The TAS Test project: a prospective longitudinal validation of new online motor-cognitive tests to detect preclinical Alzheimer's disease and estimate 5-year risks of cognitive decline and dementia

Jane Alty, Quan Bai, Renjie Li, Katherine Lawler, Rebecca J St George, Edward Hill, Aidan Bindoff, Saurabh Garg, Xinyi Wang, Guan Huang, Kaining Zhang, Kaylee D Rudd, Larissa Bartlett, Lynette R Goldberg, Jessica M Collins, Mark R Hinder, Sharon L Naismith, David C Hogg, Anna E King, James C Vickers, Jane Alty, Quan Bai, Renjie Li, Katherine Lawler, Rebecca J St George, Edward Hill, Aidan Bindoff, Saurabh Garg, Xinyi Wang, Guan Huang, Kaining Zhang, Kaylee D Rudd, Larissa Bartlett, Lynette R Goldberg, Jessica M Collins, Mark R Hinder, Sharon L Naismith, David C Hogg, Anna E King, James C Vickers

Abstract

Background: The worldwide prevalence of dementia is rapidly rising. Alzheimer's disease (AD), accounts for 70% of cases and has a 10-20-year preclinical period, when brain pathology covertly progresses before cognitive symptoms appear. The 2020 Lancet Commission estimates that 40% of dementia cases could be prevented by modifying lifestyle/medical risk factors. To optimise dementia prevention effectiveness, there is urgent need to identify individuals with preclinical AD for targeted risk reduction. Current preclinical AD tests are too invasive, specialist or costly for population-level assessments. We have developed a new online test, TAS Test, that assesses a range of motor-cognitive functions and has capacity to be delivered at significant scale. TAS Test combines two innovations: using hand movement analysis to detect preclinical AD, and computer-human interface technologies to enable robust 'self-testing' data collection. The aims are to validate TAS Test to [1] identify preclinical AD, and [2] predict risk of cognitive decline and AD dementia.

Methods: Aim 1 will be addressed through a cross-sectional study of 500 cognitively healthy older adults, who will complete TAS Test items comprising measures of motor control, processing speed, attention, visuospatial ability, memory and language. TAS Test measures will be compared to a blood-based AD biomarker, phosphorylated tau 181 (p-tau181). Aim 2 will be addressed through a 5-year prospective cohort study of 10,000 older adults. Participants will complete TAS Test annually and subtests of the Cambridge Neuropsychological Test Battery (CANTAB) biennially. 300 participants will undergo in-person clinical assessments. We will use machine learning of motor-cognitive performance on TAS Test to develop an algorithm that classifies preclinical AD risk (p-tau181-defined) and determine the precision to prospectively estimate 5-year risks of cognitive decline and AD.

Discussion: This study will establish the precision of TAS Test to identify preclinical AD and estimate risk of cognitive decline and AD. If accurate, TAS Test will provide a low-cost, accessible enrichment strategy to pre-screen individuals for their likelihood of AD pathology prior to more expensive tests such as blood or imaging biomarkers. This would have wide applications in public health initiatives and clinical trials.

Trial registration: ClinicalTrials.gov Identifier: NCT05194787 , 18 January 2022. Retrospectively registered.

Keywords: Ageing; Artificial intelligence; Computer vision; Dementia; Movement analysis, kinematics, finger tapping, dual-task; Screening.

Conflict of interest statement

JA receives royalties from Taylor and Francis publishing; has stock ownership in ClearSky Medical Diagnostics and has received Honoraria from Stada, Allergan, Merz and Medtronic. SN has received Honoraria from Nutrica and Roche pharmaceuticals.

© 2022. The Author(s).

Figures

Fig. 1
Fig. 1
TAS Test Reaction Time Tests: a) simple choice test and b) five choice test
Fig. 2
Fig. 2
Benson Figure Test (a) Viewing phase to register the shape over a 1-minute duration and (b) delayed recall phase to identify whether a sub-section was part of the original figure
Fig. 3
Fig. 3
Spatial Span test; the participant is shown a sequence of yellow circles and then asked to repeat the sequence by clicking on the circles. The length of sequence increases each time a sequence is correctly recalled, up to a maximum of 9 circles

