Elsevier

NeuroImage

Volume 22, Issue 4, August 2004, Pages 1754-1766
NeuroImage

Mapping hippocampal and ventricular change in Alzheimer disease

https://doi.org/10.1016/j.neuroimage.2004.03.040Get rights and content

Abstract

We developed an anatomical mapping technique to detect hippocampal and ventricular changes in Alzheimer disease (AD). The resulting maps are sensitive to longitudinal changes in brain structure as the disease progresses. An anatomical surface modeling approach was combined with surface-based statistics to visualize the region and rate of atrophy in serial MRI scans and isolate where these changes link with cognitive decline. Fifty-two high-resolution MRI scans were acquired from 12 AD patients (age: 68.4 ± 1.9 years) and 14 matched controls (age: 71.4 ± 0.9 years), each scanned twice (2.1 ± 0.4 years apart). 3D parametric mesh models of the hippocampus and temporal horns were created in sequential scans and averaged across subjects to identify systematic patterns of atrophy. As an index of radial atrophy, 3D distance fields were generated relating each anatomical surface point to a medial curve threading down the medial axis of each structure. Hippocampal atrophic rates and ventricular expansion were assessed statistically using surface-based permutation testing and were faster in AD than in controls. Using color-coded maps and video sequences, these changes were visualized as they progressed anatomically over time. Additional maps localized regions where atrophic changes linked with cognitive decline. Temporal horn expansion maps were more sensitive to AD progression than maps of hippocampal atrophy, but both maps correlated with clinical deterioration. These quantitative, dynamic visualizations of hippocampal atrophy and ventricular expansion rates in aging and AD may provide a promising measure to track AD progression in drug trials.

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

References (69)

  • K.L Narr et al.

    A twin study of genetic contributions to hippocampal morphology in schizophrenia

    Neurobiol. Dis.

    (2002)
  • S.J Teipel et al.

    Regional pattern of hippocampus and corpus callosum atrophy in Alzheimer's disease in relation to dementia severity: evidence for early neocortical degeneration

    Neurobiol. Aging

    (2003)
  • P.M Thompson et al.

    High-resolution random mesh algorithms for creating a probabilistic 3D surface atlas of the human brain

    NeuroImage

    (1996)
  • L Wang et al.

    Statistical analysis of hippocampal asymmetry

    NeuroImage

    (2001)
  • L Wang et al.

    Changes in hippocampal volume and shape across time distinguish dementia of the Alzheimer type from healthy aging

    NeuroImage

    (2003)
  • S.E Black

    The search for diagnostic and progression markers in AD: so near but still too far?

    Neurology

    (1999)
  • M Bobinski et al.

    The histological validation of post mortem magnetic resonance imaging-determined hippocampal volume in Alzheimer's disease

    Neuroscience

    (2000)
  • H Braak et al.

    Staging of Alzheimer-related cortical destruction

    Int. Psychogeriatr.

    (1997)
  • K.M Bradley et al.

    Serial brain MRI at 3–6 month intervals as a surrogate marker for Alzheimer's disease

    Br. J. Radiol.

    (2002)
  • D Chan et al.

    Rates of global and regional cerebral atrophy in AD and frontotemporal dementia

    Neurology

    (2001)
  • D.L Collins et al.

    Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space

    J. Comput. Assist. Tomogr.

    (1994)
  • J.G Csernansky et al.

    Early DAT is distinguished from aging by high-dimensional mapping of the hippocampus. Dementia of the Alzheimer type

    Neurology

    (2000)
  • C DeCarli et al.

    Assessment of Alzheimer's disease progression by neuroimaging

    Neurosci. News

    (2000)
  • A.T Du et al.

    Magnetic resonance imaging of the entorhinal cortex and hippocampus in mild cognitive impairment and Alzheimer's disease

    J. Neurol. Neurosurg. Psychiatry

    (2001)
  • A.T Du et al.

    Atrophy rates of entorhinal cortex in AD and normal aging

    Neurology

    (2003)
  • H Duvernoy

    The human hippocampus

  • A.C Evans et al.

    Three-dimensional correlative imaging: applications in human brain mapping

  • N.C Fox et al.

    Correlation between rates of brain atrophy and cognitive decline in AD

    Neurology

    (1999)
  • G Gerig et al.

    Shape versus size: improved understanding of the morphology of brain structures

    Proc. MICCAI

    (2001)
  • T Gomez-Isla et al.

    Profound loss of layer II entorhinal cortex neurons occurs in very mild Alzheimer's disease

    J. Neurosci.

    (1996)
  • J.H Growdon et al.

    Committee WGA: consensus report of the working group on biological markers of Alzheimer's disease. Ronald and Nancy Reagan Institute of the Alzheimer's Association and National Institute on Aging Working Group on Biological Markers of Alzheimer's Disease

    Neurobiol. Aging

    (1998)
  • M Grundman et al.

    Brain MRI hippocampal volume and prediction of clinical status in a mild cognitive impairment trial

    J. Mol. Neurosci.

    (2002)
  • J.W Haller et al.

    Three-dimensional hippocampal morphometry by high dimensional transformation of a neuroanatomical atlas

    Radiology

    (1997)
  • H Hampel et al.

    In vivo imaging of region and cell type specific neocortical neurodegeneration in Alzheimer's disease perspectives of MRI derived corpus callosum measurement for mapping disease progression and effects of therapy. Evidence from studies with MRI, EEG and PET

    J. Neural Transm.

    (2002)
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