Clinicoradiological and neuropathological evaluation of primary progressive aphasia.

BACKGROUND: Primary progressive aphasia (PPA) defines a group of neurodegenerative disorders characterised by language decline. Three PPA variants correlate with distinct underlying pathologies: semantic variant PPA (svPPA) with transactive response DNA-binding protein of 43 kD (TDP-43) proteinopathy, agrammatic variant PPA (agPPA) with tau deposition and logopenic variant PPA (lvPPA) with Alzheimer's disease (AD). Our objectives were to differentiate PPA variants using clinical and neuroimaging features, assess progression and evaluate structural MRI and a novel 18-F fluorodeoxyglucose positron emission tomography (FDG-PET) image decomposition machine learning algorithm for neuropathology prediction. METHODS: We analysed 82 autopsied patients diagnosed with PPA from 1998 to 2022. Clinical histories, language characteristics, neuropsychological results and brain imaging were reviewed. A machine learning framework using a k-nearest neighbours classifier assessed FDG-PET scans from 45 patients compared with a large reference database. RESULTS: PPA variant distribution: 35 lvPPA (80% AD), 28 agPPA (89% tauopathy) and 18 svPPA (72% frontotemporal lobar degeneration-TAR DNA-binding protein (FTLD-TDP)). Apraxia of speech was associated with 4R-tauopathy in agPPA, while pure agrammatic PPA without apraxia was linked to 3R-tauopathy. Longitudinal data revealed language dysfunction remained the predominant deficit for patients with lvPPA, agPPA evolved to corticobasal or progressive supranuclear palsy syndrome (64%) and svPPA progressed to behavioural variant frontotemporal dementia (44%). agPPA-4R-tauopathy exhibited limited pre-supplementary motor area atrophy, lvPPA-AD displayed temporal atrophy extending to the superior temporal sulcus and svPPA-FTLD-TDP had severe temporal pole atrophy. The FDG-PET-based machine learning algorithm accurately predicted clinical diagnoses and underlying pathologies. CONCLUSIONS: Distinguishing 3R-taupathy and 4R-tauopathy in agPPA may rely on apraxia of speech presence. Additional linguistic and clinical features can aid neuropathology prediction. Our data-driven brain metabolism decomposition approach effectively predicts underlying neuropathology.

Fracture Risk Among Living Kidney Donors 25 Years After Donation.

Importance: Living kidney donors may have an increased risk of fractures due to reductions in kidney mass, lower concentrations of serum 1,25-dihydroxyvitamin D, and secondary increases in serum parathyroid hormone. Objective: To compare the overall and site-specific risk of fractures among living kidney donors with strictly matched controls from the general population who would have been eligible to donate a kidney but did not do so. Design, Setting, and Participants: This survey study was conducted between December 1, 2021, and July 31, 2023. A total of 5065 living kidney donors from 3 large transplant centers in Minnesota were invited to complete a survey about their bone health and history of fractures, and 16 156 population-based nondonor controls without a history of comorbidities that would have precluded kidney donation were identified from the Rochester Epidemiology Project and completed the same survey. A total of 2132 living kidney donors and 2014 nondonor controls responded to the survey. Statistical analyses were performed from May to August 2023. Exposure: Living kidney donation. Main Outcomes and Measures: The rates of overall and site-specific fractures were compared between living kidney donors and controls using standardized incidence ratios (SIRs). Results: At the time of survey, the 2132 living kidney donors had a mean (SD) age of 67.1 (8.9) years and included 1245 women (58.4%), and the 2014 controls had a mean (SD) age of 68.6 (7.9) years and included 1140 women (56.6%). The mean (SD) time between donation or index date and survey date was 24.2 (10.4) years for donors and 27.6 (10.7) years for controls. The overall rate of fractures among living kidney donors was significantly lower than among controls (SIR, 0.89; 95% CI, 0.81-0.97). However, there were significantly more vertebral fractures among living kidney donors than among controls (SIR, 1.42; 95% CI, 1.05-1.83). Conclusions and Relevance: This survey study found a reduced rate of overall fractures but an excess of vertebral fractures among living kidney donors compared with controls after a mean follow-up of 25 years. Treatment of excess vertebral fractures with dietary supplements such as vitamin D3 may reduce the numbers of vertebral fractures and patient morbidity.

The Effect of Implant Density on Adolescent Idiopathic Scoliosis Fusion: Results of the Minimize Implants Maximize Outcomes Randomized Clinical Trial.

