Baseline MRI predictors of conversion from MCI to probable AD in the ADNI cohort

IU Center for Neuroimaging, Division of Imaging Sciences, Department of Radiology, Indiana University School of Medicine, 950 W Walnut St, R2 E124, Indianapolis, IN 46202, USA.
Current Alzheimer research (Impact Factor: 3.89). 08/2009; 6(4):347-61. DOI: 10.2174/156720509788929273
Source: PubMed

ABSTRACT The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a multi-center study assessing neuroimaging in diagnosis and longitudinal monitoring. Amnestic Mild Cognitive Impairment (MCI) often represents a prodromal form of dementia, conferring a 10-15% annual risk of converting to probable AD. We analyzed baseline 1.5T MRI scans in 693 participants from the ADNI cohort divided into four groups by baseline diagnosis and one year MCI to probable AD conversion status to identify neuroimaging phenotypes associated with MCI and AD and potential predictive markers of imminent conversion. MP-RAGE scans were analyzed using publicly available voxel-based morphometry (VBM) and automated parcellation methods. Measures included global and hippocampal grey matter (GM) density, hippocampal and amygdalar volumes, and cortical thickness values from entorhinal cortex and other temporal and parietal lobe regions. The overall pattern of structural MRI changes in MCI (n=339) and AD (n=148) compared to healthy controls (HC, n=206) was similar to prior findings in smaller samples. MCI-Converters (n=62) demonstrated a very similar pattern of atrophic changes to the AD group up to a year before meeting clinical criteria for AD. Finally, a comparison of effect sizes for contrasts between the MCI-Converters and MCI-Stable (n=277) groups on MRI metrics indicated that degree of neurodegeneration of medial temporal structures was the best antecedent MRI marker of imminent conversion, with decreased hippocampal volume (left > right) being the most robust. Validation of imaging biomarkers is important as they can help enrich clinical trials of disease modifying agents by identifying individuals at highest risk for progression to AD.

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Available from: John D West, Sep 27, 2015
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    • "pdf . et al. 2008 ; Li et al. 2012 ; Misra et al. 2009; Risacher et al. 2009 ; Wang et al. 2011b ) , and used off the - shelf machine learning tools to discriminate MCI - C from MCI - NC . However , those methods were short of high performance for clinical use . "
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    ABSTRACT: As the early stage of Alzheimer's disease (AD), mild cognitive impairment (MCI) has high chance to convert to AD. Effective prediction of such conversion from MCI to AD is of great importance for early diagnosis of AD and also for evaluating AD risk pre-symptomatically. Unlike most previous methods that used only the samples from a target domain to train a classifier, in this paper, we propose a novel multimodal manifold-regularized transfer learning (M2TL) method that jointly utilizes samples from another domain (e.g., AD vs. normal controls (NC)) as well as unlabeled samples to boost the performance of the MCI conversion prediction. Specifically, the proposed M2TL method includes two key components. The first one is a kernel-based maximum mean discrepancy criterion, which helps eliminate the potential negative effect induced by the distributional difference between the auxiliary domain (i.e., AD and NC) and the target domain (i.e., MCI converters (MCI-C) and MCI non-converters (MCI-NC)). The second one is a semi-supervised multimodal manifold-regularized least squares classification method, where the target-domain samples, the auxiliary-domain samples, and the unlabeled samples can be jointly used for training our classifier. Furthermore, with the integration of a group sparsity constraint into our objective function, the proposed M2TL has a capability of selecting the informative samples to build a robust classifier. Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database validate the effectiveness of the proposed method by significantly improving the classification accuracy of 80.1 % for MCI conversion prediction, and also outperforming the state-of-the-art methods.
    Brain Imaging and Behavior 02/2015; DOI:10.1007/s11682-015-9356-x · 4.60 Impact Factor
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    • "For hypothesis testing, GWR was analyzed with a vertex-byvertex general linear model (GLM) between conversion groups adjusting for apolipoprotein ε4 (APOE4) carrier status (i.e., positive defined as e2/e4, e3/e4, e4/e4 versus negative defined as e2/e2, e2/e3, e3/e3 (Yip et al. 2005)), scanner type (Han et al. 2006), cortical thickness (i.e., a marker of brain aging (Risacher et al. 2009)), and hippocampal volume (i.e., a marker of neurodegeneration (Braak et al. 2006)). To assess the stability of our observations, a Monte Carlo simulation technique was applied using 5000 iterations of repeated random sampling. "
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    ABSTRACT: The clinical relevance of gray/white matter contrast ratio (GWR) in mild cognitive impairment (MCI) remains unknown. This study examined baseline GWR and 3-year follow-up diagnostic status in MCI. Alzheimer's Disease Neuroimaging Initiative MCI participants with baseline 1.5 T MRI and 3-year follow-up clinical data were included. Participants were categorized into two groups based on 3-year follow-up diagnoses: 1) non-converters (n = 69, 75 ± 7, 26 % female), and 2) converters (i.e., dementia at follow-up; n = 69, 75 ± 7, 30 % female) who were matched on baseline age and Mini-Mental State Examination scores. Groups were compared on FreeSurfer generated baseline GWR from structural images in which higher values represent greater tissue contrast. A general linear model, adjusting for APOE-status, scanner type, hippocampal volume, and cortical thickness, revealed that converters evidenced lower GWR values than non-converters (i.e., more degradation in tissue contrast; p = 0.03). Individuals with MCI who convert to dementia have lower baseline GWR values than individuals who remain diagnostically stable over a 3-year period, statistically independent of cortical thickness or hippocampal volume.
    Brain Imaging and Behavior 06/2014; 9(2). DOI:10.1007/s11682-014-9291-2 · 4.60 Impact Factor
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    • "AD patients are characterized by higher average rate of hippocampal volume loss than healthy age-matched controls, whereas MCI patients have an intermediate level of volume loss between AD and control individuals (Schuff et al., 2009). Moreover, hippocampal atrophy in MCI converters is more pronounced compared to non-converters (Devanand et al., 2007; Kantarci et al., 2009; Risacher et al., 2009; Costafreda et al., 2011). "
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    ABSTRACT: The hippocampus is one of the earliest affected brain regions in Alzheimer's disease (AD) and its dysfunction is believed to underlie the core feature of the disease-memory impairment. Given that hippocampal volume is one of the best AD biomarkers, our review focuses on distinct subfields within the hippocampus, pinpointing regions that might enhance the predictive value of current diagnostic methods. Our review presents how changes in hippocampal volume, shape, symmetry and activation are reflected by cognitive impairment and how they are linked with neurogenesis alterations. Moreover, we revisit the functional differentiation along the anteroposterior longitudinal axis of the hippocampus and discuss its relevance for AD diagnosis. Finally, we indicate that apart from hippocampal subfield volumetry, the characteristic pattern of hippocampal hyperactivation associated with seizures and neurogenesis changes is another promising candidate for an early AD biomarker that could become also a target for early interventions.
    Frontiers in Cellular Neuroscience 03/2014; 8:95. DOI:10.3389/fncel.2014.00095 · 4.29 Impact Factor
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