Robust fiber orientation distribution function estimation using deep constrained spherical deconvolution for diffusion-weighted magnetic resonance imaging.

Purpose: Diffusion-weighted magnetic resonance imaging (DW-MRI) is a critical imaging method for capturing and modeling tissue microarchitecture at a millimeter scale. A common practice to model the measured DW-MRI signal is via fiber orientation distribution function (fODF). This function is the essential first step for the downstream tractography and connectivity analyses. With recent advantages in data sharing, large-scale multisite DW-MRI datasets are being made available for multisite studies. However, measurement variabilities (e.g., inter- and intrasite variability, hardware performance, and sequence design) are inevitable during the acquisition of DW-MRI. Most existing model-based methods [e.g., constrained spherical deconvolution (CSD)] and learning-based methods (e.g., deep learning) do not explicitly consider such variabilities in fODF modeling, which consequently leads to inferior performance on multisite and/or longitudinal diffusion studies. Approach: In this paper, we propose a data-driven deep CSD method to explicitly constrain the scan-rescan variabilities for a more reproducible and robust estimation of brain microstructure from repeated DW-MRI scans. Specifically, the proposed method introduces a three-dimensional volumetric scanner-invariant regularization scheme during the fODF estimation. We study the Human Connectome Project (HCP) young adults test-retest group as well as the MASiVar dataset (with inter- and intrasite scan/rescan data). The Baltimore Longitudinal Study of Aging dataset is employed for external validation. Results: From the experimental results, the proposed data-driven framework outperforms the existing benchmarks in repeated fODF estimation. By introducing the contrastive loss with scan/rescan data, the proposed method achieved a higher consistency while maintaining higher angular correlation coefficients with the CSD modeling. The proposed method is assessing the downstream connectivity analysis and shows increased performance in distinguishing subjects with different biomarkers. Conclusion: We propose a deep CSD method to explicitly reduce the scan-rescan variabilities, so as to model a more reproducible and robust brain microstructure from repeated DW-MRI scans. The plug-and-play design of the proposed approach is potentially applicable to a wider range of data harmonization problems in neuroimaging.

Leveraging longitudinal diffusion MRI data to quantify differences in white matter microstructural decline in normal and abnormal aging.

Introduction: It is unclear how rates of white matter microstructural decline differ between normal aging and abnormal aging. Methods: Diffusion MRI data from several well-established longitudinal cohorts of aging (Alzheimer's Disease Neuroimaging Initiative [ADNI], Baltimore Longitudinal Study of Aging [BLSA], Vanderbilt Memory & Aging Project [VMAP]) were free-water corrected and harmonized. This dataset included 1723 participants (age at baseline: 72.8 ± 8.87 years, 49.5% male) and 4605 imaging sessions (follow-up time: 2.97 ± 2.09 years, follow-up range: 1-13 years, mean number of visits: 4.42 ± 1.98). Differences in white matter microstructural decline in normal and abnormal agers was assessed. Results: While we found a global decline in white matter in normal/abnormal aging, we found that several white matter tracts (e.g., cingulum bundle) were vulnerable to abnormal aging. Conclusions: There is a prevalent role of white matter microstructural decline in aging, and future large-scale studies in this area may further refine our understanding of the underlying neurodegenerative processes. HIGHLIGHTS: Longitudinal data were free-water corrected and harmonized.Global effects of white matter decline were seen in normal and abnormal aging.The free-water metric was most vulnerable to abnormal aging.Cingulum free-water was the most vulnerable to abnormal aging.

Automatic Preprocessing Pipeline for White Matter Functional Analyses of Large-Scale Databases.

