Alzheimer’s Disease Neuroimaging Inniative. Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline

Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, School of Medicine, Philadelphia, PA 19104, USA.
NeuroImage (Impact Factor: 6.36). 03/2008; 39(4):1731-43. DOI: 10.1016/j.neuroimage.2007.10.031
Source: PubMed


Spatial patterns of brain atrophy in mild cognitive impairment (MCI) and Alzheimer's disease (AD) were measured via methods of computational neuroanatomy. These patterns were spatially complex and involved many brain regions. In addition to the hippocampus and the medial temporal lobe gray matter, a number of other regions displayed significant atrophy, including orbitofrontal and medial-prefrontal grey matter, cingulate (mainly posterior), insula, uncus, and temporal lobe white matter. Approximately 2/3 of the MCI group presented patterns of atrophy that overlapped with AD, whereas the remaining 1/3 overlapped with cognitively normal individuals, thereby indicating that some, but not all, MCI patients have significant and extensive brain atrophy in this cohort of MCI patients. Importantly, the group with AD-like patterns presented much higher rate of MMSE decline in follow-up visits; conversely, pattern classification provided relatively high classification accuracy (87%) of the individuals that presented relatively higher MMSE decline within a year from baseline. High-dimensional pattern classification, a nonlinear multivariate analysis, provided measures of structural abnormality that can potentially be useful for individual patient classification, as well as for predicting progression and examining multivariate relationships in group analyses.


Available from: Kayhan Batmanghelich, Jan 09, 2015
  • Source
    • "Effective and accurate diagnosis of Alzheimer's disease and its prodromal stage, MCI, are crucial for drug trials, given the urgent need for treatments to resist or slow disease progression. Many neuroimaging studies have used anatomical measures derived from T1-weighted brain MRI, such as cortical thickness, and volumetric or shape measures of subregions of the brain, to differentiate AD or MCI from NC (Fan et al., 2008; Hua et al., 2008a,b; Gerardin et al., 2009; Magnin et al., 2009; Hua et al., 2010; Cuingnet et al., 2011; Westman et al., 2011; Hua et al., 2013; Gutman et al., 2015). Moreover, measures derived from functional imaging or cerebrospinal fluid (CSF) assays have also been used to help classify individuals with cognitive impairment vs. healthy controls (De Santi et al., 2001; Morris et al., 2001; Bouwman et al., 2007; Mattsson et al., 2009; Shaw et al., 2009; Fjell et al., 2010). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Alzheimer's disease (AD) is a progressive brain disease. Accurate detection of AD and its prodromal stage, mild cognitive impairment (MCI), are crucial. There is also a growing interest in identifying brain imaging biomarkers that help to automatically differentiate stages of Alzheimer's disease. Here, we focused on brain structural networks computed from diffusion MRI and proposed a new feature extraction and classification framework based on higher order singular value decomposition and sparse logistic regression. In tests on publicly available data from the Alzheimer's Disease Neuroimaging Initiative, our proposed framework showed promise in detecting brain network differences that help in classifying different stages of Alzheimer's disease.
    Frontiers in Neuroscience 08/2015; 9:257. DOI:10.3389/fnins.2015.00257 · 3.66 Impact Factor
  • Source
    • "ation of the algorithms , the current clinical diagnosis criteria for AD ( McKhann et al . , 2011 ) and MCI ( Petersen , 2004 ) were used , which is a common practice in studies of computer - aided diagnosis methods ( Cuingnet et al . , 2011 ; Klöppel et al . , 2008 ; Falahati et al . , 2014 ; Davatzikos et al . , 2008a ; Duchesne et al . , 2008 ; Fan et al . , 2008a , b ; Gray et al . , 2013 ; Koikkalainen et al . , 2012 ; Magnin et al . , 2009 ; Vemuri et al . , 2008 ; Wolz et al . , 2011 ) . Ground truth diagnosis of dementia can only be assessed using autopsy and is therefore only rarely available . Of the previously mentioned pa - pers , only one paper included one group of 20 AD patients with "
    [Show abstract] [Hide abstract]
    ABSTRACT: Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi- center data set. Using clinical practice as starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer’s disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with in total 29 algorithms. The algorithms were trained on a small training set (n=30) and optionally on data from other sources (e.g., the Alzheimer’s Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework:
    NeuroImage 05/2015; DOI:10.1016/j.neuroimage.2015.01.048 · 6.36 Impact Factor
  • Source
    • "This step not only speeds up the diagnostic system, but can also enhance its diagnostic performance. Techniques that have been investigated for this purpose include (but are not restricted to): principal component analysis (PCA) [15], nonnegative matrix factorization (NMF) [9], filter methods based on t-test [11], Pearson correlation [7] or mutual information [7] [8] and also wrapper methods such as recursive feature elimination [10] [5]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Early diagnosis of Alzheimer disease (AD), while still at the stage known as mild cognitive impairment (MCI), is important for the development of new treatments. However, brain degeneration in MCI evolves with time and differs from patient to patient, making early diagnosis a very challenging task. Despite these difficulties, many machine learning techniques have already been used for the diagnosis of MCI and for predicting MCI to AD conversion, but the MCI group used in previous works is usually very heterogeneous containing subjects at different stages. The goal of this paper is to investigate how the disease stage impacts on the ability of machine learning methodologies to predict conversion. After identifying the converters and estimating the time of conversion (TC) (using neuropsychological test scores), we devised 5 subgroups of MCI converters (MCI-C) based on their temporal distance to the conversion instant (0, 6, 12, 18 and 24 months before conversion). Next, we used the FDG-PET images of these subgroups and trained classifiers to distinguish between the MCI-C at different stages and stable non-converters (MCI-NC). Our results show that MCI to AD conversion can be predicted as early as 24 months prior to conversion and that the discriminative power of the machine learning methods decreases with the increasing temporal distance to the TC, as expected. These findings were consistent for all the tested classifiers. Our results also show that this decrease arises from a reduction in the information contained in the regions used for classification and by a decrease in the stability of the automatic selection procedure. Copyright © 2015 Elsevier Ltd. All rights reserved.
    Computers in Biology and Medicine 01/2015; 58C:101-109. DOI:10.1016/j.compbiomed.2015.01.003 · 1.24 Impact Factor
Show more