Citations

AbstractBackgroundStudies of aging and dementia often acquire brain MRI and PET images, then de‐identify and share these widely with other researchers. It is known that research participants can potentially be re‐identified from brain MRI and CT using face recognition, but this has not been tested with PET imaging.MethodWe generated face reconstruction images of 182 volunteers using their existing amyloid (PiB), tau (FTP), and FDG PET scans from clinical GE PET/CT scanners, and we measured how often commercial automatic face recognition (Microsoft Azure) correctly matched these reconstructions with photographs of their faces. We additionally compared these match rates with face reconstructions from CT (from PET/CT) and MRI. When testing face recognition from PET scans, CT was used only during PET reconstruction, not for face recognition. We also validated our updated mri_reface software, originally developed to de‐identify (“de‐face”) faces in MRI, with new support for PET images. We measured face recognition rates before and after applying mri_reface to PET, CT, and MRI. In addition, we measured effects of de‐facing on biomarker measurements (global and regional SUVR) using 244 ADNI Florbetapir (FBP) amyloid PET scans with two automated pipelines: FreeSurfer 6.0 (PETSurfer) and an in‐house SPM12‐based pipeline.ResultRates of successful face recognition with PET ranged from 32‐41% (Table 1) across tracers. These were lower than MRI (98%) and CT (78%) but still concerning for participant privacy. Using mri_reface reduced face recognition to 0‐3.5% for PET, 5% for CT, and 8% for MRI. mri_reface had little effect on global amyloid SUVR measurements from ADNI PET images (Figure 1). ICC values between original and de‐faced images were 1.00 with biases and median absolute differences < 0.5%, across both pipelines. Effects on individual regions all had ICC values > 0.98 with biases and median absolute differences < 2%.ConclusionFace reconstructions from PET images have sufficient quality for potential re‐identification using publicly available face recognition technology. Research studies should consider using face de‐identification (de‐facing) software on PET images in addition to CT and structural MRI. Our free, automatic mri_reface software greatly reduced the potential for face recognition without substantially affecting SUVR measurements.

AbstractBackgroundPhysical and cognitive activities are associated with decreased risk of mild cognitive impairment and dementia. However, the longitudinal associations between physical and cognitive activities with trajectories of Alzheimer’s disease (AD) neuroimaging biomarkers among older adults free of dementia remain unclear.MethodWe conducted a longitudinal study derived from the population‐based Mayo Clinic Study of Aging, including individuals aged ≥ 50 years who were free of dementia. Participants had information on physical and cognitive activity engagement in midlife (ages 30‐45 for participants aged 50‐69; ages 50‐65 for participants aged ≥ 70) and late‐life (12 months prior to baseline assessment), and AD biomarker assessments. We calculated linear mixed‐effect models to examine the association between baseline physical and cognitive activity composite scores for mid‐ and late‐life, and trajectories for individual yearly change in amyloid deposition (measured by PiB‐PET), tau burden (measured by Tau‐PET), regional glucose hypometabolism (measured by FDG‐PET), cortical thickness (measured by MRI), and white matter hyper intensities (WMH, measured by FLAIR‐MRI). The models were adjusted for age, sex, and APOE ɛ4 carrier status.ResultThe sample included 2755 persons (52% males; 2462 cognitively unimpaired, 293 with MCI). The mean [SD] age was 72.7 [9.8] years; 774 participants were APOE ɛ4 carriers. Although, overall, participants showed an increase in amyloid deposition, tau burden, WMH, and a decrease in glucose metabolism and cortical thickness over time, those with higher late‐life physical activity experienced less pronounced increase in Tau‐PET SUVR over time [estimate for late‐life physical activity with time interaction: ‐0.002; 95% CI ‐0.004, ‐0.000; p = 0.034]. In addition, participants with higher cognitive activities in midlife [est. for midlife cognitive activity with time interaction: 0.001; 95% CI 0.000, 0.003; p = 0.026] and late‐life [est. for late‐life cognitive activity with time interaction 0.002; 95% CI 0.001, 0.003; p = 0.005] had less pronounced decrease in FDG‐PET SUVR over time. There were no further significant interactions.ConclusionPreliminary findings suggest significant associations between 1) late‐life physical activity and less tauopathy, and 2) cognitive activities in mid‐ and late‐life with less synaptic dysfunction over time. Further research is needed to validate study findings.

