J.N. Motter1,2,*, S.N. Rushia3,*, M. Qian4, C. Ndouli1,2, A. Nwosu5, J.R. Petrella5, P.M. Doraiswamy5, T.E. Goldberg1,2, D.P. Devanand1,2
1. Department of Psychiatry, Columbia University Irving Medical Center, USA; 2. Division of Geriatric Psychiatry, New York State Psychiatric Institute, USA; 3. University of Massachusetts Chan Medical School, USA; 4. Department of Biostatistics, Mailman School of Public Health, Columbia University Irving Medical Center; 5. Duke University School of Medicine and the Duke Institute for Brain Sciences, USA; * Both authors contributed equally to this manuscript.
Corresponding Author: Jeffrey N. Motter, Department of Psychiatry, Division of Geriatric Psychiatry, 1051 Riverside Drive, New York, NY 10032, United States. Email: email@example.com.
J Prev Alz Dis 2023;
Published online May 26, 2023, http://dx.doi.org/10.14283/jpad.2023.62
Background: Computerized cognitive training (CCT) has emerged as a potential treatment option for mild cognitive impairment (MCI). It remains unclear whether CCT’s effect is driven in part by expectancy of improvement.
OBJECTIVES: This study aimed to determine factors associated with therapeutic expectancy and the influence of therapeutic expectancy on treatment effects in a randomized clinical trial of CCT versus crossword puzzle training (CPT) for older adults with MCI.
DESIGN: Randomized clinical trial of CCT vs CPT with 78-week follow-up.
SETTING: Two-site study – New York State Psychiatric Institute and Duke University Medical Center.
PARTICIPANTS: 107 patients with MCI.
INTERVENTION: 12 weeks of intensive training with CCT or CPT with follow-up booster training over 78 weeks.
MEASUREMENTS: Patients rated their expectancies for CCT and CPT prior to randomization.
RESULTS: Patients reported greater expectancy for CCT than CPT. Lower patient expectancy was associated with lower global cognition at baseline and older age. Expectancy did not differ by sex or race. There was no association between expectancy and measures of everyday functioning, hippocampus volume, or apolipoprotein E genotype. Expectancy was not associated with change in measures of global cognition, everyday functioning, and hippocampus volume from baseline to week 78, nor did expectancy interact with treatment condition.
CONCLUSIONS: While greater cognitive impairment and increased age was associated with low expectancy of improvement, expectancy was not associated with the likelihood of response to treatment with CPT or CCT.
Key words: Expectancy, age, mild cognitive impairment, computerized cognitive training, crossword puzzles.
Mild cognitive impairment (MCI) confers high risk of transition to dementia, most commonly due to Alzheimer’s disease (AD) (1). Given the difficulty in establishing efficacy for medications to treat MCI, computerized cognitive training (CCT) has emerged as a potential treatment option. CCT uses software aimed at training and sharpening cognitive skills. A meta-analysis of CCT for older adults with MCI, based primarily on studies with small samples with variable diagnostic criteria, reported small-to-medium positive effect sizes on global cognition, episodic memory, and working memory (2). However, there are concerns that CCT’s effects are driven by factors such as expectancy, engagement, motivation, and novelty (3, 4).
Expectancy is a belief-based factor, analogous to the placebo effect, that has the potential to influence the magnitude of improvement following an intervention. This belief that a treatment will produce a particular outcome impacts the effects of psychotherapy (5–7) and pharmacological interventions (8). The literature on expectancy in CCT is sparse, with mixed findings, and has been restricted to cognitively intact adults (9–12). In samples of cognitively intact adults, younger adults tend to experience greater benefit from CCT than older adults (13, 14). It is important to determine whether this effect is influenced by differences in expectancy, as it remains possible that older adults may have greater skepticism for computerized interventions than their younger counterparts. The stage of cognitive impairment a patient is in could also conceivably influence their expectations of CCT. Because AD is an irreversible condition, individuals in more advanced stage of cognitive decline/disease state (lower cognition/more neuropathic changes) may have lower expectancy of gains, as there are no treatments that can stop or reverse cognitive decline due to AD (15, 16). On the other end of the spectrum, individuals exhibiting milder cognitive symptoms may potentially expect greater impact from participating in treatments aimed at ameliorating their deficits before they worsen, reflecting the growing interest scientifically and in the public spheres about reducing dementia progression by early intervention (17). However, to date, there have been no studies on expectancy of CCT among patients with MCI or AD. Understanding demographic, clinical, functional, and structural brain imaging characteristics that influence a patient’s expectations can help to identify individuals who view training optimistically and are likely to engage with and remain in a trial. In clinical practice, it is important to know if the patient’s expectancy of improvement affects the magnitude of cognitive and functional gains following cognitive training, because expectancy is often a factor that determines which treatment is prescribed.
