Predicting Prodromal Alzheimer's Disease in Subjects with Mild Cognitive Impairment Using Machine Learning Classification of Multimodal Multicenter Diffusion‐Tensor and Magnetic Resonance Imaging Data

Alzheimer's disease (AD) patients show early changes in white matter (WM) structural integrity. We studied the use of diffusion tensor imaging (DTI) in assessing WM alterations in the predementia stage of mild cognitive impairment (MCI).

[1]  Clifford R. Jack,et al.  Alliance for Aging Research AD Biomarkers Work Group: structural MRI , 2011, Neurobiology of Aging.

[2]  Christian Böhm,et al.  Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer's disease , 2010, NeuroImage.

[3]  Perminder S. Sachdev,et al.  Microstructural White Matter Changes, Not Hippocampal Atrophy, Detect Early Amnestic Mild Cognitive Impairment , 2013, PloS one.

[4]  Jeffrey J Neil,et al.  Diffusion imaging concepts for clinicians , 2008, Journal of magnetic resonance imaging : JMRI.

[5]  Douglas Walker,et al.  Increased A beta peptides and reduced cholesterol and myelin proteins characterize white matter degeneration in Alzheimer's disease. , 2002, Biochemistry.

[6]  Nick C Fox,et al.  Automatic classification of MR scans in Alzheimer's disease. , 2008, Brain : a journal of neurology.

[7]  A. Pfefferbaum,et al.  Replicability of diffusion tensor imaging measurements of fractional anisotropy and trace in brain , 2003, Journal of magnetic resonance imaging : JMRI.

[8]  A. Besga,et al.  Computer Aided Diagnosis system for Alzheimer Disease using brain Diffusion Tensor Imaging features selected by Pearson's correlation , 2011, Neuroscience Letters.

[9]  D. Shen,et al.  Prediction of Alzheimer's Disease and Mild Cognitive Impairment Using Cortical Morphological Patterns Chong-yaw Wee, Pew-thian Yap, and Dinggang Shen; for the Alzheimer's Disease Neuroimaging Initiative , 2022 .

[10]  K. Lovblad,et al.  Individual prediction of cognitive decline in mild cognitive impairment using support vector machine-based analysis of diffusion tensor imaging data. , 2010, Journal of Alzheimer's disease : JAD.

[11]  Daoqiang Zhang,et al.  Identification of MCI individuals using structural and functional connectivity networks , 2012, NeuroImage.

[12]  Gunnar Rätsch,et al.  Large Scale Multiple Kernel Learning , 2006, J. Mach. Learn. Res..

[13]  Clifford R. Jack,et al.  Interpreting scan data acquired from multiple scanners: A study with Alzheimer's disease , 2008, NeuroImage.

[14]  Stefan Klöppel,et al.  Anatomical MRI and DTI in the diagnosis of Alzheimer's disease: a European multicenter study. , 2012, Journal of Alzheimer's disease : JAD.

[15]  M. Albert,et al.  DTI analyses and clinical applications in Alzheimer's disease. , 2011, Journal of Alzheimer's disease : JAD.

[16]  John S. Duncan,et al.  Identical, but not the same: Intra-site and inter-site reproducibility of fractional anisotropy measures on two 3.0 T scanners , 2010, NeuroImage.

[17]  Maximilian Reiser,et al.  Multivariate network analysis of fiber tract integrity in Alzheimer’s disease , 2007, NeuroImage.

[18]  M. Torrens Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268 , 1990 .

[19]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[20]  Yu-Min Kuo,et al.  Increased Aβ Peptides and Reduced Cholesterol and Myelin Proteins Characterize White Matter Degeneration in Alzheimer's Disease† , 2002 .

[21]  H. Möller,et al.  Value of CSF β-amyloid1–42 and tau as predictors of Alzheimer's disease in patients with mild cognitive impairment , 2004, Molecular Psychiatry.

[22]  W. M. van der Flier,et al.  Amyloid-beta(1-42), total tau, and phosphorylated tau as cerebrospinal fluid biomarkers for the diagnosis of Alzheimer disease. , 2010, Clinical chemistry.

[23]  A. Brun,et al.  A white matter disorder in dementia of the Alzheimer type: A pathoanatomical study , 1986, Annals of neurology.

[24]  P. Good,et al.  Permutation Tests: A Practical Guide to Resampling Methods for Testing Hypotheses , 1995 .

[25]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[26]  Clifford R. Jack,et al.  Effects of hardware heterogeneity on the performance of SVM Alzheimer's disease classifier , 2011, NeuroImage.

[27]  Bram Stieltjes,et al.  Longitudinal changes in fiber tract integrity in healthy aging and mild cognitive impairment: a DTI follow-up study. , 2010, Journal of Alzheimer's disease : JAD.

[28]  Daoqiang Zhang,et al.  Multimodal classification of Alzheimer's disease and mild cognitive impairment , 2011, NeuroImage.

[29]  Gemma C. Garriga,et al.  Permutation Tests for Studying Classifier Performance , 2009, 2009 Ninth IEEE International Conference on Data Mining.

