Elsevier

NeuroImage

Volume 183, December 2018, Pages 401-411
NeuroImage

Using high-dimensional machine learning methods to estimate an anatomical risk factor for Alzheimer's disease across imaging databases

https://doi.org/10.1016/j.neuroimage.2018.08.040Get rights and content

Highlights

  • AD-PS scores of AD anatomical risk were estimated for the WHIMS-MRI cohort.

  • The scores and their change were associated with age and cognitive function.

  • The scores and their change were associated with white matter lesion volumes.

  • The scores and their change were associated with incident cognitive impairment.

Abstract

Introduction

The main goal of this work is to investigate the feasibility of estimating an anatomical index that can be used as an Alzheimer's disease (AD) risk factor in the Women's Health Initiative Magnetic Resonance Imaging Study (WHIMS-MRI) using MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), a well-characterized imaging database of AD patients and cognitively normal subjects. We called this index AD Pattern Similarity (AD-PS) scores. To demonstrate the construct validity of the scores, we investigated their associations with several AD risk factors. The ADNI and WHIMS imaging databases were collected with different goals, populations and data acquisition protocols: it is important to demonstrate that the approach to estimating AD-PS scores can bridge these differences.

Methods

MRI data from both studies were processed using high-dimensional warping methods. High-dimensional classifiers were then estimated using the ADNI MRI data. Next, the classifiers were applied to baseline and follow-up WHIMS-MRI GM data to generate the GM AD-PS scores. To study the validity of the scores we investigated associations between GM AD-PS scores at baseline (Scan 1) and their longitudinal changes (Scan 2 –Scan 1) with: 1) age, cognitive scores, white matter small vessel ischemic disease (WM SVID) volume at baseline and 2) age, cognitive scores, WM SVID volume longitudinal changes respectively. In addition, we investigated their associations with time until classification of independently adjudicated status in WHIMS-MRI.

Results

Higher GM AD-PS scores from WHIMS-MRI baseline data were associated with older age, lower cognitive scores, and higher WM SVID volume. Longitudinal changes in GM AD-PS scores (Scan 2 – Scan 1) were also associated with age and changes in WM SVID volumes and cognitive test scores. Increases in the GM AD-PS scores predicted decreases in cognitive scores and increases in WM SVID volume. GM AD-PS scores and their longitudinal changes also were associated with time until classification of cognitive impairment. Finally, receiver operating characteristic curves showed that baseline GM AD-PS scores of cognitively normal participants carried information about future cognitive status determined during follow-up.

Discussion

We applied a high-dimensional machine learning approach to estimate a novel AD risk factor for WHIMS-MRI study participants using ADNI data. The GM AD-PS scores showed strong associations with incident cognitive impairment and cross-sectional and longitudinal associations with age, cognitive function, cognitive status and WM SVID volume lending support to the ongoing validation of the GM AD-PS score.

Introduction

Machine learning is becoming an increasingly popular approach in biomedical research related to high-dimensional data. In Alzheimer's disease (AD) research the challenge of early detection is of paramount importance, since pathological processes develop many years before the cognitive impairment is observed. Early detection of AD might help in selection of patients for clinical trials and improve the efficacy of clinical or behavioral interventions.

Massive amounts of data from different sources are being used to develop early detection models (Weiner et al., 2013). One of the more common types of neuroimaging data is structural MRI. Brain MRI is used to identify neuropathologies such as brain atrophy. Traditional approaches to study the impact of AD on brain structure are based on specific regions of interest (ROI) or voxel-based morphometry; these are univariate and cannot reveal complex spatial patterns of atrophy related to AD. Machine learning approaches are well-suited to address these challenges because they can capture complex patterns hidden in the data and build powerful prediction models based on high-dimensional data.

Many recent studies have sought to derive machine learning models based on structural MRI to predict AD or MCI-AD conversion. Much of this work has used the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, which was developed with highly standardized protocols to distinguish individuals with and without AD. For these methods to be adopted in practice, it is important to demonstrate their utility with different protocols for identifying dementia and MRI outcomes. These efforts could enable the AD research community to more fully use large amounts of imaging data already collected.

