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Data harmonization is necessary for removing confounding effects in multi-site diffusion image analysis. One such harmonization method, LinearRISH, scales rotationally invariant spherical harmonic (RISH) features from one site ("target") to the second ("reference") to reduce confounding scanner effects. However, reference and target site designations are not arbitrary and resultant diffusion metrics (fractional anisotropy, mean diffusivity) are biased by this choice. In this work we propose MidRISH: rather than scaling reference RISH features to target RISH features, we project both sites to a mid-space. We validate MidRISH with the following experiments: harmonizing scanner differences from 37 matched patients free of cognitive impairment, and harmonizing acquisition and study differences on 117 matched patients free of cognitive impairment. We find that MidRISH reduces bias of reference selection while preserving harmonization efficacy of LinearRISH. Users should be cautious when performing LinearRISH harmonization. To select a reference site is to choose diffusion metric effect-size. Our proposed method eliminates the bias-inducing site selection step., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Introduction: The effects of sex, race, and Apolipoprotein E ( APOE ) - Alzheimer's disease (AD) risk factors - on white matter integrity are not well characterized., Methods: Diffusion MRI data from nine well-established longitudinal cohorts of aging were free-water (FW)-corrected and harmonized. This dataset included 4,702 participants (age=73.06 ± 9.75) with 9,671 imaging sessions over time. FW and FW-corrected fractional anisotropy (FA FWcorr ) were used to assess differences in white matter microstructure by sex, race, and APOE -ε4 carrier status., Results: Sex differences in FA FWcorr in association and projection tracts, racial differences in FA FWcorr in projection tracts, and APOE -ε4 differences in FW limbic and occipital transcallosal tracts were most pronounced., Discussion: There are prominent differences in white matter microstructure by sex, race, and APOE -ε4 carrier status. This work adds to our understanding of disparities in AD. Additional work to understand the etiology of these differences is warranted., Competing Interests: SCJ has served on advisory boards for Enigma Biomedical and ALZPath in the past two years. AJS receives support from multiple NIH grants (P30 AG010133, P30 AG072976, R01 AG019771, R01 AG057739, U19 AG024904, R01 LM013463, R01 AG068193, T32 AG071444, U01 AG068057, U01 AG072177, U19 AG074879, and U24 AG074855). He has also received support from Avid Radiopharmaceuticals, a subsidiary of Eli Lilly (in kind contribution of PET tracer precursor) and participated in Scientific Advisory Boards (Bayer Oncology, Eisai, Novo Nordisk, and Siemens Medical Solutions USA, Inc) and an Observational Study Monitoring Board (MESA, NIH NHLBI), as well as External Advisory Committees for multiple NIA grants. He also serves as Editor-in-Chief of Brain Imaging and Behavior, a Springer-Nature Journal.

T1-weighted (T1w) MRI has low frequency intensity artifacts due to magnetic field inhomogeneities. Removal of these biases in T1w MRI images is a critical preprocessing step to ensure spatially consistent image interpretation. N4ITK bias field correction, the current state-of-the-art, is implemented in such a way that makes it difficult to port between different pipelines and workflows, thus making it hard to reimplement and reproduce results across local, cloud, and edge platforms. Moreover, N4ITK is opaque to optimization before and after its application, meaning that methodological development must work around the inhomogeneity correction step. Given the importance of bias fields correction in structural preprocessing and flexible implementation, we pursue a deep learning approximation / reinterpretation of the N4ITK bias fields correction to create a method which is portable, flexible, and fully differentiable. In this paper, we trained a deep learning network "DeepN4" on eight independent cohorts from 72 different scanners and age ranges with N4ITK-corrected T1w MRI and bias field for supervision in log space. We found that we can closely approximate N4ITK bias fields correction with naïve networks. We evaluate the peak signal to noise ratio (PSNR) in test dataset against the N4ITK corrected images. The median PSNR of corrected images between N4ITK and DeepN4 was 47.96 dB. In addition, we assess the DeepN4 model on eight additional external datasets and show the generalizability of the approach. This study establishes that incompatible N4ITK preprocessing steps can be closely approximated by naïve deep neural networks, facilitating more flexibility. All code and models are released at https://github.com/MASILab/DeepN4 ., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Purpose: Diffusion tensor imaging (DTI) is a magnetic resonance imaging technique that provides unique information about white matter microstructure in the brain but is susceptible to confounding effects introduced by scanner or acquisition differences. ComBat is a leading approach for addressing these site biases. However, despite its frequent use for harmonization, ComBat's robustness toward site dissimilarities and overall cohort size have not yet been evaluated in terms of DTI., Approach: As a baseline, we match N = 358 participants from two sites to create a "silver standard" that simulates a cohort for multi-site harmonization. Across sites, we harmonize mean fractional anisotropy and mean diffusivity, calculated using participant DTI data, for the regions of interest defined by the JHU EVE-Type III atlas. We bootstrap 10 iterations at 19 levels of total sample size, 10 levels of sample size imbalance between sites, and 6 levels of mean age difference between sites to quantify (i)  β AGE , the linear regression coefficient of the relationship between FA and age; (ii)  γ ^ s f * , the ComBat-estimated site-shift; and (iii)  δ ^ s f * , the ComBat-estimated site-scaling. We characterize the reliability of ComBat by evaluating the root mean squared error in these three metrics and examine if there is a correlation between the reliability of ComBat and a violation of assumptions., Results: ComBat remains well behaved for β AGE when N > 162 and when the mean age difference is less than 4 years. The assumptions of the ComBat model regarding the normality of residual distributions are not violated as the model becomes unstable., Conclusion: Prior to harmonization of DTI data with ComBat, the input cohort should be examined for size and covariate distributions of each site. Direct assessment of residual distributions is less informative on stability than bootstrap analysis. We caution use ComBat of in situations that do not conform to the above thresholds., (© 2024 The Authors.)

