An MRI-Derived definition of MCI-To-AD conversion for Long-Term, automatic prognosis of MCI patients

Center for NMR Research, Department of Radiology, Penn State University College of Medicine, Hershey, Pennsylvania, United States of America.
PLoS ONE (Impact Factor: 3.23). 10/2011; 6(10):e25074. DOI: 10.1371/journal.pone.0025074
Source: PubMed


Alzheimer's disease (AD) and mild cognitive impairment (MCI) are of great current research interest. While there is no consensus on whether MCIs actually "convert" to AD, this concept is widely applied. Thus, the more important question is not whether MCIs convert, but what is the best such definition. We focus on automatic prognostication, nominally using only a baseline brain image, of whether an MCI will convert within a multi-year period following the initial clinical visit. This is not a traditional supervised learning problem since, in ADNI, there are no definitive labeled conversion examples. It is not unsupervised, either, since there are (labeled) ADs and Controls, as well as cognitive scores for MCIs. Prior works have defined MCI subclasses based on whether or not clinical scores significantly change from baseline. There are concerns with these definitions, however, since, e.g., most MCIs (and ADs) do not change from a baseline CDR = 0.5 at any subsequent visit in ADNI, even while physiological changes may be occurring. These works ignore rich phenotypical information in an MCI patient's brain scan and labeled AD and Control examples, in defining conversion. We propose an innovative definition, wherein an MCI is a converter if any of the patient's brain scans are classified "AD" by a Control-AD classifier. This definition bootstraps design of a second classifier, specifically trained to predict whether or not MCIs will convert. We thus predict whether an AD-Control classifier will predict that a patient has AD. Our results demonstrate that this definition leads not only to much higher prognostic accuracy than by-CDR conversion, but also to subpopulations more consistent with known AD biomarkers (including CSF markers). We also identify key prognostic brain region biomarkers.

