AD, the most common neurodegenerative disorder, results in profound cognitive and functional deterioration, and ultimately death. AD pathology is triggered by the aberrant processing and deposition of two proteins – Aß (beta-amyloid) and tau - leading to neuronal dysfunction and neuronal death. The cumulative loss of neurons and their connections results in macroscopic changes such as cortical and hippocampal atrophy and ventricular enlargement. Brain atrophy is a common feature of virtually all neurodegenerative disorders, and each disorder has a unique pattern of involvement. The classic AD pattern consists of early prominent involvement of the entorhinal cortex and the hippocampus, followed by significant atrophy of the posterior (i.e., temporal, parietal and occipital) and later on the frontal association cortices. Sulcal dilation and ventriculomegaly reflecting both gray and white matter loss are prominent and easily seen on conventional diagnostic images. While indeed AD is considered a primary gray matter disorder, there are significant secondary changes in the white matter in subjects with AD. Most likely white matter loss is due to secondary loss of the axons following neuronal death. We could further support this argument by the findings reported in42
. The authors of this paper report a gray/white matter ratio of 1.7 (corresponding to approximately 63% gray and 37% white matter) in healthy elderly (mean age 71 y, range 65–76 y) and a gray/white matter ratio of 1.9 (corresponding to approximately 66% gray and 34% white matter) in AD subjects (mean age 70 y, range 61–75 y).
Normal aging is also associated with hippocampal atrophy9
and ventricular dilation13, 23
. However, unlike in AD, these changes are modest and their rate of progression over time is relatively slow. Both AD and normal aging result in similar changes, so separating these two states is frequently challenging. Here we have investigated effects of age and AD independent of each other.
Despite the cross-sectional nature of this work the pervasive AD effect on the hippocampal formation is clearly demonstrable. We found significant between-group differences between NC and AD in all hippocampal subfields. Statistically significant differences between MCI and NC were largely restricted to the hippocampal head and the subiculum bilaterally as well as the CA2–3 area on the right. The structural differences between MCI and AD were most pronounced in the CA1 subfield but were also seen in the CA2–3 areas bilaterally.
The effects of full-blown AD on the lateral ventricles localized not only to the posterior portions (body/occipital horn) but also to the frontal horns. This predominantly posterior ventricular enlargement in MCI agrees with the known early posterior predominant disease effect, which later spreads to the frontal horns. Whether involvement of the frontal horns coincides or predates the conversion to full-blown AD syndrome and hence marks the transition to functional impairment remains to be proven.
Previous studies report that aging affects only selected hippocampal subfields, but no two studies have agreed on the exact regions involved. Some report involvement of the CA3/dentate gyrus and CA18
, others of CA1 and subiculum9
, the subiculum and the dentate gyrus10
or the subiculum alone11
. The disagreement between these studies likely reflects MR acquisition and methodological differences, including limited statistical power and relatively small effect sizes. Our results suggest an age effect on all these subfields and confirm all of these previous observations. Our finding of trend-level effect in the MCI group in the context of significant effects in the NC and AD groups may be due to relative lack of power due to the smaller size of our MCI group.
In agreement with prior reports14, 43
, we found a significant effect of age on the shape and size of the lateral ventricles. This effect is most pronounced in the frontal horns perhaps as the result of structural changes in one or more tissue classes in the frontal lobes44–47
. These data are also in keeping with the pattern of cognitive decline seen in the elderly, including decline in working memory, information processing speed and retrieval of previously learned information48
. We found the strongest ventricular age effect in our youngest group – the NC group, and weaker age effects in our older groups – the MCI and AD groups. This may seem counterintuitive, but may be attributable to several factors. One could speculate that either the effect of age on ventricular volume/radial distance is nonlinear with steeper slopes in the younger as opposed to older elderly subjects as was recently demonstrated49
. Further evidence is the data reported by Sowell et al.50, 51
where the investigators plotted trajectories of cortical atrophy over the lifespan, and some age effects slowed down. Another possible reason for this in a longitudinal study, is cohort attrition due to mortality or inability to remain in the study when atrophy has exceeded a certain level. This might lead to a censoring of data available to understand the age effects. Yet here we only work with cross-sectional data. It may also be that once the brain and ventricular volumes are affected by Alzheimer's pathology the strength of the association between age and ventricular volume or radial distance weakens. The only way to find out whether any of these two speculations are true would be to study the effect of aging longitudinally in sample of cognitively normal elderly who later in life have succumbed to AD.
Several strengths and limitations of this work should be recognized. The strengths of this work lie in the thorough evaluation of this relatively large subject pool and the advanced imaging software used. Additionally, we carefully modeled the effects of age and by doing so we build upon previous reports of associations between age and change in hippocampal and ventricular volume over time. Our 3D results show aging effects in all hippocampal subfields. We also demonstrated a fronto-parietal pattern of age effects on the lateral ventricles. Given these findings, we conclude that aging accounts for some of the variability of hippocampal and lateral ventricular structural measures and thus should be included as covariate in all structural hippocampal and ventricular analyses when possible. The major weakness of this study is its cross-sectional design. Subject follow-up and the use of longitudinal datasets such as those from the Alzheimer's Disease Neuroimaging Initiative are planned. The second limitation is the fact that original imaging data collection was conducted on 1.5Tesla MR scanner. It would certainly be valuable to collect high resolution hippocampal scans, such as T2-weighted scans at 3 or 7 Tesla52–55
however this type of scan is not routinely aquired in large scale database projects such as the UCLA imaging database or ADNI56
, where the whole brain needs to be assessed and scan time is limited. Finally, we corrected for potentially confounding variables such as age, sex, and education using a general linear model, but it may be that age and education effects are nonlinear in nature.