The Cognitive Training and Neuroplasticity in Mild Cognitive Impairment (COG-IT) study was a two-site, 78-week clinical trial involving older adults with MCI randomized to complete either CCT (Lumosity platform; intervention condition) or Crossword Puzzle Training (CPT; active control condition). CPT demonstrated superior efficacy to CCT for improving global cognition. Everyday functioning worsened and hippocampus volume decreased more with CCT than with CPT (18). The purpose of the present study is to determine which factors at baseline were related to expectancy of improvement among patients with MCI in the COG-IT trial, and to determine if expectancy influenced the treatment effects of CCT/CPT. Specifically, we hypothesized (1) higher cognitive functioning at baseline will be associated with higher expectancy; (2) older age will be associated with lower expectancy; (3) greater MRI-defined brain atrophy will be associated with lower expectancy; and (4) higher expectancy will be associated with greater improvement in global cognition following training in CCT/CPT.
Full study procedures (19) and main outcomes (18) have been published previously. This study is registered on ClinicalTrials.gov (NCT03205709, posted July 2, 2017). The study was conducted at two sites: Columbia University/New York State Psychiatric Institute (New York, NY; Columbia University was the lead coordinating site) and Duke University Medical Center (Durham, NC). The sites’ institutional review boards approved the study, which was funded by the National Institute on Aging. Lumos Labs provided specific games and crossword training modules with technical support from their Web-based platform without cost and had no role in study design, data interpretation, or publication. Participants were not charged and had no poststudy commitment. A data and safety monitoring board provided oversight.
Participants were recruited from clinical referrals, supplemented by advertising. All participants signed the informed consent form, which indicated random assignment to one of two cognitively stimulating exercises – web-based cognitive games or computerized crossword puzzles – without stating which treatment might be better. Key inclusion criteria were age 55 to 95 years, English-speaking ability, and meeting Alzheimer’s Disease Neuroimaging Initiative criteria for early MCI or late MCI(20). Early MCI was defined by a Wechsler Memory Scale-III (WMS-III) Logical Memory delayed recall score (scores range from 0 to 25, with higher scores indicating better verbal recall) of 3 to 6 with 0 to 7 years of education, score of 5 to 9 with 8 to 15 years of education, and score of 9 to 11 with 16 or more years of education. Late MCI was defined by a WMS-III Logical Memory delayed recall score of ≤2 with 0 to 7 years of education, score of ≤4 with 8 to 15 years of education, and score of ≤8 with 16 or more years of education. Additional inclusion criteria were a Folstein Mini-Mental State Examination score of ≥23 of 30 (range, 0 to 30; a higher score indicates better cognition) and availability of an informant, such as a family member, to provide information about the participant’s functioning. Participants were required to have a home computer with internet connection to access the study website. Key exclusion criteria were current major psychiatric or neurologic disorder, dementia, contraindication to magnetic resonance imaging (MRI; conducted on 3.0 T scanners), and use of online cognitive games or crossword puzzles two times per week or more in the past year. Prescribed cholinesterase inhibitors and memantine were continued. Use of high-dose opioids, anticholinergics, and/or benzodiazepines in lorazepam equivalents of ≥1 mg per day were also exclusion criteria.
The primary outcome was the ADAS-Cog (Alzheimer’s Disease Assessment Scale-Cognitive Subscale; ADAS-Cog-11) (21), a measure of global cognition (scores range from 0 to 70, with higher scores indicating greater cognitive impairment). The secondary cognitive outcome was a composite of 11 tests in the diagnostic neuropsychological assessment (z-score, higher values indicate better cognitive performance). Everyday functioning was measured using the University of California San Diego Performance-Based Skills Assessment (UPSA, range 0 to 100; higher scores indicate better functional performance) (22) and Functional Activities Questionnaire – Informant Version (FAQ, range, 0 to 30; higher scores indicate greater impairment in instrumental activities of daily living) (23). Apolipoprotein E (ApoE) genotype was evaluated (binary variable, ApoE e4 present/not present).
