TECHNICAL FIELD

[0001] The present disclosure relates to method and device for tracking cognitive performance in mild cognitive impairment (MCI) and for discriminating between MCI impaired and non-impaired subjects.

BACKGROUND

[0002] Cognitive performance is expected to decline with aging, as many other biological functions. Results from cross sectional studies suggest a gradual age- associated decline in most cognitive functions of normal aging elders [1] However, there is still limited research on the use and interpretation of repeated measurements to identify longitudinal trends of cognitive performance, particularly as a screening/diagnostic approach to identify individuals with cognitive decline based on these trajectories [1] Such data would allow for a better understanding of the pattern and rate of age-associated cognitive changes and is also a promising strategy to identify pre-symptomatic or early symptomatic cognitive decline.

[000S] A comprehensive neuropsychological battery performed by a trained professional is the gold standard for detecting cognitive impairment [2], however it is not a cost-effective strategy for periodic cognitive testing in large groups of individuals. The brief cognitive screening tools currently available lack the desired discriminative ability to identify mild cognitive impairment (MCI) and still require a trained external evaluator [3, 4] Furthermore, most tasks included in both comprehensive batteries and brief screening tests were not specifically designed to minimize the practice effects of repeated testing [5] Computerized cognitive tests have the potential to overcome these limitations, by allowing the use of multiple test versions and self- administered testing and have shown psychometric parameters similar to traditional tests [6, 7] Additionally, they offer the potential for easier implementation of adaptive testing, in which test difficulty can be tailored to the individual performance [8] Nevertheless, most of the existing computerized cognitive tests were designed to mirror the pen and paper testing [9]; they require a trained professional, do not take advantage of the potential for adaptive testing, and are not intended for monitoring longitudinal cognitive performance [10]

[0004] Document EP1948014B1 discloses a system and method for prediction of cognitive decline, where quantitative electroencephalogram (qEEG) and/or quantitative magnetoencephalogram (qMEG) and/or quantitative event related potential (qERP) data are used to predict future cognitive decline in an individual, such that it predicts cognitive decline (corresponding to electrical and magnetic brain activity).

[0005] References

[1] Harada CN, Natelson Love MC, Triebel KL (2013) Normal cognitive aging. Clin Geriatr Med 29, 737-752.

[2] Petersen RC, Stevens JC, Ganguli M, Tangalos EG, Cummings JL, DeKosky ST (2001) Practice parameter: early detection of dementia: mild cognitive impairment (an evidence-based review). Report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology 56, 1133-1142.

[3] Lonie JA, Tierney KM, Ebmeier KP (2009) Screening for mild cognitive impairment: a systematic review. International Journal of Geriatric Psychiatry 24, 902-915.

[4] Lischka AR, Mendelsohn M, Overend T, Forbes D (2012) A systematic review of screening tools for predicting the development of dementia. Canadian Journal on Aging 31, 295-311.

[5] Calamia M, Markon K, Tranel D (2012) Scoring higher the second time around: meta-analyses of practice effects in neuropsychological assessment. Clin Neuropsychol 26, 543-570.

[6] Wild K, Howieson D, Webbe F, Seelye A, Kaye J (2008) Status of computerized cognitive testing in aging: a systematic review. Alzheimer's & Dementia 4, 428-437.

[7] Zygouris S, Tsolaki M (2015) Computerized cognitive testing for older adults: a review. American Journal of Alzheimer's Disease and other Dementias 30, 13-28. [8] Wouters H, Appels B, Van Campen J, Lindeboom R, Buiter M, Zwinderman AH, van Gool WA, Schmand B (2010) Adaptive testing combines precision with brevity in the grading of cognitive impairment. Behav Neurol 23, 181-183.

[9] Schlegel RE, Gilliland K (2007) Development and quality assurance of computer-based assessment batteries. Archives of Clinical Neuropsychology 22 Suppl 1, S49-61.

[10] Parsey CM, Schmitter-Edgecombe M (2013) Applications of technology in neuropsychological assessment. The Clinical Neuropsychologist 27, 1328-1361.

[11] Ruano L, Sousa A, Severo M, Alves I, Colunas M, Barreto R, Mateus C, Moreira S, Conde E, Bento V, Lunet N, Pais J, Tedim Cruz V (2016) Development of a self- administered web-based test for longitudinal cognitive assessment. Sci Rep 6, 19114.

[12] Ramos E, Lopes C, Barros H (2004) Investigating the effect of nonparticipation using a population-based case-control study on myocardial infarction. Annals of Epidemiology 14, 437-441.

