Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: Exploring the influence of various parameters

By Katja Franke, Gabriel Ziegler, Stefan Klöppel, Christian Gaser. NeuroImage, 2010

772 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Neurology, Psychiatry and Mental health

View Paper: https://www.semanticscholar.org/paper/4d8ac61654da5a41754d5739b3f1cff8e6be5f5f

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Citations in the corpus

Predicting Age Using Neuroimaging: Innovative Brain Ageing Biomarkers. (2017, positive): “Using structural or functional neuroimaging data, it is now possible to predict age [7,8].”
Accurate brain age prediction with lightweight deep neural networks (2019, background): “It also receives attention for its potential clinical and biological relevance (Ashburner, 2007; Brown et al., 2012; Cole et al., 2018; Cole and Franke, 2017; Davatzikos et al., 2009; Franke et al., 2014, 2010; Franke and Gaser, 2019; Habes et al., 2016; Kaufmann et al., 2019; Neeb et al., 2006).”
Models of the Aging Brain Structure and Individual Decline (2012, background): “, 2009) down sampling might also be useful (Franke et al., 2010).”
Machine Learning in Major Depression: from Classification to Treatment Outcome Prediction. (2018, background): “Overfitting can result in very good performance on training data, but very poor performance on testing data, and poor generalization to independent datasets, 108 which may be caused by small sample size with high‐dimensional features 32,109 and complex models with too many parameters.”
Predicting brain-age from multimodal imaging data captures cognitive impairment (2016, positive): “One successful age prediction framework is based on structural brain data analyzed with voxel-based morphometry (VBM, Franke et al., 2010; Franke & Gaser, 2012).”
Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects (2014, positive): “This, in turn, would bring a possible confounding effect on the estimation of disease-speci ﬁ c differences (Franke et al., 2010; Dukart et al., 2011).”
A Review of Feature Reduction Techniques in Neuroimaging (2013, neutral): “SVM or RVM) to neuroimaging data (Bray et al. 2009; Cheng et al. 2012; De Martino et al. 2008; Duchesnay et al. 2007; Duchesnay et al. 2004; Duff et al. 2011; Franke et al. 2010; Liu et al. 2012; Lohmann et al. 2007; Mitchell et al. 2004; Mwangi et al. 2012a; Norman et al. 2006; Pereira et al. 2009;”
Multi-Modal Multi-Task Learning for Joint Prediction of Multiple Regression and Classification Variables in Alzheimer’s Disease (2011, methodology): “Besides clinical variables, regression methods have also been used for estimating age of individual subject from MR brain images (Ashburner, 2007; Franke et al., 2010).”
PRoNTo: Pattern Recognition for Neuroimaging Toolbox (2013, background): “However, the authors of Franke et al. (2010) show that feature selection (Principal Component Analysis (PCA) in the present case) improved the correlation and decreased the MAE.”
The Alzheimer’s Disease Neuroimaging Initiative: A review of papers published since its inception (2011, methodology): “Stonnington et al [91] used regression analysis based on an RVM to analyze T1-weighted MRI data and predict clinical scores, whereas Franke et al [92] used an RVM combined with an automatic preprocessing step and dimension reduction using principal component analysis to estimate the age of healthy s”
Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker (2016, methodology): “Data pre-processing is almost ubiquitous in neuroimaging, including in previous studies using brain-based age predictions (Cole et al., 2015; Franke et al., 2010; Irimia et al., 2014; Schnack et al., 2016; Steffener et al., 2016).”