Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: Results from ADNI

By Chandan Misra, Yong Fan, Christos Davatzikos. NeuroImage, 2008

611 citations recorded by the source corpus.

High-dimensional pattern classification was applied to baseline and multiple follow-up MRI scans of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants with mild cognitive impairment (MCI), in order to investigate the potential of predicting short-term conversion to Alzheimer’s Disease (AD) on an individual basis. MCI participants that converted to AD (average follow-up 15 months) displayed significantly lower volumes in a number of grey matter (GM) regions, as well as in the white matter (WM). They also displayed more pronounced periventricular small-vessel pathology, as well as an increased rate of increase of such pathology. Individual person analysis was performed using a pattern classifier previously constructed from AD patients and cognitively normal (CN) individuals to yield an abnormality score that is positive for AD-like brains and negative otherwise. The abnormality scores measured from MCI non-converters (MCI-NC) followed a bimodal distribution, reflecting the heterogeneity of this group, whereas they were positive in almost all MCI converters (MCI-C), indicating extensive patterns of AD-like brain atrophy in almost all MCI-C. Both MCI subgroups had similar MMSE scores at baseline. A more specialized classifier constructed to differentiate converters from non-converters based on their baseline scans provided good classification accuracy reaching 81.5%, evaluated via cross-validation. These pattern classification schemes, which distill spatial patterns of atrophy to a single abnormality score, offer promise as biomarkers of AD and as predictors of subsequent clinical progression, on an individual patient basis.

Subfields: Neurology

View Paper: https://www.semanticscholar.org/paper/8aad8a6255e49f1c782669f91940ca0bda5f99a5

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Citations in the corpus

A review on neuroimaging‐based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages (2017, neutral): “The MCI diagnosis based on these criteria leads to a clinically heterogeneous mix of more and less impaired patients, where each patient presents a disease stage on a continuum from CN to AD (Misra et al., 2009).”
Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality and Emotion (2017, background): “, Alzheimer’s Disease dementia, the goal is to detect longitudinal changes across consecutive sessions (Misra et al., 2009; Raj et al., 2015; Risacher et al., 2010; Zhang et al., 2016; Lindemer et al., 2017).”
Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer's disease (2010, methodology): “Similarly, the group of Davatzikos (Misra et al., 2009) applied the AD vs. HC classi ﬁ er (accuracy of 94%, (Fan et al., 2008)) to separate MCI converters vs. non-converters, ﬁ nding a high sensitivity of 85.2% but a very low speci ﬁ city of 36%, resulting in a classi ﬁ cation accuracy of 48.5%, whi”
Longitudinal Changes in Cortical Thickness Associated with Normal Aging (2010, background): “, 2003), as well as in evaluating the temporal progression of neuropathology in AD (Driscoll et al., 2009; Fox et al., 2000; Jack et al., 2004; Misra et al., 2009; Mungas et al., 2005; Schott et al., 2005).”
The Alzheimer’s Disease Neuroimaging Initiative: A review of papers published since its inception (2011, positive): “Several studies [114,115,118,119] divided the MCI group into those patients who converted to AD within a year and those who remained stable.”
Identification of MCI Individuals Using Structural and Functional Connectivity Networks (2011, background): “Recent studies show that individuals with MCI tend to progress to probable AD at a rate of approximately 10% to 15% per year [23, 15], compared with healthy controls who develop dementia at a rate of 1% to 2% per year [2].”
Brain imaging in Alzheimer disease. (2012, background): “The overall pattern of atrophy is used in clinical practice and there is interest in automated pattern classification of MRI to predict AD at an early stage and to distinguish it from other dementias (Kloppel et al. 2008; Misra et al. 2009; Vemuri et al. 2009).”
Modeling disease progression via multi-task learning (2013, positive): “This result has implications for MCI targeted studies (e.g., Julkunen et al., 2009; Misra et al., 2009; Spulber et al., 2010) in that some progression information from other populations other than MCI patients can potentially benefit the studies.”
Automatic classification of patients with Alzheimer's disease from structural MRI: A comparison of ten methods using the ADNI database (2011, methodology): “In this paper, we compared different methods for the classification of patients with AD and MCI based on anatomical T1-weighted MRI.”
Multimodal Classification of Alzheimer’s Disease and Mild Cognitive Impairment (2011, methodology): “, hippocampus, entorhinal cortex, parahippocampal gyrus, and cingulated, which are consistent with previous findings using group comparison methods (Chetelat et al., 2002; Convit et al., 2000; Fox and Schott, 2004; Jack et al., 1999; Misra et al., 2009).”
Prediction of MCI to AD conversion, via MRI, CSF biomarkers, pattern classification (2010, neutral): “…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).”
Multi-Modal Multi-Task Learning for Joint Prediction of Multiple Regression and Classification Variables in Alzheimer’s Disease (2011, methodology): “For example, in (Misra et al., 2009), the accuracy between 0.”
The Alzheimer’s Disease Neuroimaging Initiative: Progress report and future plans (2010, neutral): “[32] Misra C, Fan Y, Davatzikos C.”