AbstractBackgroundMayo Test Drive (MTD): Test Development through Rapid Iteration, Validation and Expansion, is a web‐based platform optimized for remote self‐administered assessment that includes a computer adaptive word list memory test (Stricker Learning Span; SLS) and a measure of processing speed (Symbols Test). Study aims were to determine 1) convergent validity of MTD through correlations with in‐person neuropsychological measures and 2) criterion validity of MTD by qualitatively comparing the ability of Mayo Test Drive and in‐person administered neuropsychological measures to differentiate biomarker‐defined groups, independent of clinical diagnosis.MethodsMayo Clinic Study of Aging participants (N = 282) completed a brief remote cognitive assessment (mean age = 74, SD = 12; 262 CU and 20 MCI per consensus conference diagnosis; see Table 1). A subset of participants had brain amyloid and/or brain tau PET scans available within 3 years; overlapping groups were formed for 1) those on the Alzheimer’s disease (AD) continuum (A+, n = 31) or not (A‐, n = 57) and for 2) those with biological AD (A+T+, n = 19) or without any AD biomarkers (A‐T‐, n = 47). Primary outcome variables were SLS sum of trials, AVLT sum of trials, SYM Correct Trials response time, Digit Symbol Coding, MTD composite and Mayo‐PACC (AVLT sum of trials, Trails B, animals). Bivariate correlations were performed across all participants. Linear model ANOVAs were used to investigate biomarker subgroup differences; Hedge’s g effect sizes are reported.ResultsConvergent validity was supported through significant correlation (p’s < .001) of SLS and AVLT (r = .63), SYM and Coding (r = ‐.64), and MTD composite with Mayo‐PACC (r = .67); see Table 2. MTD subtests showed expected relationships with age and education (p’s < .05; Table 3). All MTD performances were significantly lower for A+ and A+T+ participants relative to A‐ or A‐T‐ (p’s<.001; Table 4). All MTD outcome measures showed a similar or greater group difference effect size relative to traditional in‐person administered neuropsychological measures for separating A+ vs. A‐ and A+T+ vs. A‐T‐ groups. See Figure 1 and Table 4.ConclusionsMTD subtests show evidence of convergent validity through strong correlations with traditional in‐person cognitive measures. Preliminary analyses suggest MTD has promise for differentiating PET biomarker groups.

AbstractBackgroundThe three most common genes responsible for the familial Frontotemporal Lobar Degeneration (f‐FTLD) are MAPT, GRN, and C9orf72, with the C9orf72 mutation being the most common cause of f‐FTLD. The ALLFTD Consortium targets to characterize f‐FTLD, including its preclinical stage, to prepare for clinical trials in FTLD. We sought to investigate the initial symptoms and clinical picture of f‐FTLD and compare them among the common pathogenic genes using longitudinal CDR® plus NACC FTLD.MethodWe longitudinally assessed the CDR® plus NACC FTLD among 292 asymptomatic participants of the ALLFTD Consortium who are family members of f‐FTLD caused by MAPT (n = 99), GRN (n = 67), or C9orf72 (n = 126) mutations, and described the detailed profiles of the CDR® plus NACC FTLD in the participants who developed Mild Behavioral and/or Cognitive Impairment (MBCI).ResultForty‐one participants (17 MAPT, 11 GRN, 13 C9orf72) developed MBCI during follow‐up visits . Age at the diagnosis of MBCI was 52.3±13.0 years (GRN 63.2±11.8, MAPT 43.5±8.6, C9orf72 54.7±10.7). Frequencies of abnormal rating on the eight CDR® plus NACC FTLD domains at the time of MBCI diagnosis were: “Memory” 44% (MAPT 47%, GRN 55%, C9orf72 31%); “Orientation” 7% (MAPT 6%, GRN 9%, C9orf72 8%); “Judgment and Problem Solving” 39% (MAPT 41%, GRN 55%, C9orf72 23%); “Community Affairs” 22% (MAPT 24%, GRN 18%, C9orf72 23%); “Home and Hobbies” 22% (MAPT 35%, GRN 0%, C9orf72 23%); “Personal Care” 0%; “Behavior/Comportment/Personality” 51% (MAPT 59%, GRN 55%, C9orf72 38%); “Language” 29% (MAPT 18%, GRN 55%, C9orf72 23%).ConclusionInitial symptoms and clinical picture of f‐FTLD in MBCI stage differ among responsible FTLD genes, which could be useful in detecting very early features of f‐FTLD in pathogenic mutation carriers.

