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We examined medical records to determine health conditions associated with dementia at varied intervals prior to dementia diagnosis in participants from the Baltimore Longitudinal Study of Aging (BLSA).Data were available for 347 Alzheimer's disease (AD), 76 vascular dementia (VaD), and 811 control participants without dementia. Logistic regressions were performed associating ICD-9 health codes with dementia status across all timepoints, at 5 and 1 year(s) prior to dementia diagnosis, and at the year of diagnosis, controlling for age, sex and follow-up length of the medical record.In AD, the earliest and most consistent associations across all time points included depression, erectile dysfunction, gait abnormalities, hearing loss, and nervous and musculoskeletal symptoms. Cardiomegaly, urinary incontinence, non-epithelial skin cancer, and pneumonia were not significant until 1 year before dementia diagnosis. In VaD, the earliest and most consistent associations across all time points included abnormal EKG, cardiac dysrhythmias, cerebrovascular disease, non-epithelial skin cancer, depression, and hearing loss. Atrial fibrillation, occlusion of cerebral arteries, essential tremor, and abnormal reflexes were not significant until 1 year before dementia diagnosis.These findings suggest that some health conditions are associated with future dementia beginning at least 5 years before dementia diagnosis and are consistently seen over time, while others only reach significance closer to the date of diagnosis. These results also show that there are both shared and distinctive health conditions associated with AD and VaD. These results reinforce the need for medical intervention and treatment to lessen the impact of health comorbidities in the aging population. This article is protected by copyright. All rights reserved.

PURPOSE: Mapping brain white matter (WM) is essential for building an understanding of brain anatomy and function. Tractography-based methods derived from diffusion-weighted MRI (dMRI) are the principal tools for investigating WM. These procedures rely on time-consuming dMRI acquisitions that may not always be available, especially for legacy or time-constrained studies. To address this problem, we aim to generate WM tracts from structural magnetic resonance imaging (MRI) image by deep learning. METHODS: Following recently proposed innovations in structural anatomical segmentation, we evaluate the feasibility of training multiply spatial localized convolution neural networks to learn context from fixed spatial patches from structural MRI on standard template. We focus on six widely used dMRI tractography algorithms (TractSeg, RecoBundles, XTRACT, Tracula, automated fiber quantification (AFQ), and AFQclipped) and train 125 U-Net models to learn these techniques from 3870 T1-weighted images from the Baltimore Longitudinal Study of Aging, the Human Connectome Project S1200 release, and scans acquired at Vanderbilt University. RESULTS: The proposed framework identifies fiber bundles with high agreement against tractography-based pathways with a median Dice coefficient from 0.62 to 0.87 on a test cohort, achieving improved subject-specific accuracy when compared to population atlas-based methods. We demonstrate the generalizability of the proposed framework on three externally available datasets. CONCLUSIONS: We show that patch-wise convolutional neural network can achieve robust bundle segmentation from T1w. We envision the use of this framework for visualizing the expected course of WM pathways when dMRI is not available.

The g‐ratio, defined as the inner‐to‐outer diameter of a myelinated axon, is associated with the speed of nerve impulse conduction, and represents an index of axonal myelination and integrity. It has been shown to be a sensitive and specific biomarker of neurodevelopment and neurodegeneration. However, there have been very few magnetic resonance imaging studies of the g‐ratio in the context of normative aging; characterizing regional and time‐dependent cerebral changes in g‐ratio in cognitively normal subjects will be a crucial step in differentiating normal from abnormal microstructural alterations. In the current study, we investigated age‐related differences in aggregate g‐ratio, that is, g‐ratio averaged over all fibers within regions of interest, in several white matter regions in a cohort of 52 cognitively unimpaired participants ranging in age from 21 to 84 years. We found a quadratic, U‐shaped, relationship between aggregate g‐ratio and age in most cerebral regions investigated, suggesting myelin maturation until middle age followed by a decrease at older ages. As expected, we observed that these age‐related differences vary across different brain regions, with the frontal lobes and parietal lobes exhibiting slightly earlier ages of minimum aggregate g‐ratio as compared to more posterior structures such as the occipital lobes and temporal lobes; this agrees with the retrogenesis paradigm. Our results provide evidence for a nonlinear association between age and aggregate g‐ratio in a sample of adults from a highly controlled population. Finally, sex differences in aggregate g‐ratio were observed in several cerebral regions, with women exhibiting overall lower values as compared to men; this likely reflects the greater myelin content in women's brain, in agreement with recent investigations., Using quantitative MRI, we investigated age‐related differences in aggregate g‐ratio in several white matter regions in a cohort of 52 cognitively unimpaired participants ranging in age from 21 to 84 years. We found a quadratic, U‐shaped, relationship between aggregate g‐ratio and age in most cerebral regions investigated. Sex differences were also observed in several cerebral regions.

