Bove F, Mulas D, Cavallieri F, et al. Long-term outcomes (15 years) after subthalamic nucleus deep brain stimulation in patients with Parkinson disease. Neurology. 2021;97:e254–62.
Hartmann CJ, Fliegen S, Groiss SJ, et al. An update on best practice of deep brain stimulation in Parkinson’s disease. Ther Adv Neurol Disord. 2019;12:175628641983809.
Malvea A, Babaei F, Boulay C, et al. Deep brain stimulation for Parkinson’s disease: a review and future outlook. Biomed Eng Lett. 2022;12:303–16.
Article PubMed PubMed Central Google Scholar
Harary M, Segar DJ, Huang KT, et al. Focused ultrasound in neurosurgery: a historical perspective. Neurosurg Focus. 2018;44:E2.
Schlesinger I, Sinai A, Zaaroor M. MRI-guided focused ultrasound in Parkinson’s disease: a review. Parkinsons Dis. 2017;2017:8124624.
PubMed PubMed Central Google Scholar
LeWitt PA, Rezai AR, Leehey MA, et al. AAV2-GAD gene therapy for advanced Parkinson’s disease: a double-blind, sham-surgery controlled, randomised trial. The Lancet Neurology. 2011;10:309–19.
Article CAS PubMed Google Scholar
Niethammer M, Tang CC, LeWitt PA, et al. Long-term follow-up of a randomized AAV2-GAD gene therapy trial for Parkinson’s disease. JCI Insight. 2017;2:e90133.
Article PubMed PubMed Central Google Scholar
Merola A, Kobayashi N, Romagnolo A, et al. Gene therapy in movement disorders: a systematic review of ongoing and completed clinical trials. Front Neurol [Internet]. 2021 [cited 2023 Jun 4];12. Available from: https://www.frontiersin.org/articles/10.3389/fneur.2021.648532.
Barker RA. Designing stem-cell-based dopamine cell replacement trials for Parkinson’s disease. Nat Med. 2019;25:1045–53.
Article CAS PubMed Google Scholar
Mari Z, Mestre TA. The disease modification conundrum in Parkinson’s disease: failures and hopes. Front Aging Neurosci [Internet]. 2022 [cited 2023 Feb 1];14. Available from: https://www.frontiersin.org/articles/10.3389/fnagi.2022.810860.
Goetz CG, Tilley BC, Shaftman SR, et al. Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS): scale presentation and clinimetric testing results. Mov Disord. 2008;23:2129–70.
Shulman LM, Pretzer-Aboff I, Anderson KE, et al. Subjective report versus objective measurement of activities of daily living in Parkinson’s disease. Mov Disord. 2006;21:794–9.
Rovini E, Maremmani C, Cavallo F. How wearable sensors can support Parkinson’s disease diagnosis and treatment: a systematic review. Front Neurosci [Internet]. 2017 [cited 2023 Jun 4];11. Available from: https://www.frontiersin.org/articles/10.3389/fnins.2017.00555.
Schlachetzki JCM, Barth J, Marxreiter F, et al. Wearable sensors objectively measure gait parameters in Parkinson’s disease. PLoS ONE. 2017;12:e0183989.
Article PubMed PubMed Central Google Scholar
Ma Y, Tang C, Chaly T, et al. Dopamine cell implantation in Parkinson’s disease: long-term clinical and 18F-FDOPA PET outcomes. J Nucl Med. 2010;51:7–15.
Schweitzer JS, Song B, Herrington TM, et al. Personalized iPSC-derived dopamine progenitor cells for Parkinson’s disease. N Engl J Med. 2020;382:1926–32.
Article CAS PubMed PubMed Central Google Scholar
Strafella AP, Bohnen NI, Perlmutter JS, et al. Molecular imaging to track Parkinson’s disease and atypical parkinsonisms: New imaging frontiers. Mov Disord. 2017;32:181–92.
Huang C, Tang C, Feigin A, et al. Changes in network activity with the progression of Parkinson’s disease. Brain. 2007;130:1834–46.
Tang CC, Poston KL, Dhawan V, et al. Abnormalities in metabolic network activity precede the onset of motor symptoms in Parkinson’s disease. J Neurosci. 2010;30:1049–56.
Article CAS PubMed PubMed Central Google Scholar
Tang CC, Holtbernd F, Ma Y, et al. Hemispheric network expression in Parkinson’s disease: relationship to dopaminergic asymmetries. JPD. 2020;10:1737–49.
Article CAS PubMed Google Scholar
Niethammer M, Tang CC, Vo A, et al. Gene therapy reduces Parkinson’s disease symptoms by reorganizing functional brain connectivity. Sci Transl Med. 2018;10:eaau0713.
