Kremers, Walter K.

AbstractBackgroundMemory impairment is generally considered minimal to absent in those with behavioral variant frontotemporal dementia (bvFTD). We sought to assess the frequencies of subjective and objective memory impairment in individuals with mild bvFTD according to consensus criteria.MethodAssessments were performed in mildly symptomatic bvFTD [defined by global FTLD Clinical Dementia Rating scale (CDR®+NACC FTLD) = 1] mutation carriers (Familial) and non‐mutation carriers (Sporadic) who are participants in the ARTFL LEFFTDS Longitudinal Frontotemporal Lobar Degeneration (ALLFTD, www.allftd.org) study. Mutation carriers had a pathogenic mutation in the MAPT, GRN or C9orf72 gene. The presence of subjective memory impairment was defined by a rating >0 on the memory domain of the CDR®+NACC FTLD. Evidence of objective memory impairment was based on performance across 7 memory measures, which include the National Alzheimer’s Coordinating Center Uniform Data Set Version 3 – Craft Story Immediate Recall (CSImm) and Delayed Recall (CSDel), Benson Figure Delayed Recall (BenDel), and the California Verbal Learning Test – Learning (CVLearn), Immediate Recall (CVImm), Delayed Recall (CVDel), and Recognition (CVRecog). Performance was considered abnormal for z‐scores ≤ ‐1.5.ResultData from 38 Familial (mean age 60±8 years, 61% male), and 109 Sporadic (mean age 63±7 years, 70% male) participants were analyzed. Memory ratings >0 on the CDR®+NACC FTLD were common for both Familial (84%) and Sporadic (88%). The frequencies of abnormal performance in the Familial vs Sporadic groups, respectively, per memory measure were: CSImm 43.8% vs 45.9%, p = 0.83; CSDel 46.9% vs 52.6%, p = 0.58; BenDel 29.4% vs 46.4%, p = 0.08; CVLearn 54.8% vs 39.8, p = 0.14; CVImm 64.5% vs 50%, p = 0.16, CVDel 80.6% vs 55.1%, p = 0.01, and CVRecog 51.6 vs 49%, p = 0.80. The frequencies of abnormal performance did not differ (all p>0.05) between Familial and Sporadic for ≥2 tests (79.3% vs 67.7%), ≥4 tests (55.2% vs 44.1%) and ≥6 tests (34.5% vs 31.2%).ConclusionThese data suggest that among those with a mild global severity of bvFTD features: 1) subjective and objective memory impairment are relatively frequent, and 2) the frequency of impairment across memory measures is largely similar between familial and sporadic bvFTD. The presence of memory impairment should not preclude a diagnosis of bvFTD.

AbstractBackgroundFrontotemporal dementia (FTD) is a leading cause of dementia in individuals under age 65. Due to limitations in access to in‐person evaluations, there is an urgent need to examine remote, accessible, and low‐burden assessment techniques for FTD. Unobtrusive monitoring of at‐home computer use in older adults has shown that those with mild cognitive impairment (MCI) have significant declines in computer use over time. To extend these findings, we characterized daily trajectories in smartphone battery percentage, a proxy of smartphone use, and examined relationships with clinical indicators of disease severity in FTD.MethodParticipants included 250 adults enrolled in the ALLFTD Mobile App study (mean age = 53.9 [SD = 14.9]; 59% women; 94% White). To assess clinical disease severity, all participants completed the FTLD Clinical Dementia Rating scale (CDR®+NACC FTLD), a brief neuropsychological battery, the Neuropsychiatric Inventory Questionnaire (NPI‐Q), and brain MRI. The ALLFTD Mobile App was installed onto participants’ smartphones for remote, passive, continuous monitoring of smartphone use (average days of monitoring = 82 [range = 7‐541]). Battery percentage was collected every 15 minutes. Linear mixed effects models examined linear, quadratic, and cubic effects of time of day (i.e., hour; modeled as person‐specific random effects) and their interactions with each measure of disease severity on battery percentage. Models covaried for age.Result57% of participants were clinically normal, 21% had MCI, and 22% had dementia. CDR®+NACC FTLD Global score interacted with hour such that participants with MCI and dementia had flatter battery curves (i.e., less change in battery percentage throughout the day – a proxy of less smartphone use) than clinically normal participants (ps<0.001). Additional models showed that worse memory (hour3 x memory score: 𝛃 = 0.59, p<0.001) and executive functioning (hour3 x executive score: 𝛃 = 0.64, p<0.001), more neuropsychiatric symptoms (hour3 x NPI‐Q: 𝛃 = ‐0.22, p = 0.004), and smaller gray matter volumes (controlling for intracranial volume; hour3 x volume: 𝛃 = 0.49, p<0.001) were also statistically significantly related to less battery usage throughout the day.ConclusionThese findings are proof‐of‐concept that passively acquired smartphone data, specifically battery usage, can provide insights about disease progression in FTD. Results support future research focusing on the development of passively collected digital metrics sensitive to clinical declines in neurodegenerative disease.

