Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: Results from ADNI☆
Introduction
The most common cause of dementia is Alzheimer's disease, with incidence doubling approximately every 5 years after the age of 65. As life expectancy increases, AD is becoming an important health problem in the elderly, and a significant societal and financial burden. As many treatments are being developed and evaluated, it is becoming important to develop diagnostic and prognostic biomarkers that can predict which individuals are relatively more likely to progress clinically. This is especially important in individuals with MCI, who present a conversion rate of approximately 15% per year.
MCI has attracted a lot of attention in the recent years, in part because it offers opportunities for relatively early diagnosis of AD, and in part a number of pharmachological interventions typically target MCI patients. A number of studies, including both region-of-interest (ROI) and voxel-based analyses, have reported relatively reduced brain volumes in the hippocampus, parahippocampal gyrus, cingulate, and other brain regions in both MCI and AD patients (Jack et al., 1999, Jack et al., 2008, Karas et al., 2004, Fox and Schott, 2004, Chetelat et al., 2002, Convit et al., 2000, Dickerson et al., 2001, Kaye et al., 1997, Killiany et al., 2000, Stoub et al., 2005, Visser et al., 2002, De Leon et al., 2006). The spatial pattern of brain atrophy in MCI is complex and highly variable, and it evolves in time as the disease progresses. Therefore, capturing such spatio-temporal patterns of structural brain change requires sophisticated image analysis methods, including high-dimensional image warping often used to quantify the regional distribution of brain tissue. Even more challenging is the development of biomarkers that classify individuals, as opposed to characterizing group differences, as inter-individual variations and statistical overlap among groups renders it difficult to characterize individuals with sufficient sensitivity and specificity.
In order to be able to achieve individual classification, we leverage upon work on high-dimensional pattern classification that has shown great promise in the past 5 years as a means to measure subtle and spatially complex imaging patterns that have diagnostic value (Davatzikos et al., 2005, Davatzikos et al., 2008b, Liu et al., 2004, Lao et al., 2003, Lao et al., 2004, Li et al., 2007, Adeli et al., 2005, Tandon et al., 2006, Davatzikos et al., 2005, Kloppel et al., 2008a, Vemuri et al., 2008). This approach aims to provide computational tools that classify individuals, based on their imaging measurements, rather than determining statistical group differences. The current study builds upon previous work in (Lao et al., 2004, Davatzikos et al., 2008a, Fan et al., 2008a, Duchesne et al., 2008), by investigating longitudinal images from a relatively large sample of MCI individuals from ADNI. The primary goal of the current study is to utilize high-dimensional pattern classification of baseline scans to determine predictors of short-term conversion from MCI to AD. The secondary goal of this study is to measure the spatial distribution of brain atrophy, as well as its longitudinal change, in MCI converters (MCI-C) and in MCI non-converters (MCI-NC) in the ADNI cohort, and to evaluate differences between these two groups. The hypothesis was that advanced pattern analysis and classification techniques applied to baseline and longitudinal images of the regional distribution of brain tissues would allow us to predict future conversion from MCI to AD.
Section snippets
ADNI
Data used in the preparation of this article were obtained from the ADNI database (www.loni.ucla.edu\ADNI). The goal of ADNI is to recruit 800 adults, ages 55 to 90, to participate in the research – approximately 200 CN older individuals to be followed for 3 years, 400 people with MCI to be followed for 3 years, and 200 people with early AD to be followed for 2 years. For up-to-date information see www.adni-info.org.
Participants
ADNI participants with structural MR images and at least one, in most cases two
Voxel-based analysis of baseline RAVENS maps
Fig. 1, Fig. 2, Fig. 3 show representative sections obtained after applying voxel-wise t-tests to the baseline RAVENS maps of GM, WM and CSF. Several regions of relatively reduced volumes of GM in MCI-C compared to MCI-NC are evident, including the anterior hippocampus, amygdala, much of the temporal lobe GM and the insular cortex, posterior cingulate, and orbitofrontal cortex. WM was reduced primarily in the periventricular frontal region, indicating higher periventricular leukoareosis in
Discussions and conclusion
This study utilized methods of computational neuroanatomy and high-dimensional pattern classification to investigate spatial patterns of brain atrophy, as well as their longitudinal change, in a group of 103 MCI individuals from the ADNI study, aiming to determine imaging markers that predict short-term conversion within a period of approximately 15 months, on average. Despite the relatively short clinical follow-up period of this study, significant differences at baseline were measured between
Acknowledgments
This study was financially supported by a grant by the Institute for the Study of Aging. Additional support was provided by the NIH grant R01AG14971. The authors would like to thank Evi Parmpi for help with data processing.
Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI; Principal Investigator: Michael Weiner; NIH grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging
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Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (www.loni.ucla.edu\ADNI). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. ADNI investigators include (complete listing available at http://www.loni.ucla.edu/ADNI/Data/ADNI_Manuscript_Citations.doc).