BACKGROUND: Severe adolescent idiopathic scoliosis (AIS) can be treated with instrumented fusion, but the number of anchors needed for optimal correction is controversial. METHODS: We conducted a multicenter, randomized study that included patients undergoing spinal fusion for single thoracic curves between 45° and 65°, the most common form of operatively treated AIS. Of the 211 patients randomized, 108 were assigned to a high-density screw pattern and 103, to a low-density screw pattern. Surgeons were instructed to use ≥1.8 implants per spinal level fused for patients in the high-implant-density group or ≤1.4 implants per spinal level fused for patients in the low-implant-density group. The primary outcome measure was the percent correction of the coronal curve at the 2-year follow-up. The power analysis for this trial required 174 patients to show equivalence, defined as a 95% confidence interval (CI) within a ±10% correction margin with a probability of 90%. RESULTS: In the intention-to-treat analysis, the mean percent correction of the coronal curve was equivalent between the high-density and low-density groups at the 2-year follow-up (67.6% versus 65.7%; difference, -1.9% [95% CI: -6.1%, 2.2%]). In the per-protocol cohorts, the mean percent correction of the coronal curve was also equivalent between the 2 groups at the 2-year follow-up (65.0% versus 66.1%; difference, 1.1% [95% CI: -3.0%, 5.2%]). A total of 6 patients in the low-density group and 5 patients in the high-density group required reoperation (p = 1.0). CONCLUSIONS: In the setting of spinal fusion for primary thoracic AIS curves between 45° and 65°, the percent coronal curve correction obtained with use of a low-implant-density construct and that obtained with use of a high-implant-density construct were equivalent. LEVEL OF EVIDENCE: Therapeutic Level I . See Instructions for Authors for a complete description of levels of evidence.

Stricker Learning Span criterion validity: a remote self-administered multi-device compatible digital word list memory measure shows similar ability to differentiate amyloid and tau PET-defined biomarker groups as in-person Auditory Verbal Learning Test.

OBJECTIVE: The Stricker Learning Span (SLS) is a computer-adaptive digital word list memory test specifically designed for remote assessment and self-administration on a web-based multi-device platform (Mayo Test Drive). We aimed to establish criterion validity of the SLS by comparing its ability to differentiate biomarker-defined groups to the person-administered Rey's Auditory Verbal Learning Test (AVLT). METHOD: Participants (N = 353; mean age = 71, SD = 11; 93% cognitively unimpaired [CU]) completed the AVLT during an in-person visit, the SLS remotely (within 3 months) and had brain amyloid and tau PET scans available (within 3 years). Overlapping groups were formed for 1) those on the Alzheimer's disease (AD) continuum (amyloid PET positive, A+, n = 125) or not (A-, n = 228), and those with biological AD (amyloid and tau PET positive, A+T+, n = 55) vs no evidence of AD pathology (A-T-, n = 195). Analyses were repeated among CU participants only. RESULTS: The SLS and AVLT showed similar ability to differentiate biomarker-defined groups when comparing AUROCs (p's > .05). In logistic regression models, SLS contributed significantly to predicting biomarker group beyond age, education, and sex, including when limited to CU participants. Medium (A- vs A+) to large (A-T- vs A+T+) unadjusted effect sizes were observed for both SLS and AVLT. Learning and delay variables were similar in terms of ability to separate biomarker groups. CONCLUSIONS: Remotely administered SLS performed similarly to in-person-administered AVLT in its ability to separate biomarker-defined groups, providing evidence of criterion validity. Results suggest the SLS may be sensitive to detecting subtle objective cognitive decline in preclinical AD.

Mayo normative studies: regression-based normative data for ages 30-91 years with a focus on the Boston Naming Test, Trail Making Test and Category Fluency.

