Individual prediction of dyslexia by single versus multiple deficit models.

The overall goals of this study were to test single versus multiple cognitive deficit models of dyslexia (reading disability) at the level of individual cases and to determine the clinical utility of these models for prediction and diagnosis of dyslexia. To accomplish these goals, we tested five cognitive models of dyslexia--two single-deficit models, two multiple-deficit models, and one hybrid model--in two large population-based samples, one cross-sectional (Colorado Learning Disability Research Center) and one longitudinal (International longitudinal Twin Study). The cognitive deficits included in these cognitive models were in phonological awareness, language skill, and processing speed and/or naming speed. To determine whether an individual case fit one of these models, we used two methods: 1) the presence or absence of the predicted cognitive deficits, and 2) whether the individual's level of reading skill best fit the regression equation with the relevant cognitive predictors (i.e., whether their reading skill was proportional to those cognitive predictors.) We found that roughly equal proportions of cases met both tests of model fit for the multiple deficit models (30-36%) and single deficit models (24-28%); hence, the hybrid model provided the best overall fit to the data. The remaining roughly 40% of cases in each sample lacked the deficit or deficits that corresponded with their best-fitting regression model. We discuss the clinical implications of these results for both diagnosis of school-age children and preschool prediction of children at risk for dyslexia.

A multiple deficit model of reading disability and attention-deficit/hyperactivity disorder: searching for shared cognitive deficits.

BACKGROUND: This study tests a multiple cognitive deficit model of reading disability (RD), attention-deficit/hyperactivity disorder (ADHD), and their comorbidity. METHODS: A structural equation model (SEM) of multiple cognitive risk factors and symptom outcome variables was constructed. The model included phonological awareness as a unique predictor of RD and response inhibition as a unique predictor of ADHD. Processing speed, naming speed, and verbal working memory were modeled as potential shared cognitive deficits. RESULTS: Model fit indices from the SEM indicated satisfactory fit. Closer inspection of the path weights revealed that processing speed was the only cognitive variable with significant unique relationships to RD and ADHD dimensions, particularly inattention. Moreover, the significant correlation between reading and inattention was reduced to non-significance when processing speed was included in the model, suggesting that processing speed primarily accounted for the phenotypic correlation (or comorbidity) between reading and inattention. CONCLUSIONS: This study illustrates the power of a multiple deficit approach to complex developmental disorders and psychopathologies, particularly for exploring comorbidities. The theoretical role of processing speed in the developmental pathways of RD and ADHD and directions for future research are discussed.

Age-related regional network of magnetic resonance imaging gray matter in the rhesus macaque.

Human structural neuroimaging studies have supported the preferential effects of healthy aging on frontal cortex, but reductions in other brain regions have also been observed. We investigated the regional network pattern of gray matter using magnetic resonance imaging (MRI) in young adult and old rhesus macaques (RMs) to evaluate age effects throughout the brain in a nonhuman primate model of healthy aging in which the full complement of Alzheimer's disease (AD) pathology does not occur. Volumetric T1 MRI scans were spatially normalized and segmented for gray matter using statistical parametric mapping (SPM2) voxel-based morphometry. Multivariate network analysis using the scaled subprofile model identified a linear combination of two gray matter patterns that distinguished the young from old RMs. The combined pattern included reductions in bilateral dorsolateral and ventrolateral prefrontal and orbitofrontal and superior temporal sulcal regions with areas of relative preservation in vicinities of the cerebellum, globus pallidus, visual cortex, and parietal cortex in old compared with young RMs. Higher expression of this age-related gray matter pattern was associated with poorer performance in working memory. In the RM model of healthy aging, the major regionally distributed effects of advanced age on the brain involve reductions in prefrontal regions and in the vicinity of the superior temporal sulcus. The age-related differences in gray matter reflect the effects of healthy aging that cannot be attributed to AD pathology, providing support for the targeted effects of aging on the integrity of frontal lobe regions and selective temporal lobe areas and their associated cognitive functions.

Regional network of magnetic resonance imaging gray matter volume in healthy aging.

Healthy aging has been associated with brain volume reductions preferentially affecting the frontal cortex, but also involving other regions. We used a network model of regional covariance, the Scaled Subprofile Model, with magnetic resonance imaging voxel-based morphometry to identify the regional distribution of gray matter associated with aging in 26 healthy adults, 22-77 years old. Scaled Subprofile Model analysis identified a pattern that was highly correlated with age (R2=0.66, P