Recently, increasing evidence suggests that fMRI signals in white matter (WM), conventionally ignored as nuisance, are robustly detectable using appropriate processing methods and are related to neural activity, while changes in WM with aging and degeneration are also well documented. These findings suggest variations in patterns of BOLD signals in WM should be investigated. However, existing fMRI analysis tools, which were designed for processing gray matter signals, are not well suited for large-scale processing of WM signals in fMRI data. We developed an automatic pipeline for high-performance preprocessing of fMRI images with emphasis on quantifying changes in BOLD signals in WM in an aging population. At the image processing level, the pipeline integrated existing software modules with fine parameter tunings and modifications to better extract weaker WM signals. The preprocessing results primarily included whole-brain time-courses, functional connectivity, maps and tissue masks in a common space. At the job execution level, this pipeline exploited a local XNAT to store datasets and results, while using DAX tool to automatic distribute batch jobs that run on high-performance computing clusters. Through the pipeline, 5,034 fMRI/T1 scans were preprocessed. The intraclass correlation coefficient (ICC) of test-retest experiment based on the preprocessed data is 0.52 - 0.86 (N=1000), indicating a high reliability of our pipeline, comparable to previously reported ICC in gray matter experiments. This preprocessing pipeline highly facilitates our future analyses on WM functional alterations in aging and may be of benefit to a larger community interested in WM fMRI studies.

Functional alterations in bipartite network of white and grey matters during aging.

The effects of normal aging on functional connectivity (FC) within various brain networks of gray matter (GM) have been well-documented. However, the age effects on the networks of FC between white matter (WM) and GM, namely WM-GM FC, remains unclear. Evaluating crucial properties, such as global efficiency (GE), for a WM-GM FC network poses a challenge due to the absence of closed triangle paths which are essential for assessing network properties in traditional graph models. In this study, we propose a bipartite graph model to characterize the WM-GM FC network and quantify these challenging network properties. Leveraging this model, we assessed the WM-GM FC network properties at multiple scales across 1,462 cognitively normal subjects aged 22-96 years from three repositories (ADNI, BLSA and OASIS-3) and investigated the age effects on these properties throughout adulthood and during late adulthood (age ≥70 years). Our findings reveal that (1) heterogeneous alterations occurred in region-specific WM-GM FC over the adulthood and decline predominated during late adulthood; (2) the FC density of WM bundles engaged in memory, executive function and processing speed declined with age over adulthood, particularly in later years; and (3) the GE of attention, default, somatomotor, frontoparietal and limbic networks reduced with age over adulthood, and GE of visual network declined during late adulthood. These findings provide unpresented insights into multi-scale alterations in networks of WM-GM functional synchronizations during normal aging. Furthermore, our bipartite graph model offers an extendable framework for quantifying WM-engaged networks, which may contribute to a wide range of neuroscience research.

Leveraging longitudinal diffusion MRI data to quantify differences in white matter microstructural decline in normal and abnormal aging.

INTRODUCTION: It is unclear how rates of white matter microstructural decline differ between normal aging and abnormal aging. METHODS: Diffusion MRI data from several well-established longitudinal cohorts of aging [Alzheimer's Neuroimaging Initiative (ADNI), Baltimore Longitudinal Study of Aging (BLSA), Vanderbilt Memory & Aging Project (VMAP)] was free-water corrected and harmonized. This dataset included 1,723 participants (age at baseline: 72.8±8.87 years, 49.5% male) and 4,605 imaging sessions (follow-up time: 2.97±2.09 years, follow-up range: 1-13 years, mean number of visits: 4.42±1.98). Differences in white matter microstructural decline in normal and abnormal agers was assessed. RESULTS: While we found global decline in white matter in normal/abnormal aging, we found that several white matter tracts (e.g., cingulum bundle) were vulnerable to abnormal aging. CONCLUSIONS: There is a prevalent role of white matter microstructural decline in aging, and future large-scale studies in this area may further refine our understanding of the underlying neurodegenerative processes. HIGHLIGHTS: Longitudinal data was free-water corrected and harmonizedGlobal effects of white matter decline were seen in normal and abnormal agingThe free-water metric was most vulnerable to abnormal agingCingulum free-water was the most vulnerable to abnormal aging.

