Age-related Differences in White Matter Integrity in Healthy Human Brain: Evidence from Structural Mri and Diffusion Tensor Imaging

Abstract

The aim is to investigate the relationship between microstructural white matter (WM) diffusivity indices and macrostructural WM volume (WMV) among healthy individuals (20–85 years). Whole-brain diffusion measures were calculated from diffusion tensor imaging using FMRIB software library while WMV was estimated through voxel-based morphometry, and voxel-based analysis was carried out using tract-based spatial statistics. Our results revealed that mean diffusivity, axial diffusivity, and radial diffusivity had shown good correlation with WMV but not for fractional anisotropy (FA). Voxel-wise tract-based spatial statistics analysis for FA showed a significant decrease in four regions for middle-aged group compared to young-aged group, in 22 regions for old-aged group compared to middle-aged group, and in 26 regions for old-aged group compared to young-aged group (P < 0.05). We found significantly lower WMV, FA, and mean diffusivity values in females than males and inverted-U trend for FA in males. We conclude differential age- and gender-related changes for structural WMV and WM diffusion indices.

The Lifespan Trajectory of the Encoding-Retrieval Flip: A Multimodal Examination of Medial Parietal Cortex Contributions to Episodic Memory

“…We also tested for main effects of source memory on CT, controlling for sex. We did include sex as a covariate in the ROI analyses because sex is associated with differences in brain structure (Raznahan et al, 2011); however, studies using FreeSurfer do rarely detect sex differences in mean CT or trajectories of CT development Tamnes et al, 2010;Amlien et al, 2016). The analyses were performed across all vertices and the results were thresholded using pre-cached Monte Carlo simulation with a cluster-forming-threshold of p Ͻ 0.01 and and Bonferroni adjusted for analyses across both hemispheres.…”

“…This reversal of functional response is likely critical for memory and has been dubbed the encoding/retrieval (E/R) flip Huijbers et al, 2012;2013;Gilmore et al, 2015). Interestingly, the PMC is among the regions that undergo the most rapid structural (Brown and Jernigan, 2012;Tamnes et al, 2013;Amlien et al, 2016) and metabolic (Blüml et al, 2013;Degnan et al, 2014) changes during late childhood and adolescence, coinciding with episodic memory development (Ofen et al, 2007;Ghetti and Bunge, 2012). An intriguing question is whether improvement in the ability to dynamically regulate PMC activity during encoding and retrieval can account for developmental gains in memory.…”

Brain age prediction reveals aberrant brain white matter in schizophrenia and bipolar disorder: A multi-sample diffusion tensor imaging study

“…Aberrant myelination and brain wiring during adolescence has been included among the neurobiological features of severe mental disorders, and white matter (WM) aberrations have been documented prior to disease onset (7)(8)(9)(10)(11). Brain imaging has shown that normative WM development follows a characteristic non-linear trajectory with peak maturation around the third or fourth decade (12)(13)(14). Compared to healthy controls (HC), adult patients with SZ or BD exhibit anatomically distributed group-level differences in various diffusion-based indices of WM structure (15,16).…”

White Matter Degeneration after Ischemic Stroke: A Longitudinal Diffusion Tensor Imaging Study

“…Contralesional changes may have implications on how DTI parameters should be presented in future studies. DTI parameters can vary between individuals, as it is modulated in response to motor training, 29 age, 20 and cardiovascular fitness. 21 In many stroke studies, DTI parameters are normalized with regard to the contralesional hemisphere.…”

“…To assess changes over time in DTI parameters, we used mixed effect models with time, group (NIHSS‐negative and NIHSS‐positive) and the time*group interaction as fixed effects and subject as a random effect . DTI parameters have shown to be dependent on factors such as age and physical fitness, and therefore we modeled individual intercepts as a random effect. We examined the residuals of the fitted model to assess the assumptions for linear modeling (heteroscedasticity and normal distribution of the residuals).…”