Clinical trial NCT03676881: Longitudinal Validation of a Computerized Cognitive Battery (Cognigram) in the Diagnosis of Mild Cognitive Impairment and Alzheimer's Disease
Official title: Longitudinal Validation of a Computerized Cognitive Battery (Cognigram) in the Diagnosis of Mild Cognitive Impairment and Alzheimer's Disease
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Alzheimer Disease
Dementia
Neuropathology
Mild Cognitive Impairment
Lead sponsor: Bruyere Research Institute
Sponsor class: OTHER
First posted: 2018-09-19
Enrollment: 36
Sex: ALL
Minimum age (years): 60
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: c) Diagnostic criteria:

Mild Cognitive Impairment (MCI):

Diagnosis of MCI in recruitment database and corroborated at baseline by:

1. Objective cognitive impairment: expressed as ≥1.5 SD below the normative mean in at least one test of the NPS tests, AND
2. MMSE \>19, AND
3. Subjective Cognitive Impairment, expressed by participant and/or study partner: defined by GPCOG, AND
4. Absence of significant functional impairment: score ≤5 in the FAQ.

Cognitively Normal (CN):

Diagnosis of CN in recruitment database -or absence of diagnosis of MCI or dementia- and corroborated at baseline by:

1. Normal score expressed as within 1 SD from the normative mean in every test of the NPS tests, AND
2. MMSE ≥ 27

Brief summary: This research project will test two new computerized technologies in the detection of brain changes related to Mild Cognitive Impairment (MCI) and dementia due to Alzheimer's disease. These technologies are:

1. Computerized cognitive battery: Cognigram (CG) Computerized assessments have multiple advantages for the early detection of subtle changes in cognition in older adults. One of their main advantages is their higher precision when measuring accuracy and speed of responses, compared to pencil-and-paper tests. They also allow a greater reliability in measures, as tests are given in a standardized format without the interference of an evaluator. Finally, by including automatized instructions and reports, they are suitable for off-site or long-distance use.

   The present study aims to validate the Cognigram™ (CG) computerized cognitive tool, in a prospective and longitudinal fashion, determining if changes in the CG scores over 3, 6, 9, and 12 months, can predict progression to dementia at 1-year, 2-years, and 3-years, for patients with Mild Cognitive Impairment (MCI).
2. The NeuroCatch™ Platform (NCP)

Event-related potentials (ERP) are non-invasive, low-cost, electrophysiological methods that allow recording of the electrical activity of the brain in vivo through an Electroencephalogram (EGG). They are free from cultural and educational influence and can provide insights into the cognitive processes. ERP could enable to detect brain changes and determine the prognosis of MCI subjects.

The NCP, an investigational medical device system developed by NeuroCatch Inc., consists of an EEG software and hardware that captures brain health information. It offers a quick (i.e., 10 minutes for EEG preparation and 6 minutes for each task of EEG recording), simple (i.e., includes only 8 electrodes), and easy-to-use solution (i.e., includes a computerized software that automatically analyzes data and outputs graphs in less than 1 minute) for the acquisition of EEG and ERP.

Detailed description: Rationale

Today's aging population brings an increase in the incidence of dementia. In Canada, there are approximately 564,000 persons diagnosed with dementia, with an expected two-fold increase in this number by the year 2031. In this context, the early detection and prediction of cognitive decline are both imperative for achieving the prevention and/or slowing of dementia.

Standard pencil-and-paper neuropsychological tests are pivotal for the detection and follow-up of cognitive impairment; however they are labor-intensive and require the presence of a trained neuropsychologist on-site. In this regard, computerized testing may be better suited for cognitive screening in large epidemiologic studies and for longitudinal monitoring by primary care providers, due to their higher efficiency for serial assessments and their suitability for off-site or long-distance use. At the same time, computerized testing allows for higher precision in the recording of accuracy and speed of response, with a level of sensitivity not possible in standard administrations.

A number of computerized cognitive batteries have been recently developed, though intended as research tools. There is a current demand for the validation of computerized cognitive batteries in the clinical setting. One such computerized battery is the Cognigram™ (CG), which measures processing speed, attention, working memory and learning. Previous cross-sectional studies have demonstrated the validity of CG for detecting MCI and various types of dementia. However, there is no current literature on the longitudinal validity of CG, and minimal longitudinal validation of other computerized cognitive batteries currently in existence.

