Current advances in digital cognitive assessment for preclinical Alzheimer's disease

Fredrik Öhman, Jason Hassenstab, David Berron, Michael Schöll, Kathryn V Papp, Fredrik Öhman, Jason Hassenstab, David Berron, Michael Schöll, Kathryn V Papp

Abstract

There is a pressing need to capture and track subtle cognitive change at the preclinical stage of Alzheimer's disease (AD) rapidly, cost-effectively, and with high sensitivity. Concurrently, the landscape of digital cognitive assessment is rapidly evolving as technology advances, older adult tech-adoption increases, and external events (i.e., COVID-19) necessitate remote digital assessment. Here, we provide a snapshot review of the current state of digital cognitive assessment for preclinical AD including different device platforms/assessment approaches, levels of validation, and implementation challenges. We focus on articles, grants, and recent conference proceedings specifically querying the relationship between digital cognitive assessments and established biomarkers for preclinical AD (e.g., amyloid beta and tau) in clinically normal (CN) individuals. Several digital assessments were identified across platforms (e.g., digital pens, smartphones). Digital assessments varied by intended setting (e.g., remote vs. in-clinic), level of supervision (e.g., self vs. supervised), and device origin (personal vs. study-provided). At least 11 publications characterize digital cognitive assessment against AD biomarkers among CN. First available data demonstrate promising validity of this approach against both conventional assessment methods (moderate to large effect sizes) and relevant biomarkers (predominantly weak to moderate effect sizes). We discuss levels of validation and issues relating to usability, data quality, data protection, and attrition. While still in its infancy, digital cognitive assessment, especially when administered remotely, will undoubtedly play a major future role in screening for and tracking preclinical AD.

Keywords: clinical assessment; clinical trials; cognition; computerized assessment; digital cognitive biomarkers; home‐based assessment; preclinical Alzheimer's disease; smartphone‐based assessment.

Conflict of interest statement

Fredrik Öhman declares no conflict of interest. Jason Hassenstab is a paid consultant for Lundbeck, Biogen, Roche, and Takeda, outside the scope of this work. David Berron has co‐founded neotiv GmbH and owns shares. Kathryn V. Papp has served as a paid consultant for Biogen Idec and Digital Cognition Technologies. Michael Schöll has served on a scientific advisory board for Servier and received speaker honoraria by Genentech, outside the scope of this work.

© 2021 The Authors. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals, LLC on behalf of Alzheimer's Association.

Figures

FIGURE 1
FIGURE 1
A, Cogstate One Back tests. Copyright© 2020 Cogstate. All rights reserved. Used with Cogstate's permission. B, CANTAB Spatial Span and Paired Associates Learning. Copyright Cambridge Cognition. All rights reserved. C, NIH‐Toolbox Pattern Comparison Processing Speed Test Age 7+ v2.1. Used with permission NIH Toolbox, © 2020 National Institutes of Health and Northwestern University
FIGURE 2
FIGURE 2
A, Ambulatory Research in Cognition (ARC) Symbols Test, Grids Test, and Prices Test. Used with permission from J. Hassenstab. B, neotiv Objects‐in‐Rooms Recall test. Used with permission from neotiv GmbH. C, Boston Remote Assessment for Neurocognitive Health (BRANCH). Used with permission from K. V. Papp
FIGURE 3
FIGURE 3
A, Sea Hero Quest Wayfinding and Path integration. Used with permission from M. Hornberger. B, Digital Maze Test from survey perspective and landmarks from a first‐person perspective. Used with permission from D. Head. C, Data and analysis process for digital Clock Drawing Test (dCDT), from data collection, the artificial intelligence (AI) analysis steps, and the machine learning (ML) analysis and reporting. Used with permission from Digital Cognition Technologies
FIGURE 4
FIGURE 4
Overview of cognitive tests and their platforms. BRANCH, Boston Remote Assessment for Neurocognitive Health; ORCA‐LLT, Online Repeatable Cognitive Assessment‐Language Learning Test; NIH‐TB, National Institutes of Health Toolbox; CANTAB, Cambridge Neuropsychological Test Automated Battery; ARC, Ambulatory Research in Cognition; M2C2, Monitoring of Cognitive Change; dCDT, digital Clock Drawing Test. *Is available for use through a web browser

References

    1. Jack CR, Holtzman DM. Biomarker modeling of alzheimer's disease. Neuron. 2013;80(6):1347‐1358.
    1. Jack CR, Bennett DA, Blennow K, et al. NIA‐AA Research Framework: toward a biological definition of Alzheimer's disease. Alzheimer's Dement. 2018;14(4):535‐562.