Comparing voxel- and feature-wise harmonization of complex graph measures from multiple sites for structural brain network investigation of aging.

Complex graph theory measures of brain structural connectomes derived from diffusion weighted images (DWI) provide insight into the network structure of the brain. Further, as the number of available DWI datasets grows, so does the ability to investigate associations in these measures with major biological factors, like age. However, one key hurdle that remains is the presence of scanner effects that can arise from different DWI datasets and confound multisite analyses. Two common approaches to correct these effects are voxel-wise and feature-wise harmonization. However, it is still unclear how to best leverage them for graph-theory analysis of an aging population. Thus, there is a need to better characterize the impact of each harmonization method and their ability to preserve age related features. We investigate this by characterizing four complex graph theory measures (modularity, characteristic path length, global efficiency, and betweenness centrality) in 48 participants aged 55 to 86 from Baltimore Longitudinal Study of Aging (BLSA) and Vanderbilt Memory and Aging Project (VMAP) before and after voxel- and feature-wise harmonization with the Null Space Deep Network (NSDN) and ComBat, respectively. First, we characterize across dataset coefficients of variation (CoV) and find the combination of NSDN and ComBat causes the greatest reduction in CoV followed by ComBat alone then NSDN alone. Second, we reproduce published associations of modularity with age after correcting for other covariates with linear models. We find that harmonization with ComBat or ComBat and NSDN together improves the significance of existing age effects, reduces model residuals, and qualitatively reduces separation between datasets. These results reinforce the efficiency of statistical harmonization on the feature-level with ComBat and suggest that harmonization on the voxel-level is synergistic but may have reduced effect after running through the multiple layers of the connectomics pipeline. Thus, we conclude that feature-wise harmonization improves statistical results, but the addition of biologically informed voxel-based harmonization offers further improvement.

Prediagnosis Smoking Cessation and Overall Survival Among Patients With Non-Small Cell Lung Cancer.

Importance: Lung cancer remains the leading cause of cancer-related death globally; non-small cell lung cancer (NSCLC) accounts for 85% of all lung cancer cases, and cigarette smoking is the factor most significantly associated with its risk. However, little is known about the association of years since prediagnosis smoking cessation and cumulative smoking with overall survival (OS) following a lung cancer diagnosis. Objective: To characterize the association of years since smoking cessation before diagnosis and cumulative smoking pack-years with OS in patients with NSCLC in a lung cancer survivor cohort. Design, Setting, and Participants: The cohort study involved patients with NSCLC who were recruited to the Boston Lung Cancer Survival Cohort at Massachusetts General Hospital (Boston, Massachusetts) between 1992 and 2022. Patients' smoking history and baseline clinicopathological characteristics were prospectively collected through questionnaires, and OS following lung cancer diagnosis was regularly updated. Exposures: Duration of smoking cessation before a lung cancer diagnosis. Main Outcomes and Measures: The primary outcome was the association of detailed smoking history with OS following a lung cancer diagnosis. Results: Of 5594 patients with NSCLC (mean [SD] age, 65.6 [10.8] years; 2987 men [53.4%]), 795 (14.2%) were never smokers, 3308 (59.1%) were former smokers, and 1491 (26.7%) were current smokers. Cox regression analysis suggested that former smokers had 26% higher mortality (hazard ratio [HR], 1.26; 95% CI, 1.13-1.40; P < .001) and current smokers had 68% higher mortality (HR, 1.68; 95% CI, 1.50-1.89; P < .001) compared with never smokers. Log2-transformed years since smoking cessation before diagnosis were associated with significantly lower mortality among ever smokers (HR, 0.96; 95% CI, 0.93-0.99; P = .003). Subgroup analysis, stratified by clinical stage at diagnosis, revealed that former and current smokers had even shorter OS among patients with early-stage disease. Conclusions and Relevance: In this cohort study of patients with NSCLC, quitting smoking early was associated with lower mortality following a lung cancer diagnosis, and the association of smoking history with OS may have varied depending on clinical stage at diagnosis, potentially owing to the differing treatment regimens and efficacy associated with smoking exposure following diagnosis. Detailed smoking history collection should be incorporated into future epidemiological and clinical studies to improve lung cancer prognosis and treatment selection.