Imaging findings inconsistent with those expected at specific chronological age ranges may serve as early indicators of neurological disorders and increased mortality risk. Estimation of chronological age, and deviations from expected results, from structural magnetic resonance imaging (MRI) data has become an important proxy task for developing biomarkers that are sensitive to such deviations. Complementary to structural analysis, diffusion tensor imaging (DTI) has proven effective in identifying age-related microstructural changes within the brain white matter, thereby presenting itself as a promising additional modality for brain age prediction. Although early studies have sought to harness DTI's advantages for age estimation, there is no evidence that the success of this prediction is owed to the unique microstructural and diffusivity features that DTI provides, rather than the macrostructural features that are also available in DTI data. Therefore, we seek to develop white-matter-specific age estimation to capture deviations from normal white matter aging. Specifically, we deliberately disregard the macrostructural information when predicting age from DTI scalar images, using two distinct methods. The first method relies on extracting only microstructural features from regions of interest (ROIs). The second applies 3D residual neural networks (ResNets) to learn features directly from the images, which are non-linearly registered and warped to a template to minimize macrostructural variations. When tested on unseen data, the first method yields mean absolute error (MAE) of 6.11 ± 0.19 years for cognitively normal participants and MAE of 6.62 ± 0.30 years for cognitively impaired participants, while the second method achieves MAE of 4.69 ± 0.23 years for cognitively normal participants and MAE of 4.96 ± 0.28 years for cognitively impaired participants. We find that the ResNet model captures subtler, non-macrostructural features for brain age prediction.

Diffusion MRI (dMRI) streamline tractography, the gold-standard for in vivo estimation of white matter (WM) pathways in the brain, has long been considered as a product of WM microstructure. However, recent advances in tractography demonstrated that convolutional recurrent neural networks (CoRNN) trained with a teacher-student framework have the ability to learn to propagate streamlines directly from T1 and anatomical context. Training for this network has previously relied on high resolution dMRI. In this paper, we generalize the training mechanism to traditional clinical resolution data, which allows generalizability across sensitive and susceptible study populations. We train CoRNN on a small subset of the Baltimore Longitudinal Study of Aging (BLSA), which better resembles clinical scans. We define a metric, termed the epsilon ball seeding method, to compare T1 tractography and traditional diffusion tractography at the streamline level. We show that under this metric T1 tractography generated by CoRNN reproduces diffusion tractography with approximately three millimeters of error.

Subject head motion during the acquisition of diffusion-weighted imaging (DWI) of the brain induces artifacts and affects image quality. Information about the frequency and extent of motion could reveal which aspects of motion correction are most necessary. Therefore, we investigate the extent of translation and rotation among participants, and how the motion changes during the scan acquisition. We analyze 5,380 DWI scans from 1,034 participants. We measure the rotations and translations in the sagittal, coronal and transverse planes needed to align the volumes to the first and previous volumes, as well as the displacement. The different types of motion are compared with each other and compared over time. The largest rotation (per minute) is around the right - left axis (median 0.378 °/min, range 0.000 - 11.466°) and the largest translation (per minute) is along the anterior - posterior axis (median 1.867 mm/min, range 0.000 - 10.944 mm). We additionally observe that spikes in movement occur at the beginning of the scan, particularly in anterior - posterior translation. The results show that all scans are affected by subtle head motion, which may impact subsequent image analysis.

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., Competing Interests: The authors report no conflicts of interest. Author disclosures are available in the supporting information., (© 2023 The Authors. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

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., Competing Interests: Declaration of Competing Interest All authors declare no known competing financial interests or personal relationships with other people or organizations that could inappropriately influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

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., Competing Interests: The authors declare no conflicts of interest. Author disclosures are available in the Supporting Information., (© 2023 The Authors. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals, LLC on behalf of Alzheimer's Association.)

Objective: 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)., Methods: Data were available for 347 Alzheimer's disease (AD), 76 vascular dementia (VaD), and 811 control participants without dementia. Logistic regressions were performed associating International Classification of Diseases, 9th Revision (ICD-9) health codes with dementia status across all time points, 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., Results: 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 electrocardiogram (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., Interpretation: 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. ANN NEUROL 2023;93:805-818., (© 2022 American Neurological Association. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

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.

Batch size is a key hyperparameter in training deep learning models. Conventional wisdom suggests larger batches produce improved model performance. Here we present evidence to the contrary, particularly when using autoencoders to derive meaningful latent spaces from data with spatially global similarities and local differences, such as electronic health records (EHR) and medical imaging. We investigate batch size effects in both EHR data from the Baltimore Longitudinal Study of Aging and medical imaging data from the multimodal brain tumor segmentation (BraTS) challenge. We train fully connected and convolutional autoencoders to compress the EHR and imaging input spaces, respectively, into 32-dimensional latent spaces via reconstruction losses for various batch sizes between 1 and 100. Under the same hyperparameter configurations, smaller batches improve loss performance for both datasets. Additionally, latent spaces derived by autoencoders with smaller batches capture more biologically meaningful information. Qualitatively, we visualize 2-dimensional projections of the latent spaces and find that with smaller batches the EHR network better separates the sex of the individuals, and the imaging network better captures the right-left laterality of tumors. Quantitatively, the analogous sex classification and laterality regressions using the latent spaces demonstrate statistically significant improvements in performance at smaller batch sizes. Finally, we find improved individual variation locally in visualizations of representative data reconstructions at lower batch sizes. Taken together, these results suggest that smaller batch sizes should be considered when designing autoencoders to extract meaningful latent spaces among EHR and medical imaging data driven by global similarities and local variation.

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)., (Copyright © 2022 Elsevier Inc. All rights reserved.)

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., (Published by Oxford University Press on behalf of The Gerontological Society of America 2021.)

