[ad_1]
Jeppesen J, Otto M, Frederiksen Y, Hansen AK, Fedorova TD, Knudsen K, et al. Observations on muscle activity in REM sleep behavior disorder assessed with a semi-automated scoring algorithm. Clin Neurophysiol. 2018;129(3):541–7.
Google Scholar
Iranzo A, Santamaria J, Tolosa E. Idiopathic rapid eye movement sleep behaviour disorder: diagnosis, management, and the need for neuroprotective interventions. Lancet Neurol. 2016;15(4):405–19.
Google Scholar
Iranzo A, Fairfoul G, Ayudhaya ACN, Serradell M, Gelpi E, Vilaseca I, et al. Detection of α-synuclein in CSF by RT-QuIC in patients with isolated rapid-eye-movement sleep behaviour disorder: a longitudinal observational study. Lancet Neurol. 2021;20(3):203–12.
Google Scholar
Schaffrath A, Schleyken S, Seger A, Jergas H, Özdüzenciler P, Pils M, et al. Patients with isolated REM-sleep behavior disorder have elevated levels of alpha-synuclein aggregates in stool. NPJ Parkinson’s Dis. 2023;9(1):14.
Google Scholar
Kuzkina A, Panzer C, Seger A, Schmitt D, Rößle J, Schreglmann SR, et al. Dermal real-time quaking-induced conversion is a sensitive marker to confirm isolated rapid eye movement sleep behavior disorder as an early α-synucleinopathy. Mov Disord. 2023;38:1077–82. https://doi.org/10.1002/mds.29340.
Google Scholar
Berg D, Postuma RB, Adler CH, Bloem BR, Chan P, Dubois B, et al. MDS research criteria for prodromal Parkinson’s disease. Mov Disord. 2015;30(12):1600–11.
Google Scholar
Heinzel S, Berg D, Gasser T, Chen H, Yao C, Postuma RB, et al. Update of the MDS research criteria for prodromal Parkinson’s disease. Mov Disord. 2019;34(10):1464–70.
Google Scholar
McKeith IG, Ferman TJ, Thomas AJ, Blanc F, Boeve BF, Fujishiro H, et al. Research criteria for the diagnosis of prodromal dementia with Lewy bodies. Neurology. 2020;94(17):743–55.
Google Scholar
Wenning GK, Stankovic I, Vignatelli L, Fanciulli A, Calandra-Buonaura G, Seppi K, et al. The movement disorder society criteria for the diagnosis of multiple system atrophy. Mov Disord. 2022;37(6):1131–48.
Google Scholar
Postuma RB, Iranzo A, Hu M, Högl B, Boeve BF, Manni R, et al. Risk and predictors of dementia and parkinsonism in idiopathic REM sleep behaviour disorder: a multicentre study. Brain. 2019;142(3):744–59.
Google Scholar
Joza S, Hu MT, Jung K-Y, Kunz D, Stefani A, Dušek P, et al. Progression of clinical markers in prodromal Parkinson’s disease and dementia with Lewy bodies: a multicentre study. Brain. 2023;146(8):3258–72.
Google Scholar
Liepelt-Scarfone I, Ophey A, Kalbe E. Cognition in prodromal Parkinson’s disease. Cognition in Parkinson’s disease. 2022;269:93.
Google Scholar
Fengler S, Liepelt-Scarfone I, Brockmann K, Schäffer E, Berg D, Kalbe E. Cognitive changes in prodromal Parkinson’s disease: a review. Mov Disord. 2017;32(12):1655–66.
Google Scholar
Ferini-Strambi L, Fasiello E, Sforza M, Salsone M, Galbiati A. Neuropsychological, electrophysiological, and neuroimaging biomarkers for REM behavior disorder. Expert Rev Neurother. 2019;19(11):1069–87.
Google Scholar
Leitner C, D’Este G, Verga L, et al. Neuropsychological changes in isolated REM sleep behavior disorder: a systematic review and meta-analysis of cross-sectional and longitudinal studies. Neuropsychol Rev. 2024;34:41–66. https://doi.org/10.1007/s11065-022-09572-1.
