Functional Brain Networks to Evaluate Treatment Responses in Parkinson’s Disease

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  PubMed  Google Scholar 

Huang C, Tang C, Feigin A, et al. Changes in network activity with the progression of Parkinson’s disease. Brain. 2007;130:1834–46.

Article  PubMed  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  Google Scholar 

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.

PubMed  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Chapter  Google Scholar 

Himberg J, Hyvärinen A, Esposito F. Validating the independent components of neuroimaging time series via clustering and visualization. Neuroimage. 2004;22:1214–22.

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