Alzheimer's disease diagnosis in individual subjects using structural MR images: Validation studies

By Prashanthi Vemuri, Jeffrey L. Gunter, Matthew L. Senjem, Jennifer L. Whitwell, Kejal Kantarci, David S. Knopman, Brad Boeve, Ronald C. Petersen, Clifford R. Jack Jr.. 2008

479 citations recorded by the source corpus.

Subfields: Neurology, Physiology, Psychiatry and Mental health

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Citations in the corpus

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging (2015, positive): “…have been widely used in the neuroimaging community to generate diagnostic scores on the basis of brain images Mouro-Miranda et al. (2005) Wang et al. (2007) Cuingnet et al. (2011) Craddock et al. (2009) Fan et al. (2007) Batmanghelich et al. (2012) Vemuri et al. (2008) Davatzikos et al. (2011).”
Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome (2011, methodology): “Theses approaches allow capturing complex multivariate relationships in the data and have been successfully applied to the individual classification of a variety of neurological conditions (Lao et al., 2004; Fan et al., 2007; Klöppel et al., 2008a; Vemuri et al., 2008).”
Analytic estimation of statistical significance maps for support vector machine based multi-variate image analysis and classification (2013, unknown): “…Fan et al., 2007; Klöppel et al., 2008; Koutsouleris et al., 2009; Langs et al., 2011; Mingoia et al., 2012; Mouro-Miranda et al., 2005; Pereira et al., 1998; Richiardi et al., 2011; Sabuncu and Van Leemput, 2011; Vemuri et al., 2008; Venkataraman et al., 2012; Wang et al., 2007; Xu et al., 2009).”
Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer's disease (2013, methodology): “Several studies demonstrated the successful classiﬁcation of dementia based on atrophy using such machine-learning methods (e.g., Fan et al., 2008; Klöppel et al., 2008; Vemuri et al., 2008; Magnin et al., 2009; Cuingnet et al., 2011; Koikkalainen et al., 2012).”
Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer's Disease (2011, methodology): “Vemuri et al. [54] present a SEN/SPE of 86/ 86 on 380 subjects using the STAND score.”
Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge (2015, methodology): “…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).”
Issues with threshold masking in voxel-based morphometry of atrophied brains (2009, methodology): “Vemuri et al. (2008) used this approach in their image classification work, with a consensus of 50% and a threshold of 0.1.”
Predictive Markers for AD in a Multi-Modality Framework: An Analysis of MCI Progression in the ADNI Population (2010, methodology): “In a related work, Vemuri et al. (2008) demonstrated a slightly different method of applying linear SVMs on another dataset obtaining 88 − 90 % classi ﬁ cation accuracy.”
Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer's disease (2010, background): “Vemuri et al. (2008) observed some in ﬂ uence of the parameter C on theclassi ﬁ cationresult.”
Investigating the predictive value of whole-brain structural MR scans in autism: A pattern classification approach (2010, unknown): “To date, however, few studies have employed SVMs in the analysis of structural MRI data in humans, and these have mainly been used to classify patients with Alzheimer's disease (Kloppel et al., 2008; Vemuri et al., 2008) and mild cognitive impairment (Davatzikos et al.”
PRoNTo: Pattern Recognition for Neuroimaging Toolbox (2013, unknown): “…example, the classification of subjects into healthy and patients with Alzheimer ’ s Disease (AD), using structural MRI, has achieved accuracies between 86 % (Vemuri et al. 2008) and 96 % (Klöppel et al. 2008), depending on the sample size, information used and reliability of the diagnostic labels.”
Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: Exploring the influence of various parameters (2010, unknown): “…a variety of classi ﬁ cation methods have been employed to separate one or more groups of patients from healthy controls (Davatzikos et al., 2005, 2008a, 2008b; Fan et al., 2008a, 2008b; Klöppel et al., 2008a, 2008b, 2009; Liu et al., 2004; Teipel et al., 2007; Vemuri et al., 2008, 2009a,b).”
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.”
Ensemble Sparse Classification of Alzheimer’s Disease (2012, methodology): “…feature selection is another important technique for dimensionality reduction which selects the most discriminative features and at the same time eliminates the redundant features (Davatzikos et al., 2006, epub; Davatzikos et al., 2008; Fan et al., 2005a; Vemuri et al., 2008; Yoon et al., 2007).”