[13] Winblad B, Palmer K, Kivipelto M, Jelic V, Fratiglioni L, Wahlund LO, Nordberg A, Backman L, Albert M, Almkvist O, Arai H, Basun H, Blennow K, de Leon M, DeCarli C, Erkinjuntti T, Giacobini E, Graff C, Hardy J, Jack C, Jorm A, Ritchie K, van Duijn C, Visser P, Petersen RC (2004) Mild cognitive impairment--beyond controversies, towards a consensus: report of the International Working Group on Mild Cognitive Impairment. Journal of Internal Medicine 256, 240-246.

[14] Freitas S, Simoes MR, Alves L, Santana I (2011) Montreal Cognitive Assessment (MoCA): normative study for the Portuguese population. J Clin Exp Neuropsychol 33, 989-996.

[15] Joana Morgado CR, Carolina Maruta, Manuela Guerreiro, Isabel Martins (2009) New normative values of Mini-Mental State Examination. Sinapse - Publicagao da Sociedade Portuguesa de Neurologia 9, 10-16.

[16] Cavaco S, Goncalves A, Pinto C, Almeida E, Gomes F, Moreira I, Fernandes J, Teixeira-Pinto A (2013) Semantic fluency and phonemic fluency: regression-based norms for the Portuguese population. Arch Clin Neuropsychol 28, 262-271. [17] Cavaco S, Goncalves A, Pinto C, Almeida E, Gomes F, Moreira I, Fernandes J, Teixeira-Pinto A (2013) Trail Making Test: regression-based norms for the Portuguese population. Arch Clin Neuropsychol 28, 189-198.

[18] Stroop JR (1935) Studies of interference in serial verbal reactions. Journal of Experimental Psychology 18, 643-662.

[19] Santana I, Duro D, Freitas S, Alves L, Simoes MR (2013) The Clock Drawing Test: Portuguese norms, by age and education, for three different scoring systems. Arch Clin Neuropsychol 28, 375-387.

[20] Guerreiro M (1998) Universidade de Lisboa, Lisboa.

[21] Rutjes AWS, Reitsma JB, Vandenbroucke JP, Glas AS, Bossuyt PMM (2005) Case-Control and Two-Gate Designs in Diagnostic Accuracy Studies. Clinical Chemistry SI, 1335.

[22] Ho DE, Imai K, King G, Stuart EA (2007) Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference. Political Analysis 15, 199-236.

[23] Hastie TJ (1992) Generalized additive models (Chapter 7) In Statistical Models in S, Hastie JMCaTJ, ed. AT&T Bell Laboratories.

[24] Wesnes K, Pincock C (2002) Practice effects on cognitive tasks: a major problem? Lancet Neurol 1, 473.

[25] Bartels C, Wegrzyn M, Wiedl A, Ackermann V, Ehrenreich H (2010) Practice effects in healthy adults: a longitudinal study on frequent repetitive cognitive testing. BMC Neurosci 11, 118.

[26] Machulda MM, Pankratz VS, Christianson TJ, Ivnik RJ, Mielke MM, Roberts RO, Knopman DS, Boeve BF, Petersen RC (2013) Practice effects and longitudinal cognitive change in normal aging vs. incident mild cognitive impairment and dementia in the Mayo Clinic Study of Aging. The Clinical neuropsychologist 27, 1247- 1264.

[27] Krishnan K, Rossetti H, Hynan LS, Carter K, Falkowski J, Lacritz L, Cullum CM, Weiner M (2016) Changes in Montreal Cognitive Assessment Scores Over Time. Assessment. [28] Cooley SA, Heaps JM, Bolzenius JD, Salminen LE, Baker LM, Scott SE, Paul RH (2015) Longitudinal Change in Performance on the Montreal Cognitive Assessment in Older Adults. Clin Neuropsychol 29, 824-835.

[29] Goldberg TE, Harvey PD, Wesnes KA, Snyder PJ, Schneider LS (2015) Practice effects due to serial cognitive assessment: Implications for preclinical Alzheimer's disease randomized controlled trials. Alzheimers Dement (Amst) 1, 103-111.

[30] Konsztowicz S, Xie H, Higgins J, Mayo N, Koski L (2011) Development of a method for quantifying cognitive ability in the elderly through adaptive test administration. Int Psychogeriatr 23, 1116-1123.

[31] Jacobusse G, Buuren S (2007) Computerized adaptive testing for measuring development of young children. Stot Med 26, 2629-2638.

[32] Ayutyanont N, Langbaum JB, Hendrix SB, Chen K, Fleisher AS, Friesenhahn M, Ward M, Aguirre C, Acosta-Baena N, Madrigal L, Munoz C, Tirado V, Moreno S, Tariot PN, Lopera F, Reiman EM (2014) The Alzheimer's prevention initiative composite cognitive test score: sample size estimates for the evaluation of preclinical Alzheimer's disease treatments in presenilin 1 E280A mutation carriers. J Clin Psychiatry 75, 652-660.