OBJECTIVE: Normative neuropsychological data are essential for interpretation of test performance in the context of demographic factors. The Mayo Normative Studies (MNS) aim to provide updated normative data for neuropsychological measures administered in the Mayo Clinic Study of Aging (MCSA), a population-based study of aging that randomly samples residents of Olmsted County, Minnesota, from age- and sex-stratified groups. We examined demographic effects on neuropsychological measures and validated the regression-based norms in comparison to existing normative data developed in a similar sample. METHOD: The MNS includes cognitively unimpaired adults ≥30 years of age (n = 4,428) participating in the MCSA. Multivariable linear regressions were used to determine demographic effects on test performance. Regression-based normative formulas were developed by first converting raw scores to normalized scaled scores and then regressing on age, age2, sex, and education. Total and sex-stratified base rates of low scores (T < 40) were examined in an older adult validation sample and compared with Mayo's Older Americans Normative Studies (MOANS) norms. RESULTS: Independent linear regressions revealed variable patterns of linear and/or quadratic effects of age (r2 = 6-27% variance explained), sex (0-13%), and education (2-10%) across measures. MNS norms improved base rates of low performance in the older adult validation sample overall and in sex-specific patterns relative to MOANS. CONCLUSIONS: Our results demonstrate the need for updated norms that consider complex demographic associations on test performance and that specifically exclude participants with mild cognitive impairment from the normative sample.

Association of Cerebrovascular Imaging Biomarkers, Depression, and Anxiety, with Mild Cognitive Impairment.

The study included 1,738 Mayo Clinic Study of Aging participants (≥50 years old; 1,460 cognitively unimpaired and 278 with mild cognitive impairment (MCI)) and examined the cross-sectional association between cerebrovascular (CVD) imaging biomarkers (e.g., white matter hyperintensities (WMH), infarctions) and Beck Depression Inventory-II (BDI-II) and Beck Anxiety Inventory (BAI) scores, as well as their association with MCI. High (abnormal) WMH burden was significantly associated with having BDI-II>13 and BAI > 7 scores, and both (CVD imaging biomarkers and depression/anxiety) were significantly associated with MCI when included simultaneously in the model, suggesting that both were independently associated with the odds of MCI.

Effects of de-facing software mri_reface on utility of imaging biomarkers used in Alzheimer's disease research.

Brain imaging research studies increasingly use "de-facing" software to remove or replace facial imagery before public data sharing. Several works have studied the effects of de-facing software on brain imaging biomarkers by directly comparing automated measurements from unmodified vs de-faced images, but most research brain images are used in analyses of correlations with cognitive measurements or clinical statuses, and the effects of de-facing on these types of imaging-to-cognition correlations has not been measured. In this work, we focused on brain imaging measures of amyloid (A), tau (T), neurodegeneration (N), and vascular (V) measures used in Alzheimer's Disease (AD) research. We created a retrospective sample of participants from three age- and sex-matched clinical groups (cognitively unimpaired, mild cognitive impairment, and AD dementia, and we performed region- and voxel-wise analyses of: hippocampal volume (N), white matter hyperintensity volume (V), amyloid PET (A), and tau PET (T) measures, each from multiple software pipelines, on their ability to separate cognitively defined groups and their degrees of correlation with age and Clinical Dementia Rating (CDR)-Sum of Boxes (CDR-SB). We performed each of these analyses twice: once with unmodified images and once with images de-faced with leading de-facing software mri_reface, and we directly compared the findings and their statistical strengths between the original vs. the de-faced images. Analyses with original and with de-faced images had very high agreement. There were no significant differences between any voxel-wise comparisons. Among region-wise comparisons, only three out of 55 correlations were significantly different between original and de-faced images, and these were not significant after correction for multiple comparisons. Overall, the statistical power of the imaging data for AD biomarkers was almost identical between unmodified and de-faced images, and their analyses results were extremely consistent.

Plasma-derived biomarkers of Alzheimer's disease and neuropsychiatric symptoms: A community-based study.

INTRODUCTION: We examined associations between plasma-derived biomarkers of Alzheimer's disease (AD) and neuropsychiatric symptoms (NPS) in community-dwelling older adults. METHODS: Cross-sectional study involving 1005 persons ≥50 years of age (mean 74 years, 564 male, 118 cognitively impaired), who completed plasma-derived biomarker (amyloid beta 42 [Aß42]/Aß40, phosphorylated tau 181 [p-tau181], p-tau217, total tau [t-tau], neurofilament light [NfL]), and NPS assessment. RESULTS: P-tau181 (odds ratio [OR] 2.06, 95% confidence interval [CI] 1.41-3.00, p < 0.001), p-tau217 (OR 1.70, 95% CI 1.10-2.61, p = 0.016), and t-tau (OR 1.44, 95% CI 1.08-1.92, p = 0.012) were associated with appetite change. We also found that p-tau181 and p-tau217 were associated with increased symptoms of agitation (OR 1.93, 95% CI 1.20-3.11, p = 0.007 and OR 2.04, 95% CI 1.21-3.42, p = 0.007, respectively), and disinhibition (OR 2.39, 95% CI 1.45-3.93, p = 0.001 and OR 2.30, 95% CI 1.33-3.98, p = 0.003, respectively). Aß42/Aß40 and NfL were not associated with NPS. CONCLUSION: Higher plasma-derived p-tau181 and p-tau217 levels are associated with increased symptoms of appetite change, agitation, and disinhibition. These findings may support the validity of plasma tau biomarkers for predicting behavioral symptoms that often accompany cognitive impairment. HIGHLIGHTS: We studied 1005 community-dwelling persons aged ≥ 50 yearsHigher plasma tau levels are associated with increased neuropsychiatric symptomsAß42/Aß40 and NfL are not associated with neuropsychiatric symptomsClinicians should treat neuropsychiatric symptoms in persons with high plasma-derived tau.