Community detection on graphs constructed from functional magnetic resonance imaging (fMRI) data has led to important insights into brain functional organization. Large studies of brain community structure often include images acquired on multiple scanners across different studies. Differences in scanner can introduce variability into the downstream results, and these differences are often referred to as scanner effects. Such effects have been previously shown to significantly impact common network metrics. In this study, we identify scanner effects in data-driven community detection results and related network metrics. We assess a commonly employed harmonization method and propose new methodology for harmonizing functional connectivity that leverage existing knowledge about network structure as well as patterns of covariance in the data. Finally, we demonstrate that our new methods reduce scanner effects in community structure and network metrics. Our results highlight scanner effects in studies of brain functional organization and provide additional tools to address these unwanted effects. These findings and methods can be incorporated into future functional connectivity studies, potentially preventing spurious findings and improving reliability of results., Competing Interests: Declaration of Competing Interest Authors declare that they have no conflict of interest., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Along with the increasing availability of electronic medical record (EMR) data, phenome-wide association studies (PheWAS) and phenome-disease association studies (PheDAS) have become a prominent, first-line method of analysis for uncovering the secrets of EMR. Despite this recent growth, there is a lack of approachable software tools for conducting these analyses on large-scale EMR cohorts. In this article, we introduce pyPheWAS, an open-source python package for conducting PheDAS and related analyses. This toolkit includes 1) data preparation, such as cohort censoring and age-matching; 2) traditional PheDAS analysis of ICD-9 and ICD-10 billing codes; 3) PheDAS analysis applied to a novel EMR phenotype mapping: current procedural terminology (CPT) codes; and 4) novelty analysis of significant disease-phenotype associations found through PheDAS. The pyPheWAS toolkit is approachable and comprehensive, encapsulating data prep through result visualization all within a simple command-line interface. The toolkit is designed for the ever-growing scale of available EMR data, with the ability to analyze cohorts of 100,000 + patients in less than 2 h. Through a case study of Down Syndrome and other intellectual developmental disabilities, we demonstrate the ability of pyPheWAS to discover both known and potentially novel disease-phenotype associations across different experiment designs and disease groups. The software and user documentation are available in open source at https://github.com/MASILab/pyPheWAS ., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

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 .

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.

Reproducible identification of white matter pathways across subjects is essential for the study of structural connectivity of the human brain. One of the key challenges is anatomical differences between subjects and human rater subjectivity in labeling. Labeling white matter regions of interest presents many challenges due to the need to integrate both local and global information. Clearly communicating the manual processes to capture this information is cumbersome, yet essential to lay a solid foundation for comprehensive atlases. Segmentation protocols must be designed so the interpretation of the requested tasks as well as locating structural landmarks is anatomically accurate, intuitive and reproducible. In this work, we quantified the reproducibility of a first iteration of an open/public multi-bundle segmentation protocol. This allowed us to establish a baseline for its reproducibility as well as to identify the limitations for future iterations. The protocol was tested/evaluated on both typical 3 T research acquisition Baltimore Longitudinal Study of Aging (BLSA) and high-acquisition quality Human Connectome Project (HCP) datasets. The results show that a rudimentary protocol can produce acceptable intra-rater and inter-rater reproducibility. However, this work highlights the difficulty in generalizing reproducible results and the importance of reaching consensus on anatomical description of white matter pathways. The protocol has been made available in open source to improve generalizability and reliability in collaboration. The goal is to improve upon the first iteration and initiate a discussion on the anatomical validity (or lack thereof) of some bundle definitions and the importance of reproducibility of tractography segmentation., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Purpose: Diffusion weighted MRI imaging (DWI) is often subject to low signal-to-noise ratios (SNRs) and artifacts. Recent work has produced software tools that can correct individual problems, but these tools have not been combined with each other and with quality assurance (QA). A single integrated pipeline is proposed to perform DWI preprocessing with a spectrum of tools and produce an intuitive QA document., Methods: The proposed pipeline, built around the FSL, MRTrix3, and ANTs software packages, performs DWI denoising; inter-scan intensity normalization; susceptibility-, eddy current-, and motion-induced artifact correction; and slice-wise signal drop-out imputation. To perform QA on the raw and preprocessed data and each preprocessing operation, the pipeline documents qualitative visualizations, quantitative plots, gradient verifications, and tensor goodness-of-fit and fractional anisotropy analyses., Results: Raw DWI data were preprocessed and quality checked with the proposed pipeline and demonstrated improved SNRs; physiologic intensity ratios; corrected susceptibility-, eddy current-, and motion-induced artifacts; imputed signal-lost slices; and improved tensor fits. The pipeline identified incorrect gradient configurations and file-type conversion errors and was shown to be effective on externally available datasets., Conclusions: The proposed pipeline is a single integrated pipeline that combines established diffusion preprocessing tools from major MRI-focused software packages with intuitive QA., (© 2021 International Society for Magnetic Resonance in Medicine.)

The hippocampus and underlying cortices are highly susceptible to pathologic change with increasing age. Using an associative face-scene (Face-Place) encoding task designed to target these regions, we investigated activation and connectivity patterns in cognitively normal older adults. Functional MRI scans were collected in 210 older participants (mean age = 76.4 yrs) in the Baltimore Longitudinal Study of Aging (BLSA). Brain activation patterns were examined during encoding of novel Face-Place pairs. Functional connectivity of the hippocampus was also examined during encoding, with seed regions placed along the longitudinal axis in the head, body and tail of the structure. In the temporal lobe, task activation patterns included coverage of the hippocampus and underlying ventral temporal cortices. Extensive activation was also seen in frontal, parietal and occipital lobes of the brain. Functional connectivity analyses during overall encoding showed that the head of the hippocampus was connected to frontal and anterior/middle temporal regions, the body with frontal, widespread temporal and occipital regions, and the tail with posterior temporal and occipital cortical regions. Connectivity limited to encoding of subsequently remembered stimuli showed a similar pattern for the hippocampal body, but differing patterns for the head and tail regions. These results show that the Face-Place task produces activation along the occipitotemporal visual pathway including medial temporal areas. The connectivity results also show that patterns of functional connectivity vary throughout the anterior-posterior extent of the hippocampus during memory encoding. As these patterns include regions vulnerable to pathologic change in early stages of Alzheimer's disease, continued longitudinal assessment of these individuals can provide valuable information regarding changes in brain-behavior relationships that may occur with advancing age and the onset of cognitive decline.