White matter microstructural metrics are sensitively associated with clinical staging in Alzheimer's disease.

Introduction: White matter microstructure may be abnormal along the Alzheimer's disease (AD) continuum. Methods: Diffusion magnetic resonance imaging (dMRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 627), Baltimore Longitudinal Study of Aging (BLSA, n = 684), and Vanderbilt Memory & Aging Project (VMAP, n = 296) cohorts were free-water (FW) corrected and conventional, and FW-corrected microstructural metrics were quantified within 48 white matter tracts. Microstructural values were subsequently harmonized using the Longitudinal ComBat technique and inputted as independent variables to predict diagnosis (cognitively unimpaired [CU], mild cognitive impairment [MCI], AD). Models were adjusted for age, sex, race/ethnicity, education, apolipoprotein E (APOE) ε4 carrier status, and APOE ε2 carrier status. Results: Conventional dMRI metrics were associated globally with diagnostic status; following FW correction, the FW metric itself exhibited global associations with diagnostic status, but intracellular metric associations were diminished. Discussion: White matter microstructure is altered along the AD continuum. FW correction may provide further understanding of the white matter neurodegenerative process in AD. Highlights: Longitudinal ComBat successfully harmonized large-scale diffusion magnetic resonance imaging (dMRI) metrics.Conventional dMRI metrics were globally sensitive to diagnostic status.Free-water (FW) correction mitigated intracellular associations with diagnostic status.The FW metric itself was globally sensitive to diagnostic status. Multivariate conventional and FW-corrected models may provide complementary information.

Associations of baseline and longitudinal change in cerebellum volume with age-related changes in verbal learning and memory.

The cerebellum is involved in higher-order cognitive functions, e.g., learning and memory, and is susceptible to age-related atrophy. Yet, the cerebellum's role in age-related cognitive decline remains largely unknown. We investigated cross-sectional and longitudinal associations between cerebellar volume and verbal learning and memory. Linear mixed effects models and partial correlations were used to examine the relationship between changes in cerebellum volumes (total cerebellum, cerebellum white matter [WM], cerebellum hemisphere gray matter [GM], and cerebellum vermis subregions) and changes in verbal learning and memory performance among 549 Baltimore Longitudinal Study of Aging participants (2,292 visits). All models were adjusted by baseline demographic characteristics (age, sex, race, education), and APOE e4 carrier status. In examining associations between change with change, we tested an additional model that included either hippocampal (HC), cuneus, or postcentral gyrus (PoCG) volumes to assess whether cerebellar volumes were uniquely associated with verbal learning and memory. Cross-sectionally, the association of baseline cerebellum GM and WM with baseline verbal learning and memory was age-dependent, with the oldest individuals showing the strongest association between volume and performance. Baseline volume was not significantly associated with change in learning and memory. However, analysis of associations between change in volumes and changes in verbal learning and memory showed that greater declines in verbal memory were associated with greater volume loss in cerebellum white matter, and preserved GM volume in cerebellum vermis lobules VI-VII. The association between decline in verbal memory and decline in cerebellar WM volume remained after adjustment for HC, cuneus, and PoCG volume. Our findings highlight that associations between cerebellum volume and verbal learning and memory are age-dependent and regionally specific.

Short superficial white matter and aging: a longitudinal multi-site study of 1293 subjects and 2711 sessions.