Eligibility for Anti-Amyloid Treatment in a Population-Based Study of Cognitive Aging.

BACKGROUND AND OBJECTIVES: Treatment options for Alzheimer disease (AD) are limited and have focused mainly on symptomatic therapy and improving quality of life. Recently, lecanemab, an anti-ß-amyloid monoclonal antibody (mAb), received accelerated approval by the US Food and Drug Administration for treatment in the early stages of biomarker-confirmed symptomatic AD. An additional anti-ß-amyloid mAb, aducanumab, was approved in 2021, and more will potentially become available in the near future. Research on the applicability and generalizability of the anti-ß-amyloid mAb eligibility criteria on adults with biomarkers available in the general population has been lacking. The study's primary aim was to apply the clinical trial eligibility criteria for lecanemab treatment to participants with early AD of the population-based Mayo Clinic Study of Aging (MCSA) and assess the generalizability of anti-amyloid treatment. The secondary aim of this study was to apply the clinical trial eligibility criteria for aducanumab treatment in MCSA participants. METHODS: This cross-sectional study aimed to apply the clinical trial eligibility criteria for lecanemab and aducanumab treatment to participants with early AD of the population-based MCSA and assess the generalizability of anti-amyloid treatment. RESULTS: Two hundred thirty-seven MCSA participants (mean age [SD] 80.9 [6.3] years, 54.9% male, and 97.5% White) with mild cognitive impairment (MCI) or mild dementia and increased brain amyloid burden by PiB PET comprised the study sample. Lecanemab trial's inclusion criteria reduced the study sample to 112 (47.3% of 237) participants. The trial's exclusion criteria further narrowed the number of potentially eligible participants to 19 (overall 8% of 237). Modifying the eligibility criteria to include all participants with MCI (instead of applying additional cognitive criteria) resulted in 17.4% of participants with MCI being eligible for lecanemab treatment. One hundred four participants (43.9% of 237) fulfilled the aducanumab clinical trial's inclusion criteria. The aducanumab trial's exclusion criteria further reduced the number of available participants, narrowing those eligible to 12 (5.1% of 237). Common exclusions were related to other chronic conditions and neuroimaging findings. DISCUSSION: Findings estimate the limited eligibility in typical older adults with cognitive impairment for anti-ß-amyloid mAbs.

Clinicoradiologic and Neuropathologic Evaluation of Corticobasal Syndrome.

