Mark A Eckert1, Kenneth I Vaden2. 1. Department of Otolaryngology - Head and Neck Surgery, Medical University of South Carolina, Charleston, S.C., 29412, United States. Electronic address: eckert@musc.edu. 2. Department of Otolaryngology - Head and Neck Surgery, Medical University of South Carolina, Charleston, S.C., 29412, United States.
Abstract
BACKGROUND: Structural cerebral asymmetries are hypothesized to provide an architectural foundation for functional asymmetries and behavioral lateralities. Studies of structural asymmetries typically focus on gray matter measures that are influenced by gross deformation fields used for normalization, and thus characterize a combination of different morphologic influences on structural asymmetries. NEW METHOD: A deformation-based morphometry approach was developed to characterize structural asymmetries at different spatial scales of resolution, which can provide relatively more specific inference about the morphologic reason(s) for structural asymmetries, using a dataset of 347 typically developing children (7.00-12.92 years). RESULTS: Significant structural asymmetries were observed for a larger lobar spatial scale (e.g., frontal petalia) and for a smaller gyral/sulcal spatial scale of resolution (e.g., marginal sulcus). Total intracranial volume was significantly associated with asymmetries at the larger spatial scale of normalization, while age was significantly associated with asymmetries at the smaller scale of normalization. There were no significant anti- or fluctuating asymmetry effects based on Hartigan Dip Tests and Bonnett Tests, respectively. COMPARISON WITH EXISTING METHOD(S): While spatially similar asymmetries were observed in both gray matter and deformation field data (e.g., medial planum temporale/Heschl's gyrus), the deformation approach characterizes asymmetries based on three iterations of successively smaller scales of normalization. CONCLUSIONS: Structural asymmetries can be identified in normalization deformations with a procedure that is tailored for sensitivity to structures at different spatial scales of resolution where there may be different mechanisms for the expression of asymmetry.
BACKGROUND: Structural cerebral asymmetries are hypothesized to provide an architectural foundation for functional asymmetries and behavioral lateralities. Studies of structural asymmetries typically focus on gray matter measures that are influenced by gross deformation fields used for normalization, and thus characterize a combination of different morphologic influences on structural asymmetries. NEW METHOD: A deformation-based morphometry approach was developed to characterize structural asymmetries at different spatial scales of resolution, which can provide relatively more specific inference about the morphologic reason(s) for structural asymmetries, using a dataset of 347 typically developing children (7.00-12.92 years). RESULTS: Significant structural asymmetries were observed for a larger lobar spatial scale (e.g., frontal petalia) and for a smaller gyral/sulcal spatial scale of resolution (e.g., marginal sulcus). Total intracranial volume was significantly associated with asymmetries at the larger spatial scale of normalization, while age was significantly associated with asymmetries at the smaller scale of normalization. There were no significant anti- or fluctuating asymmetry effects based on Hartigan Dip Tests and Bonnett Tests, respectively. COMPARISON WITH EXISTING METHOD(S): While spatially similar asymmetries were observed in both gray matter and deformation field data (e.g., medial planum temporale/Heschl's gyrus), the deformation approach characterizes asymmetries based on three iterations of successively smaller scales of normalization. CONCLUSIONS: Structural asymmetries can be identified in normalization deformations with a procedure that is tailored for sensitivity to structures at different spatial scales of resolution where there may be different mechanisms for the expression of asymmetry.
Authors: Thomas R Barrick; Clare E Mackay; Sylvain Prima; Frederik Maes; Dirk Vandermeulen; Timothy J Crow; Neil Roberts Journal: Neuroimage Date: 2005-02-01 Impact factor: 6.556
Authors: Mark A Eckert; Christiana M Leonard; Marko Wilke; Mathew Eckert; Todd Richards; Anne Richards; Virginia Berninger Journal: Cortex Date: 2005-06 Impact factor: 4.027
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