[33] Hung SY, Fu WM (2017) Drug candidates in clinical trials for Alzheimer's disease. J Biomed Sci 24, 47.

[0006] These facts are disclosed in order to illustrate the technical problem addressed by the present disclosure.

GENERAL DESCRIPTION

[0007] The disclosure relates to the fact that repeated measurements may be helpful to identify patients with early cognitive decline. The disclosure includes comparing the variation of cognitive performance over one year in patients with mild cognitive impairment (MCI) and healthy individuals using a computer-based method, hereby referred as the Brain on Track, which can be a self-applied computerized test. The disclosure includes results from a study of 30 patients with probable MCI and 377 controls from a population-based cohort, who performed the disclosed test from home every three months for one year. The scores were compared using a linear mixed-effects model. The area under the curve to detect MCI was 0.94. The disclosure method and device identified a significant decline in cognitive performance over one year in patients with MCI and the disclosed test and index presented a high discriminative ability.

[0008] The disclosed method is a computerized cognitive test developed for self- administered web-based longitudinal cognitive screening and monitoring [11] It was previously shown to have good reproducibility, significant correlation with existing cognitive tests, ability to identify clinically relevant differences for MCI and early dementia and high test-retest reliability when performed from home [11]

[0009] In this disclosure it is aimed to develop and improve the disclosed test by expanding the domains assessed and by developing subtests with levels of difficulty adjusted to the expected individual performance. One of the objectives of this disclosure is to describe the variation of cognitive performance over one year using disclosed test in patients with mild cognitive impairment (MCI) and individuals from a population-based cohort using this improved version of the BoT. It was also assessed the ability of this disclosed test to discriminate between MCI and healthy controls in single and repeated uses.

[0010] The development and validation of the BoT test resulted in a version with a series of subtests, seven subtests [11] according to an embodiment. After critical review of these results by the neurologists and neuropsychologists of the development team, it was decided to expand the assessment of memory, executive functions and information processing speed, due to the lack of subtests assessing those domains, and four additional subtests were added: Colour Interference Task (executive functions), Delayed Verbal Memory Task (delayed verbal memory), Verbal Memory Task II (immediate verbal memory) and Attention Task III (Sustained attention, information processing speed). Furthermore, in previous work, the test showed a better discriminatory ability in individuals with middle and higher education, when compared with individuals with less than four years of schooling. Therefore, the Delayed Verbal Memory Task and Attention Task III were designed with three different levels of difficulty, adapted to the expected baseline cognitive performance of the participants, based on the educational attainment [11] These changes resulted in the revised version of disclosed test, that used in this study and expected to improve the discriminatory ability of the test. The total duration of the disclosed test is 24 minutes, and the detailed description of the subtests is detailed in the Appendix -Table 4.

[0011] This disclosure includes a method to discriminate MCI-impaired patients from normal individuals that is divided in three steps. The first step is to calculate the proposed method score in each time of assessment. The second step is to estimate a smoothed function that summarizes the trajectory over time in 3 values: the baseline value (initial point in time of testing), the increase/decrease to a predetermined time cut-point and the increase/decrease from this cut-point onwards. Then the 3 values are weighted and added up to form a score that is used to discriminate MCI-impaired patients from normal individuals.

[0012] It is disclosed a device for use in discriminating an individual between mild cognitive impairment, MCI, impaired and non-MCI impaired, comprising an electronic data processor configured for generating an index indicative of mild cognitive impairment, MCI, impairment in an individual, by carrying out the method steps of: acquiring result scores from a computer-based cognitive test administered multiple times over a period of time to said individual, in which a higher score represents a higher cognitive result; estimating a smoothed function of said scores over the period of time; calculating a first value equal to the value of said smoothed function at the beginning of said period of time; calculating a second value equal to the slope of said smoothed function between the beginning of said period of time and a predetermined cut-point in time; calculating a third value equal to the slope of said smoothed function between the predetermined cut-point in time and the end of said period of time; multiplying each said calculated value by a respective first, second, and third predetermined weighting coefficient to obtain a weighted product; summing said products to obtain an index which is indicative of a degree of presence of MCI; detecting if said index is above or below a predetermined threshold. [0013] Said predetermined weighting coefficients are calculated in order to maximize the discrimination power for MCI of the calculated index.

[0014] Alternatively, the acquired result scores from the computer-based cognitive test may have a lower score representing a higher cognitive result. In this case, the disclosure may be adapted straightforwardly taking this into account, inverting values or calculations when necessary.