Resting-state functional MRI (rsfMRI) provides important information for studying and mapping the activities and functions of the brain. Conventionally, rsfMRIs are often registered to structural images in the Euclidean space without considering cortical surface geometry. Meanwhile, a surface-based representation offers a relaxed coordinate chart, but this still requires surface registration for group-wise data analysis. In this work, we investigate the performance of two existing surface registration methods in a surface-based rsfMRI analysis framework: FreeSurfer and Hierarchical Spherical Deformation (HSD). To minimize registration bias, we establish shape correspondence using both methods in a group-wise manner that estimates the unbiased average of a given cohort. To evaluate their performance, we focus on neuroanatomical alignment as well as the amount of distortion that can potentially bias surface tessellation for secondary level rsfMRI data analyses. In the pilot analysis, we examine a single timepoint of imaging data from 100 subjects out of an aging cohort. Overall, HSD establishes improved shape correspondence with reduced mean curvature deviation (10.94% less on average per subject, paired t-test: p <10 -10 ) and reduced registration distortion (FreeSurfer: average 41.91% distortion per subject, HSD: 18.63%, paired t-test: p <10 -10 ). Furthermore, HSD introduces less distortion than FreeSurfer in the areas identified in the individual components that were extracted by surface-based independent component analysis (ICA) after spatial smoothing and time series normalization. Consequently, we show that FreeSurfer capture individual components with globally similar but locally different patterns in ICA in visual inspection.

Prior neuroimaging studies have demonstrated isolated structural and connectivity changes in the brain due to Alzheimer's Disease (AD). However, how these changes relate to each other is not well understood. We present a preliminary study to begin to fill this gap by leveraging joint independent component analysis (jICA). We explore how jICA performs in an analysis of T1 and diffusion weighted MRI by characterizing the joint changes of complex cortical surface and structural connectivity metrics in AD in subjects from the Baltimore Longitudinal Study of Aging. We calculate 588 region-based cortical metrics and 4,753 fractional anisotropy-based connectivity metrics and project them into a low-dimensional manifold with principal component analysis. We perform jICA on the manifold and subsequently backproject the independent components to the original data space. We demonstrate component stability with 3-fold cross validation and find differential component loadings between 776 cognitively unimpaired control subjects and 23 with AD that generalizes across folds. In addition, we perform the same analysis on the surface and connectivity metrics separately and find that the joint approach identifies both novel and similar components to the separate approaches. To illustrate the joint approach's primary utility, we provide an example hypothesis for how surface and connectivity components may vary together with AD. These preliminary results suggest jointly varying independent cortical surface and structural connectivity components can be consistently extracted from MRI data and provide a data-driven way for generating novel hypotheses about AD that may not be captured by separate analyses.

Investigation of relationships between age-related changes in regional brain volumes and changes in domain-specific cognition could provide insights into the neural underpinnings of individual differences in cognitive aging. Domain-specific cognition (memory, verbal fluency, visuospatial ability) and tests of executive function and attention (Trail-Making Test Part A and B) and 47 brain volumes of interest (VOIs) were assessed in 836 Baltimore Longitudinal Study of Aging participants with mean follow-up of 4.1 years (maximum 23.1 years). To examine the correlation between changes in domain-specific cognition and changes in brain volumes, we used bivariate linear mixed effects models with unstructured variance-covariance structure to estimate longitudinal trajectories for each variable of interest and correlations among the random effects of these measures. Higher annual rates of memory decline were associated with greater volume loss in 14 VOIs primarily within the temporal and occipital lobes. Verbal fluency decline was associated with greater ventricular enlargement and volume loss in 24 VOIs within the frontal, temporal, and parietal lobes. Decline in visuospatial ability was associated with volume loss in 3 temporal and parietal VOIs. Declines on the attentional test were associated with volume loss in 4 VOIs located within temporal and parietal lobes. Greater declines on the executive function test were associated with greater ventricular enlargement and volume loss in 10 frontal, parietal, and temporal VOIs. Our findings highlight domain-specific patterns of regional brain atrophy that may contribute to individual differences in cognitive aging., Competing Interests: Declaration of Competing Interest The authors report no conflicts of interest., (Copyright © 2020. Published by Elsevier Inc.)

Studies suggest that concussions may be related to increased risk of neurodegenerative diseases, such as Chronic Traumatic Encephalopathy and Alzheimer's Disease. Most neuroimaging studies show effects of concussions in frontal and temporal lobes of the brain, yet the long-term impacts of concussions on the aging brain have not been well studied. We examined neuroimaging data from 51 participants (mean age at first imaging visit=65.1 ± 11.23) in the Baltimore Longitudinal Study of Aging (BLSA) who reported a concussion in their medical history an average of 23 years prior to the first imaging visit, and compared them to 150 participants (mean age at first imaging visit=66.6 ± 10.97) with no history of concussion. Participants underwent serial structural MRI over a mean of 5.17±6.14 years and DTI over a mean of 2.92±2.22 years to measure brain structure, as well as 15 O-water PET over a mean of 5.33±2.19 years to measure brain function. A battery of neuropsychological tests was also administered over a mean of 11.62±7.41 years. Analyses of frontal and temporal lobe regions were performed to examine differences in these measures between the concussion and control groups at first imaging visit and in change over time. Compared to those without concussion, participants with a prior concussion had greater brain atrophy in temporal lobe white matter and hippocampus at first imaging visit, which remained stable throughout the follow-up visits. Those with prior concussion also showed differences in white matter microstructure using DTI, including increased radial and axial diffusivity in the fornix/stria terminalis, anterior corona radiata, and superior longitudinal fasciculus at first imaging visit. In 15 O-water PET, higher resting cerebral blood flow was seen at first imaging visit in orbitofrontal and lateral temporal regions, and both increases and decreases were seen in prefrontal, cingulate, insular, hippocampal, and ventral temporal regions with longitudinal follow-up. There were no significant differences in neuropsychological performance between groups. Most of the differences observed between the concussed and non-concussed groups were seen at the first imaging visit, suggesting that concussions can produce long-lasting structural and functional alterations in temporal and frontal regions of the brain in older individuals. These results also suggest that many of the reported short-term effects of concussion may still be apparent later in life., Competing Interests: Declaration of Competing Interest The authors report no competing interests., (Published by Elsevier Inc.)