AbstractBackgroundThe ALLFTD Consortium aims to characterize preclinical and prodromal stage of familial Frontotemporal Lobar Degeneration (f‐FTLD) to prepare for clinical trials in FTLD.MethodWe prospectively followed 320 asymptomatic participants of the ALLFTD Consortium who are family members of f‐FTLD with a mutation in MAPT (n = 106), GRN (n = 74), or C9orf72 (n = 140), and describe the ratings on the eight domains of CDR® plus NACC FTLD (FTLD‐CDR) in those who phenoconverted to Mild Behavioral and/or Cognitive Impairment (MBCI). MBCI was diagnosed by the proposed research criteria for MBCI‐FTD (Barker MS, et al. Brain 2022). Participants who were diagnosed as MBCI but returned to asymptomatic at the following visit were excluded from the FTLD‐CDR assessment. ANOVA was used to compare continuous variables, and chi‐square test was used to compare categorical variables across the groups.ResultThirty‐six participants (15 MAPT, 9 GRN, 12 C9orf72) developed MBCI during follow‐up visits, 13 participants (5 MAPT, 3 GRN, 5 C9orf72) were diagnosed as MBCI but were characterized as asymptomatic at the following visit, and 271 participants stayed asymptomatic. Percentage of male in MBCI participants was 50% (MAPT 60%, GRN 33%, C9orf72 50%, p = 0.45). Age at phenoconversion to MBCI was 53.6±13.0 years old, the youngest in MAPT and the oldest in GRN participants (MAPT 44.3±8.3, GRN 63.1±11.3, C9orf72 56.4±10.8, p<0.001). Abnormal rating on each of the eight CDR® plus NACC FTLD domains at the initial visit of MBCI phenoconversion were observed in: “Memory” 36% (MAPT 33%, GRN 33%, C9orf72 44%, p = 0.95); “Orientation” 8% (MAPT 7%, GRN 8%, C9orf72 11%, p = 0.93); “Judgment/Problem Solving” 44% (MAPT 47%, GRN 50%, C9orf72 33%, p = 0.73); “Community Affairs” 22% (MAPT 27%, GRN 17%, C9orf72 22%, p = 0.82); “Home/Hobbies” 25% (MAPT 40%, GRN 8%, C9orf72 22%, p = 0.16); “Personal Care” 0%; “Behavior/Comportment/Personality” 44% (MAPT 60%, GRN 42%, C9orf72 22%, p = 0.19); “Language” 31% (MAPT 13%, GRN 50%, C9orf72 33%, p = 0.12).ConclusionThe three most common FTLD‐CDR domains initially disturbed in the prodromal stage of fFTLD were “Behavior/Comportment/Personality”, “Judgment/Problem Solving”, and “Memory”, while “Orientation” was intact in the majority of the individuals. Memory impairment without disorientation was seen in one‐third of the prodromal fFTLD participants.