Objectives Given the need to detect subclinical changes in brain health that sometimes occur with aging in apparently healthy older adults, we assessed whether bimanual gesture imitation performance, simple to assess clinically, can detect age effects and alterations in cognition, olfaction, and movement. Design Cross-sectional study. Setting Baltimore Longitudinal Study of Aging. Participants Men and women, aged 22 to 101 years, without cognitive impairment, dementia, stroke, Parkinson disease, resting tremor, abnormal muscle tone, or abnormal coordination (N = 507). Measurements Bimanual gesture imitation was measured using a test validated in older adults. We assessed (1) cognition, including verbal memory, executive function, attention, visuospatial ability, visuoperceptual speed, and language; (2) manual dexterity with the Purdue Pegboard Test; (3) olfaction, using the 16-item Sniffin' Sticks Identification Test; (4) upper extremity motor function, using a computer-based finger tapping test; and (5) lower extremity motor function, including 6-meter usual and rapid gait speeds, 400-meter walk time, Health ABC Physical Performance Battery, and total standing balance time. Cross-sectional associations between bimanual gesture imitation performance and each measure were examined using linear regression after adjustment for age, sex, race, education, and body mass index. Models with mobility measures also adjusted for height. Results Higher gesture imitation performance was associated with younger age. After adjustment, a worse score was associated with worse olfaction, executive function, and visuospatial ability. Gesture imitation score was not associated with other cognitive measures or motor function. Conclusion In persons without clinically detectable neurological conditions, poor bimanual gesture imitation is associated with other indicators of brain health, including olfaction and selected cognitive function domains. Bimanual gesture imitation may be useful clinically to detect subtle brain changes in apparently healthy older adults. J Am Geriatr Soc 67:2581-2586, 2019.

Objective While a number of single nucleotide polymorphisms (SNPs) associated with Alzheimer's disease (AD) or cognitive impairment have been identified, independent replications remain the only way to validate proposed signals. We investigated SNPs in candidate genes associated with either cognitive impairment or AD pathogenesis and their relationships with probable dementia (PD) in the Women's Health Initiative Memory Study (WHIMS). Methods We analyzed 96 SNPs across five genes (APOE/TOMM40, BDNF, COMT, SORL1, and KIBRA) in 2857 women (ages ≥65) from the WHIMS randomized trials of hormone therapy using a custom Illumina GoldenGate assay; 19% of the sample were MCI (N = 165) or PD (N = 387), and the remaining 81% were free of cognitive impairment. SNP associations were evaluated for PD in non-Hispanic whites adjusting for age and HT using logistic regression under an additive genetic model. Results One SNP (rs157582), located in the TOMM40 gene nearby APOE, was associated with the PD phenotype based on a P value accounting for multiple comparisons. An additional 12 SNPs were associated with the PD phenotype at P ≤ 0.05 (APOE: rs405509, rs439401; TOMM40: rs8106922, and KIBRA: rs4320284, rs11740112, rs10040267, rs13171394, rs6555802, rs2241368, rs244904, rs6555805, and rs10475878). Results of the sensitivity analyes excluding MCI were similar, with addition of COMT rs737865 and BDNF rs1491850 (P ≤ 0.05). Conclusions Our results in older women provide supporting evidence that the APOE/TOMM40 genes confer dementia risk and extend these findings to COMT, BDNF, and KIBRA. Our findings may lead to a better understanding of the role these genes play in cognition and cognitive impairment.

