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Objective: White matter hyperintensity (WMH) volume is a neuroimaging marker of lesion load related to small vessel disease that has been associated with cognitive aging and Alzheimer's disease (AD) risk., Method: The present study sought to examine whether regional WMH volume mediates the relationship between APOE ε4 status, a strong genetic risk factor for AD, and cognition and if this association is moderated by age group differences within a sample of 187 healthy older adults (APOE ε4 status [carrier/non-carrier] = 56/131)., Results: After we controlled for sex, education, and vascular risk factors, ANCOVA analyses revealed significant age group by APOE ε4 status interactions for right parietal and left temporal WMH volumes. Within the young-old group (50-69 years), ε4 carriers had greater right parietal and left temporal WMH volumes than non-carriers. However, in the old-old group (70-89 years), right parietal and left temporal WMH volumes were comparable across APOE ε4 groups. Further, within ε4 non-carriers, old-old adults had greater right parietal and left temporal WMH volumes than young-old adults, but there were no significant differences across age groups in ε4 carriers. Follow-up moderated mediation analyses revealed that, in the young-old, but not the old-old group, there were significant indirect effects of ε4 status on memory and executive functions through left temporal WMH volume., Conclusions: These findings suggest that, among healthy young-old adults, increased left temporal WMH volume, in the context of the ε4 allele, may represent an early marker of cognitive aging with the potential to lead to greater risk for AD.

Limited research exists on the association between resting-state functional network connectivity in the brain and learning and memory processes in advanced age. This study examined within-network connectivity of cingulo-opercular (CON), frontoparietal control (FPCN), and default mode (DMN) networks, and verbal and visuospatial learning and memory in older adults. Across domains, we hypothesized that greater CON and FPCN connectivity would associate with better learning, and greater DMN connectivity would associate with better memory. A total of 330 healthy older adults (age range = 65-89) underwent resting-state fMRI and completed the Hopkins Verbal Learning Test-Revised (HVLT-R) and Brief Visuospatial Memory Test-Revised (BVMT-R) in a randomized clinical trial. Total and delayed recall scores were assessed from baseline data, and a learning ratio calculation was applied to participants' scores. Average CON, FPCN, and DMN connectivity values were obtained with CONN Toolbox. Hierarchical regressions controlled for sex, race, ethnicity, years of education, and scanner site, as this was a multi-site study. Greater within-network CON connectivity was associated with better verbal learning (HVLT-R Total Recall, Learning Ratio), visuospatial learning (BVMT-R Total Recall), and visuospatial memory (BVMT-R Delayed Recall). Greater FPCN connectivity was associated with better visuospatial learning (BVMT-R Learning Ratio) but did not survive multiple comparison correction. DMN connectivity was not associated with these measures of learning and memory. CON may make small but unique contributions to learning and memory across domains, making it a valuable target in future longitudinal studies and interventions to attenuate memory decline. Further research is necessary to understand the role of FPCN in learning and memory., (© 2023. The Author(s), under exclusive licence to American Aging Association.)

Declines in processing speed performance occur in aging and are a critical marker of functional independence in older adults. Numerous studies suggest that Useful Field of View (UFOV) training may ameliorate cognitive decline in older adults. Despite its efficacy, little is known about the neural correlates of this task. The current study is the first to investigate the coherence of functional connectivity during UFOV task completion. A total of 336 participants completed the UFOV task while undergoing task-based functional magnetic resonance imaging (fMRI). Ten spherical regions of interest (ROIs), selected a priori, were created based on regions with the greatest peak BOLD activation patterns in the UFOV fMRI task and regions that have been shown to significantly relate to UFOV fMRI task performance. We used a weighted ROI-to-ROI connectivity analysis to model task-specific functional connectivity strength between these a priori selected ROIs. We found that our UFOV fMRI network was functionally connected during task performance and was significantly associated to UFOV fMRI task performance. Within-network connectivity of the UFOV fMRI network showed comparable or better predictive power in accounting for UFOV accuracy compared to 7 resting state networks, delineated by Yeo and colleagues. Finally, we demonstrate that the within-network connectivity of UFOV fMRI task accounted for scores on a measure of "near transfer", the Double Decision task, better than the aforementioned resting state networks. Our data elucidate functional connectivity patterns of the UFOV fMRI task. This may assist in future targeted interventions that aim to improve synchronicity within the UFOV fMRI network., (© 2022. The Author(s), under exclusive licence to American Aging Association.)

Declines in processing speed performance occur in aging and are a critical marker of functional independence in older adults. Studies suggest that Useful Field of View (UFOV) training may ameliorate cognitive decline. Despite its efficacy, little is known about the neural correlates of this task. Within the current study, 233 healthy older adults completed a UFOV-based task while undergoing functional magnetic resonance imaging (fMRI). During the "stimulus" portion of this task, participants must identify a target in the center of the screen and the location of a target in the periphery, among distractors. During the "probe" portion, participants must decide if the object in the center and the location of the target in the periphery were identical to the "stimulus" screen. Widespread bilateral whole-brain activation was observed when activation patterns of the "probe" contrast were subtracted from the "stimulus" contrast. Conversely, the subtraction of "stimulus" from "probe" was associated with discrete activation patterns consisting of 13 clusters. Additionally, when evaluating the variance associated with task accuracy, specific subregions were identified that may be critical for task performance. Our data elucidate the functional neural correlates of a UFOV-based task, a task used in both cognitive training paradigms and assessment of function., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Speed-of-processing abilities decline with age yet are important in performing instrumental activities of daily living. The useful field of view, or Double Decision task, assesses speed-of-processing and divided attention. Performance on this task is related to attention, executive functioning, and visual processing abilities in older adults, and poorer performance predicts more motor vehicle accidents in the elderly. Cognitive training in this task reduces risk of dementia. Structural and functional neural correlates of this task suggest that higher-order resting state networks may be associated with performance on the Double Decision task, although this has never been explored. This study aimed to assess the association of within-network connectivity of the default mode network, dorsal attention network, frontoparietal control network, and cingulo-opercular network with Double Decision task performance, and subcomponents of this task in a sample of 267 healthy older adults. Multiple linear regressions showed that connectivity of the cingulo-opercular network is associated with visual speed-of-processing and divided attention subcomponents of the Double Decision task. Cingulo-opercular network and frontoparietal control network connectivity is associated with Double Decision task performance. Stronger connectivity is related to better performance in all cases. These findings confirm the unique role of the cingulo-opercular network in visual attention and sustained divided attention. Frontoparietal control network connectivity, in addition to cingulo-opercular network connectivity, is related to Double Decision task performance, a task implicated in reduced dementia risk. Future research should explore the role these higher-order networks play in reduced dementia risk after cognitive intervention using the Double Decision task., (© 2021. American Aging Association.)