    1. Sperling RA, Aisen PS, Beckett LA, 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. Alzheimer's Dement. 2011;7(3):280‐292.
    1. Weintraub S, Carrillo MC, Farias ST, et al. Measuring cognition and function in the preclinical stage of Alzheimer's disease. Alzheimer's Dement Transl Res Clin Interv. 2018;4:64‐75.
    1. Perrotin A, Mormino EC, Madison CM, Hayenga AO, Jagust WJ. Subjective cognition and amyloid deposition imaging: a Pittsburgh compound B positron emission tomography study in normal elderly individuals. Arch Neurol. 2012;69(2):223‐229.
    1. Sperling RA, Johnson KA, Doraiswamy PM, et al. Amyloid deposition detected with florbetapir F 18 (18F‐AV‐45) is related to lower episodic memory performance in clinically normal older individuals. Neurobiol Aging. 2013;34(3):822‐831.
    1. Papp KV, Rentz DM, Maruff P, et al. The computerized cognitive composite (C3) in A4, an Alzheimer's disease secondary prevention trial. J Prev Alzheimer's Dis. 2020;8(1):59‐67.
    1. Duke Han S, Nguyen CP, Stricker NH, Nation DA. Detectable neuropsychological differences in early preclinical Alzheimer's disease: a meta‐analysis. Neuropsychol Rev. 2017;27(4):305‐325.
    1. Aizenstein HJ, Nebes RD, Saxton JA, et al. Frequent amyloid deposition without significant cognitive impairment among the elderly. Arch Neurol. 2008;65(11):1509‐1517.
    1. Ying Lim Y, Ellis KA, Harrington K, et al. Cognitive consequences of high aβ amyloid in mild cognitive impairment and healthy older adults: implications for early detection of Alzheimer's disease. Neuropsychology. 2013;27(3):322‐332.
    1. Song Z, Insel PS, Buckley S, et al. Brain amyloid‐β burden is associated with disruption of intrinsic functional connectivity within the medial temporal lobe in cognitively normal elderly. J Neurosci. 2015;35(7):3240‐3247.
    1. Harrington KD, Lim YY, Ames D, et al. Amyloid β–Associated cognitive decline in the absence of clinical disease progression and systemic illness. Alzheimer's Dement. 2017;8:156‐164.
    1. Lim YY, Pietrzak RH, Bourgeat P, et al. Relationships between performance on the cogstate brief battery, neurodegeneration, and aβ accumulation in cognitively normal older adults and adults with MCI. Arch Clin Neuropsychol. 2015;30(1):49‐58.
    1. Papp KV, Rentz DM, Orlovsky I, Sperling RA, Mormino EC. Optimizing the preclinical Alzheimer's cognitive composite with semantic processing: the PACC5. Alzheimer's Dement Transl Res Clin Interv. 2017;3(4):668‐677.
    1. Rowe CC, Bourgeat P, Ellis KA, et al. Predicting Alzheimer disease with β‐amyloid imaging: results from the Australian imaging, biomarkers, and lifestyle study of ageing. Ann Neurol. 2013;74(6):905‐913.
    1. Hanseeuw BJ, Betensky RA, Jacobs HIL, et al. Association of amyloid and tau with cognition in preclinical Alzheimer disease: a longitudinal study. JAMA Neurol. 2019;76(8):915‐924.
    1. Donohue MC, Sperling RA, Salmon DP, et al. The preclinical Alzheimer cognitive composite: measuring amyloid‐related decline. JAMA Neurol. 2014;71(8):961‐970.
    1. Papp KV, Rentz DM, Mormino EC, et al. Cued memory decline in biomarker‐defined preclinical Alzheimer disease. Neurology. 2017;88(15):1431‐1438.
