Abstract of online articlePrediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification
Introduction
The incidence of Alzheimer's disease (AD) doubles every 5 years after the age of 65, rendering the disease the major cause for dementia as well as a very important health and socioecomic issue, particularly in view of increasing life expectancy (Bain et al., 2008, Hebert et al., 2001). Although most currently approved treatments are symptomatic and don't directly slow AD pathology progression, it is anticipated that new disease-modifying treatments will be available in the near future. It is also expected that treatment decisions will greatly benefit from diagnostic and prognostic tools that identify individuals likely to progress to dementia sooner. This is especially important in individuals with mild cognitive impairment (MCI), who present a conversion rate of approximately 15% per year.
Two promising and potentially complementary biomarkers of early AD are structural changes measured by magnetic resonance imaging (MRI), and cerebrospinal fluid (CSF) concentrations of Aβ42, a marker that tends to correlate inversely with amyloid plaque deposition in the brain, and tau protein, a marker of neuronal injury that correlates with neurofibrillary tangles. A number of studies have reported relatively reduced brain volumes in the hippocampus, parahippocampal gyrus, cingulate, and other brain regions in both MCI and AD patients (Chetelat et al., 2002, Convit et al., 2000, De Leon et al., 2006, Dickerson et al., 2001, Fox and Schott, 2004, Jack et al., 1999, Jack et al., 2008, Karas et al., 2004, Kaye et al., 1997, Killiany et al., 2000, Pennanen et al., 2005, Risacher et al., 2009, Stoub et al., 2005, Thompson et al., 2007, Visser et al., 2002). Studies using CSF biomarkers have also shown the promise of CSF tau and Aβ42 measures as diagnostic tests for AD as well as potential predictors of risk for developing AD in normal individuals and those with MCI (Hampel et al., 2010a, Hampel et al., 2010b, Schuff et al., 2009, Shaw et al., 2009).
The spatial patterns of brain atrophy in MCI and AD are complex and highly variable, depend on the stage of the disease, and are concurrent with structural changes occurring with normal aging not necessarily being associated with clinical progression (Driscoll et al., 2009, Resnick et al., 2003). Advanced pattern analysis and classification methods have been found in recent years to be promising tools for capturing such complex spatial patterns of brain structure (Davatzikos et al., 2009, Duchesne et al., 2008, Fan et al., 2008b, Gerardin et al., 2009, Hinrichs et al., 2009, Kloppel et al., 2008, Lao et al., 2004, McEvoy et al., 2009, Vemuri et al., 2009). Importantly, these methods have begun to provide tests of high sensitivity and specificity on an individual patient basis, in addition to characterizing group differences, hence they can potentially be used as diagnostic and prognostic tools. Herein, we use a marker termed SPARE-AD (Spatial Pattern of Abnormalities for Recognition of Early AD), which has been found in previous studies to be a good predictor of MCI to AD conversion (Misra et al., 2009), but also of conversion from normal cognition to MCI in healthy elderly individuals (Davatzikos et al., 2009). This marker was also found to be a good differential diagnostic marker between AD and frontotemporal dementia (FTD) (Davatzikos et al., 2008b). As a primary goal in this study, we investigate the SPARE-AD individually, and in combination with CSF biomarkers, aiming to utilize information from baseline measurements in order to predict MCI individuals likely to convert to AD in a relatively short period (the average follow-up period in this study was 12 months). The secondary goal of this study is to measure the spatial pattern of brain atrophy, as well as its longitudinal change, in MCI converters (MCI-C) and in MCI nonconverters (MCI-NC) in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, and to evaluate differences between these 2 groups. The hypothesis was that pattern analysis and classification techniques applied to images of the regional distribution of brain tissues, in conjunction with CSF biomarkers, would allow us to predict future conversion from MCI to AD.