Google Scholar
Berg D, Borghammer P, Fereshtehnejad S-M, Heinzel S, Horsager J, Schaeffer E, et al. Prodromal Parkinson disease subtypes—key to understanding heterogeneity. Nat Rev Neurol. 2021;17(6):349–61.
Google Scholar
Jozwiak N, Postuma RB, Montplaisir J, Latreille V, Panisset M, Chouinard S, et al. REM sleep behavior disorder and cognitive impairment in Parkinson’s disease. Sleep. 2017;40(8):zsx101.
Google Scholar
Fereshtehnejad S-M, Romenets SR, Anang JB, Latreille V, Gagnon J-F, Postuma RB. New clinical subtypes of Parkinson disease and their longitudinal progression: a prospective cohort comparison with other phenotypes. JAMA Neurol. 2015;72(8):863–73.
Google Scholar
Aarsland D, Batzu L, Halliday GM, Geurtsen GJ, Ballard C, Ray Chaudhuri K, et al. Parkinson disease-associated cognitive impairment. Nat Rev Dis Primers. 2021;7(1):1–21.
Vossius C, Larsen JP, Janvin C, Aarsland D. The economic impact of cognitive impairment in Parkinson’s disease. Mov Disord. 2011;26(8):1541–4.
Google Scholar
Mosley PE, Moodie R, Dissanayaka N. Caregiver burden in Parkinson disease: a critical review of recent literature. J Geriatr Psychiatry Neurol. 2017;30(5):235–52.
Google Scholar
Rahayel S, Gaubert M, Postuma RB, Montplaisir J, Carrier J, Monchi O, et al. Brain atrophy in Parkinson’s disease with polysomnography-confirmed REM sleep behavior disorder. Sleep. 2019;42(6):zsz062.
Google Scholar
Chen M, Li Y, Chen J, Gao L, Sun J, Gu Z, et al. Structural and functional brain alterations in patients with idiopathic rapid eye movement sleep behavior disorder. J Neuroradiol. 2022;49(1):66–72.
Google Scholar
Campabadal A, Segura B, Junque C, Iranzo A. Structural and functional magnetic resonance imaging in isolated REM sleep behavior disorder: a systematic review of studies using neuroimaging software. Sleep Med Rev. 2021;59:101495.
Google Scholar
Rahayel S, Postuma RB, Montplaisir J, Marchand DG, Escudier F, Gaubert M, et al. Cortical and subcortical gray matter bases of cognitive deficits in REM sleep behavior disorder. Neurology. 2018;90(20):e1759–70.
Google Scholar
Weil RS, Morris HR. REM sleep behaviour disorder: an early window for prevention in neurodegeneration? Brain. 2019;142(3):498–501.
Google Scholar
Postuma RB, Berg D. Prodromal Parkinson’s disease: the decade past, the decade to come. Mov Disord. 2019;34(5):665–75.
Google Scholar
Dommershuijsen LJ, Darweesh SKL, Luik AI, Kieboom BCT, Koudstaal PJ, Boon AJW, et al. Ethical considerations in screening for rapid eye movement sleep behavior disorder in the general population. Mov Disord. 2020;35:1939–44. https://doi.org/10.1002/mds.28262.
Google Scholar
Gossard TR, Teigen LN, Yoo S, Timm PC, Jagielski J, Bibi N, et al. Patient values and preferences regarding prognostic counseling in isolated REM sleep behavior disorder. Sleep. 2023;46(1):zsac244.
Google Scholar
Ngandu T, Lehtisalo J, Solomon A, Levälahti E, Ahtiluoto S, Antikainen R, et al. A 2 year multidomain intervention of diet, exercise, cognitive training, and vascular risk monitoring versus control to prevent cognitive decline in at-risk elderly people (FINGER): a randomised controlled trial. The Lancet. 2015;385(9984):2255–63.
Google Scholar
Vellas B, Carrie I, Gillette-Guyonnet S, Touchon J, Dantoine T, Dartigues J, et al. MAPT study: a multidomain approach for preventing Alzheimer’s disease: design and baseline data. The J Prev Alzheimer’s Dis. 2014;1(1):13–22.
