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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. [ABSTRACT FROM AUTHOR]

The long-term negative consequences of learning difficulties have been acknowledged. Nonetheless, research is still scarce regarding the prediction of adolescent difficulties in reading and arithmetic skills. The present study examines at which age phase and with what kind of constellation of parent- and child-related factors can adolescent difficulties in arithmetic and/or reading fluency be successfully predicted. A sample of Finnish children (N = 941) was followed from the onset of kindergarten (at age 6) through adolescence (ages 13–16). Children’s cognitive skills were assessed in kindergarten, and arithmetic and reading fluency were examined in Grades 2, 4, 6, 7, and 9. Parents’ self-report data were collected on their own learning difficulties and educational level. Scoring below the 16th percentile in both Grades 7 and 9 was set as the criterion for dysfluency either in reading (N = 87, 9.2%) or arithmetic (N = 84, 8.9%). Adolescent dysfluency in both domains was moderately predicted by parental measures and kindergarten cognitive skills. Although adding school-age fluency measures clearly increased both the predictability and specificity of models up to Grade 4 for both skills, knowledge of letters’ names, counting, and visuospatial skills remained unique predictors of dysfluency in adolescence. [ABSTRACT FROM AUTHOR]

Background: Exposure to major life stressors and aberrant brain functioning have been linked to anxiety and depression, especially during periods of heighted functional brain plasticity, such as adolescence. However, it remains unclear if specific characteristics of major life stressors and functional network disruptions differentially predict anxiety and depression symptoms over time and, if so, whether they act independently or jointly., Methods: We collected baseline lifetime stressor exposure data and resting-state functional magnetic resonance imaging data in a longitudinal sample of 107 adolescents enriched for anxiety and depressive disorders. We examined five stressor characteristics: physical danger, interpersonal loss, humiliation, entrapment, and role change/disruption. Anxiety and depression symptoms were assessed at baseline, 6-month and 12-month follow-ups. Linear mixed effect models tested if these stressor characteristics, functional connectivity within and between frontoparietal, default, and ventral attention networks, and their interactions differentially predicted anxiety and depression symptoms at 6-month and 12-month follow-ups., Results: Greater lifetime severity of physical danger and humiliation prospectively predicted increased anxiety symptoms at both follow-ups, whereas greater lifetime entrapment severity prospectively predicted higher anxiety and depression symptoms. Only the effects of lifetime entrapment severity were robust to including within- and between-network functional connectivity metrics and other significantly predictive stressor characteristics. Lifetime entrapment severity more strongly predicted anxiety symptoms in youth with higher default network connectivity. Greater functional connectivity between frontoparietal and default networks prospectively predicted increased depression symptoms., Conclusions: Taken together, these results underscore the critical importance of using stressor characteristics and functional connectivity jointly to study predictors for adolescent anxiety and depression., Competing Interests: Disclosures The authors declare no conflicts of interest with respect to this work.

The purpose of this study was to examine the power of phonological awareness and rapid naming longitudinally in predicting reading achievement in a highly transparent orthography, Turkish. Children were followed in kindergarten and first grade and assessed at four time points. While phonological awareness and rapid naming skills of children were assessed in the fall and spring semesters of kindergarten and in the fall semester of first grade, their reading skills were assessed at the end of fall semester of first grade. A total of 365 children participated in all assessments. Structural equation modeling was used to examine the prediction status of phonological awareness and rapid naming on reading achievement. Results revealed that although phonological awareness was a better predictor of reading than rapid naming, both skills predicted reading achievement significantly at all three time points, and that the contribution of these skills to reading differed at various points in time. In conclusion phonological awareness and rapid naming are strong predictors of reading achievement in a highly transparent orthography as indicated by the previous research. Thus, by assessing these skills, children at risk for reading difficulties can be identified and supported early to increase their chance of success in school. [ABSTRACT FROM AUTHOR]

Background: 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., Methods: 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., Results: 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., Conclusions: 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., (Copyright © 2024 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study investigated endogenous and exogenous predictors of early literacy in Turkish-speaking children. Whether children's language and working memory performances (as the endogenous factors) and home literacy environment (as the exogenous factor) in the beginning of kindergarten predict the children's current and year-end early literacy skills (phonological awareness, letter knowledge, receptive and expressive vocabulary) was examined. The participants consisted of 441 kindergarten children. Results showed that language development, working memory, and home reading environment predicted children's both current and year-end phonological awareness. Language and home writing activities were significant predictors of the year-end letter knowledge. Working memory was a significant predictor for both the current and year-end letter knowledge. Language, working memory and home reading environment significantly predicted the acquisition of receptive and expressive vocabulary. In conclusion, results suggest that each of the early literacy skills is related to both the developmental characteristics of children and their home literacy environment. [ABSTRACT FROM AUTHOR]

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.

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. [ABSTRACT FROM AUTHOR]

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., Competing Interests: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

• 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]

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. [ABSTRACT FROM AUTHOR]

Attention deficit hyperactivity disorder (ADHD) is emblematic of unresolved heterogeneity in psychiatric disorders-the variation in biological, clinical, and psychological correlates that impedes progress on etiology. One approach to this problem is to characterize subgroups using measures rooted in biological or psychological theory, consistent with the National Institute of Mental Health's research domain criteria initiative. Within ADHD, a promising application involves using emotion trait profiles that can address the role of irritability as a complicating feature for ADHD. Here, a new sample of 186 children with ADHD was evaluated using community detection analysis to determine if meaningful subprofiles existed and if they replicated those previously identified. The new sample and a prior sample were pooled for evaluation of (a) method dependence, (b) longitudinal assessment of the stability of classifications, and (c) clinical prediction 2 years later. Three temperament profiles were confirmed within the ADHD group: one with normative emotional functioning ("mild"), one with high surgency ("surgent"), and one with high negative affect ("irritable"). Profiles were similar across statistical clustering approaches. The irritable group had the highest external validity: It was moderately stable over time and it enhanced prospective prediction of clinical outcomes beyond standard baseline indicators. The irritable group was not reducible to ADHD + oppositional defiant disorder, ADHD + disruptive mood dysregulation disorder, or other patterns of comorbidity. Among the negative affect domains studied, trait proneness to anger uniquely contributed to clinical prediction. Results extend our understanding of chronic irritability in psychiatric disorders and provide prospects for a fresh approach to assessing ADHD heterogeneity focused on the distinction between ADHD with and without anger/irritability. (PsycINFO Database Record (c) 2019 APA, all rights reserved). [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]

