Structural parameters are superior to eigenvector centrality in detecting progressive supranuclear palsy with machine learning & multimodal MRI

[1]  W. Hao,et al.  The differential diagnosis value of radiomics-based machine learning in Parkinson’s disease: a systematic review and meta-analysis , 2023, Frontiers in aging neuroscience.

[2]  K. Fliessbach,et al.  Disentangling brain functional network remodeling in corticobasal syndrome – A multimodal MRI study , 2019, NeuroImage: Clinical.

[3]  J. Neumann,et al.  Atrophy in midbrain & cerebral/cerebellar pedunculi is characteristic for progressive supranuclear palsy – A double-validation whole-brain meta-analysis , 2019, NeuroImage: Clinical.

[4]  M. Filippi,et al.  Tracking brain damage in progressive supranuclear palsy: a longitudinal MRI study , 2018, Journal of Neurology, Neurosurgery, and Psychiatry.

[5]  Karsten Mueller,et al.  Commentary: Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates , 2017, Front. Hum. Neurosci..

[6]  A. Berardelli,et al.  Abnormal Resting-State Functional Connectivity in Progressive Supranuclear Palsy and Corticobasal Syndrome , 2017, Front. Neurol..

[7]  Murray Grossman,et al.  Clinical diagnosis of progressive supranuclear palsy: The movement disorder society criteria , 2017, Movement disorders : official journal of the Movement Disorder Society.

[8]  D. Lulé,et al.  Intrinsic functional connectivity alterations in progressive supranuclear palsy: Differential effects in frontal cortex, motor, and midbrain networks , 2017, Movement disorders : official journal of the Movement Disorder Society.

[9]  J. Rowe,et al.  Radiological biomarkers for diagnosis in PSP: Where are we and where do we need to be? , 2017, Movement disorders : official journal of the Movement Disorder Society.

[10]  E. Růžička,et al.  Disease-Specific Regions Outperform Whole-Brain Approaches in Identifying Progressive Supranuclear Palsy: A Multicentric MRI Study , 2017, Front. Neurosci..

[11]  Karl J. Friston,et al.  Analysis of family‐wise error rates in statistical parametric mapping using random field theory , 2016, Human brain mapping.

[12]  Gareth J. Barker,et al.  Cortical Thickness, Surface Area and Volume Measures in Parkinson's Disease, Multiple System Atrophy and Progressive Supranuclear Palsy , 2014, PloS one.

[13]  Gadi Goelman,et al.  Maximizing Negative Correlations in Resting-State Functional Connectivity MRI by Time-Lag , 2014, PloS one.

[14]  Timothy O. Laumann,et al.  Methods to detect, characterize, and remove motion artifact in resting state fMRI , 2014, NeuroImage.

[15]  Janaina Mourão Miranda,et al.  PRoNTo: Pattern Recognition for Neuroimaging Toolbox , 2013, Neuroinformatics.

[16]  Andrew Trujillo,et al.  Intrinsic connectivity network disruption in progressive supranuclear palsy , 2013, Annals of neurology.

[17]  Clifford R. Jack,et al.  Disrupted thalamocortical connectivity in PSP: a resting-state fMRI, DTI, and VBM study. , 2011, Parkinsonism & related disorders.

[18]  Kevin Murphy,et al.  The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced? , 2009, NeuroImage.

[19]  G Lohmann,et al.  LIPSIA--a new software system for the evaluation of functional magnetic resonance images of the human brain. , 2001, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[20]  O. Perron.  Zur Theorie der Matrices , 1907 .

[21]  F. Hillary,et al.  Hyperconnectivity is a fundamental response to neurological disruption. , 2015, Neuropsychology.

[22]  Christian Gaser,et al.  Title of the paper: CAT – A Computational Anatomy Toolbox for the Analysis of Structural MRI Data , 2022 .