Google Scholar
Kivipelto M, Mangialasche F, Ngandu T. Lifestyle interventions to prevent cognitive impairment, dementia and Alzheimer disease. Nat Rev Neurol. 2018;14(11):653–66.
Google Scholar
Rebok GW, Ball K, Guey LT, Jones RN, Kim HY, King JW, et al. Ten-year effects of the advanced cognitive training for independent and vital elderly cognitive training trial on cognition and everyday functioning in older adults. J Am Geriatr Soc. 2014;62(1):16–24.
Google Scholar
Gavelin HM, Lampit A, Hallock H, Sabates J, Bahar-Fuchs A. Cognition-oriented treatments for older adults: a systematic overview of systematic reviews. Neuropsychol Rev. 2020;30:167–93.
Google Scholar
Leung IH, Walton CC, Hallock H, Lewis SJ, Valenzuela M, Lampit A. Cognitive training in Parkinson disease: a systematic review and meta-analysis. Neurology. 2015;85(21):1843–51.
Google Scholar
Lawrence BJ, Gasson N, Bucks RS, Troeung L, Loftus AM. Cognitive training and noninvasive brain stimulation for cognition in Parkinson’s disease: a meta-analysis. Neurorehabil Neural Repair. 2017;31(7):597–608.
Google Scholar
Gavelin HM, Domellöf ME, Leung I, Neely AS, Launder NH, Nategh L, et al. Computerized cognitive training in Parkinson’s disease: a systematic review and meta-analysis. Ageing Res Rev. 2022;80:101671. https://doi.org/10.1016/j.arr.2022.101671.
Duda BM, Sweet LH. Functional brain changes associated with cognitive training in healthy older adults: a preliminary ALE meta-analysis. Brain Imaging Behav. 2019;14:1247–62.
Google Scholar
van Balkom TD, van den Heuvel OA, Berendse HW, van der Werf YD, Vriend C. The effects of cognitive training on brain network activity and connectivity in aging and neurodegenerative diseases: a systematic review. Neuropsychol Rev. 2020;30(1):267–86.
Google Scholar
Ophey A, Rehberg S, Giehl K, Eggers C, Reker P, van Eimeren T, et al. Predicting working memory training responsiveness in Parkinson’s disease: both “system hardware” and room for improvement are needed. Neurorehabil Neural Repair. 2021;35(2):117–30.
Google Scholar
Hingorani AD, van der Windt DA, Riley RD, Abrams K, Moons KG, Steyerberg EW, et al. Prognosis research strategy (PROGRESS) 4: stratified medicine research. BMJ. 2013;346:1–9.
Google Scholar
Ophey A, Roheger M, Folkerts A-K, Skoetz N, Kalbe E. A systematic review on predictors of working memory training responsiveness in healthy older adults: methodological challenges and future directions. Front Aging Neurosci. 2020;12:1–23.
Google Scholar
Roheger M, Folkerts AK, Krohm F, et al. Prognostic factors for change in memory test performance after memory training in healthy older adults: a systematic review and outline of statistical challenges. Diagn Progn Res. 2020;4:7. https://doi.org/10.1186/s41512-020-0071-8.
Google Scholar
Sommerauer M, Fedorova TD, Hansen AK, Knudsen K, Otto M, Jeppesen J, et al. Evaluation of the noradrenergic system in Parkinson’s disease: an 11C-MeNER PET and neuromelanin MRI study. Brain. 2018;141(2):496–504.
Google Scholar
Knudsen K, Fedorova TD, Hansen AK, Sommerauer M, Otto M, Svendsen KB, et al. In-vivo staging of pathology in REM sleep behaviour disorder: a multimodality imaging case-control study. Lancet Neurol. 2018;17(7):618–28.
Google Scholar
Nguyen L, Murphy K, Andrews G. Cognitive and neural plasticity in old age: a systematic review of evidence from executive functions cognitive training. Ageing Res Rev. 2019;53:1–17.
Google Scholar
Díez-Cirarda M, Ojeda N, Peña J, Cabrera-Zubizarreta A, Lucas-Jiménez O, Gómez-Esteban JC, et al. Increased brain connectivity and activation after cognitive rehabilitation in Parkinson’s disease: a randomized controlled trial. Brain Imaging Behav. 2017;11(6):1640–51.