It is estimated that short association fibers running immediately beneath the cortex may make up as much as 60% of the total white matter volume. However, these have been understudied relative to the long-range association, projection, and commissural fibers of the brain. This is largely because of limitations of diffusion MRI fiber tractography, which is the primary methodology used to non-invasively study the white matter connections. Inspired by recent anatomical considerations and methodological improvements in superficial white matter (SWM) tractography, we aim to characterize changes in these fiber systems in cognitively normal aging, which provide insight into the biological foundation of age-related cognitive changes, and a better understanding of how age-related pathology differs from healthy aging. To do this, we used three large, longitudinal and cross-sectional datasets (N = 1293 subjects, 2711 sessions) to quantify microstructural features and length/volume features of several SWM systems. We find that axial, radial, and mean diffusivities show positive associations with age, while fractional anisotropy has negative associations with age in SWM throughout the entire brain. These associations were most pronounced in the frontal, temporal, and temporoparietal regions. Moreover, measures of SWM volume and length decrease with age in a heterogenous manner across the brain, with different rates of change in inter-gyri and intra-gyri SWM, and at slower rates than well-studied long-range white matter pathways. These features, and their variations with age, provide the background for characterizing normal aging, and, in combination with larger association pathways and gray matter microstructural features, may provide insight into fundamental mechanisms associated with aging and cognition.

Joint independent component analysis for hypothesizing spatiotemporal relationships between longitudinal gray and white matter changes in preclinical Alzheimer's disease.

Characterizing relationships between gray matter (GM) and white matter (WM) in early Alzheimer's disease (AD) would improve understanding of how and when AD impacts the brain. However, modeling these relationships across brain regions and longitudinally remains a challenge. Thus, we propose extending joint independent component analysis (jICA) into spatiotemporal modeling of regional cortical thickness and WM bundle volumes leveraging multimodal MRI. We jointly characterize these GM and WM features in a normal aging (n=316) and an age- and sex-matched preclinical AD cohort (n=81) at each of two imaging sessions spaced three years apart, training on the normal aging population in cross-validation and interrogating the preclinical AD cohort. We find this joint model identifies reproducible, longitudinal changes in GM and WM between the two imaging sessions and that these changes are associated with preclinical AD and are plausible considering the literature. We compare this joint model to two focused models: (1) GM features at the first session and WM at the second and (2) vice versa. The joint model identifies components that correlate poorly with those from the focused models, suggesting the different models resolve different patterns. We find the strength of association with preclinical AD is improved in the GM to WM model, which supports the hypothesis that medial temporal and frontal thinning precedes volume loss in the uncinate fasciculus and inferior anterior-posterior association fibers. These results suggest that jICA effectively generates spatiotemporal hypotheses about GM and WM in preclinical AD, especially when specific intermodality relationships are considered a priori.

Herpes Viruses in the Baltimore Longitudinal Study of Aging: Associations With Brain Volumes, Cognitive Performance, and Plasma Biomarkers.

BACKGROUND AND OBJECTIVES: Although an infectious etiology of Alzheimer's Disease (AD) has received renewed attention with a particular focus on herpes viruses, the longitudinal effects of symptomatic herpes viruses (sHHV) infection on brain structure and cognition remain poorly understood, as does the effect of sHHV on AD/neurodegeneration biomarkers. METHODS: We used a longitudinal, community-based cohort to characterize the association of sHHV diagnoses with changes in 3T MRI brain volume and cognitive performance. Additionally, we related sHHV to cross-sectional differences in plasma biomarkers of AD (Aß42/40), astrogliosis (glial fibrillary acidic protein [GFAP]) and neurodegeneration (neurofilament light [NfL]). Baltimore Longitudinal Study of Aging (BLSA) participants were recruited from the community and assessed with serial brain MRIs and cognitive exams over an average of 3.4 (SD=3.2) and 8.6 (SD=7.7) years, respectively. sHHV classification used ICD9 codes documented at comprehensive health and functional screening evaluations at each study visit. Linear mixed effects and multivariable linear regression models were used in analyses. RESULTS: A total of 1,009 participants were included in the primary MRI analysis, 98% of whom were cognitively normal at baseline MRI (mean age = 65.7 years; 54.8% female). Having a sHHV diagnosis (N=119) was associated with longitudinal reductions in white matter volume (annual additional rate of change -0.34 cm3/year; p = 0.035), particularly in the temporal lobe. However, there was no association between sHHV and change in total brain, total gray matter, or AD signature region volume. Among the 119 participants with sHHV, exposure to antiviral treatment attenuated declines in occipital white matter (p = 0.04). Although the sHHV group had higher cognitive scores at baseline, sHHV diagnosis was associated with accelerated longitudinal declines in attention (annual additional rate of change -0.01 Z-score/year; p = 0.008). Additionally, sHHV diagnosis was associated with elevated plasma GFAP, but not related to Aß42/40 and NfL levels. DISCUSSION: These findings suggest an association of sHHV infection with white matter volume loss, attentional decline, and astrogliosis. Although the findings link sHHV to several neurocognitive features, the results do not support an association between sHHV and AD-specific disease processes.