Introduction Higher energetic costs for mobility predict gait speed decline. Slow gait is linked to cognitive decline and Alzheimer's disease (AD). Whether the energetic cost of walking is linked to AD pathology is unknown. We investigated the cross‐sectional association between the energetic cost of walking, gait speed, and amyloid beta (Aβ) status (+/−) in older adults. Methods One hundred forty‐nine cognitively normal adults (56% women, mean age 77.5 ± 8.4 years) completed customary‐paced walking assessments with indirect calorimetry and 11C‐Pittsburgh compound B positron emission tomography. Logistic regression models examined associations adjusted for demographics, body composition, comorbid conditions, and apolipoprotein E ε4. Results Each 0.01 mL/kg/m greater energy cost was associated with 18% higher odds of being Aβ+ (odds ratio [OR] = 1.18; 95% confidence interval [CI]: 1.04 to 1.34; P = .011). These findings were not observed when investigating gait speed (OR = 0.99; 95% CI: 0.97 to 1.01; P = .321). Discussion High energetic cost of walking is linked to AD pathology and may be a potential target for therapeutic intervention.

Introduction Biomarker discovery of dementia and cognitive impairment is important to gather insight into mechanisms underlying the pathogenesis of these conditions. Methods In 997 adults from the InCHIANTI study, we assessed the association of 1301 plasma proteins with dementia and cognitive impairment. Validation was conducted in two Alzheimer's disease (AD) case‐control studies as well as endophenotypes of AD including cognitive decline, brain amyloid burden, and brain volume. Results We identified four risk proteins that were significantly associated with increased odds (peptidase inhibitor 3 (PI3), trefoil factor 3 (TFF3), pregnancy associated plasma protein A (PAPPA), agouti‐related peptide (AGRP)) and two protective proteins (myostatin (MSTN), integrin aVb5 (ITGAV/ITGB5)) with decreased odds of baseline cognitive impairment or dementia. Of these, four proteins (MSTN, PI3, TFF3, PAPPA) were associated cognitive decline in subjects that were cognitively normal at baseline. ITGAV/ITGB5 was associated with lower brain amyloid burden, MSTN and ITGAV/ITGB5 were associated with larger brain volume and slower brain atrophy, and PI3, PAPPA, and AGRP were associated with smaller brain volume and/or faster brain atrophy. Discussion These proteins may be useful as non‐invasive biomarkers of dementia and cognitive impairment.

Objective To test the hypothesis that among cognitively healthy individuals, distinct groups exist in terms of amyloid and phosphorylated‐tau accumulation rates; that if rapid accumulator groups exist, their membership can be predicted by Alzheimer's disease (AD) risk factors, and that time points of significant increase in AD protein accumulation will be evident. Methods The analysis reports data from 263 individuals from the BIOCARD and 184 individuals from the Baltimore Longitudinal Study of Aging with repeated cerebrospinal fluid (CSF) and positron emission tomography (PET) sampling, respectively. We used latent class mixed‐effect models to identify distinct classes of amyloid (CSF and PET) and p‐Tau (CSF) accumulation rates and generalized additive modeling to investigate non‐linear changes to AD biomarkers. Results For both amyloid and p‐Tau latent class models we confirmed the existence of two separate classes: accumulators and non‐accumulators. The accumulator and non‐accumulator groups differed significantly in terms of baseline AD protein levels and slope of change. APOE e4 carrier status and episodic memory predicted amyloid class membership. Non‐linear models revealed time points of significant increase in the rate of amyloid and p‐Tau accumulation whereby APOE e4 carrier status associated with earlier age at onset of rapid accumulation. Conclusions The current analysis demonstrates the existence of distinct classes of amyloid and p‐Tau accumulators. Predictors of class membership were identified but the overall accuracy of the models was modest, highlighting the need for additional biomarkers that are sensitive to early disease phenotypes.

Recent studies have revealed that brain development is marked by morphological synchronization across brain regions. Regions with shared growth trajectories form structural covariance networks (SCNs) that not only map onto functionally identified cognitive systems, but also correlate with a range of cognitive abilities across the lifespan. Despite advances in within-network covariance examinations, few studies have examined lifetime patterns of structural relationships across known SCNs. In the current study, we used a big-data framework and a novel application of covariate-adjusted restricted cubic spline regression to identify volumetric network trajectories and covariance patterns across 13 networks (n = 5,019, ages = 7-90). Our findings revealed that typical development and aging are marked by significant shifts in the degree that networks preferentially coordinate with one another (i.e., modularity). Specifically, childhood showed higher modularity of networks compared to adolescence, reflecting a shift over development from segregation to desegregation of inter-network relationships. The shift from young to middle adulthood was marked by a significant decrease in inter-network modularity and organization, which continued into older adulthood, potentially reflecting changes in brain organizational efficiency with age. This study is the first to characterize brain development and aging in terms of inter-network structural covariance across the lifespan.