References

    1. Livingston G, Sommerlad A, Orgeta V, Costafreda SG, Huntley J, Ames D, et al. Dementia prevention, intervention, and care. Lancet. 2017;390(10113):2673–2734. doi: 10.1016/S0140-6736(17)31363-6.
    1. Livingston G, Huntley J, Sommerlad A, Ames D, Ballard C, Banerjee S, et al. Dementia prevention, intervention, and care: 2020 report of the lancet commission. Lancet. 2020;396(10248):413–446. doi: 10.1016/S0140-6736(20)30367-6.
    1. (WHO) WHO . Risk reduction of cognitive decline and dementia. 2019.
    1. Sperling RA, Aisen PS, Beckett LA, Bennett DA, Craft S, Fagan AM, et al. Toward defining the preclinical stages of Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 2011;7(3):280–292. doi: 10.1016/j.jalz.2011.03.003.
    1. Jack CR, Jr, Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, et al. NIA-AA research framework: toward a biological definition of Alzheimer's disease. Alzheimers Dement. 2018;14(4):535–562. doi: 10.1016/j.jalz.2018.02.018.
    1. de Leon MJ, Mosconi L, Blennow K, DeSanti S, Zinkowski R, Mehta PD, et al. Imaging and CSF studies in the preclinical diagnosis of Alzheimer's disease. Ann N Y Acad Sci. 2007;1097:114–145. doi: 10.1196/annals.1379.012.
    1. Schroeder RW, Martin PK, Walling A. Neuropsychological evaluations in adults. Am Fam Physician. 2019;99(2):101–108.
    1. Karikari TK, Pascoal TA, Ashton NJ, Janelidze S, Benedet AL, Rodriguez JL, et al. Blood phosphorylated tau 181 as a biomarker for Alzheimer's disease: a diagnostic performance and prediction modelling study using data from four prospective cohorts. Lancet Neurol. 2020;19(5):422–433. doi: 10.1016/S1474-4422(20)30071-5.
    1. Janelidze S, Mattsson N, Palmqvist S, Smith R, Beach TG, Serrano GE, et al. Plasma P-tau181 in Alzheimer’s disease: relationship to other biomarkers, differential diagnosis, neuropathology and longitudinal progression to Alzheimer's dementia. Nat Med. 2020;26(3):379–386. doi: 10.1038/s41591-020-0755-1.
    1. Morrison MS, Aparicio HJ, Blennow K, Zetterberg H, Ashton NJ, Karikari TK, et al. Antemortem plasma phosphorylated tau (181) predicts Alzheimer's disease neuropathology and regional tau at autopsy. Brain. 2022:awac175. 10.1093/brain/awac175. Epub ahead of print.
    1. Lantero Rodriguez J, Karikari TK, Suarez-Calvet M, Troakes C, King A, Emersic A, et al. Plasma p-tau181 accurately predicts Alzheimer’s disease pathology at least 8 years prior to post-mortem and improves the clinical characterisation of cognitive decline. Acta Neuropathol. 2020;140(3):267–278. doi: 10.1007/s00401-020-02195-x.
    1. Moscoso A, Grothe MJ, Ashton NJ, Karikari TK, Lantero Rodriguez J, Snellman A, et al. Longitudinal associations of blood phosphorylated Tau181 and Neurofilament light chain with neurodegeneration in Alzheimer disease. JAMA Neurol. 2021;78(4):396–406. doi: 10.1001/jamaneurol.2020.4986.
    1. Dumurgier J, Artaud F, Touraine C, Rouaud O, Tavernier B, Dufouil C, et al. Gait speed and decline in gait speed as predictors of incident dementia. J Gerontol A Biol Sci Med Sci. 2017;72(5):655–661.
    1. Beauchet O, Annweiler C, Callisaya ML, De Cock AM, Helbostad JL, Kressig RW, et al. Poor gait performance and prediction of dementia: results from a Meta-analysis. J Am Med Dir Assoc. 2016;17(6):482–490. doi: 10.1016/j.jamda.2015.12.092.
    1. Loh KK, Hadj-Bouziane F, Petrides M, Procyk E, Amiez C. Rostro-caudal Organization of Connectivity between cingulate motor areas and lateral frontal regions. Front Neurosci. 2017;11:753. doi: 10.3389/fnins.2017.00753.
    1. Minoshima S, Giordani B, Berent S, Frey KA, Foster NL, Kuhl DE. Metabolic reduction in the posterior cingulate cortex in very early Alzheimer’s disease. Ann Neurol. 1997;42(1):85–94. doi: 10.1002/ana.410420114.
