Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline

By Yong Fan, Nematollah Kayhan Batmanghelich, Christopher M. Clark, Christos Davatzikos, Alzheimer's Disease Neuroimaging Initiative. NeuroImage, 2008

530 citations recorded by the source corpus.

Subfields: Neurology, Physiology, Psychiatry and Mental health

View Paper: https://www.semanticscholar.org/paper/b33aa68a6ec026d7b2b49db14735014567395fef

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Citations in the corpus

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).”
Networks of anatomical covariance (2013, methodology): “The Davatzikos group (Fan et al., 2008;Misra et al., 2009) have used high-dimensional classification of GM density to examine the covariance patterns of patients with mild cognitive impairment (MCI) who were either converters or non-converters to a diagnosis of Alzheimer's Disease (AD) (Braak and…”
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).”
Prediction of MCI to AD conversion, via MRI, CSF biomarkers, pattern classification (2010, methodology): “This classifier was trained in (Fan et al., 2008a) on the AD and CN subjects; it provides an output that tends to be close to 1 for AD patients and 1 for CN subjects.”
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%,…”
The Alzheimer’s Disease Neuroimaging Initiative: A review of papers published since its inception (2011, unknown): “Using a leave-one-out approach, scans were then classified as having a degree of AD-like or normal-like anatomic features, as defined by Fan et al [83].”
Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging (2009, unknown): “The disease severity of our AD and MCI patients was similar to those included in Fan et al. (2008) (mean MMS=23 for AD and 27 for MCI) and in Davatzikos et al. (2008a) (mean MMS=26 for MCI).”
Multimodal Classification of Alzheimer’s Disease and Mild Cognitive Impairment (2011, methodology): “Although there exist some methods combining two modalities of biomarkers for individual classification, i.e., using both MRI and PET (Fan et al., 2008b; Hinrichs et al., 2009a, 2009b; Ye et al., 2008), both MRI and CSF (Davatzikos et al., in press), or both MRI and APOE biomarkers (Ye et al.,…”
A review on neuroimaging‐based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages (2017, unknown): “For instance, Vemuri et al. (2008) combined GM, WM, and CSF density maps with age, gender, and APOE genotype for SVM-based AD classification.”
RABBIT: Rapid Alignment of Brains by Building Intermediate Templates (2009, unknown): “05 (false discover rate (FDR) corrected) (Bozzali et al., 2006; Busatto et al., 2003; Chetelat and Baron, 2003; Convit et al., 1997; Cuenod et al., 1993; De Santi et al., 2001; Du et al., 2001; Fan et al., 2008; Fox et al., 1996; Frisoni et al., 2007; Grundman et al., 2002; Jack et al., 2000,1999; K”
Generative-Discriminative Basis Learning for Medical Imaging (2012, unknown): “[14] and has shown to perform well on ADNI dataset [59].”
Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: Results from ADNI (2008, unknown): “…in the past 5 years as a means to measure subtle and spatially complex imaging patterns that have diagnostic value (Davatzikos et al., 2008a,b; Liu et al., 2004; Lao et al., 2003, 2004; Li et al., 2007; Adeli et al., 2005; Tandon et al., 2006, 2005; Kloppel et al., 2008a; Vemuri et al., 2008).”
Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects (2014, unknown): “Over the past few years, numerous MRI biomarkers have been proposed in classifying AD patients in different disease stages (Fan et al., 2008; Duchesne et al., 2008; Chupin et al., 2009; Querbes et al., 2009; Wolz et al., 2011; Hinrichs et al., 2011; Westman et al., 2011a,b; Westman et al., 2012;…”