Three-dimensional surface mapping of hippocampal atrophy progression from MCI to AD and over normal aging as assessed using voxel-based morphometry

Abstract

The hippocampus is the brain structure of highest and earliest structural alteration in Alzheimer's disease (AD). New developments in neuroimaging methods recently made it possible to assess the respective involvement of the different hippocampal subfields by mapping atrophy on a 3D hippocampal surface view. In this longitudinal study on patients with mild cognitive impairment (MCI), we used such an approach to map the profile of hippocampal atrophy and its progression over an 18-month follow-up period in rapid converters to AD and "non-converters" compared to age-matched controls. For the sake of comparison, we also assessed the profile of hippocampal atrophy associated with AD and with increasing age in a healthy control population ranging from young adult to elderly. We found major involvement of the lateral part of the superior hippocampus mainly corresponding to the CA1 subfield in MCI and AD while increasing age was mainly associated with subiculum atrophy in the healthy population. Moreover, the CA1 subfield also showed highest atrophy rates during follow-up, in both rapid converters and "non-converters" although increased effects were observed in the former group. This study emphasizes the differences between normal aging and AD processes leading to hippocampal atrophy, pointing to a specific AD-related CA1 involvement while subiculum atrophy would represent a normal aging process. Our findings also suggest that the degree of hippocampal atrophy, more than its spatial localization, predicts rapid conversion to AD in patients with MCI.

[18F]Fluorodeoxyglucose Positron Emission Tomography in Alzheimer Disease and Related Disorders

“…MCI patients also had glucose metabolic reduction in the hippocampus, and this reduction was prominent in amnestic MCI (Clerici et al, 2009;Jauhiainen et al, 2008;Mosconi et al, 2005). After correction for the partial volume effect, glucose metabolism reduction in the temporoparietal cortex has become the core finding in MCI rather than glucose metabolism reduction in the hippocampus (Chételat et al, 2008).…”

La neuro-imagerie au service de la maladie d’Alzheimer

“…Insuffisant à lui seul, cet indice améliore cependant le diagnostic lorsqu'il est combiné à d'autres marqueurs (tels que des tests neuropsychologiques et d'autres mesures volumiques notamment [11]). Des travaux récents suggèrent également qu'une exploration plus détaillée de cette structure, distinguant ses différentes sous-parties, pourrait également accroître la sensibilité de cette mesure [12][13][14] (Figure 1B). Les résultats obtenus par TEP-FDG sont très encourageants pour le diagnostic précoce de cette maladie.…”

“…Plusieurs études ont montré la sensibilité de l'IRM pour détecter l'évolution de l'atrophie au cours du temps, en particulier le taux d'atrophie de l'hippocampe ou de la substance grise globale [17,18], et ces mesures sont déjà utilisées dans des études thérapeutiques. Plus précisément, il a été montré que, chez les patients MCI, la progression vers la MA s'accompagne spécifiquement d'une augmentation du taux d'atrophie au niveau de l'hippocampe (et particulièrement du champ CA1), du lobe temporal et du cortex cingulaire postérieur (Figure 2A) [12,19]. Les études en TEP-FDG sont beaucoup moins nombreuses, mais elles semblent indiquer une progression de l'hypométabolisme du cortex cingulaire postérieur et temporo-pariétal postérieur latéral au stade de MCI au lobe frontal au stade de démence [20].…”

P2‐231: Systematic Comparison of Different Techniques to Measure Hippocampal Subfield Volumes in ADNI2

“…This problem has been recognized by the research community and is now being addressed by an international work group that will develop a harmonized subfield labeling protocol (Yushkevich et al, 2015a;Wisse et al, 2017) using a similar approach as for the harmonization of the outer hippocampus boundaries (Boccardi et al, 2011). The other development is the variety of available labeling techniques (e.g., Pipitone et al, 2014;Kim et al, 2014;Zeineh et al, 2001;Thompson et al, 2004;Chatelat et al, 2008;Goubran et al, 2014). Each of them has its strengths and weaknesses which can complicate the comparison of findings across laboratories but also confuse researchers new to this field who must decide which of the available technique fits their needs best.…”

“…1. Approaches using a whole brain T1 weighted image typically at a resolution of 1 mm isotropic or close to that resolution, e.g., shape analysis, radial distance technique, voxel-based morphometry or deformation-based morphometry (Csernansky et al, 1998;Chatelat et al, 2008;Thompson et al, 2004;Yushkevich et al, 2010a). Of the currently available T1-based approaches, the Bayesian inference labeling as implemented in Freesurfer 5.1.…”