On the other hand, research and medicine is moving away from behavioral responses to assess brain health (e.g. verbal responses, reaction time, etc.) and are moving toward more neuroimaging focused measures. Biological tests could enable to detect pre-dementia and determine the prognosis of MCI subjects.A promising biological test is EEG/ERP. The investigators have previously shown group differences in ERPs for patients with MCI and CN. Other studies have reported promising ERP markers of pre-dementia and progression of MCI to dementia. However, ERP can be complex to process and labor-intensive, limiting its value in the clinical setting. For example, the usual time for an ERP series measuring multiple cognitive domains typically lasts 1 hour, another 25 minutes for applying the EEG cap and ensuring all electrodes are connected, and some 30 minutes per paradigm (x2-3 paradigms).

The NeuroCatch™ Platform (NCP) offers key competitive advantages compared to other EEG platforms, having a rapid test time with automated processing, analysis and results display - benefitting patients and clinicians being significantly easier to use than current EEG systems, and it alleviates training and ramp up costs amongst EEG users. In this study, the investigators will use the NPC to explore for ERPs that could predict progression to dementia in patients with MCI. The present study will make an initial assessment of the capacity of the NCP to detect cognitive decline and predict conversion to dementia in patients with MCI.

Hypotheses:

In Cognigram, the investigators hypothesize that the following will occur:

i. Significant differences in the CG scores at baseline will be found between MCI and CN groups ii. MCI subjects will show greater longitudinal changes than the CN subjects iii. Intra-individual Significant longitudinal changes in the CG performances at 3 and/or 6 months and/or 9 months and/or 12 months, will relate to the prospective clinical outcome at 12-months, and/or 24-months, and/or 36-months.

iv. The CG will show a higher sensitivity in detecting cognitive changes, and a higher predictive power of longitudinal clinical outcome than the pencil-and-paper MoCA test.

In NeuroCatch, the investigators hypothesize that the following will occur:

I. Significant differences in the ERP parameters (amplitude and latency of ERPs such as N100, N400, P300) at baseline will be found between MCI and CN groups II. MCI subjects will show greater longitudinal changes in ERP parameters than the CN subjects III. Intra-individual significant longitudinal changes in ERP parameters at 6 months and/or 12 months and/or 24 months, will relate to the prospective clinical outcome at 12-months, and/or 24-months, and/or 36-months.

IV. The NCP will show a higher sensitivity in detecting cognitive changes, and a higher predictive power of longitudinal clinical outcome than the pencil-and-paper MoCA test.

The objectives of the project are as follows:

i. To discriminate CG longitudinal changes related to neural damage from normal variations in cognitively normal (CN) older adults ii. To determine if CG baseline score is related to the clinical outcome at 12-months, 24-months, and 36-months iii. To determine if intra-individual CG changes at 3, 6, 9, and 12 months, are related to the clinical outcome at 12-months, 24-months, and 36-months.

iv. To compare the powers of CG and MoCA for predicting longitudinal clinical outcomes.

v. To discriminate ERP longitudinal changes (e.g., changes in N100, N400, P300 amplitudes and latencies) related to neural damage from normal variations in cognitively normal (CN) older adults.

vi. To determine if ERP baseline parameters (e.g. amplitude of N100, N400, P300) is related to the clinical outcome at 12-months, 24-months, and 36-months vii. To determine if intra-individual changes in ERP parameters at 6, 12, and 24 months, are related to the clinical outcome at 12-months, 24-months, and 36-months.

viii. To compare the powers of NCP and MoCA for predicting longitudinal clinical outcomes.

For the purpose of these project, a total sample of 30 MCI and their study partners, and 30 cognitively normal subjects (CN), will be recruited. Study partners of MCI subjects will answer the functional questionnaire and the cognitive questionnaire required for defining the clinical status of participants. It is mandatory for MCI participants to have a study partner available in order to participate in this study.

For the CN project, the participants will undergo CG and Montreal Cognitive Assessment (MoCA) testing sessions at baseline, 3-months, 6-months, 9-months, and 12-months. The capacities for predicting clinical longitudinal outcomes (i.e., reversion to normal cognition, significant decline within MCI spectrum, or progression to dementia) of CG and MoCA will be compared. The clinical outcome will be assessed using a Neuropsychological battery, a functional assessment and a brief cognitive questionnaire at baseline, 12-months, 24-months and 36-months. It is expected that changes in CG scores will be sensitive to cognitive decline, allowing an early prediction of progression of the cognitive impairment.