    1. Bransby L, Lim YY, Ames D, et al. Sensitivity of a Preclinical Alzheimer's Cognitive Composite (PACC) to amyloid β load in preclinical Alzheimer's disease. J Cli Exp Neuropsychol. 2019;41(6):591‐600.
    1. Mormino EC, Papp KV, Rentz DM, et al. Early and late change on the preclinical Alzheimer's cognitive composite in clinically normal older individuals with elevated amyloid β. Alzheimer's Dement. 2017;13(9):1004‐1012.
    1. Hedden T, Oh H, Younger AP, Patel TA. Meta‐analysis of amyloid‐cognition relations in cognitively normal older adults. Neurology. 2013;80(14):1341‐1348.
    1. Mortamais M, Ash JA, Harrison J, et al. Detecting cognitive changes in preclinical Alzheimer's disease: a review of its feasibility. Alzheimer's Dement. 2017;13(4):468‐492.
    1. Baker JE, Lim YY, Pietrzak RH, et al. Cognitive impairment and decline in cognitively normal older adults with high amyloid‐β: a meta‐analysis. Alzheimer's Dement. 2017;6:108‐121.
    1. Petersen RC, Wiste HJ, Weigand SD, et al. Association of elevated amyloid levels with cognition and biomarkers in cognitively normal people from the community. JAMA Neurol. 2016;73(1):85‐92.
    1. Johnson DK, Storandt M, Morris JC, Galvin JE. Longitudinal study of the transition from healthy aging to Alzheimer disease. Arch Neurol. 2009;66(10):1254‐1259.
    1. Nelson PT, Alafuzoff I, Bigio EH, et al. Correlation of Alzheimer disease neuropathologic changes with cognitive status: a review of the literature. J Neuropathol Exp Neurol. 2012;71(5):362‐381.
    1. Ossenkoppele R, Schonhaut DR, Schöll M, et al. Tau PET patterns mirror clinical and neuroanatomical variability in Alzheimer's disease. Brain. 2016;139(5):1551‐1567.
    1. Bejanin A, Schonhaut DR, La Joie R, et al. Tau pathology and neurodegeneration contribute to cognitive impairment in Alzheimer's disease. Brain. 2017;140(12):3286‐3300.
    1. Maass A, Lockhart SN, Harrison TM, et al. Entorhinal tau pathology, episodic memory decline, and neurodegeneration in aging. J Neurosci. 2018;38(3):530‐543.
    1. Schöll M, Lockhart SN, Schonhaut DR, et al. PET imaging of tau deposition in the aging human brain. Neuron. 2016;89(5):971‐982.
    1. Schöll M, Maass A. Does early cognitive decline require the presence of both tau and amyloid‐β? Brain. 2020;143(1):10‐13.
    1. Betthauser TJ, Koscik RL, Jonaitis EM, et al. Amyloid and tau imaging biomarkers explain cognitive decline from late middle‐age. Brain. 2020;143(1):320‐335.
    1. Mormino EC, Papp KV. Amyloid accumulation and cognitive decline in clinically normal older individuals: implications for aging and early Alzheimer's disease. J Alzheimer's Dis. 2018;64(s1):S633‐S646.
    1. Rentz DM, Parra Rodriguez MA, Amariglio R, Stern Y, Sperling R, Ferris S. Promising developments in neuropsychological approaches for the detection of preclinical Alzheimer's disease: a selective review. Alzheimer's Res Ther. 2013;5(6):1.
    1. Sliwinski MJ. Measurement‐burst designs for social health research. Soc Personal Psychol Compass. 2008;2(1):245‐261.
    1. Goldberg TE, Harvey PD, Wesnes KA, Snyder PJ, Schneider LS. Practice effects due to serial cognitive assessment: implications for preclinical Alzheimer's disease randomized controlled trials. Alzheimer's Dement. 2015;1(1):103‐111.
    1. Soldan A, Pettigrew C, Albert M. Evaluating cognitive reserve through the prism of preclinical Alzheimer disease. Psychiatr Clin North Am. 2018;41(1):65‐77.
    1. Schatz P, Browndyke J. Applications of computer‐based neuropsychological assessment. J Head Trauma Rehabil. 2002;17(5):395‐410.