Increasing reliance on electronic medical records at large medical centers provides unique opportunities to perform population level analyses exploring disease progression and etiology. The massive accumulation of diagnostic, procedure, and laboratory codes in one place has enabled the exploration of co-occurring conditions, their risk factors, and potential prognostic factors. While most of the readily identifiable associations in medical records are (now) well known to the scientific community, there is no doubt many more relationships are still to be uncovered in EMR data. In this paper, we introduce a novel finding index to help with that task. This new index uses data mined from real-time PubMed abstracts to indicate the extent to which empirically discovered associations are already known (i.e., present in the scientific literature). Our methods leverage second-generation p-values, which better identify associations that are truly clinically meaningful. We illustrate our new method with three examples: Autism Spectrum Disorder, Alzheimer's Disease, and Optic Neuritis. Our results demonstrate wide utility for identifying new associations in EMR data that have the highest priority among the complex web of correlations and causalities. Data scientists and clinicians can work together more effectively to discover novel associations that are both empirically reliable and clinically understudied., Competing Interests: The authors have declared that no competing interests exist.

Composite models that combine medical imaging with electronic medical records (EMR) improve predictive power when compared to traditional models that use imaging alone. The digitization of EMR provides potential access to a wealth of medical information, but presents new challenges in algorithm design and inference. Previous studies, such as Phenome Wide Association Study (PheWAS), have shown that EMR data can be used to investigate the relationship between genotypes and clinical conditions. Here, we introduce Phenome-Disease Association Study to extend the statistical capabilities of the PheWAS software through a custom Python package, which creates diagnostic EMR signatures to capture system-wide co-morbidities for a disease population within a given time interval. We investigate the effect of integrating these EMR signatures with radiological data to improve diagnostic classification in disease domains known to have confounding factors because of variable and complex clinical presentation. Specifically, we focus on two studies: First, a study of four major optic nerve related conditions; and second, a study of diabetes. Addition of EMR signature vectors to radiologically derived structural metrics improves the area under the curve (AUC) for diagnostic classification using elastic net regression, for diseases of the optic nerve. For glaucoma, the AUC improves from 0.71 to 0.83, for intrinsic optic nerve disease it increases from 0.72 to 0.91, for optic nerve edema it increases from 0.95 to 0.96, and for thyroid eye disease from 0.79 to 0.89. The EMR signatures recapitulate known comorbidities with diabetes, such as abnormal glucose, but do not significantly modulate image-derived features. In summary, EMR signatures present a scalable and readily applicable.

Disentangling the heterogeneity of brain aging in cognitively normal older adults is challenging, as multiple co-occurring pathologic processes result in diverse functional and structural changes. Capitalizing on machine learning methods applied to magnetic resonance imaging data from 400 participants aged 50 to 96 years in the Baltimore Longitudinal Study of Aging, we constructed normative cross-sectional brain aging trajectories of structural and functional changes. Deviations from typical trajectories identified individuals with resilient brain aging and multiple subtypes of advanced brain aging. We identified 5 distinct phenotypes of advanced brain aging. One group included individuals with relatively extensive structural and functional loss and high white matter hyperintensity burden. Another subgroup showed focal hippocampal atrophy and lower posterior-cingulate functional coherence, low white matter hyperintensity burden, and higher medial-temporal connectivity, potentially reflecting high brain tissue reserve counterbalancing brain loss that is consistent with early stages of Alzheimer's disease. Other subgroups displayed distinct patterns. These results indicate that brain changes should not be measured seeking a single signature of brain aging but rather via methods capturing heterogeneity and subtypes of brain aging. Our findings inform future studies aiming to better understand the neurobiological underpinnings of brain aging imaging patterns., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Background: Chronic inflammation has been linked to memory and other cognitive impairments, as well as Alzheimer's disease. Here, we investigate the association between inflammatory markers and changes in brain activity measured by regional cerebral blood flow (rCBF) to assess the relationship between inflammation and brain function in older individuals., Methods: Annual 15O water resting-state positron emission tomography (PET) scans collected over a 5-year period were assessed in 138 cognitively normal older participants (77 males; mean age at baseline = 71.3; mean scans per participant = 3.5) in the Baltimore Longitudinal Study of Aging. Voxel-wise linear mixed models were used to investigate associations between rCBF and C-reactive protein (CRP) and interleukin-6 (IL-6) at the time of scanning. We examined relationships between baseline CRP and IL-6 levels and baseline rCBF, and relationships between baseline and mean inflammatory levels over time and longitudinal rCBF changes., Results: Higher baseline CRP and IL-6 were each associated with lower baseline rCBF primarily in frontal and occipital regions, with only the lingual gyrus surviving atrophy correction. Higher baseline and mean CRP were also associated with greater rCBF declines over time in anterior cingulate and hippocampal regions, whereas higher baseline and mean IL-6 levels were associated with greater rCBF declines in orbitofrontal and hippocampal regions., Conclusions: Higher levels of inflammation are associated with longitudinal changes in brain function in regions important for cognition. These results, along with previous studies, suggest that chronic inflammation in older adults may contribute to age-associated declines in cognitive function.

