Atypical Brain Development: A Conceptual Framework for Understanding Developmental Learning Disabilities

2001 ◽  
Vol 20 (2) ◽  
pp. 465-481 ◽  
Author(s):  
Jeffrey W. Gilger ◽  
Bonnie J. Kaplan
Author(s):  
Nawaf. M. Aldhafeeri

The study aimed to identify the psychological needs (for competence, autonomy, and affiliation) with kindergarten children in Kuwait. A sample of 117 children from kindergarten (57 with developmental learning disabilities, and 60 of normal children) was selected. Two instruments were used: early detection tool and psychological needs scale. The results showed that there were significant differences (p. < 0.01) between the developmental learning disabilities and the normal children in all needs. Also, there were significant differences (p. < 0.05) between males and females in competence and autonomy needs. There were no significant differences between males and females in the affiliation need. There were significant Interaction effect between gender and the group in the autonomy need indicating that differences due to gender are not constant. 


2016 ◽  
pp. 349-367
Author(s):  
Yuhui Shi

In this article, the necessity of having developmental learning embedded in a swarm intelligence algorithm is confirmed by briefly considering brain evolution, brain development, brainstorming process, etc. Several swarm intelligence algorithms are looked at from developmental learning perspective. Finally, a framework of a developmental swarm intelligence algorithm is given to help understand developmental swarm intelligence algorithms, and to guide to design and/or implement any new developmental swarm intelligence algorithm and/or any developmental evolutionary algorithm.


Author(s):  
Julia M. Stephen ◽  
Isabel Solis ◽  
John F. L. Pinner ◽  
Felicha T. Candelaria-Cook

The use of magnetoencephalography (MEG) to understand alterations in brain development in children has increased rapidly over the past two decades. Investigators have argued that MEG is an ideal neuroimaging tool for children because the technology is quiet and it provides high-density sensor systems. This participant-friendly technology has led to exploration of the use of MEG to identify biomarkers for atypical brain development to facilitate early diagnosis and intervention. Prior studies provide evidence that MEG is sensitive to a number of pediatric clinical disorders demonstrated through significant differences (e.g., latency, amplitude, spectral power) in children with autism spectrum disorder, children born prematurely, and children with fetal alcohol spectrum disorder, to name a few. At the same time, differences in age range, stimulus parameters, and study population characteristics contribute to variability in results across independent laboratories. While the current studies provide strong evidence for the sensitivity of MEG to identify brain abnormalities in children, replication studies are needed to validate biomarkers of atypical brain development to identify children at risk for atypical brain development. Additional studies are also needed to understand the dynamic changes in these brain markers across the age spectrum. Finally, future directions include gaining a broader understanding of typical and atypical brain development to identify neural targets for intervention.


1985 ◽  
Vol 23 (2) ◽  
pp. 133-144 ◽  
Author(s):  
Susan Epps ◽  
James E. Ysseldyke ◽  
Bob Algozzine

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