. In this article, we develop a novel adversarial mutual learning framework for longitudinal disease progression prediction, allowing us to leverage multiple data modalities available for training to train a performant model that uses a single modality for prediction. Specifically, in our framework, a single-modal model (which utilizes the main modality) learns from a pretrained multimodal model (which accepts both main and auxiliary modalities as input) in a mutual learning manner to (1) infer outcome-related representations of the auxiliary modalities based on its own representations for the main modality during adversarial training and (2) successfully combine them to predict the longitudinal outcome. We apply our method to analyze the retinal imaging genetics for the early diagnosis of age-related macular degeneration (AMD) disease, that is, simultaneous assessment of the severity of AMD at the time of the current visit and the prognosis of the condition at the subsequent visit. Our experiments using the Age-Related Eye Disease Study dataset show that our method is more effective than baselines at classifying patients' current and forecasting their future AMD severity.

Deformation driven Seq2Seq longitudinal tumor and organs-at-risk prediction for radiotherapy

PURPOSE

Radiotherapy presents unique challenges and clinical requirements for longitudinal tumor and organ-at-risk (OAR) prediction during treatment. The challenges include tumor inflammation/edema and radiation-induced changes in organ geometry, whereas the clinical requirements demand flexibility in input/output sequence timepoints to update the predictions on rolling basis and the grounding of all predictions in relationship to the pre-treatment imaging information for response and toxicity assessment in adaptive radiotherapy.

METHODS

To deal with the aforementioned challenges and to comply with the clinical requirements, we present a novel 3D sequence-to-sequence model based on Convolution Long Short-Term Memory (ConvLSTM) that makes use of series of deformation vector fields (DVFs) between individual timepoints and reference pre-treatment/planning CTs to predict future anatomical deformations and changes in gross tumor volume as well as critical OARs. High-quality DVF training data are created by employing hyper-parameter optimization on the subset of the training data with DICE coefficient and mutual information metric. We validated our model on two radiotherapy datasets: a publicly available head-and-neck dataset (28 patients with manually contoured pre-, mid-, and post-treatment CTs), and an internal non-small cell lung cancer dataset (63 patients with manually contoured planning CT and 6 weekly CBCTs).

RESULTS

The use of DVF representation and skip connections overcomes the blurring issue of ConvLSTM prediction with the traditional image representation. The mean and standard deviation of DICE for predictions of lung GTV at weeks 4, 5, and 6 were 0.83 ± 0.09, 0.82 ± 0.08, and 0.81 ± 0.10, respectively, and for post-treatment ipsilateral and contralateral parotids, were 0.81 ± 0.06 and 0.85 ± 0.02.

CONCLUSION

We presented a novel DVF-based Seq2Seq model for medical images, leveraging the complete 3D imaging information of a relatively large longitudinal clinical dataset, to carry out longitudinal GTV/OAR predictions for anatomical changes in HN and lung radiotherapy patients, which has potential to improve RT outcomes.

Longitudinal Prediction of Infant MR Images With Multi-Contrast Perceptual Adversarial Learning

The infant brain undergoes a remarkable period of neural development that is crucial for the development of cognitive and behavioral capacities (Hasegawa et al., 2018). Longitudinal magnetic resonance imaging (MRI) is able to characterize the developmental trajectories and is critical in neuroimaging studies of early brain development. However, missing data at different time points is an unavoidable occurrence in longitudinal studies owing to participant attrition and scan failure. Compared to dropping incomplete data, data imputation is considered a better solution to address such missing data in order to preserve all available samples. In this paper, we adapt generative adversarial networks (GAN) to a new application: longitudinal image prediction of structural MRI in the first year of life. In contrast to existing medical image-to-image translation applications of GANs, where inputs and outputs share a very close anatomical structure, our task is more challenging as brain size, shape and tissue contrast vary significantly between the input data and the predicted data. Several improvements over existing GAN approaches are proposed to address these challenges in our task. To enhance the realism, crispness, and accuracy of the predicted images, we incorporate both a traditional voxel-wise reconstruction loss as well as a perceptual loss term into the adversarial learning scheme. As the differing contrast changes in T1w and T2w MR images in the first year of life, we incorporate multi-contrast images leading to our proposed 3D multi-contrast perceptual adversarial network (MPGAN). Extensive evaluations are performed to assess the qualityand fidelity of the predicted images, including qualitative and quantitative assessments of the image appearance, as well as quantitative assessment on two segmentation tasks. Our experimental results show that our MPGAN is an effective solution for longitudinal MR image data imputation in the infant brain. We further apply our predicted/imputed images to two practical tasks, a regression task and a classification task, in order to highlight the enhanced task-related performance following image imputation. The results show that the model performance in both tasks is improved by including the additional imputed data, demonstrating the usability of the predicted images generated from our approach.

Functional Connectivity Predicts Individual Development of Inhibitory Control during Adolescence

Derailment of inhibitory control (IC) underlies numerous psychiatric and behavioral disorders, many of which emerge during adolescence. Identifying reliable predictive biomarkers that place the adolescents at elevated risk for future IC deficits can help guide early interventions, yet the scarcity of longitudinal research has hindered the progress. Here, using a large-scale longitudinal dataset in which the same subjects performed a stop signal task during functional magnetic resonance imaging at ages 14 and 19, we tracked their IC development individually and tried to find the brain features predicting their development by constructing prediction models using 14-year-olds' functional connections within a network or between a pair of networks. The participants had distinct between-subject trajectories in their IC development. Of the candidate connections used for prediction, ventral attention-subcortical network interconnections could predict the individual development of IC and formed a prediction model that generalized to previously unseen individuals. Furthermore, we found that connectivity between these two networks was related to substance abuse problems, an IC-deficit related problematic behavior, within 5 years. Our study reveals individual differences in IC development from mid- to late-adolescence and highlights the importance of ventral attention-subcortical network interconnections in predicting future IC development and substance abuse in adolescents.