    1. Sliwinski MJ, Mogle JA, Hyun J, Munoz E, Smyth JM, Lipton RB. Reliability and validity of ambulatory cognitive assessments. Assessment. 2018;25(1):14‐30.
    1. Koo BM, Vizer LM. Mobile technology for cognitive assessment of older adults: a scoping review. Innov Aging. 2019;3(1):1‐14.
    1. Miller JB, Barr WB. The technology crisis in neuropsychology. Arch Clin Neuropsychol. 2017;32(5):541‐554.
    1. Laske C, Sohrabi HR, Frost SM, et al. Innovative diagnostic tools for early detection of Alzheimer's disease. Alzheimer's Dement. 2015;11(5):561‐578.
    1. Pratap A, Neto EC, Snyder P, et al. Indicators of retention in remote digital health studies: a cross‐study evaluation of 100,000 participants. npj Digit Med. 2020;3(1):1‐10.
    1. Bush SS. A Casebook of Ethical Challenges in Neuropsychology. Taylor & Francis; 2004.
    1. Anderson BYM, Perrin A. PI_2017.05.17_Older‐Americans‐Tech_FINAL. Pew Res Cent. 2017;(May):1‐22.
    1. Gold M, Amatniek J, Carrillo MC, et al. Digital technologies as biomarkers, clinical outcomes assessment, and recruitment tools in Alzheimer's disease clinical trials. Alzheimer's Dement Transl Res Clin Interv. 2018;4:234‐242.
    1. Wild K, Howieson D, Webbe F, Seelye A, Kaye J. Status of computerized cognitive testing in aging: a systematic review. Alzheimer's Dement. 2008;4(6):428‐437.
    1. Kourtis LC, Regele OB, Wright JM, Jones GB. Digital biomarkers for Alzheimer's disease: the mobile/wearable devices opportunity. npj Digit Med. 2019;2(1):1‐9.
    1. Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan‐a web and mobile app for systematic reviews. Syst Rev. 2016;5(1):210.
    1. Maruff P, Thomas E, Cysique L, et al. Validity of the CogState brief battery: relationship to standardized tests and sensitivity to cognitive impairment in mild traumatic brain injury, schizophrenia, and AIDS dementia complex. Arch Clin Neuropsychol. 2009;24(2):165‐178.
    1. Darby DG, Pietrzak RH, Fredrickson J, et al. Intraindividual cognitive decline using a brief computerized cognitive screening test. Alzheimer's Dement. 2012;8(2):95‐104.
    1. Fredrickson J, Maruff P, Woodward M, et al. Evaluation of the usability of a brief computerized cognitive screening test in older people for epidemiological studies. Neuroepidemiology. 2010;34(2):65‐75.
    1. Perin S, Buckley RF, Pase MP, et al. Unsupervised assessment of cognition in the healthy brain project: implications for web‐based registries of individuals at risk for Alzheimer's disease. Alzheimer's Dement Transl Res Clin Interv. 2020;6(1):1‐11.
    1. Papp KV, Amariglio RE, Dekhtyar M, et al. Development of a psychometrically equivalent short form of the face‐name associative memory exam for use along the early Alzheimers disease trajectory. Clin Neuropsychol. 2014;28(5):771‐785.
    1. Stark SM, Yassa M, Lacy JW, Stark CEL. A task to assess behavioral pattern separation (BPS) in humans. Neuropsychologia. 2013;51(12):2442‐2449.
    1. Vannini P, Hedden T, Becker JA, et al. Age and amyloid‐related alterations in default network habituation to stimulus repetition. Neurobiol Aging. 2012;33(7):1237‐1252.
    1. Kirwan CB, Stark CEL. Overcoming interference: an fMRI investigation of pattern separation in the medial temporal lobe. Learn Mem. 2007;14(9):625‐633.
    1. Buckley RF, Sparks KP, Papp KV, et al. Computerized cognitive testing for use in clinical trials: a comparison of the NIH toolbox and CogState C3 batteries. J Prev Alzheimer's Dis. 2017;4(1):3‐11.
    1. Mielke MM, Machulda MM, Hagen CE, et al. Influence of amyloid and APOE on cognitive performance in a late middle‐aged cohort. Alzheimer's Dement. 2016;12(3):281‐291.