[0015] It is also disclosed a computer-based method for generating an index indicative of mild cognitive impairment, MCI, impairment in an individual, comprising the steps of: acquiring result scores from a computer-based cognitive test administered multiple times over a period of time to said individual, in which a higher score represents a higher cognitive result; estimating a smoothed function of said scores over the period of time; calculating a first value equal to the value of said smoothed function at the beginning of said period of time; calculating a second value equal to the slope of said smoothed function between the beginning of said period of time and a predetermined cut-point in time; calculating a third value equal to the slope of said smoothed function between the predetermined cut-point in time and the end of said period of time; multiplying each said calculated value by a respective first, second, and third predetermined weighting coefficient to obtain a weighted product; summing said products to obtain an index which is indicative of a degree of presence of MCI.

[0016] In an embodiment, the first and third predetermined weighting coefficients are negative, and the second predetermined weighting coefficient is positive.

[0017] In an embodiment, the smoothed curve function is a spline-type function.

[0018] In an embodiment, the smoothed curve function is a spline-type function having an inflection knot-point at the predetermined cut-point in time. [0019] In an embodiment, the smoothed curve function is adapted to be estimated from multiple times of computer-based cognitive test administration which are not periodic.

[0020] In an embodiment, the first, second, and third predetermined weighting coefficient are within an interval having a size of 20% of the largest weighting coefficient, further in particular equal within an interval having a size of 10% of the largest weighting coefficient, further in particular being -2.14, 2.03 and 2.00, respectively.

[0021] In an embodiment, the predetermined cut-point in time is from 70 to 100 days, in particular from 80 to 90 days, further in particular from 84 days.

[0022] In an embodiment, the computer-based cognitive test comprises a series of computer-based cognitive subtests.

[0023] In an embodiment, the series of computer-based cognitive subtests is the series of tests of table 4.

[0024] In an embodiment, said period of time is longer than 180 days, in particular longer than 180 days and shorter than a year.

[0025] It is also disclosed a non-transitory storage media including program instructions for implementing a computer-based method for generating an index indicative of mild cognitive impairment, MCI, impairment in an individual, the program instructions including instructions executable by a data processor to carry out the method of any of the disclosed embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The following figures provide preferred embodiments for illustrating the disclosure and should not be seen as limiting the scope of invention.

[0027] Figure 1 shows a schematic representation of an embodiment of the trajectories of cognitive performance over one year in the disclosed test in patients with mild cognitive impairment and healthy individuals. [0028] Figure 2 illustrative representation of an embodiment of the method of the present disclosure.

DETAILED DESCRIPTION

[0029] The present disclosure relates to method and device for tracking cognitive performance in mild cognitive impairment (MCI) and for discriminating between MCI impaired and non-impaired subjects.

[0030] In an embodiment, was performed a longitudinal study in which a group of patients with probable MCI and a group of individuals from the general population were monitored with Brain on Track for one year. Overall inclusion criteria were: a) >18 years of age; b) access to a computer at home and c) being able to use a computer and mouse interface without external help.

[0031] The individuals from the general population represent a subset from the EPIPorto population-based cohort [12]. This cohort was assembled between 1999 and 2003 as a representative sample of adult (>18 years) dwellers from the city of Porto. Participants were selected by random digit dialing of landline telephones [12]. In the 2013-2015 revaluation of the cohort, the first 300 consecutive participants were invited to participate in the test-retest study of the first version of the method [11], while the remaining who attend the re-evaluation (n=676) were invited to participate in the present study. From the latter, 75 refused to participate and 289 were excluded, because they did not have continuous access to a computer connected to the internet at home (n=182) or because they could not use a computer and mouse interface without external help (n=107). Therefore, a total of 312 participants were enrolled, from whom 259 completed the one-year follow-up (83.0%). Participants from the EPIPorto cohort who presented impairment in any domain in the neuropsychological assessment were also evaluated by a neurologist to verify if they complied with the criteria for MCI; one participant from the cohort was considered to have MCI, due to probable Alzheimer's disease, and included in the MCI group for data analysis.

[0032] Patients with probable MCI were recruited in the Memory Outpatient Clinic of Centro Hospitalar de Entre Douro e Vouga. Eligibility criteria included the presence of progressive cognitive complaints over a period of at least six months, as reported by the patient or family members, impairment in at least one cognitive domain in a neuropsychological evaluation and no limitation in daily activities [13]. Eligible patients who attended the Neurological outpatient clinic in the second semester of 2015 and complied with inclusion criteria were consecutively invited to participate in the study. We recruited 30 patients with a clinical diagnosis of probable MCI from a memory clinic, from which 24 completed the one-year follow-up (80.0%). From these, 16 were confirmed as having a progressive clinical deterioration compatible with MCI in the one-year clinical re-assessment (nine due to probable Alzheimer's disease, six due to probable vascular cognitive impairment and one due to probable Lewy body disease), while in seven the final clinical diagnosis was anxiety/depression and in one obstructive sleep apnoea syndrome. Those without MCI were included in the general population group for the analyses. The final analysis comprises a total of 17 patients with confirmed MCI and a total of 267 healthy individuals.