AbstractBackgroundEasily accessible and self‐administered cognitive assessments that can aide early identification of individuals at risk for Alzheimer’s disease (AD) dementia are critical for timely intervention. Mayo Test Drive (MTD) is a remote, self‐administered, multi‐device compatible, web‐based cognitive assessment (Stricker et al., 2022). We investigated continuous associations between MTD and amyloid and tau PET biomarkers and compared the strength of association to PET biomarkers and in‐person cognitive measures.Method556 adults from the Mayo Clinic Study of Aging participated (mean age = 70.6; mean education = 16.0 years; 50.7% female; 97.5% white). Participants were predominantly cognitively unimpaired (95.3%; 4.7% had a consensus diagnosis of mild cognitive impairment). Participants completed (1) brain amyloid and tau PET scans, (2) in‐person neuropsychological assessment including Mayo Preclinical Alzheimer’s disease Cognitive Composite (Mayo‐PACC) and Global‐z, and (3) remote MTD comprised of the Stricker Learning Span (SLS) and the Symbols test, which are combined into an MTD composite. Multiple regressions adjusted for age, sex, and education queried associations between imaging biomarkers [amyloid meta‐region of interest (ROI), tau meta‐ROI, and tau entorhinal cortex (EC)‐ROI)] and composite and subtest scores from remote and in‐person measures. Davidson‐Mackinnon J‐test compared R‐squared values of models with remote versus in‐person composites.ResultLower performances on MTD composite and Mayo‐PACC were associated with higher amyloid meta‐ROI (p’s<.05; Table 1). MTD composite was also negatively associated with tau meta‐ROI and EC‐ROI (p’s<.05). For memory measures, SLS sum of trials was negatively associated with tau meta‐ROI (p = .01) and EC‐ROI (p<.001) with no associations between AVLT sum of trials and PET (p’s≥.06; Table 2). Among processing speed/executive function measures, worse Trails B performance was associated with higher amyloid meta‐ROI (p<.001). Symbols and Digit Symbol Coding were not associated with PET (p’s≥.29). A multivariable model with MTD explained significantly less variance (p = .04) for amyloid meta‐ROI (adjusted R‐squared = 0.165) than a multivariable model with Mayo‐PACC (adjusted R‐squared = 0.176); remote and in‐person composite measures were comparable for other associations (p’s>.05).ConclusionMTD is associated with continuous measures of amyloid and tau PET biomarkers of AD, demonstrating criterion validity for MTD. Several associations were comparable to relationships between gold‐standard in‐person neuropsychological tests and imaging biomarkers.

AbstractBackgroundMost studies on the relationship between neuropsychiatric symptoms (NPS) and cognitive decline focus on NPS data collected over a short period of time. To better understand the association between NPS and cognitive decline, including their temporal relationship, it is important to know NPS status across a person’s life.MethodWe utilized the unique Rochester Epidemiology Project medical record linkage system that has captured medical records as far back as 1907, to abstract the psychiatric history of 1810 cognitively unimpaired participants in the population‐based Mayo Clinic Study of Aging (MCSA). Trained nurse abstractors ascertained the presence of exposure to three common NPS (anxiety, depression, and unspecified emotional distress [UED]), both before and after entry into the MCSA. We conducted Cox Proportional Hazards (PH) models using age as the time scale and adjusting for sex, education, and APOE E4 allele for each NPS in predicting mild cognitive impairment (MCI), or dementia. Additional analyses considering medication use (yes/no) and timing of NPS were also run.ResultAnxiety (Hazard Ratio [HR] = 1.47, p<0.001), depression (HR = 1.36, p = 0.001), and UED (HR = 1.22, p = 0.039) were associated with a higher risk of MCI. These associations were present regardless of medication use for anxiety (With medication: HR = 1.51, p<0.001; without medication: HR = 1.41, p = 0.015), and for those taking medications for depression (With medication: HR = 1.47, p<0.001; without medication: 1.09, p = 0.597). Anxiety was associated with MCI regardless of timing of onset with the strongest relationship being for those with onset of anxiety before 65 that persisted into later life (HR = 1.88, p = 0.004). Depression was associated regardless of timing of onset if the depression was present in late life too. The results were similar for the outcome of dementia (Anxiety: HR = 1.62, p<0.001; depression: HR = 1.31, p = 0.040; UED: HR = 1.37, p = 0.014). The associations accounting for medication use and timing of NPS were similar for the outcome of dementia too.ConclusionNPS is associated with progression to MCI and dementia. Both medication use and timing of NPS might play a role in these associations.