    1. Baker JE, Pietrzak RH, Laws SM, et al. Visual paired associate learning deficits associated with elevated beta‐amyloid in cognitively normal older adults. Neuropsychology. 2019;33(7):964‐974.
    1. Hodes RJ, Insel TR, Landis SC, NIH Blueprint for Neuroscience Research . The NIH toolbox: setting a standard for biomedical research. Neurology. 2013;80(11 Suppl 3):S1.
    1. Weintraub S, Dikmen SS, Heaton RK, et al. Cognition assessment using the NIH Toolbox. Neurology. 2013;80(11 Suppl 3):S54‐S64.
    1. Snitz BE, Tudorascu DL, Yu Z, et al. Associations between NIH toolbox cognition battery and in vivo brain amyloid and tau pathology in non‐demented older adults. Alzheimer's Dement. 2020;12(1):1‐9.
    1. Fray PJ, Robbins TW, Sahakian BJ. Neuorpsychiatyric applications of CANTAB. Int J Geriatr Psychiatry. 1996;11(4):329‐336.
    1. Juncos‐Rabadán O, Pereiro AX, Facal D, Reboredo A, Lojo‐Seoane C. Do the Cambridge neuropsychological test automated battery episodic memory measures discriminate amnestic mild cognitive impairment? Int J Geriatr Psychiatry. 2014;29(6):602‐609.
    1. Bischof GN, Rodrigue KM, Kennedy KM, Devous MD, Park DC. Amyloid deposition in younger adults is linked to episodic memory performance. Neurology. 2016;87(24):2562‐2566.
    1. Nelson KB. 2020 Tech Trends of the 50+: Infographic; 2020.
    1. Pew Research Center. Mobile Fact Sheet. Pew Research Center: Mobile Fact Sheet. 2020.
    1. GFK‐Verein. Nuremberg Institute for Market Decisions. Consumer Study (2012, 2014, 2016, 2018). 2018. . Accessed February 1, 2021.
    1. Samaroo AH, Burnham SC, Amariglio R, et al. P1‐466: diminished practice effects on a monthly computerized and home‐administered face‐name associative memory exam are predictive of elevated pet amyloid in normal older adults. Alzheimer's Dement. 2019;15:P447‐P447.
    1. Schlemmer M, Desrichard O. Is medical environment detrimental to memory? A test of a white coat effect on older people's memory performance. Clin Gerontol. 2018;41(1):77‐81.
    1. Pillemer F, Price RA, Paone S, et al. Direct release of test results to patients increases patient engagement and utilization of care. PLoS One. 2016;11(6):1‐9.
    1. Lancaster C, Koychev I, Blane J, Chinner A, Wolters L, Hinds C. Evaluating the feasibility of frequent cognitive assessment using the Mezurio smartphone app: observational and interview study in adults with elevated dementia risk. JMIR mHealth uHealth. 2020;8(4):e16142.
    1. Hassenstab J. Presentation: Remote Assessment Approaches in the Dominantly Inherited Alzheimer Network (DIAN). In: Alzheimer's Association International Conference; 2020.
    1. Schweitzer P, Husky M, Allard M, et al. Feasibility and validity of mobile cognitive testing in the investigation of age‐related cognitive decline. Int J Methods Psychiatr Res. 2017;26(3):1‐8.
    1. Hassenstab J, Ruvolo D, Jasielec M, Xiong C, Grant E, Morris JC. Absence of practice effects in preclinical Alzheimer's disease. Neuropsychology. 2015;29(6):940‐948.
    1. Samaroo A, Properzi M, Sperling RA, et al. Diminished Learning Over Repeated Exposures (LORE) in preclinical Alzheimer's disease. Alzheimer's Dement. 2020;(September):1‐10.
    1. Rentz DM, Amariglio RE, Becker JA, et al. Face‐name associative memory performance is related to amyloid burden in normal elderly. Neuropsychologia. 2011;49(9):2776‐2783.
    1. Papp KV. Presentation: Repeated Memory‐Based Assessments: Implications for Clinical Trials and Practice. In: Alzheimer's Association International Conference; 2020.
    1. Lim YY, Baker JE, Bruns L, et al. Association of deficits in short‐term learning and Aβ and hippoampal volume in cognitively normal adults. Neurology. 2020;95(18):e2577‐e2585.
