Robust automated computational approach for classifying frontotemporal neurodegeneration: Multimodal/multicenter neuroimaging

Timely diagnosis of behavioral variant frontotemporal dementia (bvFTD) remains challenging because it depends on clinical expertise and potentially ambiguous diagnostic guidelines. Recent recommendations highlight the role of multimodal neuroimaging and machine learning methods as complementary tools to address this problem.

[1]  Anders M. Dale,et al.  An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest , 2006, NeuroImage.

[2]  Mark A. Hall,et al.  Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning , 1999, ICML.

[3]  Vince D. Calhoun,et al.  Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls , 2017, NeuroImage.

[4]  Rebecca M. E. Steketee,et al.  Multiparametric computer-aided differential diagnosis of Alzheimer’s disease and frontotemporal dementia using structural and advanced MRI , 2016, European Radiology.

[5]  Karsten Mueller,et al.  Combined Evaluation of FDG-PET and MRI Improves Detection and Differentiation of Dementia , 2011, PloS one.

[6]  Efstathios D. Gennatas,et al.  Divergent network connectivity changes in behavioural variant frontotemporal dementia and Alzheimer's disease. , 2010, Brain : a journal of neurology.

[7]  Pierluigi Nicotera,et al.  Ageing, Neuronal Connectivity and Brain Disorders: An Unsolved Ripple Effect , 2011, Molecular Neurobiology.

[8]  Thomas E. Nichols,et al.  Best practices in data analysis and sharing in neuroimaging using MRI , 2017, Nature Neuroscience.

[9]  S. Cappa,et al.  Brain connectivity in neurodegenerative diseases—from phenotype to proteinopathy , 2014, Nature Reviews Neurology.

[10]  Pedro Larrañaga,et al.  A review of feature selection techniques in bioinformatics , 2007, Bioinform..

[11]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[12]  Agustín Ibáñez,et al.  Your perspective and my benefit: multiple lesion models of self-other integration strategies during social bargaining. , 2016, Brain : a journal of neurology.

[13]  Junming Shao,et al.  Based on the Network Degeneration Hypothesis: Separating Individual Patients with Different Neurodegenerative Syndromes in a Preliminary Hybrid PET/MR Study , 2016, The Journal of Nuclear Medicine.

[14]  S. Baez,et al.  Feeling, learning from and being aware of inner states: interoceptive dimensions in neurodegeneration and stroke , 2016, Philosophical Transactions of the Royal Society B: Biological Sciences.

[15]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[16]  Stephen M. Smith,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[17]  Juha Koikkalainen,et al.  Structural MRI in Frontotemporal Dementia: Comparisons between Hippocampal Volumetry, Tensor-Based Morphometry and Voxel-Based Morphometry , 2012, PloS one.

[18]  Yu Zhang,et al.  Linking white matter integrity loss to associated cortical regions using structural connectivity information in Alzheimer's disease and fronto-temporal dementia: The Loss in Connectivity (LoCo) score , 2012, NeuroImage.

[19]  Evan M. Gordon,et al.  On the Stability of BOLD fMRI Correlations , 2016, Cerebral cortex.

[20]  M. Chun,et al.  Functional connectome fingerprinting: Identifying individuals based on patterns of brain connectivity , 2015, Nature Neuroscience.

[21]  Jennifer Farmer,et al.  Frontotemporal dementia: Clinicopathological correlations , 2006, Annals of neurology.

[22]  Neil P. Oxtoby,et al.  Imaging plus X: multimodal models of neurodegenerative disease , 2017, Current opinion in neurology.

[23]  David Huepe,et al.  Comparing moral judgments of patients with frontotemporal dementia and frontal stroke. , 2014, JAMA neurology.

[24]  Olivier Piguet,et al.  Eating Disturbance in Behavioural-Variant Frontotemporal Dementia , 2011, Journal of Molecular Neuroscience.

[25]  Roberta Lanzillo,et al.  Pregnancy decision-making in women with multiple sclerosis treated with natalizumab , 2018, Neurology.

[26]  Yufeng Zang,et al.  Toward reliable characterization of functional homogeneity in the human brain: Preprocessing, scan duration, imaging resolution and computational space , 2013, NeuroImage.

[27]  M. Frank,et al.  Computational psychiatry as a bridge from neuroscience to clinical applications , 2016, Nature Neuroscience.

[28]  V. Haughton,et al.  Frequencies contributing to functional connectivity in the cerebral cortex in "resting-state" data. , 2001, AJNR. American journal of neuroradiology.

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

[30]  D. Kong,et al.  Automatic Classification of Early Parkinson's Disease with Multi-Modal MR Imaging , 2012, PloS one.

[31]  L. Mucke,et al.  A network dysfunction perspective on neurodegenerative diseases , 2006, Nature.

[32]  Darran Yates,et al.  Neurodegenerative disease: Neurodegenerative networking , 2012, Nature Reviews Neuroscience.

[33]  Juan Carlos Gómez,et al.  Looking for Alzheimer's Disease morphometric signatures using machine learning techniques , 2017, Journal of Neuroscience Methods.

[34]  Sébastien Ourselin,et al.  A comparison of voxel and surface based cortical thickness estimation methods , 2011, NeuroImage.

[35]  Olivier Piguet,et al.  Tackling variability: A multicenter study to provide a gold‐standard network approach for frontotemporal dementia , 2017, Human brain mapping.

[36]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[37]  E. Reiman,et al.  Multicenter Standardized 18F-FDG PET Diagnosis of Mild Cognitive Impairment, Alzheimer's Disease, and Other Dementias , 2008, Journal of Nuclear Medicine.

[38]  Patrizia Rizzu,et al.  Structural and functional brain connectivity in presymptomatic familial frontotemporal dementia , 2013, Neurology.

[39]  P. Donnelly Kehoe,et al.  The changing brain in healthy aging: a multi-MRI machine and multicenter surface-based morphometry study , 2017, Symposium on Medical Information Processing and Analysis.

[40]  Nick C Fox,et al.  Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia. , 2011, Brain : a journal of neurology.

[41]  Sandra E Black,et al.  Salience network resting-state activity: prediction of frontotemporal dementia progression. , 2013, JAMA neurology.

[42]  F. Jessen,et al.  Predicting behavioral variant frontotemporal dementia with pattern classification in multi-center structural MRI data , 2017, NeuroImage: Clinical.

[43]  M. Weiner,et al.  A Network Diffusion Model of Disease Progression in Dementia , 2012, Neuron.

[44]  B. Biswal,et al.  Functional connectivity in the motor cortex of resting human brain using echo‐planar mri , 1995, Magnetic resonance in medicine.

[45]  J. Hodges,et al.  Behavioural-variant frontotemporal dementia: diagnosis, clinical staging, and management , 2011, The Lancet Neurology.

[46]  Azuraliza Abu Bakar,et al.  A review of feature selection techniques in sentiment analysis , 2019, Intell. Data Anal..

[47]  Kai Li,et al.  Computational approaches to fMRI analysis , 2017, Nature Neuroscience.

[48]  Sang Won Seo,et al.  Normalization of cortical thickness measurements across different T1 magnetic resonance imaging protocols by novel W-Score standardization , 2017, NeuroImage.