Hippocampus co-atrophy pattern in dementia deviates from covariance patterns across the lifespan

Abstract

The hippocampus is a plastic region and highly susceptible to ageing and dementia. Previous studies explicitly imposed a priori models of hippocampus when investigating ageing and dementia-specific atrophy but led to inconsistent results. Consequently, the basic question of whether macrostructural changes follow a cytoarchitectonic or functional organization across the adult lifespan and in age-related neurodegenerative disease remained open. The aim of this cross-sectional study was to identify the spatial pattern of hippocampus differentiation based on structural covariance with a data-driven approach across structural MRI data of large cohorts (n = 2594). We examined the pattern of structural covariance of hippocampus voxels in young, middle-aged, elderly, mild cognitive impairment and dementia disease samples by applying a clustering algorithm revealing differentiation in structural covariance within the hippocampus. In all the healthy and in the mild cognitive impaired participants, the hippocampus was robustly divided into anterior, lateral and medial subregions reminiscent of cytoarchitectonic division. In contrast, in dementia patients, the pattern of subdivision was closer to known functional differentiation into an anterior, body and tail subregions. These results not only contribute to a better understanding of co-plasticity and co-atrophy in the hippocampus across the lifespan and in dementia, but also provide robust data-driven spatial representations (i.e. maps) for structural studies.

Keywords: dementia; elderly; parcellation; structural covariance; temporal lobe.

Figure 1

Stability and consistency of hippocampal parcellations. (A) Stable organizational patterns were found for right and left hippocampus for cluster solutions 2–4 estimated with split-half cross-validation. All clusterings reached very high stability >0.9 adjusted Rand Index. (B) Cross-sample consistency of lower cluster solutions measured with the adjusted Rand Index. Despite overall high stability, the simple parcellation schemes 2–4 were also very consistent >0.6 across datasets and within age/disease-specific groups (e.g. young, elderly) suggesting biological relevance in those differentiations. Cluster solution 3 was exceptionally useful to study age and disease-related patterns, because this scheme demonstrated not only high within age/disease similarity but to some extent also across age/disease groups indicating relatedness, which did not apply for cluster solution 4. In contrast cluster solution 2 showed very high similarity independent of age/disease and dataset suggesting on the one hand a robust biological differentiation, but on the other hand a less flexible scheme to represent lifespan and pathological alterations. Box plots with median, 1.5 interquartile range (IQR), min. Q1–1.5×IQR, max. Q3 + 1.5 × IQR.

Figure 2

Age and disease-specific clusterings of the hippocampus and its similarity to functional differentiation into head, body and tail parcellation. In younger age the hippocampal differentiation was reminiscent of the differentiation between subiculum versus cornu ammonis (CA) 1–4 and dentate gyrus subfields. With increasing age, the lateral subregion decreased from the tail, whereas the differentiation in dementia was reminiscent of the functional differentiation into head, body and tail also suggested by the similarity estimation. Clusterings were compared using adjusted Rand Index.

Figure 3

Patterns of structural covariance of each hippocampus subregion in young, elderly and dementia groups. Relative resting state functional connectivity networks of dementia hippocampus in healthy elderly resembled structural covariation (SC) networks of dementia hippocampus in dementia group. Uncorrected (P < 0.001), thresholded T = 1.

Figure 4

Behavioural characterization of cluster covariance networks in age and disease groups using NeuroSynth. Behavioural profiles of anterior clusters covariance network remained relatively stable across the lifespan and in disease playing a major role in automatic perceptual-emotional approach-behaviour in learning, establishing self-related memories. Across the lifespan the medial (blue) subregion’s network changed from being associated with visual processing in younger years to being also motor-related in older age. The lateral body (green) subregion in the group of dementia was behaviourally associated with language and theory of mind processing while the lateral subregion did not show a clear behavioural specificity in the second half of lifespan compared to the anterior subregion.