Cognition level and change in cognition during adolescence are associated with cognition in midlife.

PURPOSE: Cognitive development during adolescence affects health long term. We investigated whether level of and change in language-based cognition during adolescence are associated with cognitive performance in midlife. METHODS: Participants were enrolled in the Child Health and Development Study and followed during midlife (47-52 years). Adolescent cognition was measured with the Peabody Picture Vocabulary Test at ages 9-11 years (PPVT-9) and 15-17 years (PPVT-15). We examined PPVT-9, as well as a PPVT change score (derived using the standardized regression-based method) in relation to midlife cognition measures of Wechsler Test of Adult Reading, Verbal Fluency, and Digit Symbol tests. Linear regression models were adjusted for childhood socioeconomic status, age, sex, race, and midlife marital status, education, and occupational score. RESULTS: In 357 participants (52.1% female, 25.6% African American), both PPVT-9 (ß [95% confidence interval [CI] = 0.26 [0.18, 0.34]) and PPVT change score (ß [95% CI] = 2.03 [1.27, 2.80]) were associated with Wechsler Test of Adult Reading at midlife. PPVT-9 was associated with midlife Verbal Fluency (ß [95% CI] = 0.18 [0.10, 0.25]), whereas PPVT change score was not (ß [95% CI] = -0.01 [-0.68, 0.67]). Neither PPVT-9 nor PPVT change score was associated with midlife Digit Symbol. CONCLUSIONS: Both level of and change in language-based cognition during adolescence were associated with midlife vocabulary and language function, even after controlling for midlife occupation and education.

Predicting the brain response to treatment using a Bayesian hierarchical model with application to a study of schizophrenia.

In vivo functional neuroimaging, including functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), is becoming increasingly important in defining the pathophysiology of psychiatric disorders such as schizophrenia, major depression, and Alzheimer's disease. Furthermore, recent studies have begun to investigate the possibility of using functional neuroimaging to guide treatment selection for individual patients. By studying the changes between a patient's pre- and post-treatment brain activity, investigators are gaining insights into the impact of treatment on behavior-related neural processing traits associated with particular psychiatric disorders. Furthermore, these studies may shed light on the neural basis of response and nonresponse to specific treatments. The practical limitation of such studies is that the post-treatment scans offer little guidance to treatment selection in clinical settings, since treatment decisions precede the availability of post-treatment brain scans. This shortcoming represents the impetus for developing statistical methodology that would provide clinicians with predictive information concerning the effect of treatment on brain activity and, ultimately, symptom-related behaviors. We present a prediction algorithm that uses a patient's pretreatment scans, coupled with relevant patient characteristics, to forecast the patient's brain activity following a specified treatment regimen. We derive our predictive method from a Bayesian hierarchical model constructed on the pre- and post-treatment scans of designated training data. We perform estimation using the expectation-maximization algorithm. We evaluate the accuracy of our proposed prediction method using K-fold cross-validation, quantifying the error using two new measures that we propose for neuroimaging data. The proposed method is applicable to both PET and fMRI studies. We illustrate its use with a PET study of working memory in patients with schizophrenia and an fMRI data example is also provided.

The neural processing of moral sensitivity to issues of justice and care.

The empirical and theoretical consideration of ethical decision making has focused on the process of moral judgment; however, a precondition to judgment is moral sensitivity, the ability to detect and evaluate moral issues [Rest, J. R. (1984). The major components of morality. In W. Kurtines & J. Gewirtz (Eds.), Morality, moral behaviour, and moral development (pp. 24-38). New York, NY: Wiley]. Using functional magnetic resonance imaging (fMRI) and contextually standardized, real life moral issues, we demonstrate that sensitivity to moral issues is associated with activation of the polar medial prefrontal cortex, dorsal posterior cingulate cortex, and posterior superior temporal sulcus (STS). These activations suggest that moral sensitivity is related to access to knowledge unique to one's self, supported by autobiographical memory retrieval and social perspective taking. We also assessed whether sensitivity to rule-based or "justice" moral issues versus social situational or "care" moral issues is associated with dissociable neural processing events. Sensitivity to justice issues was associated with greater activation of the left intraparietal sulcus, whereas sensitivity to care issues was associated with greater activation of the ventral posterior cingulate cortex, ventromedial and dorsolateral prefrontal cortex, and thalamus. These results suggest a role for access to self histories and identities and social perspectives in sensitivity to moral issues, provide neural representations of the subcomponent process of moral sensitivity originally proposed by Rest, and support differing neural information processing for the interpretive recognition of justice and care moral issues.