BACKGROUND AND OBJECTIVES: Corticobasal syndrome (CBS) is a clinical phenotype characterized by asymmetric parkinsonism, rigidity, myoclonus, and apraxia. Originally believed secondary to corticobasal degeneration (CBD), mounting clinicopathologic studies have revealed heterogenous neuropathologies. The objectives of this study were to determine the pathologic heterogeneity of CBS, the clinicoradiologic findings associated with different underlying pathologies causing CBS, and the positive predictive value (PPV) of current diagnostic criteria for CBD among patients with a CBS. METHODS: Clinical data, brain MRI, and neuropathologic data of patients followed at Mayo Clinic and diagnosed with CBS antemortem were reviewed according to neuropathology category at autopsy. RESULTS: The cohort consisted of 113 patients with CBS, 61 (54%) female patients. Mean ± SD disease duration was 7 ± 3.7 years; mean ± SD age at death was 70.5 ± 9.1 years. The primary neuropathologic diagnoses were 43 (38%) CBD, 27 (24%) progressive supranuclear palsy (PSP), 17 (15%) Alzheimer disease (AD), 10 (9%) frontotemporal lobar degeneration (FTLD) with TAR DNA-binding protein 43 (TDP) inclusions, 7 (6%) diffuse Lewy body disease (DLBD)/AD, and 9 (8%) with other diagnoses. Patients with CBS-AD or CBS-DLBD/AD were youngest at death (median [interquartile range]: 64 [13], 64 [11] years) while CBS-PSP were oldest (77 [12.5] years, p = 0.024). Patients with CBS-DLBD/AD had the longest disease duration (9 [6] years), while CBS-other had the shortest (3 [4.25] years, p = 0.04). Posterior cortical signs and myoclonus were more characteristic of patients with CBS-AD and patients with CBS-DLBD/AD. Patients with CBS-DLBD/AD displayed more features of Lewy body dementia. Voxel-based morphometry revealed widespread cortical gray matter loss characteristic of CBS-AD, while CBS-CBD and CBS-PSP predominantly involved premotor regions with greater amount of white matter loss. Patients with CBS-DLBD/AD showed atrophy in a focal parieto-occipital region, and patients with CBS-FTLD-TDP had predominant prefrontal cortical loss. Patients with CBS-PSP had the lowest midbrain/pons ratio (p = 0.012). Of 67 cases meeting clinical criteria for possible CBD at presentation, 27 were pathology-proven CBD, yielding a PPV of 40%. DISCUSSION: A variety of neurodegenerative disorders can be identified in patients with CBS, but clinical and regional imaging differences aid in predicting underlying neuropathology. PPV analysis of the current CBD diagnostic criteria revealed suboptimal performance. Biomarkers adequately sensitive and specific for CBD are needed.

Plasma biomarkers for prediction of Alzheimer's disease neuropathologic change.

While plasma biomarkers for Alzheimer's disease (AD) are increasingly being evaluated for clinical diagnosis and prognosis, few population-based autopsy studies have evaluated their utility in the context of predicting neuropathological changes. Our goal was to investigate the utility of clinically available plasma markers in predicting Braak staging, neuritic plaque score, Thal phase, and overall AD neuropathological change (ADNC).We utilized a population-based prospective study of 350 participants with autopsy and antemortem plasma biomarker testing using clinically available antibody assay (Quanterix) consisting of Aß42/40 ratio, p-tau181, GFAP, and NfL. We utilized a variable selection procedure in cross-validated (CV) logistic regression models to identify the best set of plasma predictors along with demographic variables, and a subset of neuropsychological tests comprising the Mayo Clinic Preclinical Alzheimer Cognitive Composite (Mayo-PACC). ADNC was best predicted with plasma GFAP, NfL, p-tau181 biomarkers along with APOE ε4 carrier status and Mayo-PACC cognitive score (CV AUC = 0.798). Braak staging was best predicted using plasma GFAP, p-tau181, and cognitive scores (CV AUC = 0.774). Neuritic plaque score was best predicted using plasma Aß42/40 ratio, p-tau181, GFAP, and NfL biomarkers (CV AUC = 0.770). Thal phase was best predicted using GFAP, NfL, p-tau181, APOE ε4 carrier status and Mayo-PACC cognitive score (CV AUC = 0.754). We found that GFAP and p-tau provided non-overlapping information on both neuritic plaque and Braak stage scores whereas Aß42/40 and NfL were mainly useful for prediction of neuritic plaque scores. Separating participants by cognitive status improved predictive performance, particularly when plasma biomarkers were included. Plasma biomarkers can differentially inform about overall ADNC pathology, Braak staging, and neuritic plaque score when combined with demographics and cognitive variables and have significant utility for earlier detection of AD.

A face-off of MRI research sequences by their need for de-facing.

It is now widely known that research brain MRI, CT, and PET images may potentially be re-identified using face recognition, and this potential can be reduced by applying face-deidentification ("de-facing") software. However, for research MRI sequences beyond T1-weighted (T1-w) and T2-FLAIR structural images, the potential for re-identification and quantitative effects of de-facing are both unknown, and the effects of de-facing T2-FLAIR are also unknown. In this work we examine these questions (where applicable) for T1-w, T2-w, T2*-w, T2-FLAIR, diffusion MRI (dMRI), functional MRI (fMRI), and arterial spin labelling (ASL) sequences. Among current-generation, vendor-product research-grade sequences, we found that 3D T1-w, T2-w, and T2-FLAIR were highly re-identifiable (96-98%). 2D T2-FLAIR and 3D multi-echo GRE (ME-GRE) were also moderately re-identifiable (44-45%), and our derived T2* from ME-GRE (comparable to a typical 2D T2*) matched at only 10%. Finally, diffusion, functional and ASL images were each minimally re-identifiable (0-8%). Applying de-facing with mri_reface version 0.3 reduced successful re-identification to ≤8%, while differential effects on popular quantitative pipelines for cortical volumes and thickness, white matter hyperintensities (WMH), and quantitative susceptibility mapping (QSM) measurements were all either comparable with or smaller than scan-rescan estimates. Consequently, high-quality de-facing software can greatly reduce the risk of re-identification for identifiable MRI sequences with only negligible effects on automated intracranial measurements. The current-generation echo-planar and spiral sequences (dMRI, fMRI, and ASL) each had minimal match rates, suggesting that they have a 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 full-face scan coverage, or if newer developments reduce the current levels of artifacts and distortion around the face.