Google Scholar
Vriend C, van Balkom TD, Berendse HW, van der Werf YD, van den Heuvel OA. Cognitive training in Parkinson’s disease induces local, not global, changes in white matter microstructure. Neurotherapeutics. 2021;18(4):2518–28.
Google Scholar
Musiek ES, Holtzman DM. Mechanisms linking circadian clocks, sleep, and neurodegeneration. Science. 2016;354(6315):1004–8.
Google Scholar
Doppler CE, Smit JA, Hommelsen M, Seger A, Horsager J, Kinnerup MB, et al. Microsleep disturbances are associated with noradrenergic dysfunction in Parkinson’s disease. Sleep. 2021;44(8):zsab040.
Google Scholar
Sommerauer M, Valko PO, Werth E, Poryazova R, Hauser S, Baumann CR. Revisiting the impact of REM sleep behavior disorder on motor progression in Parkinson’s disease. Parkinsonism Relat Disord. 2014;20(4):460–2.
Google Scholar
Schreiner SJ, Imbach LL, Valko PO, Maric A, Maqkaj R, Werth E, et al. Reduced regional NREM sleep slow-wave activity is associated with cognitive impairment in Parkinson disease. Front Neurol. 2021;12:618101.
Google Scholar
Chan A-W, Tetzlaff JM, Altman DG, Laupacis A, Gøtzsche PC, Krleža-Jerić K, et al. SPIRIT 2013 statement: defining standard protocol items for clinical trials. Ann Intern Med. 2013;158(3):200–7.
Google Scholar
Seger A, Ophey A, Heitzmann W, Doppler CE, Lindner MS, Brune C, et al. Evaluation of a structured screening assessment to detect isolated rapid eye movement sleep behavior disorder. Mov Disord. 2023;38(6):990–9. https://doi.org/10.1002/mds.29389.
Google Scholar
D’Agostino RB Sr. The delayed-start study design. N Engl J Med. 2009;361(13):1304–6.
Google Scholar
Nasreddine ZS, Phillips NA, Bédirian V, Charbonneau S, Whitehead V, Collin I, et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc. 2005;53(4):695–9.
Google Scholar
Faul F, Erdfelder E, Lang A-G, Buchner A. G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods. 2007;39(2):175–91.
Google Scholar
Chiu H-L, Chu H, Tsai J-C, Liu D, Chen Y-R, Yang H-L, et al. The effect of cognitive-based training for the healthy older people: a meta-analysis of randomized controlled trials. PLoS ONE. 2017;12(5):e0176742.
Google Scholar
Borm GF, Fransen J, Lemmens WA. A simple sample size formula for analysis of covariance in randomized clinical trials. J Clin Epidemiol. 2007;60(12):1234–8.
Google Scholar
Calamia M, Markon K, Tranel D. The robust reliability of neuropsychological measures: meta-analyses of test–retest correlations. Clin Neuropsychol. 2013;27(7):1077–105.
Google Scholar
Ophey A, Giehl K, Rehberg S, Eggers C, Reker P, van Eimeren T, et al. Effects of working memory training in patients with Parkinson’s disease without cognitive impairment: a randomized controlled trial. Parkinsonism Relat Disord. 2020;72:13–22.
Google Scholar
Meinders MJ, Marks WJ, van Zundert SB, Kapur R, Bloem BR. Enhancing participant engagement in clinical studies: strategies applied in the personalized Parkinson project. Journal of Parkinson’s Disease. 2023;13(4):637–40.
Google Scholar
Jahn T, Beitlich D, Hepp S, Knecht R, Köhler K, Ortner C, et al. Drei Sozialformeln zur Schätzung der (prämorbiden) Intelligenzquotienten nach Wechsler. Zeitschrift für Neuropsychologie. 2013. https://doi.org/10.1024/1016-264X/a000084.
Jin M, Polis A, Hartzel J. Algorithms for minimization randomization and the implementation with an R package. Commun Stat Simul Comput. 2021;50(10):3077–87.
Google Scholar
Petrelli A, Kaesberg S, Barbe M, Timmermann L, Rosen J, Fink G, et al. Cognitive training in Parkinson’s disease reduces cognitive decline in the long term. Eur J Neurol. 2015;22(4):640–7.
