Motor progression phenotypes in early-stage Parkinson’s Disease: A clinical prediction model and the role of glymphatic system imaging biomarkers

[1]  D. Peng,et al.  Evaluation of glymphatic system activity by diffusion tensor image analysis along the perivascular space (DTI-ALPS) in dementia patients. , 2023, The British journal of radiology.

[2]  Yilong Wang,et al.  Impaired glymphatic system as evidenced by low diffusivity along perivascular spaces is associated with cerebral small vessel disease: a population-based study , 2023, Stroke and vascular neurology.

[3]  Xin Li,et al.  DTI-ALPS: An MR biomarker for motor dysfunction in patients with subacute ischemic stroke , 2023, Frontiers in Neuroscience.

[4]  M. Lou,et al.  Choroid Plexus Enlargement Exacerbates White Matter Hyperintensity Growth through Glymphatic Impairment , 2023, Annals of neurology.

[5]  T. Taoka,et al.  Neuroimaging uncovers distinct relationships of glymphatic dysfunction and motor symptoms in Parkinson’s disease , 2023, Journal of Neurology.

[6]  Guo-Xing Zhang,et al.  Interaction Between the Glymphatic System and α-Synuclein in Parkinson’s Disease , 2023, Molecular Neurobiology.

[7]  T. Taoka,et al.  Neuroimaging findings related to glymphatic system alterations in older adults with metabolic syndrome , 2023, Neurobiology of Disease.

[8]  J. Pu,et al.  Diffusion along perivascular spaces as marker for impairment of glymphatic system in Parkinson’s disease , 2022, NPJ Parkinson's disease.

[9]  Yi Zhou,et al.  Long-term trajectories of BMI and cumulative incident metabolic syndrome: A cohort study , 2022, Frontiers in Endocrinology.

[10]  Y. Sohn,et al.  Patterns of striatal dopamine depletion and motor deficits in de novo Parkinson’s disease , 2022, Journal of Neural Transmission.

[11]  T. Khoo,et al.  Glymphatic System Dysfunction and Sleep Disturbance May Contribute to the Pathogenesis and Progression of Parkinson’s Disease , 2022, International journal of molecular sciences.

[12]  C. Toh,et al.  Magnetic Resonance Images Implicate That Glymphatic Alterations Mediate Cognitive Dysfunction in Alzheimer Disease , 2022, Annals of Neurology.

[13]  K. Nie,et al.  Diffusion along perivascular spaces provides evidence interlinking compromised glymphatic function with aging in Parkinson's disease , 2022, CNS neuroscience & therapeutics.

[14]  T. Bohr,et al.  The glymphatic system: Current understanding and modeling , 2022, iScience.

[15]  M. Nedergaard,et al.  Periarteriolar spaces modulate cerebrospinal fluid transport into brain and demonstrate altered morphology in aging and Alzheimer’s disease , 2022, Nature Communications.

[16]  Xiao-jun Guan,et al.  Neuroimaging evidence of glymphatic system dysfunction in possible REM sleep behavior disorder and Parkinson’s disease , 2022, NPJ Parkinson's disease.

[17]  Ian F. Harrison,et al.  Propagation of tau and α-synuclein in the brain: therapeutic potential of the glymphatic system , 2022, Translational Neurodegeneration.

[18]  C. Colosimo,et al.  Lewy body disease or diseases with Lewy bodies? , 2022, NPJ Parkinson's disease.

[19]  J. Wardlaw,et al.  Perivascular space in Parkinson's disease: Association with CSF amyloid/tau and cognitive decline. , 2022, Parkinsonism & related disorders.

[20]  M. Sommerauer,et al.  Clinical and imaging evidence of brain-first and body-first Parkinson's disease , 2022, Neurobiology of Disease.

[21]  Xiaojun Xu,et al.  Association between cigarette smoking and Parkinson’s disease: a neuroimaging study , 2022, Therapeutic advances in neurological disorders.

[22]  Yanxin Zhao,et al.  Trajectory Analysis of Orthostatic Hypotension in Parkinson’s Disease: Results From Parkinson’s Progression Markers Initiative Cohort , 2021, Frontiers in Aging Neuroscience.

[23]  Min Chen,et al.  Diffusion Tensor Imaging Along the Perivascular Space Index in Different Stages of Parkinson’s Disease , 2021, Frontiers in Aging Neuroscience.

[24]  Jiong Cai,et al.  Glymphatic clearance function in patients with cerebral small vessel disease , 2021, NeuroImage.

[25]  S. Fereshtehnejad,et al.  Prodromal Parkinson disease subtypes — key to understanding heterogeneity , 2021, Nature Reviews Neurology.