Background: Recent studies show links between metabolic syndrome and Alzheimer's disease (AD) neuropathology. Understanding the link between vascular-related health conditions and dementia will help target at risk populations and inform clinical strategies for early detection and prevention of AD., Objective: To determine whether metabolic syndrome is associated with global cerebral amyloid-β (Aβ) positivity and longitudinal Aβ accumulation., Methods: Prospective study of 165 participants who underwent (11)C-Pittsburgh compound B (PiB) PET neuroimaging to measure Aβ, from June 2005 to May 2016. Metabolic syndrome was defined using the revised Third Adults Treatment Panel of the National Cholesterol Education Program criteria. Participants were classified as PiB+/-. Linear mixed effects models assessed the relationships between baseline metabolic syndrome and PiB status and regional Aβ change over time., Results: A total of 165 cognitively normal participants of the Baltimore Longitudinal Study of Aging (BLSA) Neuroimaging substudy, aged 55-92 years (mean baseline age = 76.4 years, 85 participants were male), received an average of 2.5 PET-PiB scans over an average interval of 2.6 (3.08 SD) years between first and last visits. Metabolic syndrome was not associated with baseline PiB positivity or concurrent regional Aβ. Metabolic syndrome was associated with increased rates of Aβ accumulation in superior parietal and precuneus regions over time in the PiB+ group. Elevated fasting glucose and blood pressure showed individual associations with accelerated Aβ accumulation., Conclusion: Metabolic syndrome was associated with accelerated Aβ accumulation in PiB+ individuals and may be an important factor in the progression of AD pathology.

We recently reported that alpha-2 macroglobulin (A2M) is a biomarker of neuronal injury in Alzheimer's disease (AD) and identified a network of nine genes co-expressed with A2M in the brain. This network includes the gene encoding SPARCL1, a protein implicated in synaptic maintenance. Here, we examine whether SPARCL1 is associated with longitudinal changes in brain structure and function in older individuals at risk for AD in the Baltimore Longitudinal Study of Aging. Using data from the Gene-Tissue Expression Project, we first identified two single nucleotide polymorphisms (SNPs), rs9998212 and rs7695558, associated with lower brain SPARCL1 gene expression. We then analyzed longitudinal trajectories of cognitive performance in 591 participants who remained cognitively normal (average follow-up interval: 11.8 years) and 129 subjects who eventually developed MCI or AD (average follow-up interval: 9.4 years). Cognitively normal minor allele carriers of rs7695558 who developed incident AD showed accelerated memory loss prior to disease onset. Next, we compared longitudinal changes in brain volumes (MRI; n = 120 participants; follow-up = 6.4 years; 826 scans) and resting-state cerebral blood flow (rCBF; 15O-water PET; n = 81 participants; follow-up = 7.7 years; 664 scans) in cognitively normal participants. Cognitively normal minor allele carriers of rs9998212 showed accelerated atrophy in several global, lobar, and regional brain volumes. Minor allele carriers of both SNPs showed longitudinal changes in rCBF in several brain regions, including those vulnerable to AD pathology. Our findings suggest that SPARCL1 accelerates AD pathogenesis and thus link neuroinflammation with widespread changes in brain structure and function during aging.

One theory of age-related cognitive decline proposes that changes within the default mode network (DMN) of the brain impact the ability to successfully perform cognitive operations. To investigate this theory, we examined functional covariance within brain networks using regional cerebral blood flow data, measured by 15 O-water PET, from 99 participants (mean baseline age 68.6 ± 7.5) in the Baltimore Longitudinal Study of Aging collected over a 7.4 year period. The sample was divided in tertiles based on longitudinal performance on a verbal recognition memory task administered during scanning, and functional covariance was compared between the upper (improvers) and lower (decliners) tertile groups. The DMN and verbal memory networks (VMN) were then examined during the verbal memory scan condition. For each network, group differences in node-to-network coherence and individual node-to-node covariance relationships were assessed at baseline and in change over time. Compared with improvers, decliners showed differences in node-to-network coherence and in node-to-node relationships in the DMN but not the VMN during verbal memory. These DMN differences reflected greater covariance with better task performance at baseline and both increasing and declining covariance with declining task performance over time for decliners. When examined during the resting state alone, the direction of change in DMN covariance was similar to that seen during task performance, but node-to-node relationships differed from those observed during the task condition. These results suggest that disengagement of DMN components during task performance is not essential for successful cognitive performance as previously proposed. Instead, a proper balance in network processes may be needed to support optimal task performance.

Unlabelled: Quantification of β-amyloid (Aβ) in vivo is often accomplished using the distribution volume ratio (DVR), based on a simplified reference tissue model. We investigated the local relationships between DVR and cerebral blood flow (CBF), as well as relative CBF (R1), in nondemented older adults., Methods: Fifty-five nondemented participants (mean age, 78.5 y) in the Baltimore Longitudinal Study of Aging underwent (15)O-H2O PET CBF and dynamic (11)C-PiB PET. (15)O-H2O PET images were normalized and smoothed using SPM. A simplified reference tissue model with linear regression and spatial constraints was used to generate parametric DVR images. The DVR images were regressed on CBF images on a voxel-by-voxel basis using robust biologic parametric mapping, adjusting for age and sex (false discovery rate, P = 0.05; spatial extent, 50 voxels). DVR images were also regressed on R1 images, a measure of the transport rate constant from vascular space to tissue. All analyses were performed on the entire sample, and on high and low tertiles of mean cortical DVR., Results: Voxel-based analyses showed that increased DVR is associated with increased CBF in the frontal, parietal, temporal, and occipital cortices. However, this association appears to spare regions that typically show early Aβ deposition. A more robust relationship between DVR and CBF was observed in the lower tertile of DVR, that is, negligible cortical Aβ load, compared with the upper tertile of cortical DVR and Aβ load. The spatial distributions of the DVR-CBF and DVR-R1 correlations showed similar patterns. No reliable negative voxelwise relationships between DVR and CBF or R1 were observed., Conclusion: Robust associations between DVR and CBF at negligible Aβ levels, together with similar spatial distributions of DVR-CBF and DVR-R1 correlations, suggest that regional distribution of DVR reflects blood flow and tracer influx rather than pattern of Aβ deposition in those with minimal Aβ load. DVR-CBF associations in individuals with a higher DVR are more likely to reflect true associations between patterns of Aβ deposition and CBF or neural activity. These findings have important implications for analysis and interpretation of voxelwise correlations with external variables in individuals with varying amounts of Aβ load., (© 2015 by the Society of Nuclear Medicine and Molecular Imaging, Inc.)