For the NCP project, the participants will undergo NCP (Appendix 14: NCP) and MoCA testing sessions at baseline, 6-months, 12-months, 24-months, and 36-months. The capacities for predicting clinical outcomes (i.e., reversion to normal cognition, significant decline within MCI spectrum, or progression to dementia) of the NCP and MoCA tests will be compared. The clinical outcome will be assessed using a Neuropsychological battery, a functional assessment and a brief cognitive questionnaire at baseline, 12-months, 24-months, and 36-months. It is expected that changes in ERP will be sensitive to cognitive decline, allowing an early prediction of progression of the cognitive impairment.

Prospective longitudinal validation, if demonstrated, would greatly increase confidence in using CG and NCP in a clinical setting for the assessment of MCI patients and would allow early prediction of future clinical outcomes. This could help to develop preventive therapeutic strategies, while providing additional time for patients and their families to prepare for the future (e.g., financial arrangements, treatment options, and community services). CG and NCP would be of greatest use in primary care, where multiple constraints make cognitive assessments problematic (e.g., lack of time to perform cognitive testing, and lack of access to expensive neuropsychological testing not covered by OHIP) and EEG/ERP assessments prohibitive (e.g., lack of time to perform EEG, lack of access to trained experts for analyzing and interpreting the results of the EEG/ERP).

Eligibility criteria: Inclusion Criteria:

* Age ≥ 60
* Capable of giving consent, as stated by the University of California, San Diego Brief Assessment of Capacity to Consent (Appendix 9: UBACC)
* Meeting the diagnostic criteria of MCI or CN (described below)
* \*\*For MCI subjects: availability of a Study partner, defined as a person that knows the participant for at least 5 years, has frequent contact with them (≥2 days/week) and is knowledgeable of their functioning in activities of daily living

Exclusion Criteria:

* Significant visual, hearing, or hand-motor impairment that may interfere with the CG testing sessions or Neuropsychological Assessment
* Currently participating in Clinical Drug Trials
* Currently participating in multiple observational studies (≥2)
* Meeting the DSM-IV criteria for dementia at baseline
* Color blindness
* No consent to UBACC administration in MCI subjects
* Non-fluent in English
* Active Major depression, Stroke, Traumatic Brain Injury, substance abuse, any other neurological disease (with the exception of MCI in the MCI group).
* For NCP project only:

In-ear hearing aid or cochlear implant, hearing device

* Implanted pacemaker
* Metal or plastic implants in skull
* History of seizures
* Allergy to rubbing alcohol or EEG gel
* Unhealthy scalp (apparent open wounds and/or bruised or weakened skin)