[0033] All of the participants from the EPIPorto cohort and patients with probable MCI performed the same baseline neuropsychological evaluation, using a battery of cognitive tests validated for the Portuguese population, including the Montreal Cognitive Assessment (MoCA) test [14], the Mini Mental State Examination (MMSE) [15] the Wechsler Memory Scale III [16], Trail Making Test A and B [17], Stroop Test [18], Clock Drawing Test [19] and Token test (short version) [20] Impairment in any given cognitive domain was defined has having a performance bellow 1.5 standard deviations (SD) from age and education-adjusted norms in tests assessing that domain. All the patients with probable MCI performed brain imaging and lab studies and were re-evaluated at the end of the one year follow up, by repeating the neuropsychological evaluation and the clinical observation by neurologist, both blinded for the results of disclosed method.

[0034] The following pertains to Assessment with the disclosed method, BoT. All participants underwent the first testing with BoT in the hospital clinic or research lab; the test was self-administered, though under the observation of a member from the research team. This session had two main goals: a) teaching the participant how to login to the BoT website and accustoming the participant with the user interface and b) guaranteeing that the participant understood the instructions and mechanics of each subtest, to minimize learning effects in subsequent testing. One week after the training session, the participants were asked, by e-mail and mobile text messaging, to access the web site from their home computer and to perform the test autonomously. They were asked to repeat the test every three months for one year.

[0035] The following pertains to Statistical analysis. Final test scores of the Brain on Track test were calculated by summing the subtests' z-scores (standardized using the mean and standard deviation (SD) of the general population sample as the reference), and then standardizing this sum to a t-score (using the mean and standard deviation of the general population sample as the reference, and then multiplying by 10 and adding 50). Student's T test for independent samples was used to compare the differences in age, education and test scores between MCI patients and controls, since all variables presented a normal distribution (p>0.05 in Kolmogorov-Smirnov test).

[0036] Linear mixed-effects models (LMEM) fit by restricted maximum likelihood were built to describe and compare BoT scores between patients with MCI and individuals from the general population over one year. To build the model, we included, a priori, the variables age, education and MCI vs. non-MCI in the model, and separately tested linear and quadratic factors of time, retaining them in the model if they reached statistical significance (p<0.05). Then, we separately tested interaction factors between all the variables in the model (MCI, age, education, the linear and quadratic terms for time) retaining the interaction factors in the final model if they reached statistical significance (p<0.05).

[0037] To estimate the discriminative ability of a screening strategy for early cognitive impairment in individuals with memory complaints based on the BoT test we performed a direct comparison between patients with MCI and age and education- matched controls and estimated the area under the curve (AUC) of the BoT test scores to identify MCI, using a two-gate design to estimate the diagnostic accuracy[21]. We selected the best matched healthy controls for each patient with MCI using the nearest neighbor matching based on the distance measure on logistic regression method [22] Age and education were selected as the matching variables, as these were the only variables associated with BoT performance in this and in previous samples [11]

[0038] To estimate the AUC to distinguish between MCI and controls based on the 12- month follow-up with BoT, we first built a LMEM in the matched sample, to estimate the trend in time of BoT scores in MCI vs. matched controls using natural cubic splines with one knot (fixed for all the sample) and random effects for each spline and intercept individual [23] To estimate the fixed knot that allowed for the best fit of the data one-dimension optimized function defined using Bayesian-information criteria was employed [23] Then, the random effects of the LMEM were used to predict the probability of MCI. These probability measures were used to define the AUC.

[0039] Two cut points were defined for the first BoT test: all the subjects scoring above the high cut point would be considered probably not affected and dismissed from further testing; subjects scoring below the low cut point would be classified as probably affected and immediately referred to a Memory Clinic; subjects scoring between these two points would be monitored through regular repetitions of the test. The high cut point was defined based on the AUC to reach the highest possible sensitivity, so that none affected subject was dismissed, and the low-cut point for a specificity of 85%, so that those immediately referred to the Memory Clinic have a high probability of being affected. For the 12-month monitoring strategy with BoT a single cut point was defined, with the higher possible sensitivity, to guarantee that no affected subject was ruled out. Statistical analysis was performed in R statistical package.

[0040] The following pertains to Ethics. The study was performed in accordance with the Helsinki Declaration. The research protocol was approved by the institutional ethics committees. The web-based system for data collection of the Brain on Track test is encrypted and anonymized, and its use has been approved by the Portuguese Data Protection Authority. All subjects provided written informed consent for participation.