A few groups have developed machine learning models using one imaging database, and then applied them to other databases using different populations and imaging protocols. Kloppel and colleagues showed the feasibility of automatic classification of AD using whole brain MRI scans collected in different centers and support vector machines (SVM) (Kloppel et al., 2008). Davatzikos and colleagues used the ADNI dataset to train a SVM classifier used to estimate an index of AD anatomical risk called Spatial Pattern of Abnormality for Recognition of Early Alzheimer's disease (SPARE-AD). They investigated the association of this index with cognitive decline using longitudinal structural MRI data from cognitively normal participants and those with mild cognitive impairment (MCI) in the Baltimore Longitudinal Aging Study (BLSA) (Davatzikos et al., 2009). Associations between the SPARE-AD index, plasma analytes, and AD risk factors have been investigated using other cohorts (Habes et al., 2016a; Habes et al., 2016b; Toledo et al., 2013).

This study extends our previous research on AD pattern similarity (AD-PS) scores developed using structural MRI from ADNI (Casanova et al., 2013). Our main goal here is to create a metric based on MRI to be used by Women Health Initiative investigators, as an AD risk factor reflecting the presence of AD-related spatial patterns in the brain. This study is part of an ongoing effort to investigate the feasibility and constructive validity of estimating a novel AD risk factor based on elastic net regularization and WHIMS-MRI data. To do this we computed AD-PS scores for the Women's Health Initiative Memory MRI study cohort, and then we evaluated whether these scores are associated with classifications of cognitive impairment (normal, MCI, and probable dementia), incident cognitive impairment, age, performance on a test of cognitive function, and white matter hyperintensity volume and other risk factors.

Section snippets

WHIMS-MRI

The Women's Health Initiative Memory Study (WHIMS) investigated the effects of postmenopausal hormone therapy on the risk of dementia and changes in cognitive function in women aged 65–79 at enrollment (1996–1998) into the WHI randomized placebo-controlled hormone therapy clinical trials (Espeland et al., 2004; Shumaker et al., 1998). The WHIMS-MRI study enrolled WHIMS participants from 14 of 39 sites (Jaramillo et al., 2007; Resnick et al., 2009) from January 2005 through April 2006, an

Results

Women who had only a baseline MRI scan in WHIMS-MRI were older, more likely to be minority, hypertensive and diabetic, and had lower 3MS scores compared with women who had both baseline and follow-up MRI scans (p < 0.05) (Table 1). Baseline AD-PS scores were significantly higher for the 653 women who only had baseline MRIs compared with the 712 who had baseline and repeat MRIs (mean ± SD: 0.39 ± 0.25 versus 0.28 ± 0.20; p = 0.0002). This is consistent with a previous report by WHIMS-MRI

Discussion

WHIMS is a cohort where AD biomarkers (e.g. amyloid PET, blood or cerebrospinal fluid biomarkers, etc.) are not available however MRI scans are available for a relatively large number of participants. The long term goal of this work is to provide the WHIMS-MRI database with a metric (AD-PS scores) based on MRI that can be used by WHIMS investigators as an anatomical AD risk factor to address research questions. This is part of an ongoing work aiming at validating the AD-PS scores in the

Conclusions

In this work we applied a high-dimensional machine learning approach to estimate the AD-PS scores based on GM for WHIMS-MRI study participants, using ADNI imaging data. To investigate the value of the scores as an AD anatomical risk factor we studied their associations with several AD risk factors using WHIMS-MRI data. The scores of AD risk showed associations with incident cognitive impairment, age, cognitive function, cognitive status and WM SVID volume. Our work lends additional support to

Acknowledgements

This research was supported by NIH grant R21AG051113 (Casanova and Chen). Also RC, MAE, KMH and SRR receive funding from the Wake Forest Alzheimer's Disease Core Center (P30AG049638–01A1). The WHI program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services through contracts HHSN268201600018C, HHSN268201600001C, HHSN268201600002C, HHSN268201600003C, HHSN268201600004C and HHSN271200002C. We acknowledge the

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