Comparison of cognition abilities between groups of children with specific learning disability having average, bright normal and superior nonverbal intelligence

Karande, Sunil; Sawant, Sulaxna; Kulkarni, Madhuri; Galvankar, P; Sholapurwala, Rukhshana

doi:10.4103/0019-5359.15085

Cited by 5 publications

(1 citation statement)

References 6 publications

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“…In Waldmann's et al [78] study, subjects between the ages of 18 and 30 were divided into groups based on their Satz-Mogel Wechsler Adult Intelligence Scale-Revised FSIQ scores: (a) Borderline (70 to 79); (b) Low Average (80 to 89); (c) Average (90 to 109); (d) High Average (110 to 119); (e) Superior (120 to 129). In another study by Karande et al [79], ninety-five children with specific learning disabilities (aged 9–14 years) were divided into groups based on their nonverbal IQ scores obtained on the Wechsler Intelligence Scale for Children test: (i) average-nonverbal intelligence group (IQ 90–109), bright normal-nonverbal intelligence group (IQ 110–119), and (iii) superior-nonverbal intelligence group (IQ 120–129). In both studies, an IQ score of 120 was used as the cutoff for identifying the “superior group”.…”

Section: Methodsmentioning

confidence: 99%

Brain Anatomical Network and Intelligence

et al. 2009

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Intuitively, higher intelligence might be assumed to correspond to more efficient information transfer in the brain, but no direct evidence has been reported from the perspective of brain networks. In this study, we performed extensive analyses to test the hypothesis that individual differences in intelligence are associated with brain structural organization, and in particular that higher scores on intelligence tests are related to greater global efficiency of the brain anatomical network. We constructed binary and weighted brain anatomical networks in each of 79 healthy young adults utilizing diffusion tensor tractography and calculated topological properties of the networks using a graph theoretical method. Based on their IQ test scores, all subjects were divided into general and high intelligence groups and significantly higher global efficiencies were found in the networks of the latter group. Moreover, we showed significant correlations between IQ scores and network properties across all subjects while controlling for age and gender. Specifically, higher intelligence scores corresponded to a shorter characteristic path length and a higher global efficiency of the networks, indicating a more efficient parallel information transfer in the brain. The results were consistently observed not only in the binary but also in the weighted networks, which together provide convergent evidence for our hypothesis. Our findings suggest that the efficiency of brain structural organization may be an important biological basis for intelligence.

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Section: Methodsmentioning

confidence: 99%