Contrastive semi-supervised harmonization of single-shell to multi-shell diffusion MRI.

Diffusion weighted MRI (DW-MRI) harmonization is necessary for multi-site or multi-acquisition studies. Current statistical methods address the need to harmonize from one site to another, but do not simultaneously consider the use of multiple datasets which are comprised of multiple sites, acquisitions protocols, and age demographics. This work explores deep learning methods which can generalize across these variations through semi-supervised and unsupervised learning while also learning to estimate multi-shell data from single-shell data using the Multi-shell Diffusion MRI Harmonization Challenge (MUSHAC) and Baltimore Longitudinal Study on Aging (BLSA) datasets. We compare disentanglement harmonization models, which seek to encode anatomy and acquisition in separate latent spaces, and a CycleGAN harmonization model, which uses generative adversarial networks (GAN) to perform style transfer between sites, to the baseline preprocessing and to SHORE interpolation. We find that the disentanglement models achieve superior performance in harmonizing all data while at the same transforming the input data to a single target space across several diffusion metrics (fractional anisotropy, mean diffusivity, mean kurtosis, primary eigenvector).

Aging and white matter microstructure and macrostructure: a longitudinal multi-site diffusion MRI study of 1218 participants.

Quantifying the microstructural and macrostructural geometrical features of the human brain's connections is necessary for understanding normal aging and disease. Here, we examine brain white matter diffusion magnetic resonance imaging data from one cross-sectional and two longitudinal data sets totaling in 1218 subjects and 2459 sessions of people aged 50-97 years. Data was drawn from well-established cohorts, including the Baltimore Longitudinal Study of Aging data set, Cambridge Centre for Ageing Neuroscience data set, and the Vanderbilt Memory & Aging Project. Quantifying 4 microstructural features and, for the first time, 11 macrostructure-based features of volume, area, and length across 120 white matter pathways, we apply linear mixed effect modeling to investigate changes in pathway-specific features over time, and document large age associations within white matter. Conventional diffusion tensor microstructure indices are the most age-sensitive measures, with positive age associations for diffusivities and negative age associations with anisotropies, with similar patterns observed across all pathways. Similarly, pathway shape measures also change with age, with negative age associations for most length, surface area, and volume-based features. A particularly novel finding of this study is that while trends were homogeneous throughout the brain for microstructure features, macrostructural features demonstrated heterogeneity across pathways, whereby several projection, thalamic, and commissural tracts exhibited more decline with age compared to association and limbic tracts. The findings from this large-scale study provide a comprehensive overview of the age-related decline in white matter and demonstrate that macrostructural features may be more sensitive to heterogeneous white matter decline. Therefore, leveraging macrostructural features may be useful for studying aging and could facilitate comparisons in a variety of diseases or abnormal conditions.

Longitudinal changes of connectomes and graph theory measures in aging.