AbstractBackgroundBrief self‐administered cognitive assessments deployed remotely and combined with plasma biomarkers of Alzheimer’s disease (AD) are well suited to assess clinical symptoms and underlying biology in large‐scale decentralized studies. We investigated whether associations of self‐administered cognitive measures and plasma biomarkers were comparable to associations of in‐person cognitive measures and plasma biomarkers.MethodParticipants included 327 adults from the Mayo Clinic Study of Aging (mean age = 70.8; 48% female; mean education = 16.0 years; 96.9% white). Most (94.5%) were cognitively unimpaired; 5.5% had consensus diagnosis of mild cognitive impairment. Participants completed Mayo Test Drive (MTD; Stricker et al., 2022), a remote, self‐administered, multi‐device compatible web‐based cognitive assessment comprised of the Stricker Learning Span (SLS) and Symbols test that are combined into a MTD composite. Participants also completed in‐person neuropsychological assessment (global‐z; Mayo Preclinical Alzheimer’s disease Cognitive Composite [Mayo‐PACC]) and fasting venipuncture. Plasma was assayed via HD‐X simoa platform for amyloid‐beta (Aβ)‐42 and Aβ‐40 to create an Aβ42/40 ratio, glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL) using the N4PE Quanterix kit as well as phosphorylated‐tau protein 181 (p‐tau181). P‐tau181, GFAP, and NfL were log10‐transformed. Multiple regression analyses adjusting for age, sex, and education examined associations between plasma biomarkers and composite and subtest scores from remote and in‐person measures. Davidson‐Mackinnon J‐test compared R‐squared values of models with remote versus in‐person composites.ResultLower performances on MTD, global‐z, and Mayo‐PACC (Table 1) were associated with higher GFAP (p’s≤.02), NfL (p’s≤.003), and p‐tau181 (p’s = .04). For memory, worse SLS and AVLT sum of trials were associated with higher GFAP (p’s≤.01) and NfL (p’s≤.03). For processing speed/executive function, worse Symbols and Trails B were associated with higher p‐tau181 (p’s≤.02). Worse Trails B and Digit Symbol Coding were associated with higher NfL (p’s≤.02), and worse Digit Symbol Coding was associated with lower Aβ42/40 (p = 0.03; Table 2). A multivariable model with MTD explained significantly more variance (p = .04) for NfL (adjusted R‐squared = 0.461) than a multivariable model with Mayo‐PACC (adjusted R‐squared = 0.447); remote and in‐person composite measures were comparable for other associations (p’s>.05).ConclusionRemote and in‐person composite measures showed comparable associations with AD‐related plasma biomarkers, supporting MTD’s criterion validity.

AbstractBackgroundWe have previously shown that 3D T1‐weighted and 3D T2‐FLAIR brain MRI can potentially be re‐identified using face recognition, but this has not been tested for other MRI sequences commonly used in imaging studies of aging and dementia. Here we compare match rates of automated face recognition to re‐identify 3D T1‐weighted, 3D T2‐FLAIR, 3D T2‐weighted, 2D Axial T2‐FLAIR, multi‐echo GRE (T2*), diffusion MRI, functional MRI, ASL, and T2* GRE images.MethodWe used a dataset of 182 Mayo Clinic participants each with MRI and face photographs. MRIs used Siemens and GE vendor‐product sequences similar to ADNI3 and ADNI4 protocols. We created face reconstructions (“basic”, with minimal preprocessing, and “advanced” that automatically filled missing face regions using a template) from each MRI sequence, and we trained a Microsoft Azure PersonGroup face recognition classifier to recognize these MRI‐based faces. We then queried the classifier with five face photographs for each participant, and we recorded how often the classifier chose the correct MRI as the top‐ranked match. For image types with match rates > = 10%, we then repeated the experiment after de‐facing each MRI with mri_reface, our leading software that replaces faces in each image (“re‐faces”) with an average face.ResultResults are presented in Table 1. 3D T2‐FLAIR, 3D T1‐weighted, and 3D T2‐weighted images were matched at 96‐98%, and mri_reface reduced all to 8%. Axial T2‐FLAIR and ME‐GRE were matched at 32‐45%, and mri_reface reduced both to 1%. T2*‐GRE matched at 10%, and mri_reface reduced this to 1%. ASL matched at 8%, while dMRI and fMRI matched at only 0‐2%.ConclusionAmong current‐generation vendor‐product sequences, we recommend that de‐facing be applied before research data sharing of T1‐weighted, T2‐weighted, T2‐FLAIR, T2*, and ME‐GRE images. Minimal match rates for current‐generation product fMRI, ASL, and fMRI images suggest that they present low risk of re‐identification and can be shared without de‐facing, but this conclusion should be re‐evaluated if they are acquired without fat‐suppression, with a larger field of view, or if newer developments reduce the current levels of artifacts and distortion around the face.