At-Home Use of a Pregnancy-Specific Zone-MPC Closed-Loop System for Pregnancies Complicated by Type 1 Diabetes: A Single-Arm, Observational Multicenter Study.

OBJECTIVE: There are no commercially available hybrid closed-loop insulin delivery systems customized to achieve pregnancy-specific glucose targets in the U.S. This study aimed to evaluate the feasibility and performance of at-home use of a zone model predictive controller-based closed-loop insulin delivery system customized for pregnancies complicated by type 1 diabetes (CLC-P). RESEARCH DESIGN AND METHODS: Pregnant women with type 1 diabetes using insulin pumps were enrolled in the second or early third trimester. After study sensor wear collecting run-in data on personal pump therapy and 2 days of supervised training, participants used CLC-P targeting 80-110 mg/dL during the day and 80-100 mg/dL overnight running on an unlocked smartphone at home. Meals and activities were unrestricted throughout the trial. The primary outcome was the continuous glucose monitoring percentage of time in the target range 63-140 mg/dL versus run-in. RESULTS: Ten participants (HbA1c 5.8 ± 0.6%) used the system from mean gestational age of 23.7 ± 3.5 weeks. Mean percentage time in range increased 14.1 percentage points, equivalent to 3.4 h per day, compared with run-in (run-in 64.5 ± 16.3% versus CLC-P 78.6 ± 9.2%; P = 0.002). During CLC-P use, there was significant decrease in both time over 140 mg/dL (P = 0.033) and the hypoglycemic ranges of less than 63 mg/dL and 54 mg/dL (P = 0.037 for both). Nine participants exceeded consensus goals of above 70% time in range during CLC-P use. CONCLUSIONS: The results show that the extended use of CLC-P at home until delivery is feasible. Larger, randomized studies are needed to further evaluate system efficacy and pregnancy outcomes.

Default mode network failure and neurodegeneration across aging and amnestic and dysexecutive Alzheimer's disease.

From a complex systems perspective, clinical syndromes emerging from neurodegenerative diseases are thought to result from multiscale interactions between aggregates of misfolded proteins and the disequilibrium of large-scale networks coordinating functional operations underpinning cognitive phenomena. Across all syndromic presentations of Alzheimer's disease, age-related disruption of the default mode network is accelerated by amyloid deposition. Conversely, syndromic variability may reflect selective neurodegeneration of modular networks supporting specific cognitive abilities. In this study, we leveraged the breadth of the Human Connectome Project-Aging cohort of non-demented individuals (N = 724) as a normative cohort to assess the robustness of a biomarker of default mode network dysfunction in Alzheimer's disease, the network failure quotient, across the aging spectrum. We then examined the capacity of the network failure quotient and focal markers of neurodegeneration to discriminate patients with amnestic (N = 8) or dysexecutive (N = 10) Alzheimer's disease from the normative cohort at the patient level, as well as between Alzheimer's disease phenotypes. Importantly, all participants and patients were scanned using the Human Connectome Project-Aging protocol, allowing for the acquisition of high-resolution structural imaging and longer resting-state connectivity acquisition time. Using a regression framework, we found that the network failure quotient related to age, global and focal cortical thickness, hippocampal volume, and cognition in the normative Human Connectome Project-Aging cohort, replicating previous results from the Mayo Clinic Study of Aging that used a different scanning protocol. Then, we used quantile curves and group-wise comparisons to show that the network failure quotient commonly distinguished both dysexecutive and amnestic Alzheimer's disease patients from the normative cohort. In contrast, focal neurodegeneration markers were more phenotype-specific, where the neurodegeneration of parieto-frontal areas associated with dysexecutive Alzheimer's disease, while the neurodegeneration of hippocampal and temporal areas associated with amnestic Alzheimer's disease. Capitalizing on a large normative cohort and optimized imaging acquisition protocols, we highlight a biomarker of default mode network failure reflecting shared system-level pathophysiological mechanisms across aging and dysexecutive and amnestic Alzheimer's disease and biomarkers of focal neurodegeneration reflecting distinct pathognomonic processes across the amnestic and dysexecutive Alzheimer's disease phenotypes. These findings provide evidence that variability in inter-individual cognitive impairment in Alzheimer's disease may relate to both modular network degeneration and default mode network disruption. These results provide important information to advance complex systems approaches to cognitive aging and degeneration, expand the armamentarium of biomarkers available to aid diagnosis, monitor progression and inform clinical trials.

