Dataset Information

Quantitative diffusion tensor tractography of association and projection fibers in normally developing children and adolescents.

ABSTRACT: Whole-brain diffusion tensor tractography (DTT) at high signal-to-noise ratio and angular and spatial resolutions were utilized to study the effects of age, sex differences, and lateral asymmetries of 6 white matter pathways (arcuate fasciculus [AF], inferior longitudinal fasciculus, inferior fronto-occipital fasciculus [IFOF], uncinate fasciculus [UF], corticospinal tract [CST], and somatosensory pathway [SS]) in 31 right-handed children (6-17 years). Fractional anisotropy (FA), a measure of the orientational variance in water molecular diffusivity, and the magnitude of water diffusivity (parallel, perpendicular, and mean diffusivity) along the pathways were quantified. Three major patterns of maturation were observed: 1) significant increase in FA with age, accompanied by significant decreases in all 3 diffusivities (e.g., left IFOF); 2) significant decreases in all three diffusivities with age without significant changes in FA (e.g., left CST); and 3) no significant age-related changes in FA or diffusivity (e.g., SS). Sex differences were minimal. Many pathways showed lateral asymmetries. In the right hemisphere, the frontotemporal (FT) segment of AF was not visualized in a substantial (29%) number of participants. FA was higher in the left hemisphere in the FT segment of AF, UF, and CST, whereas it was lower in the frontoparietal segment of AF. This study provides normative data essential for the interpretation of pediatric brain DTT measurements in both health and disease.

SUBMITTER: Eluvathingal TJ

PROVIDER: S-EPMC2084482 | biostudies-literature | 2007 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Quantitative diffusion tensor tractography of association and projection fibers in normally developing children and adolescents.

Eluvathingal Thomas J TJ Hasan Khader M KM Kramer Larry L Fletcher Jack M JM Ewing-Cobbs Linda L

Cerebral cortex (New York, N.Y. : 1991) 20070216 12

Whole-brain diffusion tensor tractography (DTT) at high signal-to-noise ratio and angular and spatial resolutions were utilized to study the effects of age, sex differences, and lateral asymmetries of 6 white matter pathways (arcuate fasciculus [AF], inferior longitudinal fasciculus, inferior fronto-occipital fasciculus [IFOF], uncinate fasciculus [UF], corticospinal tract [CST], and somatosensory pathway [SS]) in 31 right-handed children (6-17 years). Fractional anisotropy (FA), a measure of th ...[more]

PMID: 17307759

Similar Datasets

Project description:Diffusion tensor tractography is increasingly used to examine structural connectivity in the brain in various conditions, but its test-retest reliability is understudied. The main purposes of this study were to evaluate 1) the reliability of quantitative measurements of diffusion tensor tractography and 2) the effect on reliability of the number of gradient sampling directions and scan repetition. Images were acquired from ten healthy participants. Ten fiber regions of nine major fiber tracts were reconstructed and quantified using six fiber variables. Intra- and inter-session reliabilities were estimated using intraclass correlation coefficient (ICC) and coefficient of variation (CV), and were compared to pinpoint major error sources. Additional pairwise comparisons were made between the reliability of images with 30 directions and NEX 2 (DTI30-2), 30 directions and NEX 1 (DTI30-1), and 15 directions and NEX 2 (DTI15-2) to determine whether increasing gradient directions and scan repetition improved reliability. Of the 60 tractography measurements, 43 showed intersession CV ? 10%, ICC ? .70, or both for DTI30-2, 40 measurements for DTI30-1, and 37 for DTI15-2. Most of the reliable measurements were associated with the tracts corpus callosum, cingulum, cerebral peduncular fibers, uncinate fasciculus, and arcuate fasciculus. These reliable measurements included factional anisotropy (FA) and mean diffusivity of all 10 fiber regions. Intersession reliability was significantly worse than intra-session reliability for FA, mean length, and tract volume measurements from DTI15-2, indicating that the combination of MRI signal variation and physiological noise/change over time was the major error source for this sequence. Increasing the number of gradient directions from 15 to 30 while controlling the scan time, significantly affected values for all six variables and reduced intersession variability for mean length and tract volume measurements. Additionally, while increasing scan repetition from 1 to 2 had no significant effect on the reliability for DTI with 30 directions, this significantly reduced the upward bias in FA values from all 10 fiber regions and fiber count, mean length, and tract volume measurements from 5 to 7 fiber regions. In conclusion, diffusion tensor tractography provided many measurements with high test-retest reliability across different fiber variables and various fiber tracts even for images with 15 directions (NEX 2). Increasing the number of gradient directions from 15 to 30 with equivalent scan time reduced variability whereas increasing repetition from 1 to 2 for 30-direction DTI improved the accuracy of tractography measurements.

Project description:The "sensory processing disorder" (SPD) refers to brain's inability to organize sensory input for appropriate use. In this study, we determined the diffusion tensor imaging (DTI) microstructural and connectivity correlates of SPD, and apply machine learning algorithms for identification of children with SPD based on DTI/tractography metrics. A total of 44 children with SPD and 41 typically developing children (TDC) were prospectively recruited and scanned. In addition to fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD), we applied probabilistic tractography to generate edge density (ED) and track density (TD) from DTI maps. For identification of children with SPD, accurate classification rates from a combination of DTI microstructural (FA, MD, AD, and RD), connectivity (TD) and connectomic (ED) metrics with different machine learning algorithms - including naïve Bayes, random forest, support vector machine, and neural networks - were determined. In voxel-wise analysis, children with SPD had lower FA, ED, and TD but higher MD and RD compared to TDC - predominantly in posterior white matter tracts including posterior corona radiata, posterior thalamic radiation, and posterior body and splenium of corpus callosum. In stepwise penalized logistic regression, the only independent variable distinguishing children with SPD from TDC was the average TD in the splenium (p < 0.001). Among different combinations of machine learning algorithms and DTI/connectivity metrics, random forest models using tract-based TD yielded the highest accuracy in classification of SPD - 77.5% accuracy, 73.8% sensitivity, and 81.6% specificity. Our findings demonstrate impaired microstructural and connectivity/connectomic integrity in children with SPD, predominantly in posterior white matter tracts, and with reduced TD of the splenium of corpus callosum as the most distinctive pattern. Applying machine learning algorithms, these connectivity metrics can be used to devise novel imaging biomarkers for neurodevelopmental disorders.

Dataset Information

Quantitative diffusion tensor tractography of association and projection fibers in normally developing children and adolescents.

Publications

Quantitative diffusion tensor tractography of association and projection fibers in normally developing children and adolescents.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets