Longitudinal Study of Callosal Microstructure in the Normal Adult Aging Brain Using Quantitative DTI Fiber Tracking

Abstract

We present a review of neuroimaging studies of normal adult aging conducted with Diffusion Tensor Imaging (DTI) and data from one of the first longitudinal studies using DTI to study normal aging. To date, virtually all DTI studies of normal adult aging have been cross-sectional and have identified several patterns of white matter microstructural sparing and compromise that differentiate regional effects, fiber type, and diffusivity characteristics: 1) fractional anisotropy (FA) is lower and mean diffusivity is higher in older than younger adults, 2) aging is characterized by an anterior-to-posterior gradient of greater-to-lesser compromise also seen in superior-to-inferior fiber systems, and 3) association fibers connecting cortical sites appear to be more vulnerable to aging than projection fibers. The results of this longitudinal study of the macrostructure and microstructure of the corpus callosum yielded a consistent pattern of differences between healthy, young (20s to 30s) and elderly (60s to 70s) men and women without change over 2 years. We then divided the fibers of the corpus callosum into the midsagittal strip and the lateral distal fibers in an attempt to identify the locus of the age-related differences. The results indicated that, on average, mean values of FA and longitudinal diffusivity (λL) were lower in the distal than midsagittal fibers in both groups, but the age effects and the anterior-to-posterior gradients were more pronounced for the distal than midsagittal fibers and extended more posteriorly in the distal than midsagittal fibers. Despite lack of evidence for callosal aging over 2 years, ventricular enlargement occurred and was disproportionately greater in the elderly relative to the young group, being 8.2% in the elderly but only 1.2% in the young group. Thus, different brain regions can express different rates of change with aging. Our longitudinal DTI data indicate that normal aging is associated with declining FA and increasing diffusivity in both λL (longitudinal diffusivity) and λT (transverse diffusivity), perhaps defining the normal ontological condition rather than a pathological one, which can be marked by low FA and low diffusivity.There is little doubt that even healthy aging is marked by decline in sensory, motor, and selective cognitive functions, well delineated by hundreds of careful, quantitative, crosssectional and longitudinal assessments (for volumes of reviews, Birren & Schaie, 2001;Craik & Salthouse, 2008). Less known are the neural mechanisms responsible for involutional changes in function, but quantitative in vivo neuroimaging studies conducted over the last quarter century provide critical leads to identify the selective brain structures and systems that contribute to functional decline.We and others have recently reviewed the neuroimaging findings in normal adult aging (Madden, Bennet & Song, 2009;, 2010Zahr, Pfefferbaum & Sullivan, 2009) based on magnetic resonance imaging (MRI), Correspondence: Edith V. Sullivan, Ph.D., Stanford University School o...

The Role of Diffusion Tensor Imaging in the Study of Cognitive Aging

“…Such uncertainty information may allow users to quantify the effects of DTI data quality on connectivity measurements, account for data quality in downstream analyses, and identify corrupted scans for which uncertainty is especially high [see Koltchinskii et al (2007) for more information] neuropathological processes presumably represent the most viable path toward understanding the relative time course of late-life white matter changes and how that time course is modulated by a complex array of inter-connected biological events. Yet, to our knowledge, there may be as few as three published studies related to cognitive aging that involve multiple DTI scans per human subject (Charlton et al 2010;Teipel et al 2010;Sullivan et al 2010). As noted above methodological issues related to DTI measurement reliability have made serial DTI studies difficult, but their importance for decoding the biology of cognitive aging is clear, and they will remain a future direction of study.…”

“…One study has suggested that by investigating relationships between FLAIR signal intensity and DTI measures within WMHs, we can effectively categorize WMHs according to the severity of WMH dysfunction, but the clinical value of such deeper characterization of WMH severity is currently unclear (Zhan et al 2009). Second, while several serial FLAIR studies have used multiple scans per individual to directly chart the longitudinal course of WMH changes in relation to risk factors and cognition, only three analogous serial DTI studies have appeared that report associations between DTI-based measures and ancillary variables of interest (Charlton et al 2010;Teipel et al 2010;Sullivan et al 2010). Third, while FLAIR is a longstanding MRI technology that is relatively easy to acquire, DTI is a recent development, with attendant problems related to data reliability and biases (see Sect.…”

Changes in Callosal Motor Fiber Integrity after Subcortical Stroke of the Pyramidal Tract

“…Indeed, even normal aging is characterized by an anterior-to-posterior gradient of microstructural degeneration along the CC (Sullivan et al, 2010) making regions of the isthmus/body of the CC, where CMF cross, particularly vulnerable to thinning. Fibers of the COF, which cross more posteriorly in the splenium of the CC, were not affected and thus delineated in all subjects.…”

Automated longitudinal intra-subject analysis (ALISA) for diffusion MRI tractography

“…Boosted by the advent of more stable and high performance MR equipment, the interest to perform longitudinal DTI studies for capturing such subject-specific changes in microstructural organization is increasing rapidly (e.g., Concha et al, 2007;Deprez et al, 2012;Gong et al, 2008;Keller and Just, 2009;Kumar et al, 2009;Ljungqvist et al, 2011;Schlaug et al, 2009;Scholz et al, 2009;Sullivan et al, 2010;Yogarajah et al, 2010). Although there are several well-established methods for analyzing cross-sectional DTI data sets, they may not be 'optimal' for longitudinal studies.…”