AbstractBackgroundRemote assessment tools offer significant promise for aiding early detection of cognitive impairment. Mayo Test Drive (MTD): Test Development through Rapid Iteration, Validation and Expansion, is a web‐based platform for remote self‐administered assessment that includes a computer adaptive word list memory test (Stricker Learning Span; SLS) and a measure of processing speed (Symbols Test). We examined the diagnostic accuracy of the SLS and a MTD composite (SLS max span, SLS trials 1–5 total correct, SLS delay correct, [Symbols correct item response time*‐1]) for amnestic mild cognitive impairment (aMCI). We also explored diagnostic accuracy for a broader group that included individuals with possible MCI (pMCI; see Figure 1).MethodsParticipants were recruited from the Mayo Clinic Study of Aging for this ancillary remote study. 226 were cognitively unimpaired (CU; concordant CU diagnosis by 3 independent raters). Fifty‐six participants had possible MCI (at least 1 of 3 raters indicated MCI) and 16 had a consensus diagnosis of aMCI. Primary outcome variables were SLS sum of trials, AVLT sum of trials, Symbols correct items response time, and MTD composite. Mean difference analyses used linear model ANOVAs (alpha = .05). Receiver operating characteristic (ROC) curves were applied; we derived optimal cutoff scores based on the Youden index method.ResultsBoth aMCI and possible MCI groups showed significantly lower performance than the CU group on SLS (Hedge’s g aMCI = ‐1.72, pMCI = ‐1.04), Symbols (Hedge’s g aMCI = 1.38, pMCI = 0.64), and MTD composite (Hedge’s g aMCI = ‐1.89, pMCI = ‐1.09); see Tables 1 and 2 (all p‘s<.01 even when additionally covarying age, education, sex). Total area under the curve was high for differentiating of CU vs. aMCI (MTD Composite = 0.91, SLS = 0.91) and acceptable for CU vs. pMCI (MTD Composite = 0.77, SLS = 0.77); see Table 2. Consistent with our flexible platform, a variety of device types were used to complete remote testing (32% smartphone, 12% tablet, 56% PC).ConclusionsMTD and the SLS show high diagnostic accuracy for aMCI. MTD is a flexible, brief, and easy‐to‐use remote cognitive assessment tool with great potential for scalable use in future studies seeking to maximize inclusion of individuals with aMCI in clinical trials or for cognitive screening in clinical settings.

AbstractBackgroundMayo Test Drive (MTD): Test Development through Rapid Iteration, Validation and Expansion, is a web‐based platform for self‐administered assessment for novel (Stricker Learning Span) and open access (Symbols Test) cognitive measures with previously demonstrated validity and reliability. Our study aim was to examine the usability of MTD, defined as the extent to which MTD was used to achieve the desired study goal of completing a testing session among participants willing to engage in an all‐remote study.Methods248 Mayo Clinic Study of Aging and Mayo Alzheimer’s Disease Research Center participants were invited to participate in this ancillary, uncompensated remote pilot study via email. To examine 1) willingness to participate in a remote cognitive assessment study and 2) usability of MTD, the initiation and completion rates of MTD were examined by cognitively unimpaired (CU; n = 142) and cognitively impaired (CI; n = 106) groups (see Figure 1). These rates and other factors associated with MTD feasibility were further explored across age in CU (i.e., 50–69, 70–79, and 80+ years) and specific diagnostic groups in CI (i.e., individuals with dementia, mild cognitive impairment (MCI), and possible MCI).ResultsSession completion rate (usability) was 98% across all participants who chose to participate, and, surprisingly, completion rates did not vary by age or diagnostic groups (p = .69; see Table 1). As expected, more individuals chose to participate in the CU (77%) versus CI (52%) group (p < .001). In terms of feasibility, individuals over 80 and with CI were more likely to have some contact with the study coordinator. Use of smartphones decreased with age, with most CU individuals over 70 using a personal computer (69‐73%; Table 2).ConclusionsThough the CI group was less likely to choose to participate than the CU group, out of those that did choose to participate the rate of completion was 98%. This demonstrates high usability of MTD. Study coordinator support and a flexible testing platform is critical for facilitating participation in remote testing across the aging population, and more supports and incentives may be needed for individuals with cognitive impairment to improve participation rates.