Author(s): Espeland, Mark A; Chen, Jiu-Chiuan; Weitlauf, Julie; Hayden, Kathleen M; Rapp, Stephen R; Resnick, Susan M; Garcia, Lorena; Cannell, Brad; Baker, Laura D; Sachs, Bonnie C; Tindle, Hilary A; Wallace, Robert; Casanova, Ramon; Women's Health Initiative Memory Study Magnetic Resonance Imaging Study Group | Abstract: ObjectivesTo examine whether trajectories of global cognitive function over time in studies that change assessment protocols may be modeled based on an individual's performance relative to others in the study cohort.DesignExtended follow-up of a cohort originally enrolled in a clinical trial of postmenopausal hormone therapy.SettingThe Women's Health Initiative Memory Study switched from an in-person interview with the Modified Mini-Mental State Examination to a telephone-based interview with the modified Telephone Interview for Cognitive Status to assess global cognitive function over long-term follow-up.ParticipantsWomen aged 75 to 92 (N=2,561).MeasurementsAnnual cognitive assessments from participants, ranked according to age-, race- and ethnicity-adjusted performance levels, were used to identify distinct trajectories. Participants assigned to the resulting trajectories were compared for selected risk factor profiles.ResultsOur approach grouped participants into five trajectories according to relative cognitive performance over time. These groups differed significantly according to 3 known risk factors for cognitive decline-education level, apolipoprotein E-ϵ4 genotype, and type 2 diabetes mellitus-and a biomarker based on brain structure that has been linked to cognitive decline and Alzheimer's disease. Participants with consistently low relative levels of cognitive function over time and those whose relative performance over time declined to these levels tended to have poorer risk factor profiles.ConclusionLongitudinal measures of an individual's relative performance on different assessment protocols for global cognitive function can be used to identify trajectories of change over time that appear to have internal validity with respect to known risk factors.

The Alzheimer's Association's Research Roundtable met in November 2017 to explore the new National Institute on Aging and the Alzheimer's Association Research Framework for Alzheimer's disease. The meeting allowed experts in the field from academia, industry, and government to provide perspectives on the new National Institute on Aging and the Alzheimer's Association Research Framework. This review will summarize the “A, T, N System” (Amyloid, Tau, and Neurodegeneration) using biomarkers and how this may be applied to clinical research and drug development. In addition, challenges and barriers to the potential adoption of this new framework will be discussed. Finally, future directions for research will be proposed.

Introduction We established a method for diagnostic harmonization across multiple studies of preclinical Alzheimer's disease and validated the method by examining its relationship with clinical status and cognition. Methods Cognitive and clinical data were used from five studies ( N = 1746). Consensus diagnoses established in each study used criteria to identify progressors from normal cognition to mild cognitive impairment. Correspondence was evaluated between these consensus diagnoses and three algorithmic classifications based on (1) objective cognitive impairment in 2+ tests only; (2) a Clinical Dementia Rating (CDR) of ≥0.5 only; and (3) both. Associations between baseline cognitive performance and cognitive change were each tested in relation to progression to algorithm-based classifications. Results In each study, an algorithmic classification based on both cognitive testing cutoff scores and a CDR ≥0.5 provided optimal balance of sensitivity and specificity (areas under the curve: 0.85–0.95). Over an average 6.6 years of follow-up (up to 28 years), N = 186 initially cognitively normal participants aged on average 64 years at baseline progressed (incidence rate: 15.3 people/1000 person-years). Baseline cognitive scores and cognitive change were associated with future diagnostic status using this algorithmic classification. Discussion Both cognitive tests and CDR ratings can be combined across multiple studies to obtain a reliable algorithmic classification with high specificity and sensitivity. This approach may be applicable to large cohort studies and to clinical trials focused on preclinical Alzheimer's disease because it provides an alternative to implementation of a time-consuming adjudication panel.