Primary outcomes:
measure: CG scores (accuracy and reaction speed)
timeFrame: Baseline visit during year 1
measure: MoCA scores
timeFrame: Baseline visit during year 1
measure: ERP´s amplitudes for Auditory sensation (N100)
timeFrame: Baseline visit during year 1
measure: ERP´s amplitude for Cognitive processing (N400)
timeFrame: Baseline visit during year 1
measure: ERP´s amplitude for Basic Attention (P300)
timeFrame: Baseline visit during year 1.
measure: ERP´s latency for Auditory sensation (N100)
timeFrame: Baseline visit during year 1.
measure: ERP´s latency for Cognitive processing (N400)
timeFrame: Baseline visit during year 1.
measure: ERP´s latency for Basic Attention (P300)
timeFrame: Baseline visit during year 1.
measure: Longitudinal changes in CG scores
timeFrame: Complete 3 year period
measure: Longitudinal changes in MoCA scores
timeFrame: Complete 3 year period
measure: Longitudinal change in Mini-mental state examination (MMSE)
timeFrame: baseline, 12 months, 24 months, and 36 months follow ups
measure: Longitudinal change in FAQ
timeFrame: baseline 12 months, 24 months, and 36 months follow ups
measure: Longitudinal change in GPCOG scores
timeFrame: baseline 12 months, 24 months, and 36 months follow ups
measure: Longitudinal change in Rey-Osterrieth Complex Figure test (RCFT)
timeFrame: baseline, 12 months, 24 months, and 36 months follow ups
measure: Longitudinal change in Hopkins Verbal Learning test (HVLT-R)
timeFrame: baseline, 12 months, 24 months, and 36 months follow ups
measure: Longitudinal change in Trail Making Tests A and B
timeFrame: baseline, 12 months, 24 months, and 36 months follow ups
measure: Longitudinal change in Semantic Verbal Fluency test (animals).
timeFrame: baseline, 12 months, 24 months, and 36 months follow ups
measure: Longitudinal change in ERP´s amplitudes for Auditory sensation (N100)
timeFrame: baseline, 6 months, 12 months, 24 months, and 36 months follow ups.
measure: Longitudinal change in ERP´s amplitudes for Cognitive processing (N400)
timeFrame: baseline, 6 months, 12 months, 24 months, and 36 months follow ups.]
measure: Longitudinal change in ERP´s amplitudes for Basic Attention (P300)
timeFrame: baseline, 6 months, 12 months, 24 months, and 36 months follow ups.]
measure: Longitudinal change in ERP´s latencies for Auditory sensation (N100)
timeFrame: baseline, 6 months, 12 months, 24 months, and 36 months follow ups
measure: Longitudinal change in ERP´s latencies for Cognitive processing (N400)
timeFrame: baseline, 6 months, 12 months, 24 months, and 36 months follow ups
measure: Longitudinal change in ERP´s latencies for Basic Attention (P300)
timeFrame: baseline, 6 months, 12 months, 24 months, and 36 months follow ups
Secondary outcomes:
Other outcomes:
Locations:
city: Ottawa
country: Canada
facility: Bruyere Research Institute
state: Ontario
Study officials:
affiliation: Bruyere Research Institute
name: Frank Frank, MD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Wild K, Howieson D, Webbe F, Seelye A, Kaye J. Status of computerized cognitive testing in aging: a systematic review. Alzheimers Dement. 2008 Nov;4(6):428-37. doi: 10.1016/j.jalz.2008.07.003.
citation: Manuel DG, Garner R, Fines P, Bancej C, Flanagan W, Tu K, Reimer K, Chambers LW, Bernier J. Alzheimer's and other dementias in Canada, 2011 to 2031: a microsimulation Population Health Modeling (POHEM) study of projected prevalence, health burden, health services, and caregiving use. Popul Health Metr. 2016 Nov 3;14:37. doi: 10.1186/s12963-016-0107-z. eCollection 2016.
citation: Mielke MM, Machulda MM, Hagen CE, Edwards KK, Roberts RO, Pankratz VS, Knopman DS, Jack CR Jr, Petersen RC. Performance of the CogState computerized battery in the Mayo Clinic Study on Aging. Alzheimers Dement. 2015 Nov;11(11):1367-76. doi: 10.1016/j.jalz.2015.01.008. Epub 2015 Apr 6.
citation: Gates NJ, Kochan NA. Computerized and on-line neuropsychological testing for late-life cognition and neurocognitive disorders: are we there yet? Curr Opin Psychiatry. 2015 Mar;28(2):165-72. doi: 10.1097/YCO.0000000000000141.
citation: Maruff, P., Lim, Y. Y., Ames, D., Ellis, K., Pietrzak, R., Savage, G., ... & Villemagne, V. (2013). Clinical utility of the cogstate brief battery in Alzheimer's disease-related memory impairment. Alzheimer's & Dementia: The Journal of the Alzheimer's Association 9, 636-P637.