As expectancy effects have not been studied explicitly in CCT trials of participants with MCI, there is no widely accepted form of measurement of such effects. Therefore, we asked patients two questions to rate their expectancy on a Likert-type scale during the baseline visit prior to randomization: ‘Doing computerized games online will have the following effect on my memory and other intellectual abilities’, and ‘Doing crossword puzzles will have the following effect on my memory and other intellectual abilities’, with response options: ‘Markedly Worsen’, ‘Mildly Worsen’, ‘No Effect’, ‘Mildly Improve’, and ‘Markedly Improve’ quantified from 1-5. Because responses were highly skewed (n=0 patients expected CCT and n=2 expected CPT to ‘Markedly Worsen’ their abilities, whereas n=33 expected CCT and n=25 expected CPT to ‘Markedly Improve’ their abilities, respectively), expectancy scores were dichotomized into ‘High Expectancy’, and ‘Low Expectancy’. Within patients, those who expected to mildly or markedly improve were classified as ‘High Expectancy’, and those who expected to mildly worsen, markedly worsen, or experience no effect from either CCT or CPT were classified as ‘Low Expectancy’. As a secondary measure, scores from CCT and CPT were combined to create a ‘Total Expectancy’ dichotomous variable.
Randomization and Treatment
Patients were randomly assigned 1:1 to games or crosswords, stratified by site, age (≤70 and >70 years), and early MCI versus late MCI (based on ADNI criteria (20)). An unblinded research coordinator conducted training sessions, and a blinded research coordinator administered cognitive and functional assessments. Participants were unblinded.
Games and Crosswords
Lumos Labs provided Web-based cognitive games and crossword puzzles. Lumos Labs account credentials included a study-specific email address and password. Each games session was composed of 6 modules randomly selected from 18 available modules that included tasks of memory, matching, spatial recognition, and processing speed (19). Participants received their overall games performance score at the end of each session. Lumos Labs also provided computerized crossword puzzles of medium difficulty, intended to be equivalent to the New York Times’ Thursday crossword puzzles, without performance-based scaling over time. The participant could view the correct answers at the end of the session but did not receive a score.
Participants were evaluated in person at five scheduled visits (weeks 0, 12, 32, 52, and 78), and research staff conducted three additional scheduled phone calls (weeks 20, 42, and 64). Initial intensive, home-based computerized training for games or crosswords consisted of four 30-minute training sessions per week for 12 weeks. Subsequent booster training was composed of four 30-minute sessions, completed over 1 week and occurring at weeks 20, 32, 42, 52, 64, and 78. During weeks 32, 52, and 78, participants completed three sessions at home and the fourth in clinic. During weeks 20, 42, and 64, participants completed all four sessions at home. Unblinded study coordinators received weekly electronic reports of completed sessions and contacted participants who had not completed sessions to attempt to improve adherence.
The consent form included descriptions of the exercises participants would be randomized to. The following sections contained descriptions most relevant to the shaping of participant’s expectations (Full consent form language is included in supplementary material).
From ‘Purpose of Study’ section
“This study is being conducted to evaluate if systematic cognitive training can improve cognitive performance in participants with memory loss. You have been asked to participate because you reported having difficulty with your memory. This study will evaluate the effects of Computerized Cognitive Training (CCT) for improvement in everyday cognitive and functional status, in addition to long-term changes in brain networks over an 18-month period. In this study, participants will be randomly assigned to one of two cognitively stimulating exercises: either a crossword puzzle training (CPT) condition or a computerized cognitive training condition (CCT).”
From ‘Procedures’ section
“Computerized cognitive training involves cognitive exercises on the computer that target specific abilities/neural networks that may improve cognitive functioning. Computerized cognitive training may include crossword puzzles, matching puzzles, and math puzzles. Computerized cognitive training tasks include Speed Pack, Disillusion, Editor’s Choice, Continuum, Familiar Faces, Tidal Treasures, Speed Match, Color Match, Word Bubbles, Train of Thought, Memory Matrix, Lost in Migration, Brain Shift, Trouble Brewing, Ebb and Flow, Masterpiece, River Ranger, and Word Snatchers. Crosswords do not become more difficult over time, since they are very similar to crosswords done in daily newspapers.”
From ‘Benefits’ section
“You may or may not benefit from participating in this study.”
