Structural and functional biomarkers of prodromal Alzheimer's disease: A high-dimensional pattern classification study

By Yong Fan, Susan M. Resnick, Xiaoying Wu, Christos Davatzikos. NeuroImage, 2008

317 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Physiology, Psychiatry and Mental health

View Paper: https://www.semanticscholar.org/paper/b152496603d1f027228354f24350092dd7091eb2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Physiology

In Psychiatry and Mental health

Citations in the corpus

Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer's Disease (2011, background): “Usually these studies have combined MRI-based markers with biomarkers based on positron emission tomography (PET) [13,14], cerebrospinal fluid (CSF) [15,16] or both [17–19].”
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).”
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).”
Automatic classification of patients with Alzheimer's disease from structural MRI: A comparison of ten methods using the ADNI database (2011, methodology): “The classification results we obtained for AD vs CN with Atlas and COMPARE methods are lower than those reported in the respective papers: 94% accuracy for the COMPARE method in (Fan et al., 2008a) and 92% sensitivity and 97% specificity for the Atlas in Magnin et al. (2009).”
Multi-Modal Multi-Task Learning for Joint Prediction of Multiple Regression and Classification Variables in Alzheimer’s Disease (2011, background): “, 2010b), and thus a lot of works on combining different modalities of biomarkers have been reported for multi-modal classification (Bouwman et al., 2007a; Chetelat et al., 2005; Fan et al., 2008; Fellgiebel et al., 2007; Geroldi et al., 2006; Vemuri et al., 2009; Visser et al., 2002; Walhovd et al.”
Ensemble Sparse Classification of Alzheimer’s Disease (2012, methodology): “Specifically, machine learning methods can provide a useful means to recognize a pattern for a new test sample based on the information learned from the training samples (Fan et al., 2005b; Fan et al., 2008b).”
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, unknown): “…been found in recent years to be promising tools for capturing such complex spatial patterns of brain structure (Davatzikos et al., 2009; Duchesne et al., 2008; Fan et al., 2008b; Gerardin et al., 2009; Hinrichs et al., 2009; Kloppel et al., 2008; Lao et al., 2004; McEvoy et al., 2009; Vemuri…”
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.”
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.,…”
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): “As described in the Materials and methods, a classi ﬁ er that was previously constructed from the AD and CN individuals (Fan et al., 2008a) was applied to the current MCI sample; as reported in (Fan et al., 2008a), this classi ﬁ er had achieved classi ﬁ cation accuracy between AD and CN equal to…”
Identification of MCI Individuals Using Structural and Functional Connectivity Networks (2011, background): “It has been proven that information extracted from different modalities and different brain tissues might complement each other and provides more comprehensive characterization of brain abnormalities [9, 11, 12, 18, 36].”