Section snippets
Participants
ADNI participants of this study include 239 MCI patients, whose preprocessed baseline and follow-up examinations we had downloaded from the ADNI web site by April 2009 (see below) and were available in our database. Data from the MCI patients were followed for an average period of approximately 12 months with a standard deviation of 6 months (range: 6–36 months). According to their Clinical Dementia Rating (CDR) scores during the follow-up period, MCI subjects were divided into 2 subgroups:
Group comparisons via voxel-based analysis
The voxel-by-voxel analysis between 2 MCI subgroups showed significant reduction of GM and WM in MCI-C compared with MCI-NC, at baseline. The results are shown in Fig. 1. Several regions of relatively reduced volumes of GM in MCI-C compared with MCI-NC are evident (red/yellow colors), including the hippocampus, amygdala, and entorhinal cortex, much of the temporal lobe GM, and the insular cortex (especially the superior temporal gyrus), posterior cingulate and precuneous, and orbitofrontal
Discussion
We present a study here of 239 MCI patients from the ADNI cohort. Pattern analysis and classification were used to: (1) examine the spatial distribution of atrophy and small vessel disease; and (2) to derive a classification score, the SPARE-AD, whose predictive value at baseline was measured relative to future clinical progression. We first summarize and discuss the main findings of this study.
Disclosure statement
The authors disclose no actual or potential conflicts of interest.
Written informed consent was obtained for participation in these studies, as approved by the Institutional Review Board (IRB) at each participating center.
Acknowledgements
The authors thank Evi Parmpi for her help with handling MRI datasets.
Financial support was partially provided by grants R01AG14971, a grant by the Institute for the Study of Aging, AG-10124 and AG-024904.
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
References (43)
- et al.
Healthy brain aging: A meeting report from the Sylvan MCohen Annual Retreat of the University of Pennsylvania Institute on Aging
Alzheimers Dement
(2008) - et al.
Atrophy of the medial occipitotemporal, inferior, and middle temporal gyri in non-demented elderly predict decline to Alzheimer's disease
Neurobiol. Aging
(2000) - et al.
Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging
Neurobiol. Aging
(2008) - et al.
Individual patient diagnosis of AD and FTD via high-dimensional pattern classification of MRI
Neuroimage
(2008) - et al.
Longitudinal CSF and MRI biomarkers improve the diagnosis of mild cognitive impairment
Neurobiol. Aging
(2006) - et al.
MRI-derived entorhinal and hippocampal atrophy in incipient and very mild Alzheimer's disease
Neurobiol. Aging
(2001) - et al.
Alzheimer's Disease Neuroimaging InitiativeSpatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline
Neuroimage
(2008) - et al.
Structural and functional biomarkers of prodromal Alzheimer's disease: A high-dimensional pattern classification study
Neuroimage
(2008) - et al.
Imaging cerebral atrophy: normal ageing to Alzheimer's disease
Lancet
(2004) - et al.
Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging
Neuroimage
(2009)
Total and phosphorylated tau protein as biological markers of Alzheimer's disease
Exp. Gerontol
Biological markers of amyloid beta-related mechanisms in Alzheimer's disease
Exp. Neurol
Alzheimer's Disease Neuroimaging InitiativeSpatially augmented LPBoosting for AD classification with evaluations on the ADNI dataset
Neuroimage
A 3D atlas of the human brain
Neuroimage
Global and local gray matter loss in mild cognitive impairment and Alzheimer's disease
Neuroimage
Morphological classification of brains via high-dimensional shape transformations and machine learning methods
Neuroimage
Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: Results from ADNI
Neuroimage
Mapping gray matter loss with voxel-based morphometry in mild cognitive impairment
Neuroreport
Longitudinal progression of Alzheimer's-like patterns of atrophy in normal older adults: the SPARE-AD index
Brain
Longitudinal pattern of regional brain volume change differentiates normal aging from MCI
Neurology
MRI-Based Automated Computer Classification of Probable AD Versus Normal Controls
IEEE Trans. Med. Imaging
Cited by (453)
AD-BERT: Using pre-trained language model to predict the progression from mild cognitive impairment to Alzheimer's disease
2023, Journal of Biomedical InformaticsDetermination of affected brain regions at various stages of Alzheimer's disease
2023, Neuroscience ResearchDeep learning-based classification of healthy aging controls, mild cognitive impairment and Alzheimer's disease using fusion of MRI-PET imaging
2023, Biomedical Signal Processing and ControlA review on Alzheimer's disease classification from normal controls and mild cognitive impairment using structural MR images
2023, Journal of Neuroscience MethodsDetecting conversion from mild cognitive impairment to Alzheimer's disease using FLAIR MRI biomarkers
2023, NeuroImage: ClinicalClinical utility of self- and informant-reported memory, attention, and spatial navigation in detecting biomarkers associated with Alzheimer disease in clinically normal adults
2024, Journal of the International Neuropsychological Society