    1. Duff K, Hammers DB, Dalley BCA, et al. Short‐term practice effects and amyloid deposition: providing information above and beyond baseline cognition. J Prev Alzheimer's Dis. 2017;4(2):87‐92.
    1. Egan MF, Kost J, Voss T, et al. Randomized trial of verubecestat for prodromal Alzheimer's disease. N Engl J Med. 2019;380(15):1408‐1420.
    1. Berron D, Neumann K, Maass A, et al. Age‐related functional changes in domain‐specific medial temporal lobe pathways. Neurobiol Aging. 2018;65:86‐97.
    1. Grande X, Berron D, Horner AJ, Bisby JA, Düzel E, Burgess N. Holistic recollection via pattern completion involves hippocampal subfield CA3. J Neurosci. 2019;39(41):8100‐8111.
    1. Polcher A, Frommann I, Koppara A, Wolfsgruber S, Jessen F, Wagner M. Face‐name associative recognition deficits in subjective cognitive decline and mild cognitive impairment. J Alzheimer's Dis. 2017;56(3):1185‐1196.
    1. Düzel E, Schütze H, Yonelinas AP, Heinze H‐J. Functional phenotyping of successful aging in long‐term memory: preserved performance in the absence of neural compensation. Hippocampus. 2010;814:n/a‐n/a.
    1. Ranganath C, Ritchey M. Two cortical systems for memory‐guided behaviour. Nat Rev Neurosci. 2012;13(10):713‐726.
    1. Maass A, Berron D, Harrison TM, et al. Alzheimer's pathology targets distinct memory networks in the ageing brain. Brain. 2019;142(8):2492‐2509.
    1. Berron D, Cardenas‐Blanco A, Bittner D, et al. Higher CSF tau levels are related to hippocampal hyperactivity and object mnemonic discrimination in older adults. J Neurosci. 2019;39(44):8788‐8797.
    1. Düzel E, Berron D, Schütze H, et al. CSF total tau levels are associated with hippocampal novelty irrespective of hippocampal volume. Alzheimer's Dement. 2018;10:782‐790.
    1. Berron D. Presentation: Remote Mobile App‐Based Memory Assessments Reflect Traditional Memory Measures and are Sensitive to Measures of Tau Pathology. In: International Conference on Clinical Trials for Alzheimer's Disease; 2020.
    1. Duzel E. Presentation: Remote Smartphone‐Based and Supervised Neuropsychological Assessments of Episodic Memory Recall are Highly Correlated. In: International Conference on Clinical Trials for Alzheimer's Disease; 2020.
    1. Güsten J, Ziegler G, Düzel E BD. Age impairs mnemonic discrimination of objects more than scenes: a web‐based, large scale approach across the lifespan. Cortex. 2020;137:138‐148. Published online.
    1. Follett R, Strezov V. An analysis of citizen science based research: usage and publication patterns. PLoS One. 2015;10(11):1‐14.
    1. Passell E, Dillon D, Baker J, et al. Digital Cognitive Assessment : Results from the TestMyBrain NIMH Research Domain Criteria (RDoC) Field Test Battery Report. Digital Cognitive Assessment; 2019.
    1. Rodríguez‐Gómez O, Rodrigo A, Iradier F, et al. The MOPEAD project: advancing patient engagement for the detection of “hidden” undiagnosed cases of Alzheimer's disease in the community. Alzheimer's Dement. 2019;15(6):828‐839.
    1. Berron D. Presentation: Fesibility of Mobile App‐Based Assessment of Memory Functions—Insights from a Citizen Science Study. In: Alzheimer's Association International Conference; 2020.
    1. Papoutsaki A, Daskalova N, Sangkloy P, Huang J, Laskey J, Hays J. WebGazer: Scalable webcam eye tracking using user interactions. IJCAI Int Jt Conf Artif Intell. 2016;2016:3839‐3845.
    1. Gaubert S, Houot M, Raimondo F, et al. A machine learning approach to screen for preclinical Alzheimer's disease. Neurobiol Aging. 2021;105:205‐216.