Background: Cross-sectional studies of the association between hypertension (HTN) and brain atrophy have shown reductions in prefrontal, temporal, and hippocampal volumes, and have identified thinner cortices across the cortical mantle., Method: In the current study, we followed 96 participants enrolled in the Baltimore Longitudinal Study of Aging over a mean interval of 8 years (mean age at baseline = 68.7) and compared those who are hypertensive (n = 49) throughout the study with those who are normotensive (n = 47)., Results: Hypertensive individuals show an increased rate of thinning compared with normotensive individuals in several regions, including the frontomarginal gyrus in the left hemisphere, and the superior temporal, fusiform, and lateral orbitofrontal cortex in the right hemisphere. We also investigated the effects of midlife blood pressure (BP), intervisit variability in BP prior to imaging, and duration of HTN on areas that show subsequent differences in the rates of cortical thinning between groups. We found that higher midlife BP and longer durations of HTN predicted a higher rate of thinning in the right superior temporal gyrus. We also found that greater variability in SBP but not DBP predicted a higher rate of thinning in the right superior temporal gyrus, frontomarginal gyrus, and occipital pole., Conclusion: These findings demonstrate that hypertensive individuals show increased rates of thinning compared with normotensive individuals and suggest intervisit BP variability and midlife BP contribute to these longitudinal differences.

Although overweight and obesity are associated with poor health outcomes in the elderly, the biological bases of obesity-related behaviors during aging are poorly understood. Common variants in the FTO gene are associated with adiposity in children and younger adults as well as with adverse mental health in older individuals. However, it is unclear whether FTO influences longitudinal trajectories of adiposity and other intermediate phenotypes relevant to mental health during aging. We examined whether a commonly carried obesity-risk variant in the FTO gene (rs1421085 single-nucleotide polymorphism) influences adiposity and is associated with changes in brain function in participants within the Baltimore Longitudinal Study of Aging, one of the longest-running longitudinal aging studies in the United States. Our results show that obesity-related risk allele carriers of FTO gene show dose-dependent increments in body mass index during aging. Moreover, the obesity-related risk allele is associated with reduced medial prefrontal cortical function during aging. Consistent with reduced brain function in regions intrinsic to impulse control and taste responsiveness, risk allele carriers of FTO exhibit dose-dependent increments in both impulsivity and intake of fatty foods. We propose that a common neural mechanism may underlie obesity-associated impulsivity and increased consumption of high-calorie foods during aging.

To develop targeted intervention strategies for the treatment of Alzheimer's disease, we first need to identify early markers of brain changes that occur before the onset of cognitive impairment. Here, we examine changes in resting-state brain function in humans from the Baltimore Longitudinal Study of Aging. We compared longitudinal changes in regional cerebral blood flow (rCBF), assessed by (15)O-water PET, over a mean 7 year period between participants who eventually developed cognitive impairment (n = 22) and those who remained cognitively normal (n = 99). Annual PET assessments began an average of 11 years before the onset of cognitive impairment in the subsequently impaired group, so all participants were cognitively normal during the scanning interval. A voxel-based mixed model analysis was used to compare groups with and without subsequent impairment. Participants with subsequent impairment showed significantly greater longitudinal rCBF increases in orbitofrontal, medial frontal, and anterior cingulate regions, and greater longitudinal decreases in parietal, temporal, and thalamic regions compared with those who maintained cognitive health. These changes were linear in nature and were not influenced by longitudinal changes in regional tissue volume. Although all participants were cognitively normal during the scanning interval, most of the accelerated rCBF changes seen in the subsequently impaired group occurred within regions thought to be critical for the maintenance of cognitive function. These changes also occurred within regions that show early accumulation of pathology in Alzheimer's disease, suggesting that there may be a connection between early pathologic change and early changes in brain function.

We investigated whether individuals with impaired glucose tolerance (IGT) in midlife subsequently show regionally specific longitudinal changes in regional cerebral blood flow (rCBF) relative to those with normal glucose tolerance (NGT). Sixty-four cognitively normal participants in the neuroimaging substudy of the Baltimore Longitudinal Study of Aging underwent serial (15)O-water positron emission tomography scans (age at first scan, 69.6 ± 7.5 years) and oral glucose tolerance tests 12 years earlier (age at first oral glucose tolerance test, 57.2 ± 11.1 years). Using voxel-based analysis, we compared changes in rCBF over an 8-year period between 15 participants with IGT in midlife and 49 with NGT. Significant differences were observed in longitudinal change in rCBF between the IGT and NGT groups. The predominant pattern was greater rCBF decline in the IGT group in the frontal, parietal, and temporal cortices. Some brain regions in the frontal and temporal cortices also showed greater longitudinal increments in rCBF in the IGT group. Our findings suggest that IGT in midlife is associated with subsequent longitudinal changes in brain function during aging even in cognitively normal older individuals., (Published by Elsevier Inc.)

Longitudinal studies on aging brain function have shown declines in frontal activity as opposed to the over-recruitment shown in cross-sectional studies. Such mixed findings suggest that age-related changes in frontal activity may be process- and region-specific, having varied associations across different frontal regions involved in distinct cognitive processes, rather than generalized across the frontal cortex. Using data from the Baltimore Longitudinal Study of Aging (BLSA), we examined individual differences through cross-sectional associations at baseline evaluation and longitudinal changes in regional cerebral blood flow (rCBF) in relation to different executive abilities in cognitively normal older adults. We found that, at baseline, greater rCBF in middle frontal regions correlated with better performance in abstraction and chunking, but greater rCBF in the insula and a distinct middle frontal region correlated with poorer inhibition and discrimination, respectively. In addition, increases in frontal rCBF over time were associated with longitudinal declines in abstraction, chunking, inhibition, discrimination, switching, and manipulation. These findings indicate process- and region-specific, rather than uniform, age-related changes in frontal brain-behavior associations, and also suggest that longitudinally high-levels of frontal engagement reflect declining rather than stable cognition., (Published by Elsevier Inc.)