[26]  Ling-Yan Ma,et al.  Motor Progression in Early-Stage Parkinson's Disease: A Clinical Prediction Model and the Role of Cerebrospinal Fluid Biomarkers , 2021, Frontiers in Aging Neuroscience.

[27]  B. Tang,et al.  Impaired meningeal lymphatic drainage in patients with idiopathic Parkinson’s disease , 2021, Nature Medicine.

[28]  Hisashi Noma,et al.  Re-evaluation of the comparative effectiveness of bootstrap-based optimism correction methods in the development of multivariable clinical prediction models , 2020, BMC Medical Research Methodology.

[29]  J. Trojanowski,et al.  Amyloid-Beta (Aβ) Plaques Promote Seeding and Spreading of Alpha-Synuclein and Tau in a Mouse Model of Lewy Body Disorders with Aβ Pathology , 2019, Neuron.

[30]  Elizabeth Qian,et al.  Subtyping of Parkinson’s Disease - Where Are We Up To? , 2019, Aging and disease.

[31]  Paolo Eusebi,et al.  CSF and blood biomarkers for Parkinson's disease , 2019, The Lancet Neurology.

[32]  R. Kayed,et al.  Tau Interacts with the C-Terminal Region of α-Synuclein, Promoting Formation of Toxic Aggregates with Distinct Molecular Conformations. , 2019, Biochemistry.

[33]  Ming Lu,et al.  Blocking meningeal lymphatic drainage aggravates Parkinson’s disease-like pathology in mice overexpressing mutated α-synuclein , 2019, Translational Neurodegeneration.

[34]  Ben Van Calster,et al.  Reporting and Interpreting Decision Curve Analysis: A Guide for Investigators. , 2018, European urology.

[35]  Han-Joon Kim,et al.  CSF β-amyloid42 and risk of freezing of gait in early Parkinson disease , 2018, Neurology.

[36]  Matthieu Resche-Rigon,et al.  Multiple imputation by chained equations for systematically and sporadically missing multilevel data , 2018, Statistical methods in medical research.

[37]  Peter Szolovits,et al.  3D-MICE: integration of cross-sectional and longitudinal imputation for multi-analyte longitudinal clinical data , 2017, J. Am. Medical Informatics Assoc..

[38]  Steven G. Luke,et al.  Evaluating significance in linear mixed-effects models in R , 2016, Behavior Research Methods.

[39]  A. Dagher,et al.  Clinical criteria for subtyping Parkinson’s disease: biomarkers and longitudinal progression , 2017, Brain : a journal of neurology.

[40]  Rui Yan,et al.  Cortical thickness and subcortical structure volume abnormalities in patients with major depression with and without anxious symptoms , 2017, Brain and behavior.

[41]  H. Kawai,et al.  Evaluation of glymphatic system activity with the diffusion MR technique: diffusion tensor image analysis along the perivascular space (DTI-ALPS) in Alzheimer’s disease cases , 2017, Japanese Journal of Radiology.

[42]  D. Krainc,et al.  α-synuclein toxicity in neurodegeneration: mechanism and therapeutic strategies , 2017, Nature Medicine.

[43]  Arthur W. Toga,et al.  CSF biomarkers associated with disease heterogeneity in early Parkinson’s disease: the Parkinson’s Progression Markers Initiative study , 2016, Acta Neuropathologica.

[44]  A. Lang,et al.  Parkinson's disease , 2015, The Lancet.

[45]  Benoit Liquet,et al.  Estimation of extended mixed models using latent classes and latent processes: the R package lcmm , 2015, 1503.00890.

[46]  Henrik Zetterberg,et al.  CSF biomarkers and clinical progression of Parkinson disease , 2015, Neurology.

[47]  M. Mcdermott,et al.  Longitudinal assessment of tau and amyloid beta in cerebrospinal fluid of Parkinson disease , 2013, Acta Neuropathologica.

[48]  G. E. Vates,et al.  A Paravascular Pathway Facilitates CSF Flow Through the Brain Parenchyma and the Clearance of Interstitial Solutes, Including Amyloid β , 2012, Science Translational Medicine.

[49]  Bart Post,et al.  UvA-DARE ( Digital Academic Repository ) Clinimetrics , clinical profile and prognosis in early Parkinson ’ s disease , 2009 .

[50]  J. Zimmerman,et al.  Assessing the calibration of mortality benchmarks in critical care: The Hosmer-Lemeshow test revisited* , 2007, Critical care medicine.

[51]  E. Elkin,et al.  Decision Curve Analysis: A Novel Method for Evaluating Prediction Models , 2006, Medical decision making : an international journal of the Society for Medical Decision Making.