    1. Souillard‐Mandar W, Davis R, Rudin C, et al. Learning classification models of cognitive conditions from subtle behaviors in the digital Clock Drawing Test. Mach Learn. 2016;102(3):393‐441.
    1. Antila K, Lötjönen J, Thurfjell L, et al. The PredictAD project: development of novel biomarkers and analysis software for early diagnosis of the Alzheimer's disease. Interface Focus. 2013;3(2):20120072.
    1. Bucholc M, Ding X, Wang H, et al. A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual. bioRxiv. 2019. 10.1101/573899
    1. Boschi V, Catricalà E, Consonni M, Chesi C, Moro A, Cappa SF. Connected speech in neurodegenerative language disorders: a review. Front Psychol. 2017;8(MAR).
    1. Ross ED. The Assessment of Aphasia and Related Disorders. Lea & Febiger; Vol 35; 1985.
    1. Mueller KD, Koscik RL, Hermann BP, Johnson SC, Turkstra LS. Declines in connected language are associated with very early mild cognitive impairment: results from the Wisconsin Registry for Alzheimer's Prevention. Front Aging Neurosci. 2018;9(JAN):1‐14.
    1. König A, Linz N, Tröger J, Wolters M, Alexandersson J, Robert P. Fully automatic speech‐based analysis of the semantic verbal fluency task. Dement Geriatr Cogn Disord. 2018;45(3‐4):198‐209.
    1. Konig A, Satt A, Sorin A, et al. Use of speech analyses within a mobile application for the assessment of cognitive impairment in elderly people. Curr Alzheimer Res. 2017;15(2):120‐129.
    1. Taler V, Phillips NA. Language performance in Alzheimer's disease and mild cognitive impairment: a comparative review. J Clin Exp Neuropsychol. 2008;30(5):501‐556.
    1. Verfaillie SCJ, Witteman J, Slot RER, et al. High amyloid burden is associated with fewer specific words during spontaneous speech in individuals with subjective cognitive decline. Neuropsychologia. 2019;131(August 2018):184‐192.
    1. Peltsch A, Hemraj A, Garcia A, Munoz DP. Saccade deficits in amnestic mild cognitive impairment resemble mild Alzheimer's disease. Eur J Neurosci. 2014;39(11):2000‐2013.
    1. Seligman SC, Giovannetti T. The potential utility of eye movements in the detection and characterization of everyday functional difficulties in mild cognitive impairment. Neuropsychol Rev. 2015;25(2):199‐215.
    1. Bott N, Madero EN, Glenn J, et al. Device‐embedded cameras for eye tracking‐based cognitive assessment: validation with paper‐pencil and computerized cognitive composites. J Med Internet Res. 2018;20(7):e11143.
    1. Rentz DM. Presentation: Capturing Cognitive Changes at the Earliest Stage of Alzheimer's Disease: A New Approach. In: Alzheimer's Association International Conference; 2020.
    1. Moodley K, Minati L, Contarino V, et al. Diagnostic differentiation of mild cognitive impairment due to Alzheimer's disease using a hippocampus‐dependent test of spatial memory. Hippocampus. 2015;25(8):939‐951.
    1. Ritchie K, Carri I. Allocentric and egocentric spatial processing in middle‐aged adults at high risk of late‐onset Alzheimer's disease : the PREVENT Dementia Study. J Alzheimers Dis. 2018;65:885‐896.
    1. Howett D, Castegnaro A, Krzywicka K, et al. Differentiation of mild cognitive impairment using an entorhinal cortex‐based test of virtual reality navigation. Brain. 2019;142(6):1751‐1766.
    1. Coutrot A, Schmidt S, Coutrot L, et al. Virtual navigation tested on a mobile app is predictive of real‐world wayfinding navigation performance. PLoS One. 2019;14(3):e0213272.
    1. Coughlan G, Coutrot A, Khondoker M, Minihane AM, Spiers H, Hornberger M. Toward personalized cognitive diagnostics of at‐genetic‐risk Alzheimer's disease. Proc Natl Acad Sci U S A. 2019;116(19):9285‐9292.
    1. Allison S, Fagan A, Morris J, Head D. Spatial navigation in preclinical Alzheimer's disease. J Alzheimer's Dis. 2016;52(1):77‐90.

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