AbstractBackgroundAutomated face recognition can potentially re‐identify de‐identified research brain MRI, PET, and CT, and direct differences of brain measurements from original and de‐faced images are statistically significant but very minor, i.e. below scan‐rescan differences. Their effects on analyses correlating brain imaging biomarkers with clinical variables remain unknown.MethodWe sampled 3 age‐and‐sex‐matched groups (CU, MCI, and clinical AD) from Mayo Clinic aging studies, each with 61 participants with same‐visit 3D T1‐weighted and 3D T2‐FLAIR MRI, PiB amyloid PET, and Flortaucipir tau PET (total n = 183). We automatically measured hippocampal volume, PIB global SUVR, and tau temporal meta‐ROI SUVR using a) our in‐house pipeline with SPM12, MCALT, and ANTs, and b) FreeSurfer 7.3.2 (PETSurfer), and we measured voxels associated with clinical group separation using SPM12. We also measured WMH from FLAIR using a) our in‐house pipeline, b) Lesion Segmentation Tool (LST), and c) FreeSurfer’s Samseg. For each measurement, we calculated pair‐wise group separation AUC values and Spearman’s rho with age and CDR‐SOB. We then de‐faced all PET and MRI using mri_reface, our top‐performing automated de‐facing software that replaces imaged faces with an average face (“re‐facing”), and we repeated all measurements and analyses with de‐faced images. We then compared the magnitudes of clinical correlations with original images and those of de‐faced images.ResultAnalyses of original and de‐faced images had very high agreement. Only 2/55 paired comparisons were significant (Tables 1‐2): FreeSurfer‐measured global PIB SUVR from de‐faced images was more correlated with age (rho 0.003 vs ‐0.227, p = 0.046), and Mayo‐measured WMH volume from original images was more correlated with CDR‐SOB (rho 0.321 vs 0.308, p = 0.020). Another comparison‐pair tested p = 0.041 for an AUC change +0.008, which we consider not practically meaningful. All others had p>0.05. In voxel‐based analyses of CU vs AD participants, all comparisons were qualitatively similar between original and de‐faced variants (Figure 1).ConclusionDe‐facing PET and MRI using mri_reface did not significantly affect 53/55 correlations between imaging and clinical variables; of the two affected, one became stronger with re‐faced images. Stronger correlations with clinical variables may be explained by de‐facing’s standardizing face voxels and artifacts that irrelevantly affect brain segmentations.