[0041] The following pertains to Results. At baseline, patients with MCI were older, less educated and had worse performance in cognitive screening tests than healthy controls. The performance in disclosed method was also significantly worse in patients (Table 1). The matched sample of healthy controls selected by propensity score presented no significant differences in age, sex or education when compared with MCI patients while having a significant better performance in disclosed method (Table 1). Table 1 - Participant demographics and test scores at baseline

Patients with Healthy controls confirmed MCI

Matched sample Whole sample (n=17)

(n=17) (n=267)

Age, mean (SD), years 70.2 (8.0) 66.5 (7.3) 57.4 (11.4)*

Sex (female), n (%) 64.7% 58.8% 49.9%

Education, mean (SD), years 5.3 (1.9) 5.7 (2.6) 13.6 (4.5)*

BoT score 27.8 (5.4) 45.4 (8.8)* 50.0 (10.0)*

MMSE score 26.0 (2.4) 28.9 (1.0)* 29.3 (1.0)*

MoCA score 16.5 (4.9) 24.7 (3.3)* 26.3 (2.8)*

*p<0.05 when compared to patients with MCI

[0042] When analysing the performance in the disclosed method using the LMEM model, patients with MCI presented, on average, an overall significantly worse performance than healthy individuals. There was also a significant association of older age and lower education with lower average scores on disclosed test (Table 2). There was a significant trend to a linear increase in performance over time in both patients and controls, with a slope that did not differ significantly between the groups (p=0.34 for interaction). The quadratic term for the effect of time on cognitive performance also reached statistical significance, even after including the linear effect of time in the model (p<0.001). This quadratic term presented a negative concavity, denoting a decrease in performance after the initial increase. Moreover, there was a significant interaction between the quadratic term for time and having MCI, implying that in patients with MCI the decrease is significantly more pronounced than in healthy controls. There was no significant interaction between time (linear or quadratic) with education and age. Table 2 - Linear mixed-effects model for the test scores of the disclosed test over one year

Variables in the model Linear coefficient Standard error p-value

Age (years) -0.37 0.04 <0.001

Education 4-9 years 6.09 1.66 >0.001 >10 years 11.96 1.52 >0.001

MCI -29.75 9.28 >0.01

Time (years) 5.11 1.13 >0.001

Time ^A 2 0.11 0.004 >0.001

Time ^A 2*MCI 0.11 0.004 >0.01

MCI - Mild cognitive impairment

Table 3 - Sensitivity and specificity for single use and for repeated use of the Brain on Track test

Single BoT test BoT 12-month at baseline follow-up*

Area under the ROC curve 0.862 0.944

Higher cut-point for referral to specialized care

Sensitivity 76.5%

Specificity 88.3%

Lower cut point for dismissal from further testing

Sensitivity 100.0% 100.0%

Specificity 47.0% 73.0%

BoT - Brain on Track; ROC - receiver operating characteristic

^* Probability of MCI defined using a linear mixed-effects model to estimate the trend in time of BoT scores using natural cubic splines with one fixed knot and random effects for intercept and splines by individual. [0043] In Figure 1, the predicted model scores are depicted, comparing the performance over one year in patients with MCI and healthy controls. The peak in performance in patients with MCI is at around 100 days (coinciding approximately with the 3 ^rd test), with a decline after that, while in controls the performance tends to stabilize at around 180 days (coinciding approximately with the 4 ^th test).

[0044] Concerning the diagnostic accuracy of BoT for single use, the AUC to identify MCI was 0.862. Based on this, we propose a rule-in cut point, for immediate referral to a Memory Clinic, with a specificity of 88.3% and sensitivity of 76.5%, while the rule-out cut point, for dismissing subjects from further testing, with a sensitivity of 100% and a specificity of 47.0%. Using the data collected over one year in the monitoring strategy, the AUC increased to 0.944, while the single cut point for rule-out would have a sensitivity of 100.0% and a specificity of 73.0%.

[0045] In an embodiment, we were able to implement a cognitive monitoring strategy based on the disclosed method computerized self-applied test in healthy individuals from a subset of a population-based cohort and in patients with probable MCI from a memory clinic. After an initial increase in test scores in all participants, patients with MCI presented a significant cognitive decline, when compared with controls, after a peak at 120 days. The repeated test measurements reached an AUC of 0.94 in the one year monitoring, compared with 0.86 in for single use.

