[The proposed research tests the hypothesis that atypical cerebral symmetries increase the risk for dyslexia through the expression of dyslexia-related genes that are known to regulate brain development.] While there were early promising findings linking planum temporale symmetry to dyslexia, study limitations due to small sample size, inconsistent measurement methods, and varied behavioral and genetic profiles of the subjects produced inconsistent results. [Here we examine planum temporale and other cerebral symmetries associated with dyslexia]. We address the limitations of previous studies by using a large dataset of existing genetic, neuroimaging, and behavioral data, as well as multi-site methods that we developed in the current funding period that make it possible to address dyslexia hypotheses with large multisite datasets. We have demonstrated the ability to deal with missing data, varied image acquisitions, and the behavioral heterogeneity of dyslexia samples that is influenced by sampling approaches. [Specific Aim 1 is to test the hypothesis that atypical cerebral asymmetries are observed for specific reading disability profiles, which are theoretically and empirically-grounded and map to different genetic risks.
Specific Aim 2 is to examine the degree to which specific genetic risk variants for dyslexia influence the development of cerebral asymmetries.
Specific Aim 3 is to develop the cloud-based infrastructure to provide investigators with secondary data for use in their studies and to replicate our findings (e.g., cerebral asymmetry measures related to dyslexia). The results will provide a consensus on the cerebral asymmetry hypothesis for dyslexia because of our large dataset and collaborative approach, provide behavioral neurogenetic explanations for dyslexia, and provide resources to the research community to advance our understanding of dyslexia and other developmental disorders.]
The neurogenetic paths to the varied expression of dyslexia are unclear. [We will test a long-standing cerebral asymmetry hypothesis for dyslexia and its potential genetic underpinnings. We will develop cloud-based computing tools for data sharing and delivering automated brain morphology measures to contributors as an incentive for data sharing, and so that contributors can replicate our findings and pursue new questions with the brain morphology measures.]
| Eckert, Mark A; Vaden Jr, Kenneth I; Maxwell, Amanda B et al. (2017) Common Brain Structure Findings Across Children with Varied Reading Disability Profiles. Sci Rep 7:6009 |
| Eckert, Mark A; Berninger, Virginia W; Hoeft, Fumiko et al. (2016) A case of Bilateral Perisylvian Syndrome with reading disability. Cortex 76:121-4 |
| Eckert, Mark A; Berninger, Virginia W; Vaden Jr, Kenneth I et al. (2016) Gray Matter Features of Reading Disability: A Combined Meta-Analytic and Direct Analysis Approach(1,2,3,4). eNeuro 3: |
| Song, Xuebo; Wang, James; Wang, Anlin et al. (2015) DeID - a data sharing tool for neuroimaging studies. Front Neurosci 9:325 |
| Wang, James Z; Zhang, Yuanyuan; Dong, Liang et al. (2014) G-Bean: an ontology-graph based web tool for biomedical literature retrieval. BMC Bioinformatics 15 Suppl 12:S1 |
| Li, Lin; Zhang, Qizhi; Ding, Yihua et al. (2014) Automatic diagnosis of melanoma using machine learning methods on a spectroscopic system. BMC Med Imaging 14:36 |
| Song, Xuebo; Li, Lin; Srimani, Pradip K et al. (2014) Measure the Semantic Similarity of GO Terms Using Aggregate Information Content. IEEE/ACM Trans Comput Biol Bioinform 11:468-76 |
| Raskind, Wendy H; Peter, Beate; Richards, Todd et al. (2012) The genetics of reading disabilities: from phenotypes to candidate genes. Front Psychol 3:601 |