AbstractBackgroundPlasma biomarkers for Alzheimer’s disease (AD) such as phosphorylated tau protein 181 (p‐tau 181), neurofilament light chain (NfL), amyloid‐β 42/40 ratio (Aβ42/40), and glial fibrillary acidic protein (GFAP) have shown high performance in the discrimination of those with clinical mild cognitive impairment (MCI) or dementia from cognitively unimpaired individuals, but few population‐based autopsy studies have evaluated the neuropathological correlates of plasma biomarkers.MethodWe utilized a population‐based prospective study of 350 participants with autopsy and antemortem plasma biomarker data. Plasma biomarkers Aβ42/40 ratio, p‐tau181, GFAP, and NfL were measured using Quanterix Simoa assays. We predicted AD neuropathological change (ADNC), Braak staging, and neuritic plaque score using these plasma biomarkers and investigated the added utility of cognitive and demographic information. We utilized a variable selection procedure in cross‐validated (CV) logistic regression models by minimizing the Bayesian Information Criterion to identify the best set of predictors for a given outcome among plasma biomarkers, demographic variables, and neuropsychological tests included in the Mayo Clinic Preclinical Alzheimer Cognitive Composite (Mayo‐PACC).ResultADNC was best predicted with plasma GFAP, NfL, p‐tau181 biomarkers along with APOE ε4 carrier status and Mayo‐PACC cognitive score (CV AUC = 0.821). Braak staging was best predicted using plasma GFAP, p‐tau181, and cognitive scores (CV AUC = 0.788). Neuritic plaque score was best predicted using plasma Aβ42/40 ratio, p‐tau181, GFAP, and NfL biomarkers (CV AUC = 0.778). Inclusion of plasma biomarkers demonstrated significantly better prediction of Braak staging (p<0.01) and neuritic plaque score (p<0.01), but not ADNC (p = 0.13), when compared to models that included only cognitive and demographic information. Univariate models consistently underperformed in comparison to multivariate models.ConclusionPlasma biomarkers can provide valuable predictive information about the presence of AD pathology, which can facilitate earlier recognition and diagnosis of AD. Incorporation of demographic and cognitive variables along with plasma variables in multivariable models will improve prediction accuracy of AD pathology, specifically Braak staging and ADNC.

Abstract Background Easily accessible and self‐administered cognitive assessments that can aide early identification of individuals at risk for Alzheimer’s disease (AD) dementia are critical for timely intervention. Mayo Test Drive (MTD) is a remote, self‐administered, multi‐device compatible, web‐based cognitive assessment (Stricker et al., 2022). We investigated continuous associations between MTD and amyloid and tau PET biomarkers and compared the strength of association to PET biomarkers and in‐person cognitive measures. Method 556 adults from the Mayo Clinic Study of Aging participated (mean age = 70.6; mean education = 16.0 years; 50.7% female; 97.5% white). Participants were predominantly cognitively unimpaired (95.3%; 4.7% had a consensus diagnosis of mild cognitive impairment). Participants completed (1) brain amyloid and tau PET scans, (2) in‐person neuropsychological assessment including Mayo Preclinical Alzheimer’s disease Cognitive Composite (Mayo‐PACC) and Global‐z, and (3) remote MTD comprised of the Stricker Learning Span (SLS) and the Symbols test, which are combined into an MTD composite. Multiple regressions adjusted for age, sex, and education queried associations between imaging biomarkers [amyloid meta‐region of interest (ROI), tau meta‐ROI, and tau entorhinal cortex (EC)‐ROI)] and composite and subtest scores from remote and in‐person measures. Davidson‐Mackinnon J‐test compared R‐squared values of models with remote versus in‐person composites. Result Lower performances on MTD composite and Mayo‐PACC were associated with higher amyloid meta‐ROI (p’s<.05; Table 1). MTD composite was also negatively associated with tau meta‐ROI and EC‐ROI (p’s<.05). For memory measures, SLS sum of trials was negatively associated with tau meta‐ROI (p = .01) and EC‐ROI (p<.001) with no associations between AVLT sum of trials and PET (p’s = .06; Table 2). Among processing speed/executive function measures, worse Trails B performance was associated with higher amyloid meta‐ROI (p<.001). Symbols and Digit Symbol Coding were not associated with PET (p’s = .29). A multivariable model with MTD explained significantly less variance (p = .04) for amyloid meta‐ROI (adjusted R‐squared = 0.165) than a multivariable model with Mayo‐PACC (adjusted R‐squared = 0.176); remote and in‐person composite measures were comparable for other associations (p’s>.05). Conclusion MTD is associated with continuous measures of amyloid and tau PET biomarkers of AD, demonstrating criterion validity for MTD. Several associations were comparable to relationships between gold‐standard in‐person neuropsychological tests and imaging biomarkers.