Achievement correlates of the Woodcock-Johnson Reading and Mathematics Subtests, KeyMath, and Woodcock Reading in an elementary aged learning disabled population

Using a semi-automated approach to quantify Unidentified Bright Objects in Neurofibromatosis type 1 and linkages to cognitive and academic outcomes

Neurofibromatosis type 1 (NF1) is an autosomal dominant neurocutaneous syndrome that affects multiple organ systems resulting in widespread symptoms, including cognitive deficits. In addition to the criteria required for an NF1 diagnosis, approximately 70% of children with NF1 present with Unidentified Bright Objects (UBOs) or Focal Areas of Signal Intensity, which are hyperintense bright spots seen on T2-weighted magnetic resonance images and seen more prominently on FLAIR magnetic resonance images (Sabol et al., 2011). Current findings relating the presence/absence, quantities, sizes, and locations of these bright spots to cognitive abilities are mixed. To contribute to and hopefully disentangle some of these mixed findings, we explored potential relationships between metrics related to UBOs and cognitive abilities in a sample of 28 children and adolescents with NF1 (M=12.52 years; SD=3.18 years; 16 male). We used the Lesion Segmentation Tool (LST) to automatically detect and segment the UBOs. The LST was able to qualitatively and quantitatively reliably detect UBOs in images of children with NF1. Using these automatically detected and segmented lesions, we found that while controlling for age, biological sex, perceptual IQ, study, and scanner, "total UBO volume", defined as the sum of all the voxels representing all of the UBOs for each participant, helped explain differences in word reading, phonological awareness, and visuospatial skills. These findings contribute to the emerging NF1 literature and help parse the specific deficits that children with NF1 have, to then help improve the efficacy of reading interventions for children with NF1.

The Woodcock-Johnson Tests of Cognitive Ability: A Comparison of Two Methods of Cluster Scale Analysis for Three Learning Disability Subtypes

The Woodcock-Johnson Oral Language/Verbal Ability clusters and Broad Reasoning/Reasoning clusters were compared for three samples of learning-disabled children: (a) high math and low reading, (b) high reading and low math, and (c) equally low math and reading abilities. Data indicated each pair of clusters to be highly correlated across subtype (.73-.93). In all instances but one, the Oral Language and Broad Reasoning clusters generated significantly lower mean standard scores than the Verbal Ability and Reasoning clusters, respectively. Implications for interpretation are discussed.

Similarity among common measures of academic achievement: implications for assessing disabled children

Four commonly used measures of academic achievement were administered in counterbalanced order to 32 elementary and middle school-aged children enrolled in exceptional education programs for the learning disabled. Measures chosen were the Kaufman Test of Educational Achievement, KeyMath-R, Test of Written Spelling 2, and the Gray Oral Reading Test-Revised. Analysis suggested corresponding measures of reading, mathematics, and spelling were highly correlated, although in some instances generated significantly different mean standard scores. Implications for practitioners are discussed.