Neuropsychiatric Symptoms and Commonly Used Biomarkers of Alzheimer's Disease: A Literature Review from a Machine Learning Perspective.

There is a growing interest in the application of machine learning (ML) in Alzheimer's disease (AD) research. However, neuropsychiatric symptoms (NPS), frequent in subjects with AD, mild cognitive impairment (MCI), and other related dementias have not been analyzed sufficiently using ML methods. To portray the landscape and potential of ML research in AD and NPS studies, we present a comprehensive literature review of existing ML approaches and commonly studied AD biomarkers. We conducted PubMed searches with keywords related to NPS, AD biomarkers, machine learning, and cognition. We included a total of 38 articles in this review after excluding some irrelevant studies from the search results and including 6 articles based on a snowball search from the bibliography of the relevant studies. We found a limited number of studies focused on NPS with or without AD biomarkers. In contrast, multiple statistical machine learning and deep learning methods have been used to build predictive diagnostic models using commonly known AD biomarkers. These mainly included multiple imaging biomarkers, cognitive scores, and various omics biomarkers. Deep learning approaches that combine these biomarkers or multi-modality datasets typically outperform single-modality datasets. We conclude ML may be leveraged to untangle the complex relationships of NPS and AD biomarkers with cognition. This may potentially help to predict the progression of MCI or dementia and develop more targeted early intervention approaches based on NPS.

Interactions Between Neuropsychiatric Symptoms and Alzheimer's Disease Neuroimaging Biomarkers in Predicting Longitudinal Cognitive Decline.

Objective: To examine interactions between Neuropsychiatric symptoms (NPS) with Pittsburgh Compound B (PiB) and fluorodeoxyglucose positron emission tomography (FDG-PET) in predicting cognitive trajectories. Methods: We conducted a longitudinal study in the setting of the population-based Mayo Clinic Study of Aging in Olmsted County, MN, involving 1581 cognitively unimpaired (CU) persons aged ≥50 years (median age 71.83 years, 54.0% males, 27.5% APOE ɛ4 carriers). NPS at baseline were assessed using the Neuropsychiatric Inventory Questionnaire (NPI-Q). Brain glucose hypometabolism was defined as a SUVR ≤ 1.47 (measured by FDG-PET) in regions typically affected in Alzheimer's disease. Abnormal cortical amyloid deposition was measured using PiB-PET (SUVR ≥ 1.48). Neuropsychological testing was done approximately every 15 months, and we calculated global and domain-specific (memory, language, attention, and visuospatial skills) cognitive z-scores. We ran linear mixed-effect models to examine the associations and interactions between NPS at baseline and z-scored PiB- and FDG-PET SUVRs in predicting cognitive z-scores adjusted for age, sex, education, and previous cognitive testing. Results: Individuals at the average PiB and without NPS at baseline declined over time on cognitive z-scores. Those with increased PiB at baseline declined faster (two-way interaction), and those with increased PiB and NPS declined even faster (three-way interaction). We observed interactions between time, increased PiB and anxiety or irritability indicating accelerated decline on global z-scores, and between time, increased PiB and several NPS (e.g., agitation) showing faster domain-specific decline, especially on the attention domain. Conclusions: NPS and increased brain amyloid deposition synergistically interact in accelerating global and domain-specific cognitive decline among CU persons at baseline.

Physiological Age by Artificial Intelligence-Enhanced Electrocardiograms as a Novel Risk Factor of Mortality in Kidney Transplant Candidates.