A Bayesian hierarchical framework for spatial modeling of fMRI data.

Applications of functional magnetic resonance imaging (fMRI) have provided novel insights into the neuropathophysiology of major psychiatric, neurological, and substance abuse disorders and their treatments. Modern activation studies often compare localized task-induced changes in brain activity between experimental groups. Complementary approaches consider the ensemble of voxels constituting an anatomically defined region of interest (ROI) or summary statistics, such as means or quantiles, of the ROI. In this work, we present a Bayesian extension of voxel-level analyses that offers several notable benefits. Among these, it combines whole-brain voxel-by-voxel modeling and ROI analyses within a unified framework. Secondly, an unstructured variance/covariance matrix for regional mean parameters allows for the study of inter-regional (long-range) correlations, and the model employs an exchangeable correlation structure to capture intra-regional (short-range) correlations. Estimation is performed using Markov Chain Monte Carlo (MCMC) techniques implemented via Gibbs sampling. We apply our Bayesian hierarchical model to two novel fMRI data sets: one considering inhibitory control in cocaine-dependent men and the second considering verbal memory in subjects at high risk for Alzheimer's disease.

Improving point predictions of random effects for subjects at high risk.

The prediction of random effects corresponding to subject-specific characteristics (e.g. means or rates of change) can be very useful in medical and epidemiologic research. At times, one may be most interested in obtaining accurate and/or precise predictions for subjects whose characteristic places them in a tail of the distribution. While the typical posterior mean predictor dominates others in terms of overall mean squared error of prediction (MSEP), its tendency to 'overshrink' has motivated research into alternatives emphasizing other criteria. Here, we specifically target MSEP within a certain region (e.g. above a known cut-off for high risk or a specified percentile of the random effect distribution), and we consider minimizing this quantity with and without constraints on overall MSEP efficiency. We use the normal-theory random intercept model to derive prediction methods with potential to yield markedly better performance for subjects in the specified region, given a well-controlled and (if desired) modest concession of overall MSEP. Criteria geared toward classification as well as overall and regional prediction unbiasedness are also provided. We evaluate the proposed techniques and illustrate them using repeated measures data on fasting blood glucose from type 2 diabetes patients. A simulation study verifies that theoretical properties and relative performances of the proposed predictors are essentially maintained when calculating them in practice based on estimated mixed linear model parameters. Straightforward extensions to incorporate covariates and additional random effects are briefly outlined.

Identifying spatial relationships in neural processing using a multiple classification approach.

The application of statistical classification methods to in vivo functional neuroimaging data makes it possible to explore spatial patterns in task-related changes in neural processing. Cluster analysis is one group of descriptive statistical procedures that can assist in identifying classes of brain regions that exhibit similar task-related functionality. In practice, a limitation of cluster analysis is that the performances of clustering algorithms rely on unknown characteristics of the data, making it difficult to determine which procedure best suits a particular analysis. We present a multiple classification approach that incorporates numerous algorithms, evaluates the associated classifications, and either selects a plausible partition relative to the others considered or pools the results from the numerous methods. The multiple classification approach utilizes a new performance criterion, called the relative information (RI) measure, to evaluate the quality of the candidate partitions and as the basis for producing a composite classification image. Employing multiple classifications, rather than a single algorithm, our methodology increases the chance of detecting the functional relationships within the data and, therefore, produces more reliable results. We apply our methodology to a PET study to explore spatial relationships in measured brain function associated with increasing blood alcohol concentration levels, and we perform a simulation study to evaluate the performance of RI.

Modeling intra-subject correlation among repeated scans in positron emission tomography (PET) neuroimaging data.

Many in vivo positron emission tomography (PET) neuroimaging studies record correlates of regional cerebral blood flow (rCBF) in a series of scans for each individual, usually under different experimental conditions. Typical methods for statistical analysis involve fitting voxel-specific general linear models (GLM) that assume spherical normal errors, implying that all voxel-specific rCBF measurements are independent and arise from identical normal probability distributions. While the spherical GLM provides a unified and computationally efficient approach to estimation, the likely correlations among an individual's repeated scans and heteroscedasticity between conditions prompt the use of extended statistical methodology. We outline a more general method to analyze PET data using random effects and correlated errors to model unequal variances across conditions as well as covariances (correlations) among the repeated scans for each individual. We introduce correlation maps to display intra-subject correlations between an individual's rCBF measurements from different scans. We illustrate the application of our model using data from a study of social anxiety and highlight analytical advantages over the spherical GLM.