Google Scholar
Petrelli A, Kaesberg S, Barbe MT, Timmermann L, Fink GR, Kessler J, et al. Effects of cognitive training in Parkinson’s disease: a randomized controlled trial. Parkinsonism Relat Disord. 2014;20(11):1196–202.
Google Scholar
Rahe J, Becker J, Fink GR, Kessler J, Kukolja J, Rahn A, et al. Cognitive training with and without additional physical activity in healthy older adults: cognitive effects, neurobiological mechanisms, and prediction of training success. Front Aging Neurosci. 2015;7:187.
Google Scholar
Kalbe E, Roheger M, Paluszak K, Meyer J, Becker J, Fink GR, et al. Effects of a cognitive training with and without additional physical activity in healthy older adults: a follow-up 1 year after a randomized controlled trial. Front Aging Neurosci. 2018;10:407.
Google Scholar
Kalbe E, Folkerts A-K, Ophey A, Eggers C, Elben S, Dimenshteyn K, et al. Enhancement of executive functions but not memory by multidomain group cognitive training in patients with Parkinson’s disease and mild cognitive impairment: a multicenter randomized controlled trial. Parkinson’s Disease. 2020;2020:1–15.
Google Scholar
Craig CL, Marshall AL, Sjöström M, Bauman AE, Booth ML, Ainsworth BE, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35(8):1381–95.
Google Scholar
Lee PH, Macfarlane DJ, Lam TH, Stewart SM. Validity of the international physical activity questionnaire short form (IPAQ-SF): a systematic review. Int J Behav Nutr Phys Act. 2011;8(1):1–11.
Google Scholar
Carlson MC, Parisi JM, Xia J, Xue Q-L, Rebok GW, Bandeen-Roche K, et al. Lifestyle activities and memory: variety may be the spice of life The Women’s Health and Aging Study II. J Int Neuropsychol Soc. 2011;18(2):286.
Google Scholar
Parisi JM, Rebok GW, Xue Q-L, Fried LP, Seeman TE, Tanner EK, et al. The role of education and intellectual activity on cognition. J Aging Res. 2012;2012:416132.
Google Scholar
Schröder H, Fitó M, Estruch R, Martínez-González MA, Corella D, Salas-Salvadó J, et al. A short screener is valid for assessing Mediterranean diet adherence among older Spanish men and women. J Nutr. 2011;141(6):1140–5.
Google Scholar
Hebestreit K, Yahiaoui-Doktor M, Engel C, Vetter W, Siniatchkin M, Erickson N, et al. Validation of the German version of the Mediterranean Diet Adherence Screener (MEDAS) questionnaire. BMC Cancer. 2017;17(1):1–10.
Google Scholar
Valenzuela MJ, Sachdev P. Assessment of complex mental activity across the lifespan: development of the Lifetime of Experiences Questionnaire (LEQ). Psychol Med. 2007;37(7):1015–25.
Google Scholar
Roeske S, Wolfsgruber S, Kleineidam L, Zulka L, Buerger K, Ewers M, et al. P3–591: a German version of the Lifetime of Experiences Questionnaire (LEQ) to measure cognitive reserve: validation results from the DELCODE study. Alzheimer’s Dementia. 2018;14((7S_Part_25)):P1352–3.
Baron-Cohen S, Jolliffe T, Mortimore C, Robertson M. Another advanced test of theory of mind: evidence from very high functioning adults with autism or Asperger syndrome. J Child Psychol Psychiatry. 1997;38(7):813–22.
Google Scholar
Kynast J, Polyakova M, Quinque EM, Hinz A, Villringer A, Schroeter ML. Age-and sex-specific standard scores for the Reading the Mind in the Eyes Test. Front Aging Neurosci. 2021;12:607107.
Google Scholar
Aschenbrenner S, Tucha O, Lange K. Regensburger Wortflüssigkeitstest Hogrefe Göttingen. Göttingen, Germany: Hogrefe; 2000.
Reitan R. Trail Making Test: manual for administration and scoring. Tucson, Arizona: Reitan Neuropsychology Laboratory; 1992.