At the New York State Psychiatric Institute (NYSPI), images were acquired across three scanners: (1) NYSPI GE MR 750 scanner with an 8-channel head coil; (2) NYSPI MBBI Siemens Prisma scanner with a 64-channel head coil; and (3) Weill Cornell GE MR 750 scanner with an 8-channel head coil. At Duke University Medical Center, images were acquired on a GE MR 750 scanner with 8-channel head coil. At screen, patients underwent scanning including the following sequences: Localiser, high-resolution T1-weighted inversion recovery prepped spoiled gradient recalled echo (3D IR-SPGR), T2-weighted fluid attenuated inversion recovery (FLAIR) and gradient echo echo-planar imaging (GE-EPI) resting-state fMRI scans. Bilateral hippocampal volume was extracted from the 3D IR-SPGR acquisition using FreeSurfer version 6.0, a publicly-available software package that assesses cortical gray, deep gray, CSF and white matter volumes based on an automated segmentation algorithm (24, 25). Mean total hippocampal volume was generated through the hippocampal subfield segmentation tool (26). Visual quality control was modeled after the methods used in ADNI (27) and was completed by two independent reviewers. 104 scans passed quality control and underwent the hippocampal subfield processing procedure, whereas 3 scans failed quality control because of excess motion or artifacts and were excluded from further processing.
All statistical tests were two-tailed and performed at α=0.05 for inference. The ADAS-Cog was the primary outcome measure. For secondary outcomes, there was no adjustment for multiple statistical comparisons. For baseline analyses, chi square and t-tests were used to evaluate differences between expectancy groups. Logistic regression models were used to evaluate the study hypotheses, with each cognitive, functional, genetic, or anatomical measure as independent variables, and expectancy groups as dependent variables. Because age differed significantly across all expectancy groups and site differed significantly between ‘Low CCT Expectancy’ and ‘High CCT Expectancy’ groups, age and site were evaluated as covariates in the models. When age and site were entered together, age was significantly associated with expectancy across models, but site was not (likely owing to the significant difference in age between sites), thus site was removed from the final models.
The effect of expectancy on cognitive/functional outcomes was evaluated using random intercept linear mixed-effects model repeated-measures analysis. For each outcome, the change in the measure (baseline minus study time point) was the dependent measure, and the expectancy variable, study time point, and their interaction were the predictors, adjusting for the baseline value of the outcome measure. Each model included all time points, with a focus on 78 weeks (primary end point). Similar mixed effects models were conducted to evaluate the difference in treatment effects on cognitive/functional outcomes for high versus low expectancy groups. Predictors include treatment, expectancy variable, study time point and their three-way interactions. Effects of expectancy on change in MRI outcomes (week 0 to week 78) were evaluated in linear regression models.
Expectancy ratings at baseline
109 patients met criteria and were enrolled in the trial. Two were excluded because they did not complete any training sessions, bringing the final analytic sample to n=107. The average age was 71.2 years (SD 8.8) and 57.9% of the sample was female (Table 1). 86.0% of patients were classified as ‘High CCT Expectancy’, while 14.0% were classified as ‘Low CCT Expectancy’. 78.5% of patients were classified as ‘High CPT Expectancy’, while 21.5% were classified as ‘Low CPT Expectancy’. When CCT/CPT ratings were combined to create an overall expectancy index, 72.0% of patients were classified as ‘High Total Expectancy’ and 28.0% were classified as ‘Low Total Expectancy.’ Patients classified as ‘High CCT Expectancy’, ‘High CPT Expectancy’, and ‘High Total Expectancy’ were significantly younger than those classified as ‘Low CCT Expectancy’, ‘Low CPT Expectancy’, and ‘Low Total Expectancy’ respectively (Table 1; Figure 1).’ Patients were significantly more likely to report higher expectancy for CCT than CPT (χ2 [2, n=107]=6.7, p=0.010). Few patients (n=6) anticipated worsening of cognition due to training (Table 2).
Unadjusted t-test and chi square comparison of Low Expectancy and High Expectancy groups at baseline on demographic variables. All values are expressed as mean (SD) or n (%).
Patient ratings in response to the statement: “Doing ____ (crossword puzzles/CCT) will have the following effect on my memory and other intellectual abilities:” CPT=crossword puzzles; CCT= computerized cognitive training
Lower CPT, CCT, and Total patient expectancy was linked with significantly older age. See text for details. Middle line=median. Top and bottom box lines=first and third quartiles. Whiskers=maximum and minimum values.