    1. Palmqvist S, Scholl M, Strandberg O, Mattsson N, Stomrud E, Zetterberg H, et al. Earliest accumulation of beta-amyloid occurs within the default-mode network and concurrently affects brain connectivity. Nat Commun. 2017;8(1):1214. doi: 10.1038/s41467-017-01150-x.
    1. Shima K, Aya K, Mushiake H, Inase M, Aizawa H, Tanji J. Two movement-related foci in the primate cingulate cortex observed in signal-triggered and self-paced forelimb movements. J Neurophysiol. 1991;65(2):188–202. doi: 10.1152/jn.1991.65.2.188.
    1. Mollica MA, Tort-Merino A, Navarra J, Fernandez-Prieto I, Valech N, Olives J, et al. Early detection of subtle motor dysfunction in cognitively normal subjects with amyloid-beta positivity. Cortex. 2019;121:117–124. doi: 10.1016/j.cortex.2019.07.021.
    1. Mollica MA, Navarra J, Fernandez-Prieto I, Olives J, Tort A, Valech N, et al. Subtle visuomotor difficulties in preclinical Alzheimer's disease. J Neuropsychol. 2017;11(1):56–73. doi: 10.1111/jnp.12079.
    1. Andriuta D, Diouf M, Roussel M, Godefroy O. Is reaction time slowing an early sign of Alzheimer’s disease? A meta-analysis. Dement Geriatr Cogn Disord. 2019;47(4–6):281–288. doi: 10.1159/000500348.
    1. Buchman AS, Bennett DA. Loss of motor function in preclinical Alzheimer’s disease. Expert Rev Neurother. 2011;11(5):665–676. doi: 10.1586/ern.11.57.
    1. LaMonica HM, English A, Hickie IB, Ip J, Ireland C, West S, et al. Examining internet and eHealth practices and preferences: survey study of Australian older adults with subjective memory complaints, mild cognitive impairment, or dementia. J Med Internet Res. 2017;19(10):e358. doi: 10.2196/jmir.7981.
    1. Williams S, Zhao Z, Hafeez A, Wong DC, Relton SD, Fang H, Alty JE. The discerning eye of computer vision: Can it measure Parkinson's finger tap bradykinesia? J Neurol Sci. 2020;416:117003. 10.1016/j.jns.2020.117003. Epub 2020 Jun 30.
    1. Wong DRSFH, Qhawaji R, Graham CD, Alty J, Williams S. IEEE 32nd International Symposium on Computer-Based Medical Systems. 2019. Supervised classification of bradykinesia for Parkinson’s disease diagnosis from smartphone videos; pp. 32–27.
    1. Li R, Wang X, Lawler K, Garg S, Bai Q, Alty J. Applications of artificial intelligence to aid early detection of dementia: a scoping review on current capabilities and future directions. J Biomed Inform. 2022;127:104030. doi: 10.1016/j.jbi.2022.104030.
    1. Williams S, Fang H, Relton SD, Wong DC, Alam T, Alty JE. Accuracy of smartphone video for contactless measurement of hand tremor frequency. Mov Disord Clin Pract. 2021;8(1):69–75. doi: 10.1002/mdc3.13119.
    1. Cosgrove J, Hinder MR, St George RJ, Picardi C, Smith SL, Lones MA, et al. Significant cognitive decline in Parkinson's disease exacerbates the reliance on visual feedback during upper limb reaches. Neuropsychologia. 2021;157:107885. doi: 10.1016/j.neuropsychologia.2021.107885.
    1. Alty JE, Clissold BG, McColl CD, Reardon KA, Shiff M, Kempster PA. Longitudinal study of the levodopa motor response in Parkinson’s disease: relationship between cognitive decline and motor function. Mov Disord. 2009;24(16):2337–2343.
    1. Williams S, Fang H, Alty J, Qahwaji R, Patel P, Graham CD. A smartphone camera reveals an ‘invisible’ parkinsonian tremor: a potential pre-motor biomarker? J Neurol. 2018;265(12):3017–3018. doi: 10.1007/s00415-018-9060-z.
    1. Summers MJ, Saunders NLJ, Valenzuela MJ, Summers JJ, Ritchie K, Robinson A, et al. The Tasmanian healthy brain project (THBP): a prospective longitudinal examination of the effect of university-level education in older adults in preventing age-related cognitive decline and reducing the risk of dementia. Int Psychogeriatr. 2013;25(7):1145–1155. doi: 10.1017/S1041610213000380.
    1. Diamond K, Mowszowski L, Cockayne N, Norrie L, Paradise M, Hermens DF, et al. Randomized controlled trial of a healthy brain ageing cognitive training program: effects on memory, mood, and sleep. J Alzheimers Dis. 2015;44(4):1181–1191. doi: 10.3233/JAD-142061.