Aebi C. Validierung der neuropsychologischen Testbatterie CERAD-NP: eine Multi-Center Studie [Dissertation]. Basel: University of Basel; 2002.
Bäumler G, Stroop J. Farbe-Wort-Interferenztest nach JR Stroop (FWIT). Hogrefe, Verlag für Psychologie; 1985.
Sturm W, Willmes K, Horn W. Leistungsprüfsystem für 50–90jährige. Handanweisung. Göttingen: Hogrefe; 1993.
Rey A. L’examen psychologique dans les cas d’encéphalopathie traumatique: (Les problèmes). Librairie Naville & Cie; 1941.
Strauss E, Sherman EM, Spreen O. A compendium of neuropsychological tests: administration, norms, and commentary. American Chemical Society; 2006.
Benton A, Hannay HJ, Varney NR. Visual perception of line direction in patients with unilateral brain disease. Neurology. 1975;25(10):907.
Google Scholar
Benton AL. Contributions to neuropsychological assessment: a clinical manual. USA: Oxford University Press; 1994.
Wechsler D. WMS-R: Wechsler memory scale-revised: manual. Psychological Corporation; 1984.
Schretlen D, Bobholz JH, Brandt J. Development and psychometric properties of the Brief Test of Attention. Clin Neuropsychol. 1996;10(1):80–9.
Google Scholar
Helmstaedter C, Durwen HF. VLMT: Verbaler Lern-und Merkfähigkeitstest: Ein praktikables und differenziertes Instrumentarium zur Prüfung der verbalen Gedächtnisleistungen. Schweizer Archiv für Neurologie, Neurochirurgie und Psychiatrie. 1990.
Kalbe E, Reinhold N, Brand M, Kessler J. Aphasie-Check-Liste (ACL): Protokollheft, Testheft, Lösungsfolien, Vorlagen, Manual. Köln: ProLog, Therapie-und Lernmittel; 2002.
Wechsler D. Die Messung der Intelligenz Erwachsener. Textband zum Hamburg-Wechsler-Intelligenztest für Erwachsene (HAWIE); Deutsche Bearbeitung Anne von Hardesty, und Hans Lauber; 1956.
Beck AT, Steer RA, Brown GK. Beck depression inventory-II. San Antonio. 1996;78(2):490–8.
Penner I-K, Raselli C, Stöcklin M, Opwis K, Kappos L, Calabrese P. The Fatigue Scale for Motor and Cognitive Functions (FSMC): validation of a new instrument to assess multiple sclerosis-related fatigue. Mult Scler J. 2009;15(12):1509–17.
Google Scholar
Ware JE, Kosinski M, Bayliss MS, McHorney CA, Rogers WH, Raczek A. Comparison of methods for the scoring and statistical analysis of SF-36 health profile and summary measures: summary of results from the Medical Outcomes Study. Med Care. 1995;33:AS264–79.
Google Scholar
Bullinger M. German translation and psychometric testing of the SF-36 health survey: preliminary results from the IQOLA project. Soc Sci Med. 1995;41(10):1359–66.
Google Scholar
Levenstein S, Prantera C, Varvo V, Scribano ML, Berto E, Luzi C, et al. Development of the perceived stress questionnaire: a new tool for psychosomatic research. J Psychosom Res. 1993;37(1):19–32.
Google Scholar
Fliege H, Rose M, Arck P, Levenstein S, Klapp BF. Validierung des “perceived stress questionnaire” (PSQ) an einer deutschen Stichprobe. [Validation of the “Perceived Stress Questionnaire” (PSQ) in a German sample.]. Diagnostica. 2001;47(3):142–52.
Google Scholar
Jerusalem M, Schwarzer R. Skala zur allgemeinen Selbstwirksamkeitserwartung. Skalen zur Erfassung von Lehrer-und Schülermerkmalen Dokumentation der psychometrischen Verfahren im Rahmen der Wissenschaftlichen Begleitung des Modellversuchs Selbstwirksame Schulen. Berlin: Freie Universität Berlin; 1999.