Baseline logistical regression models
Better ADAS-Cog performance was significantly associated with ‘High CCT Expectancy’, ‘High CPT Expectancy’, and ‘High Total Expectancy’ (Table 3). After adjustment for age, better performance on ADAS-Cog remained significantly associated with ‘High Total Expectancy’, but not with ‘High CCT Expectancy’ or ‘High CPT Expectancy’. Older age was significantly associated with lower expectancy in each model (p<0.05)
Logistic regression models comparing high vs. low patient expectancy of CCT, CPT, and Total expectancy. Each model includes the baseline measure plus age as covariates, with patient expectancy group as dependent variable. ADAS-Cog=Alzheimer’s Disease Assessment Scale-Cognitive Subscale (higher scores=lower performance). NeuroComp=neuropsychological composite of 11 tests in the diagnostic neuropsychological assessment (higher scores=better performance). UPSA=University of California San Diego Performance-based Skills Assessment (higher scores=higher functioning). FAQ=Functional Activities Questionnaire (higher scores=lower functioning). Hipp Vol=hippocampus volume. ApoE=apolipoprotein E genotype (e4 not present vs e4 present).
For secondary outcomes, after adjusting for age, expectancy was not associated with the neuropsychological composite, UPSA, FAQ, hippocampus volume, or ApoE genotype. Older age was significantly associated with lower expectancy in each model (p<0.05). The effect of sex and race on expectancy was not significant.
Longitudinal linear mixed-effects models: both groups, total expectancy
Expectancy was not significantly associated with change in ADAS-Cog or any secondary outcome measures over time. There was no significant difference between ‘Low Total Expectancy’ and ‘High Total Expectancy’ on change in ADAS-Cog or change in any secondary outcome measure between baseline and week 12 or between baseline and week 78 (Table 4).
Results of linear mixed-effects model repeated-measures analysis. Change in each outcome variable is analyzed as week 0 minus either week 12 or week 78, adjusting for baseline values of the corresponding measure, with the final least squares mean treatment difference representing the low expectancy group change minus the high expectancy group change. ADAS-Cog=Alzheimer’s Disease Assessment Scale-Cognitive Subscale (higher scores=lower performance). NeuroComp=neuropsychological composite of 11 tests in the diagnostic neuropsychological assessment (higher scores=better performance). UPSA=University of California San Diego Performance-based Skills Assessment (higher scores=higher functioning). FAQ=Functional Activities Questionnaire (higher scores=lower functioning). Hipp Vol=hippocampus volume.
Longitudinal linear mixed-effects models: treatment-specific expectancy analyses
Next, we examined whether there was a difference in treatment effects between low vs high expectancies for the randomized treatment that was received. For patients randomized to CCT, there was no significant difference between ‘Low CCT Expectancy’ and ‘High CCT Expectancy’ on change in ADAS-Cog or any secondary outcome measure between baseline and week 12 or between baseline and week 78. Similarly, for patients randomized to CPT, there was no significant difference between ‘Low CPT Expectancy’ and ‘High CPT Expectancy’ on change in ADAS-Cog or any secondary outcome measure.
To our knowledge, this is the first study to examine expectancy in a trial of CCT for patients with MCI. A key finding from this study is that older age was consistently associated with lower patient expectancy in all models, even when corrected for cognitive measures, structural brain variables, and other demographic factors. We speculate this may be due in part to less frequent computer use, lower familiarity with computers, and greater skepticism of computerized interventions among older patients. Nevertheless, all patients were required to have access to a computer at home, thus narrowing the sample to individuals with at least some degree of computer familiarity. Although age significantly moderates the effect of CCT in samples of cognitively intact adults, such that younger adults tend to experience larger effects and broader transfer to untrained domains than older adults (13, 14), it appears unlikely that expectancy mediates this relationship given the present null findings for an impact of expectancy on longitudinal change. As hypothesized, higher cognitive performance at baseline was generally associated with higher expectancy, albeit with select measures only (ADAS-Cog, though not the neuropsychological composite). This result indicates that individuals who are in more advanced stages of cognitive decline and thus possibly at a greater risk of progression to AD are less likely to anticipate positive benefits from training in CCT/CPT. Contrary to our hypothesis, baseline hippocampus volume was not associated with expectancy. Additionally, baseline measures of everyday functioning were not associated with expectancy. Given that MCI is defined by lack of impairment in these activities, there may be a floor effect on the UPSA and FAQ that assess functioning, thus potentially obscuring our ability to detect any effect.