Changes in brain structure and connectivity in aging can be probed through diffusion weighted MRI and summarized with structural connectome matrices. Complex network analysis based on graph theory has been applied to provide measures that are correlated with neurobiological variations and can help guide quantitative study of brain function. However, the understanding of how connectomes change longitudinally is limited. In this work, we evaluate modern pipelines to obtain the connectomics data from diffusion weighted MRI scans across different sessions from control subjects (55-99 years old) in the Baltimore Longitudinal Study of Aging and Cambridge Centre for Ageing and Neuroscience. From the connectivity matrices, we compute graph theory measures to understand their brain networks and apply a linear mixed-effects model to study their longitudinal changes with respect to age. With this approach, we computed 14 graph theory measures of 1469 healthy subjects (2476 scans) and found statistically significant correlations between all 14 measures and age. In this analysis: 1) the brain becomes more segregated but less integrated in aging; 2) the overall network cost increases for older subjects; 3) the small-world organizations remain stable; and 4) due to high intra-subject variance, there is not clear trend for longitudinal changes of graph theory measures of individual subjects. Therefore, while useful to investigate brain evolution in aging at the population level, improvements in the connectome reconstruction are needed to decrease single subject variability for individual inference.

Accelerated decline in white matter microstructure in subsequently impaired older adults and its relationship with cognitive decline.

Little is known about a longitudinal decline in white matter microstructure and its associations with cognition in preclinical dementia. Longitudinal diffusion tensor imaging and neuropsychological testing were performed in 50 older adults who subsequently developed mild cognitive impairment or dementia (subsequently impaired) and 200 cognitively normal controls. Rates of white matter microstructural decline were compared between groups using voxel-wise linear mixed-effects models. Associations between change in white matter microstructure and cognition were examined. Subsequently impaired individuals had a faster decline in fractional anisotropy in the right inferior fronto-occipital fasciculus and bilateral splenium of the corpus callosum. A decline in right inferior fronto-occipital fasciculus fractional anisotropy was related to a decline in verbal memory, visuospatial ability, processing speed and mini-mental state examination. A decline in bilateral splenium fractional anisotropy was related to a decline in verbal fluency, processing speed and mini-mental state examination. Accelerated regional white matter microstructural decline is evident during the preclinical phase of mild cognitive impairment/dementia and is related to domain-specific cognitive decline.

Recognition Memory is Associated with Distinct Patterns of Regional Gray Matter Volumes in Young and Aged Monkeys.

Cognitive aging varies tremendously across individuals and is often accompanied by regionally specific reductions in gray matter (GM) volume, even in the absence of disease. Rhesus monkeys provide a primate model unconfounded by advanced neurodegenerative disease, and the current study used a recognition memory test (delayed non-matching to sample; DNMS) in conjunction with structural imaging and voxel-based morphometry (VBM) to characterize age-related differences in GM volume and brain-behavior relationships. Consistent with expectations from a long history of neuropsychological research, DNMS performance in young animals prominently correlated with the volume of multiple structures in the medial temporal lobe memory system. Less anticipated correlations were also observed in the cingulate and cerebellum. In aged monkeys, significant volumetric correlations with DNMS performance were largely restricted to the prefrontal cortex and striatum. Importantly, interaction effects in an omnibus analysis directly confirmed that the associations between volume and task performance in the MTL and prefrontal cortex are age-dependent. These results demonstrate that the regional distribution of GM volumes coupled with DNMS performance changes across the lifespan, consistent with the perspective that the aged primate brain retains a substantial capacity for structural reorganization.

Disease Burden Affects Aging Brain Function.