Article CAS PubMed Google Scholar
Perovnik M, Rus T, Schindlbeck KA, et al. Functional brain networks in the evaluation of patients with neurodegenerative disorders. Nat Rev Neurol. 2022;19:73–90.
Eidelberg D. Metabolic brain networks in neurodegenerative disorders: a functional imaging approach. Trends Neurosci. 2009;32:548–57.
Article CAS PubMed PubMed Central Google Scholar
Spetsieris PG, Eidelberg D. Scaled subprofile modeling of resting state imaging data in Parkinson’s disease: methodological issues. Neuroimage. 2011;54:2899–914.
Sala A, Perani D. Brain molecular connectivity in neurodegenerative diseases: recent advances and new perspectives using positron emission tomography. Front Neurosci. 2019;13:617.
Article PubMed PubMed Central Google Scholar
Meles SK, Renken RJ, Pagani M, et al. Abnormal pattern of brain glucose metabolism in Parkinson’s disease: replication in three European cohorts. Eur J Nucl Med Mol Imaging. 2020;47:437–50.
Article CAS PubMed Google Scholar
Alexander GE, Moeller JR. Application of the scaled subprofile model to functional imaging in neuropsychiatric disorders: a principal component approach to modeling brain function in disease. Hum Brain Mapp. 1994;2:79–94.
Habeck C, Stern Y, the Alzheimer’s Disease Neuroimaging Initiative. Multivariate data analysis for neuroimaging data: overview and application to Alzheimer’s disease. Cell Biochem Biophys. 2010;58:53–67.
Article CAS PubMed PubMed Central Google Scholar
Spetsieris PG, Eidelberg D. Spectral guided sparse inverse covariance estimation of metabolic networks in Parkinson’s disease. Neuroimage. 2021;226:117568.
Article CAS PubMed Google Scholar
Ko JH, Spetsieris PG, Eidelberg D. Network structure and function in Parkinson’s Disease. Cereb Cortex. 2018;28:4121–35.
Habeck C, Krakauer JW, Ghez C, et al. A new approach to spatial covariance modeling of functional brain imaging data: ordinal trend analysis. Neural Comput. 2005;17:1602–45.
Carbon M, Argyelan M, Habeck C, et al. Increased sensorimotor network activity in DYT1 dystonia: a functional imaging study. Brain. 2010;133:690–700.
Article PubMed PubMed Central Google Scholar
Mure H, Tang CC, Argyelan M, et al. Improved sequence learning with subthalamic nucleus deep brain stimulation: evidence for treatment-specific network modulation. J Neurosci. 2012;32:2804–13.
Article CAS PubMed PubMed Central Google Scholar
Ko JH, Mure H, Tang CC, et al. Parkinson’s disease: increased motor network activity in the absence of movement. J Neurosci. 2013;33:4540–9.
Article CAS PubMed PubMed Central Google Scholar
Tang CC, Feigin A, Ma Y, et al. Metabolic network as a progression biomarker of premanifest Huntington’s disease. J Clin Invest. 2013;123:4076–88.
Article CAS PubMed PubMed Central Google Scholar
Perovnik M, Tang CC, Namías M, et al. Longitudinal changes in metabolic network activity in early Alzheimer’s disease. Alzheimers Dement. 2023 May 19. https://doi.org/10.1002/alz.13137. Online ahead of print.
Brakedal B, Dölle C, Riemer F, et al. The NADPARK study: A randomized phase I trial of nicotinamide riboside supplementation in Parkinson’s disease. Cell Metab. 2022;34:396-407.e6.
Article CAS PubMed Google Scholar
Ko JH, Feigin A, Mattis PJ, et al. Network modulation following sham surgery in Parkinson’s disease. J Clin Invest. 2014;124:3656–66.
Article CAS PubMed PubMed Central Google Scholar
Mure H, Hirano S, Tang CC, et al. Parkinson’s disease tremor-related metabolic network: Characterization, progression, and treatment effects. Neuroimage. 2011;54:1244–53.
Christie IN, Wells JA, Kasparov S, et al. Volumetric spatial correlations of neurovascular coupling studied using single pulse opto-fMRI. Sci Rep. 2017;7:41583.
Article CAS PubMed PubMed Central Google Scholar
Tang C, Wei Y, Zhao J, et al. The dynamic measurements of regional brain activity for resting-state fMRI: d-ALFF, d-fALFF and d-ReHo. In: Frangi AF, Schnabel JA, Davatzikos C, et al., editors. Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. Cham: Springer International Publishing; 2018. p. 190–7.
Himberg J, Hyvärinen A, Esposito F. Validating the independent components of neuroimaging time series via clustering and visualization. Neuroimage. 2004;22:1214–22.
留言 (0)