Contrary to predictions, there was no relationship between expectancy and change in outcome measures across the trial. Expectancy did not significantly influence the treatment effect of CCT or CPT. As previously reported, the main findings of the COG-IT trial were an improvement in ADAS-Cog for CPT versus a decline in ADAS-Cog for CCT. FAQ improved more with CPT than CCT. Decreases in hippocampus volume and cortical thickness were greater for CCT than for CPT(18). That CPT demonstrated greater effects than CCT despite patients having lower expectation for improvement using CPT provides evidence against the notion that expectancy effects were responsible for the benefits of CPT.
Although an impact of expectancy on treatment outcomes has been reported in psychotherapy (5–7, 28) and pharmacological interventions(8,29), there is reason to remain skeptical as to whether it influences treatment outcomes in CCT/CPT. In CCT/CPT trials, the outcome is typically change in cognitive performance, which may be less susceptible to placebo effects than outcomes for depression, anxiety, pain, and related measures. One study of CCT induced varying expectancies by posting two different recruitment flyers that advertised beneficial effects of CCT on fluid intelligence or advertised a generic opportunity for college course credit. Greater improvements on tests of fluid intelligence were reported following CCT in the high expectancy flyer (9). However, all studies of CCT that have experimentally manipulated participant’s expectations after enrollment by providing varying training instructions to induce belief of improved performance from CCT have found no differences between high expectancy and low expectancy conditions on cognitive gains (10–12). This suggests that expectancies are not a universal phenomenon in CCT, nor are they the sole driver of CCT’s effects, possibly because of the objective nature of testing for cognitive outcomes in a blinded trial.
In conclusion, higher expectancy of MCI patients participating in a CCT/CPT trial was associated with better baseline global cognition. Older individuals had lower expectations of improvement. However, in the clinical trial, expectancy was not associated with change in primary or secondary outcome measures from baseline to weeks 12 or 78, nor did expectancy interact with treatment condition. The findings suggest that expectancy does not impact cognitive and functional outcomes using computerized cognitive interventions in patients with MCI. If these findings are applied clinically, an older adult skeptical of crossword puzzles may be counseled that, despite low expectations, they may still potentially benefit.
Funding: This work is supported by National Institute on Aging grant number 1R01AG052440-01A1 and National Institute of Mental Health grant number 2T32MH020004-21. Lumos Labs provided the gaming platform at no cost. Lumos Labs had no role in the design and conduct of the study; in the collection, analysis, and interpretation of data; in the preparation of the manuscript; or in the review or approval of the manuscript.
Trial registration: ClinicalTrials.gov identifier (NCT03205709).
Ethical standards: Study protocols were approved by the Columbia University/New York State Psychiatric Institute and Duke University Medical Center Institutional Review Boards, and all participants provided informed consent before completing any study procedures.
Conflict of interest: Dr. Motter reports grants from National Institute of Mental Health, during the conduct of the study. Dr. Rushia has nothing to disclose. Dr. Qian has nothing to disclose. Charlie Ndouli has nothing to disclose. Adaora Nwosu has nothing to disclose. Dr. Petrella reports grants from National Institute on Aging, during the conduct of the study; grants from National Science Foundation, personal fees from Biogen, personal fees from Icometrix, other from Cortechs.ai, outside the submitted work. Dr. Doraiswamy reports grants from National Institute of Health, other from Lumos Labs, during the conduct of the study; grants from National Institute of Health, grants from Lilly/Advid, grants from US Highbush Blueberry Council, grants from Cure Alzheimer’s Fund, grants from Karen L Wrenn Trust, grants from Steve Aoki Fund, personal fees from Lumos Labs, personal fees from UMethod, personal fees from Vivli, personal fees from Nutricia, personal fees from Clearview, personal fees from Brain Forum, personal fees from Otsuka, personal fees from Cornell, personal fees from Nestle, non-financial support from AHEL, non-financial support from Live Love Laugh, other from Alzheon, other from Lumos Labs, other from Lululemon, other from Transposon, from Apollo, from Live Laugh Love, from Goldie Hawn Foundation, other from Transposon, other from UMethod, other from Evidation, other from Marvel Biome, other from Alzheon, outside the submitted work; In addition, Dr. Doraiswamy has a patent Diagnosis and treatment of dementia. Dr. Goldberg has nothing to disclose. Dr. Devanand reports grants from National Institute on Aging, during the conduct of the study; grants from Alzheimer’s Association, personal fees from Acadia, personal fees from Eisai, personal fees from Genentech, personal fees from Jazz, personal fees from TauRx, personal fees from Novo, personal fees from Nordisk, personal fees from Biogen, personal fees from BioExcel, outside the submitted work.
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