AbstractBackgroundStudies of aging and dementia often acquire brain MRI and PET images, then de‐identify and share these widely with other researchers. It is known that research participants can potentially be re‐identified from brain MRI and CT using face recognition, but this has not been tested with PET imaging.MethodWe generated face reconstruction images of 182 volunteers using their existing amyloid (PiB), tau (FTP), and FDG PET scans from clinical GE PET/CT scanners, and we measured how often commercial automatic face recognition (Microsoft Azure) correctly matched these reconstructions with photographs of their faces. We additionally compared these match rates with face reconstructions from CT (from PET/CT) and MRI. When testing face recognition from PET scans, CT was used only during PET reconstruction, not for face recognition. We also validated our updated mri_reface software, originally developed to de‐identify (“de‐face”) faces in MRI, with new support for PET images. We measured face recognition rates before and after applying mri_reface to PET, CT, and MRI. In addition, we measured effects of de‐facing on biomarker measurements (global and regional SUVR) using 244 ADNI Florbetapir (FBP) amyloid PET scans with two automated pipelines: FreeSurfer 6.0 (PETSurfer) and an in‐house SPM12‐based pipeline.ResultRates of successful face recognition with PET ranged from 32‐41% (Table 1) across tracers. These were lower than MRI (98%) and CT (78%) but still concerning for participant privacy. Using mri_reface reduced face recognition to 0‐3.5% for PET, 5% for CT, and 8% for MRI. mri_reface had little effect on global amyloid SUVR measurements from ADNI PET images (Figure 1). ICC values between original and de‐faced images were 1.00 with biases and median absolute differences < 0.5%, across both pipelines. Effects on individual regions all had ICC values > 0.98 with biases and median absolute differences < 2%.ConclusionFace reconstructions from PET images have sufficient quality for potential re‐identification using publicly available face recognition technology. Research studies should consider using face de‐identification (de‐facing) software on PET images in addition to CT and structural MRI. Our free, automatic mri_reface software greatly reduced the potential for face recognition without substantially affecting SUVR measurements.

AbstractBackgroundMayo Test Drive (MTD): Test Development through Rapid Iteration, Validation and Expansion, is a web‐based platform for self‐administered assessment for novel (Stricker Learning Span) and open access (Symbols Test) cognitive measures with previously demonstrated validity and reliability. Our study aim was to examine the usability of MTD, defined as the extent to which MTD was used to achieve the desired study goal of completing a testing session among participants willing to engage in an all‐remote study.Methods248 Mayo Clinic Study of Aging and Mayo Alzheimer’s Disease Research Center participants were invited to participate in this ancillary, uncompensated remote pilot study via email. To examine 1) willingness to participate in a remote cognitive assessment study and 2) usability of MTD, the initiation and completion rates of MTD were examined by cognitively unimpaired (CU; n = 142) and cognitively impaired (CI; n = 106) groups (see Figure 1). These rates and other factors associated with MTD feasibility were further explored across age in CU (i.e., 50‐69, 70‐79, and 80+ years) and specific diagnostic groups in CI (i.e., individuals with dementia, mild cognitive impairment (MCI), and possible MCI).ResultsSession completion rate (usability) was 98% across all participants who chose to participate, and, surprisingly, completion rates did not vary by age or diagnostic groups (p =.69; see Table 1). As expected, more individuals chose to participate in the CU (77%) versus CI (52%) group (p < .001). In terms of feasibility, individuals over 80 and with CI were more likely to have some contact with the study coordinator. Use of smartphones decreased with age, with most CU individuals over 70 using a personal computer (69‐73%; Table 2).ConclusionsThough the CI group was less likely to choose to participate than the CU group, out of those that did choose to participate the rate of completion was 98%. This demonstrates high usability of MTD. Study coordinator support and a flexible testing platform is critical for facilitating participation in remote testing across the aging population, and more supports and incentives may be needed for individuals with cognitive impairment to improve participation rates.

AbstractBackgroundKidney transplant recipients are at increased risk for invasive aspergillosis (IA), a disease with poor outcomes and substantial economic burden. We aimed to determine risk factors for posttransplant IA by using a national database and to assess the association of IA with mortality and allograft failure.MethodsUsing the United States Renal Data System database, we performed a retrospective case-control study of patients who underwent kidney transplant from 1998 through 2017. To evaluate risk factors for IA, we performed conditional logistic regression analysis by comparing characteristics between IA-infected patients and their matched uninfected controls. We performed Cox regression analysis to evaluate the effects of IA on mortality and death-censored allograft failure.ResultsWe matched 359 patients with IA to 1436 uninfected controls (1:4). IA was diagnosed at a median of 22.5 months (interquartile range, 5.4–85.2 months) after kidney transplant. Risk factors for IA were Black/African American race, duration of pretransplant hemodialysis, higher Elixhauser Comorbidity Index score, weight loss, chronic pulmonary disease, need for early posttransplant hemodialysis, and a history of cytomegalovirus infection. Receiving an allograft from a living donor was protective against IA. IA was a strong independent predictor of 1-year mortality (adjusted hazard ratio [aHR], 5.02 [95% confidence interval {CI}, 3.58–7.04], P &lt; .001). Additionally, IA was associated with 1-year allograft failure (aHR, 3.37 [95% CI, 1.96–5.77], P &lt; .001).ConclusionsOur findings emphasize the importance of timely transplant to mitigate the risk of posttransplant IA. An individualized approach to disease prevention is essential to decrease mortality and allograft failure.