BACKGROUND: Mortality risk assessment before kidney transplantation (KT) is imperfect. An emerging risk factor for death in nontransplant populations is physiological age as determined by the application of artificial intelligence to the electrocardiogram (ECG). The aim of this study was to examine the relationship between ECG age and KT waitlist mortality. METHODS: We applied a previously developed convolutional neural network to the ECGs of KT candidates evaluated 2014 to 2019 to determine ECG age. We used a Cox proportional hazard model to examine whether ECG age was associated with waitlist mortality. RESULTS: Of the 2183 patients evaluated, 59.1% were male, 81.4% were white, and 11.4% died during follow-up. Mean ECG age was 59.0 ± 12.0 y and mean chronological age at ECG was 53.3 ± 13.6 y. After adjusting for chronological age, comorbidities, and other characteristics associated with mortality, each increase in ECG age of >10 y than the average ECG age for patients of a similar chronological age was associated with an increase in mortality risk (hazard ratio 3.59 per 10-y increase; 95% confidence interval, 2.06-5.72; P < 0.0001). CONCLUSIONS: ECG age is a risk factor for KT waitlist mortality. Determining ECG age through artificial intelligence may help guide risk-benefit assessment when evaluating candidates for KT.

Mayo-PACC: A parsimonious preclinical Alzheimer's disease cognitive composite comprised of public-domain measures to facilitate clinical translation.

INTRODUCTION: We aimed to define a Mayo Preclinical Alzheimer's disease Cognitive Composite (Mayo-PACC) that prioritizes parsimony and use of public domain measures to facilitate clinical translation. METHODS: Cognitively unimpaired participants aged 65 to 85 at baseline with amyloid PET imaging were included, yielding 428 amyloid negative (A-) and 186 amyloid positive (A+) individuals with 7 years mean follow-up. Sensitivity to amyloid-related cognitive decline was examined using slope estimates derived from linear mixed models (difference in annualized change across A+ and A- groups). We compared differences in rates of change between Mayo-PACC and other composites (A+ > A- indicating more significant decline in A+). RESULTS: All composites showed sensitivity to amyloid-related longitudinal cognitive decline (A+ > A- annualized change p < 0.05). Comparisons revealed that Mayo-PACC (AVLT sum of trials 1-5+6+delay, Trails B, animal fluency) showed comparable longitudinal sensitivity to other composites. DISCUSSION: Mayo-PACC performs similarly to other composites and can be directly translated to the clinic.

Mid- and Late-Life Physical Activity and Neuropsychiatric Symptoms in Dementia-Free Older Adults: Mayo Clinic Study of Aging.

OBJECTIVE: This study examined associations between physical activity (PA) and neuropsychiatric symptoms (NPS) in older adults free of dementia. METHODS: This cross-sectional study included 3,222 individuals ≥70 years of age (1,655 men; mean±SD age=79.2±5.6; cognitively unimpaired, N=2,723; mild cognitive impairment, N=499) from the population-based Mayo Clinic Study of Aging. PA (taken as a presumed predictor) in midlife (i.e., when participants were 50-65 years of age) and late life (i.e., the year prior to assessment) was assessed with a self-reported, validated questionnaire; PA intensity and frequency were used to calculate composite scores. NPS (taken as presumed outcomes) were assessed with the Neuropsychiatric Inventory Questionnaire, Beck Depression Inventory (BDI-II), and Beck Anxiety Inventory (BAI). Regression analyses included midlife and late-life PA in each model, which were adjusted for age, sex, education, apolipoprotein E ɛ4 status, and medical comorbidity. RESULTS: Higher late-life PA was associated with lower odds of having apathy (OR=0.89, 95% CI=0.84-0.93), appetite changes (OR=0.92, 95% CI=0.87-0.98), nighttime disturbances (OR=0.95, 95% CI=0.91-0.99), depression (OR=0.94, 95% CI=0.90-0.97), irritability (OR=0.93, 95% CI=0.89-0.97), clinical depression (OR=0.92, 95% CI=0.88-0.97), and clinical anxiety (OR=0.90, 95% CI=0.86-0.94), as well as lower BDI-II (ß estimate=-0.042, 95% CI=-0.051 to -0.033) and BAI (ß estimate=-0.030, 95% CI=-0.040 to -0.021) scores. Higher midlife PA was associated only with higher BDI-II scores (ß estimate=0.011, 95% CI=0.004 to 0.019). Sex modified the associations between PA and NPS. CONCLUSIONS: Late-life PA was associated with a lower likelihood of clinical depression or anxiety and subclinical NPS. These findings need to be confirmed in a cohort study.