BACKGROUND: Most older adults live with multiple chronic disease conditions, yet the effect of multiple diseases on brain function remains unclear. METHODS: We examine the relationship between disease multimorbidity and brain activity using regional cerebral blood flow (rCBF) 15O-water PET scans from 97 cognitively normal participants (mean baseline age 76.5) in the Baltimore Longitudinal Study of Aging (BLSA). Multimorbidity index scores, generated from the presence of 13 health conditions, were correlated with PET data at baseline and in longitudinal change (n = 74) over 5.05 (2.74 SD) years. RESULTS: At baseline, voxel-based analysis showed that higher multimorbidity scores were associated with lower relative activity in orbitofrontal, superior frontal, temporal pole and parahippocampal regions, and greater activity in lateral temporal, occipital, and cerebellar regions. Examination of the individual health conditions comprising the index score showed hypertension and chronic kidney disease individually contributed to the overall multimorbidity pattern of altered activity. Longitudinally, both increases and decreases in activity were seen in relation to increasing multimorbidity over time. These associations were identified in orbitofrontal, lateral temporal, brainstem, and cerebellar areas. CONCLUSION: Together, these results show that greater multimorbidity is associated with widespread areas of altered brain activity, supporting a link between health and changes in aging brain function.

Epigenome-wide three-way interaction study identifies a complex pattern between TRIM27, KIAA0226, and smoking associated with overall survival of early-stage NSCLC.

The interaction between DNA methylation of tripartite motif containing 27 (cg05293407TRIM27 ) and smoking has previously been identified to reveal histologically heterogeneous effects of TRIM27 DNA methylation on early-stage non-small-cell lung cancer (NSCLC) survival. However, to understand the complex mechanisms underlying NSCLC progression, we searched three-way interactions. A two-phase study was adopted to identify three-way interactions in the form of pack-year of smoking (number of cigarettes smoked per day × number of years smoked) × cg05293407TRIM27 × epigenome-wide DNA methylation CpG probe. Two CpG probes were identified with FDR-q ≤ 0.05 in the discovery phase and P ≤ 0.05 in the validation phase: cg00060500KIAA0226 and cg17479956EXT2 . Compared to a prediction model with only clinical information, the model added 42 significant three-way interactions using a looser criterion (discovery: FDR-q ≤ 0.10, validation: P ≤ 0.05) had substantially improved the area under the receiver operating characteristic curve (AUC) of the prognostic prediction model for both 3-year and 5-year survival. Our research identified the complex interaction effects among multiple environment and epigenetic factors, and provided therapeutic target for NSCLC patients.

Vascular risk is not associated with PET measures of Alzheimer's disease neuropathology among cognitively normal older adults.

Cardiovascular disease (CVD) is associated with a higher risk of developing dementia. Studies have found that vascular risk factors are associated with greater amyloid-ß (Aß) and tau burden, which are hallmark neuropathologies of Alzheimer's disease (AD). Evidence for these associations during the preclinical stages of AD, when Aß and tau pathologies first become detectable, is mixed. Quantifying the effect of vascular risk among cognitively normal individuals can help focus the efforts to develop therapeutic approaches aimed at modifying the course of preclinical AD. Using Bayesian analysis, we examined the relationship of Aß and tau pathology with concurrent vascular risk among 87 cognitively normal individuals (median age 77, interquartile range 70-83) in the Baltimore Longitudinal Study of Aging. We quantified vascular risk as the probability of developing CVD within 10 years using published equations from the Framingham Heart Study. Aß and tau pathologies were measured using positron emission tomography. As expected, Aß positive participants had greater tau in the entorhinal cortex (EC) and inferior temporal gyrus (ITG) (difference in means = 0.09, p < 0.05 for each region), and 10-year CVD risk was positively correlated with white matter lesion burden (r = 0.24, p = 0.03). However, we did not find any associations between CVD risk and Aß or tau. The data provided over two- and four-fold evidence towards the lack of a correlation between CVD risk and tau in the EC (Bayes factor BF = 2.4) and ITG (BF = 4.0), respectively. We found over three-fold evidence towards the lack of a difference in mean CVD risk by Aß group (BF = 3.4). These null findings were replicated using a data-driven vascular risk score in the BLSA based on a principal component analysis of eight indicators of vascular health. Our data provide moderate evidence towards the lack of an association between vascular risk and concurrent AD neuropathology among cognitively normal older adults. This finding suggests that vascular risk and AD neuropathology may constitute independent pathways in the development of cognitive impairment and dementia.