Download full-text


Available from: Qing X Yang
  • Source
    • "Classifiers using morphometric MRI data have presented high indices of diagnostic accuracy that reinforce the diagnosis of AD, the commonest form of dementia (Vemuri et al., 2008; Magnin et al., 2009; Plant et al., 2010; Dai et al., 2012; Liu et al., 2012). These techniques have also afforded promising results in studies related to MCI – cognitive decline not severe enough to fulfill the criteria for established dementia (Misra et al., 2009; Aksu et al., 2011; Chincarini et al., 2011; Cui et al., 2011; Davatzikos et al., 2011; Zhang and Shen, 2012). However , the latter is still an open area with intense investigations, as predicting whether individuals who are at increased risk convert to AD is more challenging than classifying AD versus control individuals. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Recent literature has presented evidence that cardiovascular risk factors (CVRF) play an important role on cognitive performance in elderly individuals, both those who are asymp-tomatic and those who suffer from symptoms of neurodegenerative disorders. Findings from studies applying neuroimaging methods have increasingly reinforced such notion. Studies addressing the impact of CVRF on brain anatomy changes have gained increasing importance, as recent papers have reported gray matter loss predominantly in regions traditionally affected in Alzheimer's disease (AD) and vascular dementia in the presence of a high degree of cardiovascular risk. In the present paper, we explore the association between CVRF and brain changes using pattern recognition techniques applied to structural MRI and the Framingham score (a composite measure of cardiovascular risk largely used in epidemiological studies) in a sample of healthy elderly individuals. We aim to answer the following questions: is it possible to decode (i.e., to learn information regarding cardio-vascular risk from structural brain images) enabling individual predictions? Among clinical measures comprising the Framingham score, are there particular risk factors that stand as more predictable from patterns of brain changes? Our main findings are threefold: (i) we verified that structural changes in spatially distributed patterns in the brain enable statistically significant prediction of Framingham scores. This result is still significant when controlling for the presence of the APOE 4 allele (an important genetic risk factor for both AD and cardiovascular disease). (ii) When considering each risk factor singly, we found different levels of correlation between real and predicted factors; however, single factors were not significantly predictable from brain images when considering APOE4 allele presence as covariate. (iii) We found important gender differences, and the possible causes of that finding are discussed.
    Full-text · Article · Dec 2014 · Frontiers in Aging Neuroscience
  • Source
    • "In distinguishing MCI - S versus MCI - P , the ICA model achieved better accuracy ( 79 . 6% ) than other techniques ( Aksu et al . , 2011 ; Chincarini et al . , 2011 ; Cuingnet et al . , 2011 ; Gomar et al . , 2011 ; Hinrichs et al . , 2011 ; Chu et al . , 2012 ; Liu et al . , 2013b ; Shaffer et al . , 2013 ) . Shaffer et al . ( 2013 ) also used baseline structural ICA as a data - reduction tool to perform diagnostic classification for MCI - S vs . MCI - P . They derived f"
    [Show abstract] [Hide abstract]
    ABSTRACT: Identifying predictors of mild cognitive impairment (MCI) and Alzheimer׳s disease (AD) can lead to more accurate diagnosis and facilitate clinical trial participation. We identified 320 participants (93 cognitively normal or CN, 162 MCI, 65 AD) with baseline magnetic resonance imaging (MRI) data, cerebrospinal fluid biomarkers, and cognition data in the Alzheimer׳s Disease Neuroimaging Initiative database. We used independent component analysis (ICA) on structural MR images to derive 30 matter covariance patterns (ICs) across all participants. These ICs were used in iterative and stepwise discriminant classifier analyses to predict diagnostic classification at 24 months for CN vs. MCI, CN vs. AD, MCI vs. AD, and stable MCI (MCI-S) vs. MCI progression to AD (MCI-P). Models were cross-validated with a "leave-10-out" procedure. For CN vs. MCI, 84.7% accuracy was achieved based on cognitive performance measures, ICs, p-tau181p, and ApoE ε4 status. For CN vs. AD, 94.8% accuracy was achieved based on cognitive performance measures, ICs, and p-tau181p. For MCI vs. AD and MCI-S vs. MCI-P, models achieved 83.1% and 80.3% accuracy, respectively, based on cognitive performance measures, ICs, and p-tau181p. ICA-derived MRI biomarkers achieve excellent diagnostic accuracy for MCI conversion, which is little improved by CSF biomarkers and ApoE ε4 status.
    Full-text · Article · Aug 2014 · Psychiatry Research: Neuroimaging
  • Source
    • "of conversion from mild cognitive impairment to dementia (Aksu et al., 2011). The Nogo condition was more difficult than the Go condition for both groups, as expected. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Advances in breast cancer (BC) treatments have resulted in significantly improved survival rates. However, BC chemotherapy is often associated with several side effects including cognitive dysfunction. We applied multivariate pattern analysis (MVPA) to functional magnetic resonance imaging (fMRI) to find a brain connectivity pattern that accurately and automatically distinguishes chemotherapy-treated (C+) from non-chemotherapy treated (C-) BC females and healthy female controls (HC). Twenty-seven C+, 29 C-, and 30 HC underwent fMRI during an executive-prefrontal task (Go/Nogo). The pattern of functional connectivity associated with this task discriminated with significant accuracy between C+ and HC groups (72%, p = .006) and between C+ and C- groups (71%, p = .012). However, the accuracy of discrimination between C- and HC was not significant (51%, p = .46). Compared with HC, behavioral performance of the C+ and C- groups during the task was intact. However, the C+ group demonstrated altered functional connectivity in the right frontoparietal and left supplementary motor area networks compared to HC, and in the right middle frontal and left superior frontal gyri networks, compared to C-. Our results provide further evidence that executive function performance may be preserved in some chemotherapy-treated BC survivors through recruitment of additional neural connections. (JINS, 2013, 19, 1-11).
    Full-text · Article · Oct 2013 · Journal of the International Neuropsychological Society
Show more