Cognitive deficits in working memory (WM) are characteristic features of Attention-Deficit/Hyperactivity Disorder (ADHD) and autism. However, few studies have investigated cognitive deficits using a wide range of cognitive measures. We compared children with ADHD (n = 49) and autism (n = 33) with a demographically matched control group (n = 79) on a multidimensional battery of cognitive ability. Results confirmed previous research that both groups were characterized by deficits in WM. However, results also suggest verbal WM measures were better predictors than nonverbal WM measures. In addition, measures of visual-motor integration are equally discriminating of children with ADHD and autism from a matched control group. In all, 81% discrimination accuracy was obtained using only WM and visual-motor integration measures. Demonstrated shared deficits in WM and visual-motor integration are explained based on proposed neurological mechanisms common across the two disorders. Clinical implications are discussed.

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Development of early math skill depends on a prerequisite level of cognitive development. Identification of specific cognitive skills that are important for math development may not only inform instructional approaches but also inform assessment approaches to identifying children with specific learning problems in math. This study investigated the specific cognitive correlates of math problem solving across early grade levels (1-4) while controlling for basic calculation skills. As expected, basic calculation skill was a significant predictor of math problem solving across the entire sample. However, the addition of cognitive measures almost doubled the variance explained (R 2 = .61). Additionally, only select cognitive variables contributed to the prediction of math problem solving, and these variables change in importance as children develop higher-level math skills. Results are discussed within a developmental model, which emphasizes the increasing importance of abstract code representations required in higher levels of math performance. C 2015 Wiley Periodicals, Inc.

Measurement reliability is an important aspect of establishing the utility of scores used in clinical practice. Although much is known about the reliability of quantitative electroencephalographic (qEEG) metrics related to absolute power, less is known about the reliability of coherence metrics. The current study examined the measurement reliability of coherence metrics across standard frequency bands during an eyes-closed resting state. Reliability was examined both within channel pairs, and averaged across spatially contiguous channels, to summarize global patterns. We found that while most channel pairs were highly reliable on average, there was substantial variability across channels. Finally, we estimated the effect of measurement reliability on the detection of treatment-related neural change. We concluded that estimates of reliability for treated channels are crucial, and should factor into clinical assessment of treatment efficacy for EEG biofeedback (neurofeedback), especially in cases where large cross-channel variability is present.

Factor-analytic studies support a hierarchical four-factor model for the Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV) with a prominent general, third-order factor. However, there is substantial disagreement on which type of higher-order model best fits the data and how different models should guide test interpretation in clinical practice, with many studies concluding interpretation should primarily be focused on general indicators of intelligence. We performed a series of confirmatory factor analyses with the WISC-IV standardization sample (N = 2,200, ages 6-16 years) to examine model fit and reexamined models used to support test interpretation at the general level. Consistent with previous research, bifactor models were difficult to identify; however, compared with bifactor and hierarchical models, the correlated factors model with no general higher-order factor provided the best fit to the data. Results from this study support the basic four-factor model specified in the WISC-IV technical manual, with test interpretation primarily focused at the factor level, rather than the general level suggested in previous studies.