Mapping hippocampal and ventricular change in Alzheimer disease
Introduction
MRI has greatly advanced our power to map how Alzheimer disease (AD) spreads in the living brain. Dynamic measures of AD progression are vital to quantify brain atrophy and visualize its spatial profile. MRI measures of whole brain and hippocampal atrophy are now used as outcome measures in therapeutic trials for AD DeCarli et al., 2000, Grundman et al., 2002. These measures can be more consistent than currently employed mental state examinations and clinical rating scales, allowing smaller sample sizes to be used in drug trials (Jack et al., 2003). Especially useful are techniques to evaluate subtle or diffuse effects of pharmacological interventions in slowing atrophy (Ashburner et al., 2003). Better MRI analysis techniques may also detect AD earlier when neuroprotective treatments are most effective. Regional brain changes can also be related to the progression of cognitive impairment or genetic risk factors Growdon et al., 1998, O'Brien et al., 2001.
Hippocampal volume measures are sensitive to early brain change in dementia. They are readily derived from repeat (longitudinal) 3D MRI scans to assess tissue loss rates. Yearly rates of atrophy for medial temporal lobe structures correlate with rates of cognitive decline (Fox et al., 1999). They also predict time to disease onset in cognitively normal individuals Jack et al., 1999, Kaye et al., 1997, Smith and Jobst, 1996, Visser et al., 1999. In AD patients, the earliest atrophy takes place in the hippocampus and entorhinal cortex, where neurofibrillary tangle (NFT) pathology begins (e.g., Convit et al., 2000, Du et al., 2001, Du et al., 2003, Gomez-Isla et al., 1996, Jobst et al., 1994). Here, gross atrophy is detectable on MRI up to 5 years before the disease is clinically expressed Fox et al., 1999, Schott et al., 2003. MRI-derived hippocampal volumes also correlate well with neuronal loss and extent of neurofibrillary lesions observed at autopsy Bobinski et al., 2000, Smith, 2002. After disease onset, a spreading sequence of neocortical atrophy ensues, which mirrors the progressive spread of amyloid plaques and neurofibrillary tangles in the brain (NFT; Braak and Braak, 1997, Thompson et al., 2003).
Maps of these medial temporal lobe changes, described in this paper, provide several advantages. They visualize the spatial profile of the disease and can map whether it is spreading spatially and at what rate. Statistical mapping techniques can also relate changes in specific brain systems to functional and cognitive measures Janke et al., 2001, Thompson et al., 2003. Maps offer additional anatomic localization if a disease process is spatially selective or spreads over time Thompson et al., 2000a, Thompson et al., 2000b, Thompson et al., 2001a, Thompson et al., 2001b, Thompson et al., 2001c.
Here, we present a simple, practical approach to create maps of hippocampal and ventricular change over time. The technique is applicable to any disease or developmental process in which these structures change, but here, it is applied to dementia. Healthy elderly individuals and AD patients were evaluated with MRI as their disease progressed. Maps of radial atrophy (MRA), explained below, were developed to pinpoint the location and rate of atrophy and visualize group differences in the spatial profile of changes. These maps are related to ongoing work in the computer vision field on ‘medial representations’ (M-reps; Styner and Gerig, 2001) but are used here to isolate brain changes over time. We also present the first animations of these dynamic brain changes, revealing how dementia progresses (video sequences are provided as Supplementary Data on the Internet, URL: http://www.loni.ucla.edu/~thompson/AD_4D/HP/dynamic.html). We also ensured that the maps were linked with functional changes by identifying regions where atrophic rates were linked with cognitive decline. The result is a visual index of how AD impacts the hippocampus and ventricles over time. The study has two goals: (1) to map 3D profiles of hippocampal and ventricular change over time and compare them in AD and healthy elderly subjects and (2) to map where these changes correlate with cognitive decline. Although most MRI studies in dementia measure volumes of brain structures, dynamic maps of the hippocampus and temporal horns may be potential biomarkers of AD progression. The maps better localize disease effects and may help identify factors that speed up or slow down brain degeneration in clinical trials or genetic studies of dementia.
Section snippets
Subjects
The subject cohort was exactly the same as in our recent study that mapped changes in the cortex (Thompson et al., 2003). Briefly, we used longitudinal MRI scanning (two scans: baseline and follow-up) and cognitive testing to study a group of AD subjects as their disease progressed. A second, demographically matched group of healthy elderly control subjects was also imaged longitudinally (two scans) as they aged normally. The 12 AD patients were scanned identically on two occasions a year and a
Overall dynamic changes
In AD, greatest dynamic change rates were found in the inferior ventricular horns which expanded at a striking rate (Fig. 3; L: +18.1% +/− 3.8% per year; R: +12.8% +/− 4.7% per year), significantly more rapidly than in controls (P < 0.0005). Annualized expansion rates correlated with rates of cognitive decline, as measured by MMSE scores (L: P < 0.017, R: P < 0.029); those with faster ventricular expansion declined faster. Significant ventricular expansion rates were found bilaterally even in
Conclusion
In this study, we developed a surface-based anatomical modeling method (maps of radial atrophy or MRA) to isolate dynamic changes in the hippocampus and temporal horns in aging and AD. Hippocampal volume reductions and ventricular expansions progressed over time, with different patterns in aging and dementia. Significant changes were even detected in healthy controls. Brain maps identifying these regional abnormalities reveal how they spread, dissociating disease-specific changes from those
Acknowledgements
This work was supported by research grants from the National Center for Research Resources (P41 RR13642, R21 RR19771), the National Library of Medicine (LM/MH05639), National Institute of Neurological Disorders and Stroke and the National Institute of Mental Health (NINDS/NIMH NS38753), the National Institute for Biomedical Imaging and Bioengineering (EB 001561), GlaxoSmithKline Pharmaceuticals UK, and by a Human Brain Project grant to the International Consortium for Brain Mapping, funded
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