[0046] One of the biggest challenges faced in clinical practice and dementia research is to distinguish the age-associated cognitive decline from the early onset of dementia, particularly in patients with memory complaints, but without the interference in the daily performance or social activities that defines dementia. The results of the disclosed method highlight the potential of a screening monitoring strategy to identify patients with MCI from the pool of elderly individuals with early memory complaints. Nevertheless, there are still some issues concerning its use. One important potential limitation of all monitoring strategies is practice effects. These are a major concern on longitudinal cognitive monitoring, because of the capacity of the individual to learn and adjust, and therefore individuals perform better at cognitive function tests with repeated testing, interfering on the results interpretation [24-26] This can be illustrated in the few studies in which the MoCA test was applied repeatedly, at different intervals of time, in patients with MCI. While in a follow-up of 3.5 years 42% of MCI patients declined in the MoCA, with an average of 1.7 points [27], in shorter time spans, such as 12 months, the MoCA test result increases, demonstrating important practice effects [28] Taking this limitation into account, it is important to know the factors that can minimize or enhance this effect. One of this factors is the task familiarity [29] We tried to optimize this by starting the monitoring strategy with a self-administered disclosed test in the hospital clinic or research lab, under the observation of a member from the research team, who repeated the instructions in case of any difficulty. Another strategy to minimize this problem is the use of alternate forms [29] The disclosed subtests are designed with a wide variety of elements and different combinations of these elements, so that each trial is different from test to test. The frequency of the evaluations is also an important factor. A previous study in healthy individuals compared two groups with high (baseline, weeks 2-3, week 6, week 9 and month 3) and low (month 6 and month 12 ) cognitive test frequency over one year, with the high frequency group presenting with prominent practice effects [25] In our study, we opted for an intermediate frequency (every 3 months for 1 year), which we considered to be low enough to minimize practice effects, but high enough to make an efficient monitoring and to detect changes in the cognitive status of the participants over time [25] Despite of the implemented measures, our results show that practice effects probably played a role in the performance of both groups in the initial evaluations. The initial slope of the linear increase was similar in patients and controls, but posteriorly, the MCI group started to decline, following a parabola like trajectory that was significantly different from the healthy controls, that maintained a more stable performance. We cannot discard that, at least in part, the apparent practice effect in patients with MCI could be due to a cognitive improvement secondary to the effects of anti-dementia medication, as most patients in the sample have started cholinesterase inhibitors and/or memantine close to the start of the cognitive monitoring. Ultimately, some degree of learning effects are unavoidable, for that reason the existence of control groups that undergo the same protocol is essential [29], as it allows a direct comparison between the two groups for each successive trials. [0047] Another key point to the efficiency of cognitive testing is addressing the individual pre-morbid differences in cognitive performance, known as cognitive reserve. A possible solution for this problem is the application of adaptive testing, in which the difficulty grade of a question is determined by the performance in the previous question, therefore adapting the test to the patient's abilities. Several authors have argued in favor of that strategy and proposed theoretical models of adaptive testing in the cognitive assessment of the elders [8, 30] However, although adaptive tests have already been developed to monitor the development of young children [31], such tools have never been used in the monitoring of cognitive changes over time in an elderly population. In this study, we performed a first step towards adaptive testing, by adjusting the difficulty of some subtests to the expected performance of the participants, based on academic achievement, making the evaluation process more adapted to each individual. This could be a crucial feature for successful long-term cognitive monitoring by limiting ceiling and ground effects, allowing shorter testing sessions without sacrificing precision, and the possibility to monitor patients with some degree of previous impairment. The inclusion of additional subtests to disclosed test resulted in an increase in the diagnostic accuracy in single use, with the AUC improving from 0.75 in the previous version [11] to 0.86 in the present version. We aim to further explore this strategy in future studies.

[0048] There are some limitations to this study. The number of individuals enrolled with probable MCI that had anxiety/depression and not a neurologic disorder was higher than expected, resulting in a relatively small sample of patients with definitive MCI. Furthermore, it would be interesting to better characterize their pattern of cognitive performance over time and compare them with the MCI and healthy controls, but the small number of patients in this group prevented any meaningful analysis.

[0049] The adherence to the monitoring strategy was quite high in the study, similar in both settings, and represents an interesting proof of concept for the feasibility of monitoring patients with cognitive impairment. Nevertheless, 42% of the general population sample did not participate in the study because they did not have access to a computer with internet connection or lacked familiarity with this interface. This is still a considerable number, but it is expected to decrease as the penetration of technology increases and as the younger, more educated strata of the population reaches older age.

