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Background and Purpose: Human entorhinal cortex (ERC) connects the temporal neocortex with hippocampus and is essential for memory retrieval and navigation. Markedly, there have been only few quantitative MRI works on the ERC geometric measurements in pediatric and adult healthy subjects across the lifespan. Here, we sought to fill this gap in knowledge by quantifying the ERC thickness in a very large cohort of subjects spanning 9 decades of life., Methods: Using magnetic resonance imaging data from multiple centers (IXI, MMRR, NKI, OASIS combined with the NIH-Child Dev database and locally recruited healthy subjects), we analyzed the lifespan trajectory of ERC thickness in 1,660 healthy controls ranging from 2 to 94 years of age., Results: The ERC thickness increased with age, reached a peak at about 44 years, and then decreased with age. ERC thickness is hemispherically rightward-asymmetric with no gender differences. Mean ERC thickness was found to vary between 2.943 ± .438 mm and 3.525 ± .355 mm across different age populations. Also, more pronounced loss of the ERC thickness in healthy aging men was noticeable., Discussion: Our report with high spatial resolution brain MRI data from 1,660 healthy controls provided important clues about ERC thickness across lifespan. We believe that our report will pave the way for the future studies investigating distinct neural pathologies related with cognitive dysfunctions., (Copyright © 2015 by the American Society of Neuroimaging.)

Neuroimaging has uncovered both long-range and short-range connectivity abnormalities in the brains of individuals with autism spectrum disorders (ASD). However, the precise connectivity abnormalities and the relationship between these abnormalities and cognition and ASD symptoms have been inconsistent across studies. Indeed, studies find both increases and decreases in connectivity, suggesting that connectivity changes in the ASD brain are not merely due to abnormalities in specific connections, but rather, due to changes in the structure of the network in which the brain areas interact (i.e., network topology). In this study, we examined the differences in the network topology between high-functioning ASD patients and age and gender matched typically developing (TD) controls. After quantitatively characterizing the whole-brain connectivity network using diffusion tensor imaging (DTI) data, we searched for brain regions with different connectivity between ASD and TD. A measure of oral language ability was then correlated with the connectivity changes to determine the functional significance of such changes. Whole-brain connectivity measures demonstrated greater local connectivity and shorter path length in ASD as compared to TD. Stronger local connectivity was found in ASD, especially in regions such as the left superior parietal lobule, the precuneus and angular gyrus, and the right supramarginal gyrus. The relationship between oral language ability and local connectivity within these regions was significantly different between ASD and TD. Stronger local connectivity was associated with better performance in ASD and poorer performance in TD. This study supports the notion that increased local connectivity is compensatory for supporting cognitive function in ASD., (Copyright © 2012 Wiley Periodicals, Inc.)

Few researchers agree about the relationship between fronto-temporo-parietal white matter microstructure and reading skills. Unlike many previous reports, which only measured fractional anisotropy, we have also measured macroscopic volume (regional white matter tract volume) and three microstructural indices (axial, radial, and mean diffusivity) to increase interpretability of our findings. We examined the reading-related skills and white matter structure in 10 adolescents and adults with a history of poor reading and 20 age-matched typical readers. We applied a diffusion tensor imaging atlas-based algorithm to major white matter pathways. The relation of white matter structural indices to reading group, hemisphere, and reading-related skill was analyzed using linear models. White matter microstructural indices were related to performance on a sublexical decoding task, but the relations between particular microstructural indices and sublexical decoding ability and reading group were different for association (i.e., cortical-cortical) and projection (i.e., subcortical-cortical) white matter pathways. Changes in projection pathways were consistent with alterations in white matter organization and axonal size, whereas changes in association pathways were consistent with alternations in pathway complexity. Changes in macrostructure paralleled changes in microstructure. We conclude that the relations between several microstructural indices and factors related to reading ability are different for association and projection pathways., (Copyright © 2010 Wiley-Liss, Inc.)

In this communication, we extended a previously described and validated diffusion tensor imaging (DTI) method for segmenting whole brain cerebrospinal fluid (CSF) and gray and white matter (WM) tissue to provide regional volume and DTI metrics of WM tract and cortical and subcortical gray matter. This DTI-based regional segmentation was implemented using the statistical parametric mapping (SPM) toolbox and used the international consortium for brain mapping atlases and Montreal Neurological Institute brain templates. We used our DTI-based segmentation approach to calculate the left putamen volume in a cohort of 136 healthy right-handed males and females aged 15.8-62.8 years. We validated our approach by demonstrating its sensitivity to age-related changes of the putamen. Indeed, our method found that the putamen volume decreased with age (r = -0.30; P < 0.001) while the corresponding fractional anisotropy (FA) increased with advancing age (r = 0.5; P < 0.00001). It is then demonstrated, on a subset of our cohort (n = 31), that the putamen volume obtained by our method correlated with measurements obtained from FreeSurfer (r = 0.396, P < 0.05). Our novel approach increases the information obtained with a DTI examination by providing routine volumetry measure, thereby eliminating separate scans to obtain volumetry data. In addition, the labeled volumes obtained with our method have the potential to increase the accuracy of fiber tracking. In the future, this new approach can be automated to analyze large data sets to help discover noninvasive neuroimaging markers for clinical trials and brain-function studies in both health and disease., (Hum Brain Mapp, 2010. © 2010 Wiley-Liss, Inc.)