citation: Lim, Y. Y., Villemagne, V. L., Pietrzak, R. H., Snyder, P. J., Ames, D., Ellis, K. A., ... & Maruff, P. (2014). Utility of the Cognigram brief battery in assessing cognitive changes associated with AB and APOE genotype in preclinical Alzheimer's Disease. Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 10(4), 429-430.
citation: Fowler KS, Saling MM, Conway EL, Semple JM, Louis WJ. Paired associate performance in the early detection of DAT. J Int Neuropsychol Soc. 2002 Jan;8(1):58-71.
citation: Pfeffer RI, Kurosaki TT, Harrah CH Jr, Chance JM, Filos S. Measurement of functional activities in older adults in the community. J Gerontol. 1982 May;37(3):323-9. doi: 10.1093/geronj/37.3.323.
citation: American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed., text rev.). Washington, DC
citation: Jeste DV, Palmer BW, Appelbaum PS, Golshan S, Glorioso D, Dunn LB, Kim K, Meeks T, Kraemer HC. A new brief instrument for assessing decisional capacity for clinical research. Arch Gen Psychiatry. 2007 Aug;64(8):966-74. doi: 10.1001/archpsyc.64.8.966.
citation: Maruff P, Collie A, Darby D, Weaver-Cargin J, Masters C, Currie J. Subtle memory decline over 12 months in mild cognitive impairment. Dement Geriatr Cogn Disord. 2004;18(3-4):342-8. doi: 10.1159/000080229. Epub 2004 Aug 13.
citation: Darby D, Maruff P, Collie A, McStephen M. Mild cognitive impairment can be detected by multiple assessments in a single day. Neurology. 2002 Oct 8;59(7):1042-6. doi: 10.1212/wnl.59.7.1042.
citation: Darby DG, Pietrzak RH, Fredrickson J, Woodward M, Moore L, Fredrickson A, Sach J, Maruff P. Intraindividual cognitive decline using a brief computerized cognitive screening test. Alzheimers Dement. 2012;8(2):95-104. doi: 10.1016/j.jalz.2010.12.009.
citation: Howe AS, Bani-Fatemi A, De Luca V. The clinical utility of the auditory P300 latency subcomponent event-related potential in preclinical diagnosis of patients with mild cognitive impairment and Alzheimer's disease. Brain Cogn. 2014 Apr;86:64-74. doi: 10.1016/j.bandc.2014.01.015. Epub 2014 Feb 22.
citation: Ewers M, Walsh C, Trojanowski JQ, Shaw LM, Petersen RC, Jack CR Jr, Feldman HH, Bokde AL, Alexander GE, Scheltens P, Vellas B, Dubois B, Weiner M, Hampel H; North American Alzheimer's Disease Neuroimaging Initiative (ADNI). Prediction of conversion from mild cognitive impairment to Alzheimer's disease dementia based upon biomarkers and neuropsychological test performance. Neurobiol Aging. 2012 Jul;33(7):1203-14. doi: 10.1016/j.neurobiolaging.2010.10.019. Epub 2010 Dec 14.
citation: Lopez Zunini RA, Knoefel F, Lord C, Dzuali F, Breau M, Sweet L, Goubran R, Taler V. Event-related potentials elicited during working memory are altered in mild cognitive impairment. Int J Psychophysiol. 2016 Nov;109:1-8. doi: 10.1016/j.ijpsycho.2016.09.012. Epub 2016 Sep 24.
citation: Lopez Zunini RA, Knoefel F, Lord C, Breau M, Sweet L, Goubran R, Taler V. P300 amplitude alterations during inhibitory control in persons with Mild Cognitive Impairment. Brain Res. 2016 Sep 1;1646:241-248. doi: 10.1016/j.brainres.2016.06.005. Epub 2016 Jun 4.
citation: Quiroz YT, Ally BA, Celone K, McKeever J, Ruiz-Rizzo AL, Lopera F, Stern CE, Budson AE. Event-related potential markers of brain changes in preclinical familial Alzheimer disease. Neurology. 2011 Aug 2;77(5):469-75. doi: 10.1212/WNL.0b013e318227b1b0. Epub 2011 Jul 20.
citation: Rodriguez R, Lopera F, Alvarez A, Fernandez Y, Galan L, Quiroz Y, Bobes MA. Spectral Analysis of EEG in Familial Alzheimer's Disease with E280A Presenilin-1 Mutation Gene. Int J Alzheimers Dis. 2014;2014:180741. doi: 10.1155/2014/180741. Epub 2014 Jan 2.
citation: Leiser SC, Dunlop J, Bowlby MR, Devilbiss DM. Aligning strategies for using EEG as a surrogate biomarker: a review of preclinical and clinical research. Biochem Pharmacol. 2011 Jun 15;81(12):1408-21. doi: 10.1016/j.bcp.2010.10.002. Epub 2010 Oct 19.
citation: Jackson CE, Snyder PJ. Electroencephalography and event-related potentials as biomarkers of mild cognitive impairment and mild Alzheimer's disease. Alzheimers Dement. 2008 Jan;4(1 Suppl 1):S137-43. doi: 10.1016/j.jalz.2007.10.008. Epub 2007 Dec 21.