Different factors predict adolescent substance use versus adult substance abuse: Lessons from a social-developmental approach

This 17-year prospective study applied a social-developmental lens to the challenge of distinguishing predictors of adolescent-era substance use from predictors of longer term adult substance use problems. A diverse community sample of 168 individuals was repeatedly assessed from age 13 to age 30 using test, self-, parent-, and peer-report methods. As hypothesized, substance use within adolescence was linked to a range of likely transient social and developmental factors that are particularly salient during the adolescent era, including popularity with peers, peer substance use, parent-adolescent conflict, and broader patterns of deviant behavior. Substance abuse problems at ages 27-30 were best predicted, even after accounting for levels of substance use in adolescence, by adolescent-era markers of underlying deficits, including lack of social skills and poor self-concept. The factors that best predicted levels of adolescent-era substance use were not generally predictive of adult substance abuse problems in multivariate models (either with or without accounting for baseline levels of use). Results are interpreted as suggesting that recognizing the developmental nature of adolescent-era substance use may be crucial to distinguishing factors that predict socially driven and/or relatively transient use during adolescence from factors that predict long-term problems with substance abuse that extend well into adulthood.

Longitudinal Prediction of Infant Diffusion MRI Data via Graph Convolutional Adversarial Networks

Missing data is a common problem in longitudinal studies due to subject dropouts and failed scans. We present a graph-based convolutional neural network to predict missing diffusion MRI data. In particular, we consider the relationships between sampling points in the spatial domain and the diffusion wave-vector domain to construct a graph. We then use a graph convolutional network to learn the non-linear mapping from available data to missing data. Our method harnesses a multi-scale residual architecture with adversarial learning for prediction with greater accuracy and perceptual quality. Experimental results show that our method is accurate and robust in the longitudinal prediction of infant brain diffusion MRI data.

The Role of the Hostile-World Scenario in Predicting Physical and Mental Health Outcomes in Older Adults

OBJECTIVE

The hostile-world scenario (HWS) denotes a personal belief system regarding threats to one's physical and mental integrity. We examined whether the HWS predicted health among older adults.

METHOD

The Israeli branch of the Survey of Health, Ageing and Retirement in Europe (SHARE-Israel) provided data on 1,286 participants, aged 50+, interviewed in two waves 4 years apart. A special measure assembled items pertinent to the HWS throughout the SHARE survey. Nine outcomes indicated physical health (e.g., activities of daily living, medical conditions) and mental health (e.g., depressive symptoms, satisfaction with life).

RESULTS

The HWS at Wave 1 predicted all physical and mental outcomes at Wave 2, except cognitive functioning, beyond effects of sociodemographics and the respective outcome's baseline at Wave 1. This predictive effect was stronger among older participants.

DISCUSSION

The results support the conception of the HWS as a psychological monitor that senses approaching functional declines in later life.

Social cognition in schizophrenia, Part 2: 12-month stability and prediction of functional outcome in first-episode patients

This study evaluated the longitudinal stability and functional correlates of social cognition during the early course of schizophrenia. Fifty-five first-episode schizophrenia patients completed baseline and 12-month follow-up assessments of 3 key domains of social cognition (emotional processing, theory of mind, and social/relationship perception), as well as clinical ratings of real-world functioning and symptoms. Scores on all 3 social cognitive tests demonstrated good longitudinal stability with test-retest correlations exceeding .70. Higher baseline and 12-month social cognition scores were both robustly associated with significantly better work functioning, independent living, and social functioning at the 12-month follow-up assessment. Furthermore, cross-lagged panel analyses were consistent with a causal model in which baseline social cognition drove later functional outcome in the domain of work, above and beyond the contribution of symptoms. Social cognitive impairments are relatively stable, functionally relevant features of early schizophrenia. These results extend findings from a companion study, which showed stable impairments across patients in prodromal, first-episode, and chronic phases of illness on the same measures. Social cognitive impairments may serve as useful vulnerability indicators and early clinical intervention targets.

Stability of different subtypes of mild cognitive impairment among the elderly over a 2- to 3-year follow-up period

BACKGROUND/AIMS

To investigate the longitudinal stability and progression of different subtypes of mild cognitive impairment (MCI) in older adults.

METHODS

We classified 217 individuals with no cognitive impairment (NCI), amnestic MCI (aMCI) based on a single test (aMCI-1) or multiple tests (aMCI-2+), nonamnestic MCI (naMCI) based on a single test (naMCI-1) or multiple tests (naMCI-2+), or amnestic + nonamnestic MCI (a+naMCI), using their baseline neuropsychological test scores, and performed annual follow-up evaluations for up to 3 years.

RESULTS

None of the subjects with aMCI-2+ reverted to normal during follow-up, with 50% of these subjects remaining stable and 50% worsening over time. Similarly, less than 20% of subjects with aMCI-2+ and a+naMCI reverted to NCI during the follow-up period, whereas 50% of aMCI-1 and 37% with naMCI-1 reverted to NCI during this same period.

CONCLUSION

Reversion to NCI occurs much more frequently when the diagnosis of MCI is based on the results of a single neuropsychological test than when it is based on the results of more memory tests. In epidemiological studies and clinical trials the diagnosis of MCI will likely be more stable if impairment on more than one test is required for amnestic and/or nonamnestic domains.