Background: We examined the effect of the novel Alzheimer's disease (AD) risk variant rs11136000 single nucleotide polymorphism in the clusterin gene (CLU) on longitudinal changes in resting state regional cerebral blood flow (rCBF) during normal aging and investigated its influence on cognitive decline in presymptomatic stages of disease progression., Methods: Subjects were participants in the Baltimore Longitudinal Study of Aging. A subset of 88 cognitively normal older individuals had longitudinal (15)O-water positron emission tomography measurements of rCBF at baseline and up to eight annual follow-up visits. We also analyzed trajectories of cognitive decline among CLU risk carriers and noncarriers in individuals who remained cognitively normal (n = 599), as well as in those who subsequently converted to mild cognitive impairment or AD (n = 95)., Results: Cognitively normal carriers of the CLU risk allele showed significant and dose-dependent longitudinal increases in resting state rCBF in brain regions intrinsic to memory processes. There were no differences in trajectories of memory performance between CLU risk carriers and noncarriers who remained cognitively normal. However, in cognitively normal individuals who eventually converted to mild cognitive impairment or AD, CLU risk carriers showed faster rates of decline in memory performance relative to noncarriers in the presymptomatic stages of disease progression., Conclusions: The AD risk variant CLU influences longitudinal changes in brain function in asymptomatic individuals and is associated with faster cognitive decline in presymptomatic stages of disease progression. These results suggest mechanisms underlying the role of CLU in AD and may be important in monitoring disease progression in at-risk elderly., (Copyright © 2013 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Events have clear and consistent boundaries that are defined during perception in a manner that influences memory performance. The natural process of event segmentation shapes event definitions during perception, and appears to play a critical role in defining distinct episodic memories at encoding. However, the role of retrieval processes in modifying event definitions is not clear. We explored how such processes changed event boundary definitions at recall. In Experiment 1 we showed that distance from encoding is related to boundary flexibility. Participants were more likely to move self-reported event boundaries to include information reported beyond those boundaries when recalling more distant events compared to more recent events. In Experiment 2 we showed that age also influenced boundary flexibility. Older Age adults were more likely to move event boundaries than College Age adults, and the relationship between distance from encoding and boundary flexibility seen in Experiment 1 was present only in College Age and Middle Age adults. These results suggest that factors at retrieval have a direct impact on event definitions in memory and that, although episodic memories may be initially defined at encoding, these definitions are not necessarily maintained in long-term memory.

This article investigates longitudinal imaging characteristics of early cognitive decline during normal aging, leveraging on high-dimensional imaging pattern classification methods for the development of early biomarkers of cognitive decline. By combining magnetic resonance imaging (MRI) and resting positron emission tomography (PET) cerebral blood flow (CBF) images, an individualized score is generated using high-dimensional pattern classification, which predicts subsequent cognitive decline in cognitively normal older adults of the Baltimore Longitudinal Study of Aging. The resulting score, termed SPARE-CD (Spatial Pattern of Abnormality for Recognition of Early Cognitive Decline), analyzed longitudinally for 143 cognitively normal subjects over 8 years, shows functional and structural changes well before (2.3-2.9 years) changes in neurocognitive testing (California Verbal Learning Test [CVLT] scores) can be measured. Additionally, this score is found to be correlated to the [(11)C] Pittsburgh compound B (PiB) PET mean distribution volume ratio at a later time. This work indicates that MRI and PET images, combined with advanced pattern recognition methods, may be useful for very early detection of cognitive decline., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Background: Anemia has been associated with elevated cerebral blood flow (CBF) in animal models and certain clinical conditions (eg, renal disease), but whether hemoglobin level variations across a relatively normal range are associated with local or diffuse CBF changes is unclear. We investigated whether lower hemoglobin is associated with regional increases in relative CBF in older individuals, and if these increases occur in watershed regions., Methods: Seventy-four older nondemented adults underwent serial (15)O water positron emission tomography scans. Voxel-based analysis was used to investigate regional relative CBF patterns in association with hemoglobin level and in individuals with and without anemia. Analyses of cross-sectional relations between regional CBF and anemia were performed separately at two time points, 2 years apart, to identify replicable patterns of associations., Results: Restricting results to associations replicated across two cross-sectional analyses, lower hemoglobin was associated with higher relative CBF within the middle/inferior frontal, occipital, precuneus, and cerebellar regions. In addition, individuals with anemia (n = 15) showed higher relative CBF in superior frontal, middle temporal, hippocampal, and gyrus rectus regions than those without anemia. In some regions (right superior temporal gyrus, left inferior frontal gyrus, midline cuneus, and right precuneus); however, lower hemoglobin was associated with lower relative CBF., Conclusions: In nondemented individuals, lower hemoglobin is associated with elevated relative CBF in specific cortical areas but reduced CBF in other areas. Whether this association between anemia and CBF in the absence of chronic diseases and in a normal physiologic range is related to clinical endpoints warrants further study.

Background and Purpose: The Framingham Heart Study group cardiovascular disease risk profile (FCRP) score was used to assess the relationship between baseline cardiovascular risk and subsequent changes in resting state cerebral blood flow (CBF) in cognitively normal older participants from the Baltimore Longitudinal Study of Aging., Methods: Ninty-seven cognitively normal participants underwent annual resting-state positron emission tomography scans at baseline and over a period of up to 8 years (mean interval, 7.4 years). Images quantifying voxel-wise longitudinal rates of CBF change were calculated and used to examine the relationship between baseline FCRP score and changes over time in regional CBF. Individual components of the FCRP score (age, cholesterol, blood pressure, smoking status, and type 2 diabetes) were also correlated with changes in regional CBF to examine the independent contributions of each component to the overall pattern of change., Results: Higher baseline FCRP scores were associated with accelerated longitudinal decline in CBF in orbitofrontal, medial frontal/anterior cingulate, insular, precuneus, and brain stem regions. Of the components that comprise the FCRP score, higher diastolic blood pressure and diabetes were associated independently with greater decline in the medial frontal/anterior cingulate and insular regions, respectively., Conclusions: Baseline cardiovascular risk factors are associated with greater rates of decline in resting state regional brain function. The regions showing accelerated decline participate in higher-order cognitive processes and are also vulnerable to age-related neuropathology. These results, in conjunction with other studies, encourage early treatment of cardiovascular risk factors in older individuals.