Elsevier

Neurobiology of Aging

Volume 29, Issue 4, April 2008, Pages 514-523
Neurobiology of Aging

Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging

https://doi.org/10.1016/j.neurobiolaging.2006.11.010Get rights and content

Abstract

We report evidence that computer-based high-dimensional pattern classification of magnetic resonance imaging (MRI) detects patterns of brain structure characterizing mild cognitive impairment (MCI), often a prodromal phase of Alzheimer's disease (AD). Ninety percent diagnostic accuracy was achieved, using cross-validation, for 30 participants in the Baltimore Longitudinal Study of Aging. Retrospective evaluation of serial scans obtained during prior years revealed gradual increases in structural abnormality for the MCI group, often before clinical symptoms, but slower increase for individuals remaining cognitively normal. Detecting complex patterns of brain abnormality in very early stages of cognitive impairment has pivotal importance for the detection and management of AD.

Introduction

Prevalence of AD doubles every 5 years of life after age 60, with more than 4 million individuals affected in the US alone. AD is the most common dementing illness and a major public health issue of increasing importance as life expectancy increases. Although noninvasive approaches for antemortem diagnosis of AD are under development, definitive diagnosis of AD requires neuropathologic confirmation of the characteristic amyloid plaques and neurofibrillary tangles (Braak et al., 1998). New drugs under development will target different stages of disease pathophysiology, and efficacious AD treatments likely will require early initiation before irreversible brain tissue damage. Thus, a great deal of attention has been paid recently to the prodromal stage of AD, referred to as mild cognitive impairment (MCI), which includes individuals with memory problems who do not meet criteria for dementia. Although MCI definitions vary across studies (Petersen, 2003), MCI individuals convert to AD with rates of 6–15% annually (Petersen, 2003). Therefore, MCI individuals are a high risk group likely to benefit from effective treatments.

Structural magnetic resonance imaging (MRI) promises to aid diagnosis and treatment monitoring of MCI and AD, offering the potential for easily obtainable surrogate markers of diagnostic status and disease progression. Unlike relatively more advanced stages of MCI and AD, quantifying patterns of structural change during early stages of AD or during clinically normal stages is a major challenge. Brain atrophy in the early stages of AD may be relatively subtle and spatially distributed over many brain regions (Chetelat et al., 2002, Convit et al., 2000, Dickerson et al., 2001, Kaye et al., 1997, Killiany et al., 2000b), including the entorhinal cortex, the hippocampus, lateral and inferior temporal structures, anterior and posterior cingulate, and possibly other regions that have only recently been investigated (Medina et al., 2006). Furthermore, spatially heterogeneous patterns of atrophy have been found within the hippocampus, with regions known to correspond to the CA1 field presenting relatively more pronounced atrophy (Frisoni et al., 2006, Wang et al., 2006). Patterns of atrophy associated with pathology are confounded by complex patterns of atrophy associated with normal aging (Resnick et al., 2000). Moreover, the error associated with structural measurements can vary throughout the brain, since some structures are more difficult to delineate, especially via computer algorithms, thereby rendering the measurement of certain brain regions more informative than others merely for methodological reasons (Bookstein, 2001). Therefore, powerful and sensitive statistical image analysis methods must be used to capture morphological characteristics that are different between normal aging and MCI, and to determine which are most informative, from a diagnostic perspective.

Most MRI studies in MCI and AD have relied on measurement of volumes of specific brain regions (Chetelat, 2003, Nestor et al., 2004), especially the hippocampus and the entorhinal cortex, which show histopathogical changes at early stages of AD (Braak et al., 1998). Computational neuroanatomy has also been used to evaluate voxel-by-voxel brain changes in healthy aging, MCI and AD (Ashburner et al., 2003). These studies have confirmed patterns of atrophy involving medial temporal lobe structures in MCI and AD. They have reinforced the value of MRI as a potential surrogate marker of disease at the group-analysis level, i.e. for examining overall differences between individuals with and without pathology. However, their diagnostic value is limited, especially at early stages of brain pathology, since their sensitivity and specificity are not sufficient for prediction of the status of a given individual.

Herein we report results from a longitudinal study that provide strong evidence that there is a subtle and spatially distributed pattern of brain structure that is characteristic of MCI, and which often begins developing prior to the recognition of cognitive deficits. Moreover, this pattern can be detected with high sensitivity and specificity using a high-dimensional image analysis and pattern classification method that examines spatial patterns of brain atrophy in their entirety, instead of applying separate region-by-region evaluations. Therefore, detection of this structural pattern can lead to very early diagnosis of prodromal AD. This study adds to mounting evidence in the literature for the importance of pattern classification methods in detecting subtle and complex structural and functional patterns (Davatzikos et al., 2005a, Davatzikos et al., 2005b, Golland et al., 2002, Liu et al., 2004).

Section snippets

Subjects

MRI scans from 30 elderly individuals were obtained annually as part of the Baltimore Longitudinal Study of Aging neuroimaging substudy (Resnick et al., 2000). At initial enrollment, all individuals were free of dementia and other central nervous system disorders, severe cardiovascular disease, and metastatic cancer (detailed in (Resnick et al., 2000)). Screening of mental status by the blessed-information-memory-concentration (BIMC) test was performed at each annual visit in conjunction with a

Spatial pattern of MCI-specific abnormalities

The spatial map of brain regions that was formed as described in Section 2 is shown in Fig. 1a. This set of regions forms a structural network, which according to our classification approach, carries the most distinctive characteristics of MCI relative to unimpaired individuals. This map highlights several regions including the lateral and inferior aspects of both hippocampi, which is where the CA1 field of the hippocampus is located, bilateral superior, middle and inferior temporal gyri (GM),

Discussion

To our knowledge, our study is the first to demonstrate that complex and subtle structural patterns that characterize the trajectory of increasing brain abnormalities in individuals with mild MCI can be identified from cross-sectional MR scans via high-dimensional image analysis and pattern classification methods. Importantly, these patterns of structural change can be measured even before cognitive decline brings the individuals to clinical attention. The spatial pattern of distribution of

Disclosure statement

All co-authors have seen and approved this submission. There are no conflicts of interest including any financial, personal or other relationships with other people or organizations, by any of the co-authors, related to the work described in the paper. The submission is not under review by any other archival journal. An oral and poster presentation of this work has been made at the International Conference of Alzheimer's disease, in Madrid, July 2006.

Acknowledgements

We gratefully acknowledge the assistance of Yang An, M.Sc., National Institute on Aging, in statistical analysis, the BLSA participants and staff, the staff of the MRI facility at Johns Hopkins Hospital, and Dr. Juan Troncoso, Department of Pathology, Johns Hopkins University, for neuropathologic assessments. This study was supported in part by NIH funding sources N01-AG-3-2124 and R01-AG14971 and by the Intramural Research Program of the NIH, National Institute on Aging.

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