[0050] In all, the results from this method imply that the disclosed test could be a suitable tool for an early identification and monitoring of cognitive impairment in elderly individuals, and hopefully improve the current approaches to manage individuals with early memory complaints in the primary care setting and their referral for specialized care. Additionally, this tool could prove useful to identify candidates for future pre-symptomatic or early symptomatic treatments for Alzheimer's disease. Pre- symptomatic cognitive decline has been demonstrated in unimpaired presenilin-1 carriers using a composite score of neuropsychological tests over five years of follow up [32] If, as hoped, pharmaceutical treatments for Alzheimer's disease, currently under phase 2 and 3 clinical trials [33], prove successful in the pre-symptomatic phase, monitoring the population at risk with BoT could effectively identify individuals with probable early cognitive impairment, who would then perform more expensive confirmatory imaging or molecular biomarker tests to demonstrate beta-amyloid pathology, and start treatments with potential to delay or avoid the evolution to dementia.

[0051] It is disclosed an embodiment for a device for use in discriminating an individual between mild cognitive impairment, MCI, impaired and non-MCI impaired, comprising an electronic data processor configured for generating an index indicative of mild cognitive impairment, MCI, impairment in an individual, by carrying out the steps of: acquiring result scores from a computer-based cognitive test administered multiple times over a period of time to said individual, in which a higher score represents a higher cognitive result; estimating a smoothed function of said scores over the period of time; calculating a first value (1) equal to the value of said smoothed function at the beginning (2) of said period of time; calculating a second value (3) equal to the slope of said smoothed function between the beginning of said period of time and a predetermined cut-point (4) in time; calculating a third value (5) equal to the slope of said smoothed function between the predetermined cut-point in time and the end (6) of said period of time; multiplying each said calculated value by a respective first, second, and third predetermined weighting coefficient to obtain a weighted product; summing said products to obtain an index which is indicative of a degree of presence of MCI; detecting if said index is above or below a predetermined threshold.

[0052] The term "comprising" whenever used in this document is intended to indicate the presence of stated features, integers, steps, components, but not to preclude the presence or addition of one or more other features, integers, steps, components or groups thereof.

[0053] The disclosure should not be seen in any way restricted to the embodiments described and a person with ordinary skill in the art will foresee many possibilities to modifications thereof. The above described embodiments are combinable.

[0054] The following claims further set out particular embodiments of the disclosure.

Table 4 - Appendix - Subtest Description

Name Target Domains Subtest Description

The screen is divided into two parts: on the right the target image is shown, on the left Puzzles Constructive ability several of the image composing pieces are scattered. The purpose of the task is to complete the image using the scattered pieces.

Inhibitory control In a central position of the screen, a large arrow is shown. The participant must press the

Opposite Task

Executive functioning keyboard arrow in the opposite direction to that shown by the large arrow.

On the screen, three cubes of different colors light up in a random sequence. The

Sustained attention

Visual Memory Task II participant must memorize this sequence and reproduce it using the mouse to click on Short term memory the cubes in the correct order.

The participant should perform the numerical calculation shown on screen and input the number via keyboard or by using the mouse to click on a keypad with numbers on

Calculus Task Calculus screen. The operation should be completed before a balloon reaches the top of the screen.

The upper part of the screen displays a set of figures that follows a certain logic

Executive function

Sequences sequence. The participant should select the figure that completes the sequence from four Abstract thought possible figures.

The participant is asked to memorize a list of four words. After a short delay, 10 words

Immediate verbal

Verbal Memory Task II are shown on screen (four correct and six distracters) and the participant must click on memory the correct words.

Written Language On the screen, there are several sets of geometric objects of different shapes and colors

Comprehension comprehension The participant must select the set that matches the description of the written command.

The participant must select the correct category for the word that is shown on the screen

Word categories Language by dragging the word to the corresponding box. If the word does not belong to any of the categories, the participant must drag it to the garbage can.

A name of a colour is shown inside a coloured frame. The colour name, the colour of the word font and the colour of the frame are random. In the first set, the participant must

Colour Interference Executive function, select YES when the word and the colour of the frame match, and NO when they are task inhibitory control different. In the middle of the subtest, a new instruction appears on screen, and now the participant must select YES when the colour of the frame matches the colour of the word font.

The participant is asked to memorize a list of two, three and five words on the first, second and third levels, respectively. After this, the participant is asked to recall the words after 90 seconds, after 180 seconds and after 360 seconds, performing an

Delayed Verbal

Short term memory interference task between recalls. In the recall, four, six or 10 words, respectively for Memory Task each level, are shown on screen. Half the words on screen are correct and half are distracters, the participant must click on the correct words. The words are randomly selected from a list of 50 words for each level, with increasing complexity.

Two pictures are shown on screen, each picture is composed of with several geometrical

Sustained attention, shapes of different colours, three shapes in each picture on level one; eight shapes on

Attention task III Information processing level two; and 15 words on level three. The participant must decide if the two pictures are speed equal or different. The shapes and colours are randomized for each trial.