Cross-sectional studies have established that performance on paired-associate learning (PAL) tasks is associated with reading performance. There are good reasons to expect such a relationship because learning to read involves learning the sounds of individual letters and possibly also sounds of strings of letters (e.g., spelling patterns). However, results from longitudinal studies have been mixed. A closer look at these studies suggests that PAL may be related to development of accuracy rather than speed in reading. This suggestion was investigated directly in the current longitudinal study. The study followed 137 students from Grade 0 (kindergarten) to Grade 5. In Grade 0, they completed measures of PAL, letter knowledge, phoneme awareness, and rapid automatized naming (RAN). In Grades 1 and 5, decoding accuracy was measured with the addition of decoding speed in Grade 5. PAL in Grade 0 was found to be a unique predictor of decoding accuracy in Grades 1 and 5 after controlling for Grade 0 letter knowledge, phoneme awareness, and RAN. PAL in Grade 0 even contributed to Grade 5 decoding accuracy after also controlling for Grade 1 decoding. Zero-order correlations between PAL and Grade 5 decoding speed were nonsignificant and close to zero. The results indicate that PAL measures a trait that may influence reading development over a substantial amount of time. Possible roles of PAL in decoding development over time are discussed, for example, how verbal learning may be a core component in the acquisition of associations between letter patterns (spelling patterns) and their pronunciation. [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]

To enhance the quality of economically efficient healthcare, we propose a preventive planning service for next-generation screening based on a longitudinal prediction. This newly proposed framework may bring important advancements in prevention by identifying the early stages of cancer, which will help in further diagnoses and initial treatment planning. The preventive service may also solve the obstacles of cost and availability of scanners in screening. For nonstationary medical data, anomaly detection is the key problem in the prediction of cancer staging. To address anomaly detection in a huge stream of databases, we applied a composite kernel to the prediction of cancer staging for the first time. The proposed longitudinal analysis of composite kernels (LACK) is designed for the prediction of anomaly status and cancer stage for further diagnosis and the future likelihood of cancer stage progression. The prediction error of LACK is relatively small even if the prediction is made far ahead of time. The computation time for nonstationary learning is reduced by \text33\% compared with stationary learning. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

In this longitudinal study, we examined teachers' self-efficacy as a long-term predictor of their mastery goal orientation and three dimensions of instructional quality: supportive classroom climate, effective classroom management, and cognitive activation. Mastery goal orientation was also analyzed as a predictor of instructional quality. Teachers' optimism, engagement, and strain were assessed to gain information about the construct validity of the scales on self-efficacy and mastery goal orientation. We analyzed the self-report data of 203 German in-service teachers who participated in all of three time points of assessment (the years 2001, 2008, and 2011). Confirmatory factor analyses supported the assumed three-dimensionality of instructional quality. Teacher self-efficacy was found to be relatively stable and to be a long-term predictor of instructional quality as indicated by the results of latent variable modeling. Moreover, instructional quality is predicted by mastery goal orientation, which in turn is regressed on self-efficacy. As supported also by bias-corrected bootstrapping, mastery goal orientation partially mediated the relationship between classroom climate and self-efficacy. Results and an outlook for future research are discussed. [ABSTRACT FROM AUTHOR]

The present study examined phoneme awareness, phonological short term memory, letter knowledge, rapid automatized naming (RAN), and visual-verbal paired associate learning (PAL) as longitudinal predictors of spelling skills in an early phase (Grade 2) and a later phase (Grade 5) of development in a sample of 140 children learning to spell in the opaque Danish orthography. Important features of the study were the inclusion of PAL measures and the fact that the children were followed up to Grade 5. Findings from other orthographies were replicated, in that phonological processing (awareness and memory) and RAN accounted for unique variance in early spelling skills. For later spelling skills, Grade 2 spelling was by far the most powerful predictor. PAL-nonwords was the only measure to explain additional unique variance. It is suggested that PAL-nonwords taps the ability to establish representations of new phonological forms and that this ability is important for the acquisition of orthographic spelling knowledge. [ABSTRACT FROM AUTHOR]

The cross-sectional and longitudinal influences of positive youth development and life satisfaction on adolescent problem behaviour were examined in this study. The respondents were 4,523 Chinese students recruited from 43 secondary schools in Hong Kong, and the study followed them from Grade 7, Grade 9 to Grade 11. Repeated measurements employing validated self-reported assessment tools were used. Analyses using structural equation modelling showed that positive youth development directly influenced life satisfaction and reduced problem behaviour at Grades 7 and 9. However, the direct effect of positive youth development on problem behaviour was mediated by life satisfaction at Grade 11. The mediating effect of life satisfaction on the enhancement of future positive youth development was also discovered. These pioneer longitudinal findings contribute to the existing literature in delineating the causal and mediating roles of positive youth development and life satisfaction in adolescent problem behaviour, particularly in the Chinese context. [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]

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. [ABSTRACT FROM PUBLISHER]

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. Copyright © 2009 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]