[30]  Peter Stoeter,et al.  Functional implications of hippocampal volume and diffusivity in mild cognitive impairment , 2005, NeuroImage.

[31]  Stefan Klöppel,et al.  Subregional basal forebrain atrophy in Alzheimer's disease: a multicenter study. , 2014, Journal of Alzheimer's disease : JAD.

[32]  Sterling C. Johnson,et al.  Mapping the structural brain changes in Alzheimer's disease: the independent contribution of two imaging modalities. , 2011, Journal of Alzheimer's disease : JAD.

[33]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[34]  D. Prvulovic,et al.  Using Support Vector Machines with Multiple Indices of Diffusion for Automated Classification of Mild Cognitive Impairment , 2012, PloS one.

[35]  Eric D. Smith,et al.  Sensitivity Analysis, a Powerful System Validation Technique , 2007 .

[36]  Luis Concha,et al.  Diffusion tensor imaging of time-dependent axonal and myelin degradation after corpus callosotomy in epilepsy patients , 2006, NeuroImage.

[37]  K. Blennow,et al.  Basic Research , 2022 .

[38]  Vikas Singh,et al.  Predictive markers for AD in a multi-modality framework: An analysis of MCI progression in the ADNI population , 2011, NeuroImage.

[39]  Marie Chupin,et al.  Automatic classi fi cation of patients with Alzheimer ' s disease from structural MRI : A comparison of ten methods using the ADNI database , 2010 .

[40]  D. Rueckert,et al.  Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer's Disease , 2011, PloS one.

[41]  Stefan Klöppel,et al.  Combining DTI and MRI for the Automated Detection of Alzheimer's Disease Using a Large European Multicenter Dataset , 2012, MBIA.

[42]  R. Turner,et al.  Diffusion MR imaging: clinical applications. , 1992, AJR. American journal of roentgenology.

[43]  S. Kiebel,et al.  Detecting Structural Changes in Whole Brain Based on Nonlinear Deformations—Application to Schizophrenia Research , 1999, NeuroImage.

[44]  Massimo Filippi,et al.  White matter damage in Alzheimer disease and its relationship to gray matter atrophy. , 2011, Radiology.

[45]  Xing Qiu,et al.  Quantification of accuracy and precision of multi-center DTI measurements: A diffusion phantom and human brain study , 2011, NeuroImage.

[46]  Matthias J. Müller,et al.  Predicting conversion to dementia in mild cognitive impairment by volumetric and diffusivity measurements of the hippocampus , 2006, Psychiatry Research: Neuroimaging.

[47]  Stephen M. Smith,et al.  DTI measures in crossing-fibre areas: Increased diffusion anisotropy reveals early white matter alteration in MCI and mild Alzheimer's disease , 2011, NeuroImage.

[48]  Stefan Klöppel,et al.  Automated tractography of the cingulate bundle in Alzheimer's disease: A multicenter DTI study , 2012, Journal of magnetic resonance imaging : JMRI.

[49]  Norbert Schuff,et al.  MRI Markers for Mild Cognitive Impairment: Comparisons between White Matter Integrity and Gray Matter Volume Measurements , 2013, PloS one.

[50]  P. Jaccard,et al.  Etude comparative de la distribution florale dans une portion des Alpes et des Jura , 1901 .

[51]  Peter Stoeter,et al.  Diagnostic utility of hippocampal size and mean diffusivity in amnestic MCI , 2007, Neurobiology of Aging.

[52]  Stefan Klöppel,et al.  Multicenter stability of diffusion tensor imaging measures: A European clinical and physical phantom study , 2011, Psychiatry Research: Neuroimaging.

[53]  Jesse S. Jin,et al.  Identification of Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using Multivariate Predictors , 2011, PloS one.

[54]  Lars Kai Hansen,et al.  Visualization of nonlinear kernel models in neuroimaging by sensitivity maps , 2011, NeuroImage.

[55]  M. Jorge Cardoso,et al.  Accurate multimodal probabilistic prediction of conversion to Alzheimer's disease in patients with mild cognitive impairment☆ , 2013, NeuroImage: Clinical.

[56]  Tao Liu,et al.  Automated detection of amnestic mild cognitive impairment in community-dwelling elderly adults: A combined spatial atrophy and white matter alteration approach , 2012, NeuroImage.

[57]  M. Filippi,et al.  Robust Automated Detection of Microstructural White Matter Degeneration in Alzheimer’s Disease Using Machine Learning Classification of Multicenter DTI Data , 2013, PloS one.

[58]  P. Scheltens,et al.  Research criteria for the diagnosis of Alzheimer's disease: revising the NINCDS–ADRDA criteria , 2007, The Lancet Neurology.

[59]  Hideyuki Okano,et al.  Visualization of peripheral nerve degeneration and regeneration: Monitoring with diffusion tensor tractography , 2009, NeuroImage.

[60]  C. Jack,et al.  Tracking pathophysiological processes in Alzheimer's disease: an updated hypothetical model of dynamic biomarkers , 2013, The Lancet Neurology.