    1. Junkkila J, Oja S, Laine M, Karrasch M. Applicability of the CANTAB-PAL computerized memory test in identifying amnestic mild cognitive impairment and Alzheimer's disease. Dement Geriatr Cogn Disord. 2012;34(2):83–89. doi: 10.1159/000342116.
    1. Smirnov DS, Ashton NJ, Blennow K, Zetterberg H, Simren J, Lantero-Rodriguez J, et al. Plasma biomarkers for Alzheimer's disease in relation to neuropathology and cognitive change. Acta Neuropathol. 2022;143(4):487–503. doi: 10.1007/s00401-022-02408-5.
    1. Camicioli R, Howieson D, Oken B, Sexton G, Kaye J. Motor slowing precedes cognitive impairment in the oldest old. Neurology. 1998;50(5):1496–1498. doi: 10.1212/WNL.50.5.1496.
    1. Montero-Odasso M, Oteng-Amoako A, Speechley M, Gopaul K, Beauchet O, Annweiler C, et al. The motor signature of mild cognitive impairment: results from the gait and brain study. J Gerontol A Biol Sci Med Sci. 2014;69(11):1415–1421. doi: 10.1093/gerona/glu155.
    1. Noyce AJ, Treacy C, Budu C, Fearnley J, Lees AJ, Giovannoni G. The new bradykinesia akinesia Incoordination (BRAIN) test: preliminary data from an online test of upper limb movement. Mov Disord. 2012;27(1):157–158. doi: 10.1002/mds.23947.
    1. Weil RS, Pappa K, Schade RN, Schrag AE, Bahrami B, Schwarzkopf DS, et al. The cats-and-dogs test: a tool to identify visuoperceptual deficits in Parkinson's disease. Mov Disord. 2017;32(12):1789–1790. doi: 10.1002/mds.27176.
    1. Possin KL, Laluz VR, Alcantar OZ, Miller BL, Kramer JH. Distinct neuroanatomical substrates and cognitive mechanisms of figure copy performance in Alzheimer's disease and behavioral variant frontotemporal dementia. Neuropsychologia. 2011;49(1):43–48. doi: 10.1016/j.neuropsychologia.2010.10.026.
    1. Vandierendonck A, Kemps E, Fastame MC, Szmalec A. Working memory components of the Corsi blocks task. Br J Psychol. 2004;95(Pt 1):57–79. doi: 10.1348/000712604322779460.
    1. Menn L, Ramsberger G, Helmestabrooks N. A linguistic communication measure for aphasic narratives. Aphasiology. 1994;8(4):343–359. doi: 10.1080/02687039408248664.
    1. Sahakian BJ, Owen AM. Computerized assessment in neuropsychiatry using CANTAB: discussion paper. J R Soc Med. 1992;85(7):399–402.
    1. Bartlett L, Doherty K, Farrow M, Kim S, Hill E, King A, et al. Island study linking aging and neurodegenerative disease (ISLAND) targeting dementia risk reduction: protocol for a prospective web-based cohort study. JMIR Res Protoc. 2022;11(3):e34688. doi: 10.2196/34688.
    1. Fowler KS, Saling MM, Conway EL, Semple JM, Louis WJ. Paired associate performance in the early detection of DAT. J Int Neuropsych Soc. 2002;8(1):58–71. doi: 10.1017/S1355617701020069.
    1. Tatebe H, Kasai T, Ohmichi T, Kishi Y, Kakeya T, Waragai M, et al. Quantification of plasma phosphorylated tau to use as a biomarker for brain Alzheimer pathology: pilot case-control studies including patients with Alzheimer's disease and Down syndrome. Mol Neurodegener. 2017;12(1):63. doi: 10.1186/s13024-017-0206-8.
    1. Wolff RF, Moons KGM, Riley RD, Whiting PF, Westwood M, Collins GS, et al. PROBAST: a tool to assess the risk of Bias and applicability of prediction model studies. Ann Intern Med. 2019;170(1):51–58. doi: 10.7326/M18-1376.
    1. Lobo A, Lopez-Anton R, Santabarbara J, de-la- Camara C, Ventura T, Quintanilla MA, et al. Incidence and lifetime risk of dementia and Alzheimer’s disease in a southern European population. Acta Psychiatr Scand. 2011;124(5):372–383. doi: 10.1111/j.1600-0447.2011.01754.x.
    1. WHO . Global status report on the public health response to dementia. Geneva: World Health Organization; 2021.
    1. Riley RD, Ensor J, Snell KIE, Harrell FE, Jr, Martin GP, Reitsma JB, et al. Calculating the sample size required for developing a clinical prediction model. BMJ. 2020;368:m441. doi: 10.1136/bmj.m441.

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