Chaudhuri KR, Martinez-Martin P, Schapira AH, Stocchi F, Sethi K, Odin P, et al. International multicenter pilot study of the first comprehensive self-completed nonmotor symptoms questionnaire for Parkinson’s disease: the NMSQuest study. Mov Disord. 2006;21(7):916–23.
Google Scholar
Washburn RA, Smith KW, Jette AM, Janney CA. The Physical Activity Scale for the Elderly (PASE): development and evaluation. J Clin Epidemiol. 1993;46(2):153–62.
Google Scholar
Goetz CG, Tilley BC, Shaftman SR, Stebbins GT, Fahn S, Martinez-Martin P, 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(15):2129–70.
Google Scholar
Podsiadlo D, Richardson S. The timed “Up & Go”: a test of basic functional mobility for frail elderly persons. J Am Geriatr Soc. 1991;39(2):142–8.
Google Scholar
Zirek E, Ersoz Huseyinsinoglu B, Tufekcioglu Z, Bilgic B, Hanagasi H. Which cognitive dual-task walking causes most interference on the Timed Up and Go test in Parkinson’s disease: a controlled study. Neurol Sci. 2018;39:2151–7.
Google Scholar
Tiffin J, Asher EJ. The Purdue Pegboard: norms and studies of reliability and validity. J Appl Psychol. 1948;32(3):234.
Google Scholar
Agnew J, Bolla-Wilson K, Kawas CH, Bleecker ML. Purdue pegboard age and sex norms for people 40 years old and older. Dev Neuropsychol. 1988;4(1):29–35.
Google Scholar
Trenkwalder C, Kohnen R, Högl B, Metta V, Sixel-Döring F, Frauscher B, et al. Parkinson’s disease sleep scale—validation of the revised version PDSS-2. Mov Disord. 2011;26(4):644–52.
Google Scholar
Stiasny-Kolster K, Mayer G, Schäfer S, Möller JC, Heinzel-Gutenbrunner M, Oertel WH. The REM sleep behavior disorder screening questionnaire—a new diagnostic instrument. Mov Disord. 2007;22(16):2386–93.
Google Scholar
Frauscher B, Ehrmann L, Zamarian L, Auer F, Mitterling T, Gabelia D, et al. Validation of the Innsbruck REM sleep behavior disorder inventory. Mov Disord. 2012;27(13):1673–8.
Google Scholar
Doppler CE, Kinnerup MB, Brune C, Farrher E, Betts M, Fedorova TD, et al. Regional locus coeruleus degeneration is uncoupled from noradrenergic terminal loss in Parkinson’s disease. Brain. 2021;144(9):2732–44.
Google Scholar
Farrher E, Chiang C-W, Cho K-H, Grinberg F, Buschbeck RP, Chen M-J, et al. Spatiotemporal characterisation of ischaemic lesions in transient stroke animal models using diffusion free water elimination and mapping MRI with echo time dependence. Neuroimage. 2021;244:118605.
Google Scholar
Volz LJ, Cieslak M, Grafton S. A probabilistic atlas of fiber crossings for variability reduction of anisotropy measures. Brain Struct Funct. 2018;223(2):635–51.
Google Scholar
Greve KW. The WCST-64: a standardized short-form of the Wisconsin Card Sorting Test. Clin Neuropsychol. 2001;15(2):228–34.
Google Scholar
Lie C-H, Specht K, Marshall JC, Fink GR. Using fMRI to decompose the neural processes underlying the Wisconsin Card Sorting Test. Neuroimage. 2006;30(3):1038–49.
Google Scholar
Hensel L, Seger A, Farrher E, Bonkhoff AK, Shah NJ, Fink GR, et al. Fronto-striatal dynamic connectivity is linked to dopaminergic motor response in Parkinson’s disease. Parkinsonism Relat Disord. 2023;114:105777. https://doi.org/10.1016/j.parkreldis.2023.105777.
Langkammer C, Pirpamer L, Seiler S, Deistung A, Schweser F, Franthal S, et al. Quantitative susceptibility mapping in Parkinson’s disease. PLoS ONE. 2016;11(9):e0162460.
Google Scholar
R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. 2022.
Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. J Stat Softw. 2015;67:1–48.
Google Scholar
[ad_2]
Source link