Purpose: Magnetic resonance imaging (MRI) is widely used in human brain research to evaluate the effects of healthy aging and development, as well as neurological disorders. Although standardized methods for quality assurance of human MRI instruments have been established, such approaches have typically not been translated to small animal imaging. We present a method for the generation and analysis of customized phantoms for small animal MRI systems that allows rapid and accurate system stability monitoring., Methods: Computer-aided design software was used to produce a customized phantom using a rapid prototyping printer. Automated registration algorithms were used on three-dimensional images of the phantom to allow system stability to be easily monitored over time., Results: The design of the custom phantom allowed reliable placement relative to the imaging coil. Automated registration showed superior ability to detect gradient changes reflected in the images than with manual measurements. Registering images acquired over time allowed monitoring of gradient drifts of less than one percent., Conclusion: A low cost, MRI compatible phantom was successfully designed using computer-aided design software and a three-dimensional printer. Registering phantom images acquired over time allows monitoring of gradient stability of the MRI system., (Copyright © 2013 Wiley Periodicals, Inc.)

We previously introduced a voxel-based, multi-modal application of the partial least square algorithm (MMPLS) to characterize the linkage between patterns in a person's complementary complex datasets without the need to correct for multiple regional comparisons. Here we used it to demonstrate a strong correlation between MMPLS scores to characterize the linkage between the covarying patterns of fluorodeoxyglucose positron emission tomography (FDG PET) measurements of regional glucose metabolism and magnetic resonance imaging (MRI) measurements of regional gray matter associated with apolipoprotein E (APOE) ε4 gene dose (i.e., three levels of genetic risk for late-onset Alzheimer's disease (AD)) in cognitively normal, late-middle-aged persons. Coregistered and spatially normalized FDG PET and MRI images from 70% of the subjects (27 ε4 homozygotes, 36 ε4 heterozygotes and 67 ε4 non-carriers) were used in a hypothesis-generating MMPLS analysis to characterize the covarying pattern of regional gray matter volume and cerebral glucose metabolism most strongly correlated with APOE-ε4 gene dose. Coregistered and spatially normalized FDG PET and MRI images from the remaining 30% of the subjects were used in a hypothesis-testing MMPLS analysis to generate FDG PET-MRI gray matter MMPLS scores blind to their APOE genotype and characterize their relationship to APOE-ε4 gene dose. The hypothesis-generating analysis revealed covarying regional gray matter volume and cerebral glucose metabolism patterns that resembled those in traditional univariate analyses of AD and APOE-ε4 gene dose and PET-MRI scores that were strongly correlated with APOE-ε4 gene dose (p<1 × 10(-16)). The hypothesis-testing analysis results showed strong correlations between FDG PET-MRI gray matter scores and APOE-ε4 gene dose (p = 8.7 × 10(-4)). Our findings support the possibility of using the MMPLS to analyze complementary datasets from the same person in the presymptomatic detection and tracking of AD., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Neuroimaging centers and pharmaceutical companies are working together to evaluate treatments that might slow the progression of Alzheimer's disease (AD), a common but devastating late-life neuropathology. Recently, automated brain mapping methods, such as tensor-based morphometry (TBM) of structural MRI, have outperformed cognitive measures in their precision and power to track disease progression, greatly reducing sample size estimates for drug trials. In the largest TBM study to date, we studied how sample size estimates for tracking structural brain changes depend on the time interval between the scans (6-24 months). We analyzed 1309 brain scans from 91 probable AD patients (age at baseline: 75.4+/-7.5 years) and 189 individuals with mild cognitive impairment (MCI; 74.6+/-7.1 years), scanned at baseline, 6, 12, 18, and 24 months. Statistical maps revealed 3D patterns of brain atrophy at each follow-up scan relative to the baseline; numerical summaries were used to quantify temporal lobe atrophy within a statistically-defined region-of-interest. Power analyses revealed superior sample size estimates over traditional clinical measures. Only 80, 46, and 39 AD patients were required for a hypothetical clinical trial, at 6, 12, and 24 months respectively, to detect a 25% reduction in average change using a two-sided test (alpha=0.05, power=80%). Correspondingly, 106, 79, and 67 subjects were needed for an equivalent MCI trial aiming for earlier intervention. A 24-month trial provides most power, except when patient attrition exceeds 15-16%/year, in which case a 12-month trial is optimal. These statistics may facilitate clinical trial design using voxel-based brain mapping methods such as TBM., (Copyright (c) 2010 Elsevier Inc. All rights reserved.)

Tensor-based morphometry (TBM) is a powerful method to map the 3D profile of brain degeneration in Alzheimer's disease (AD) and mild cognitive impairment (MCI). We optimized a TBM-based image analysis method to determine what methodological factors, and which image-derived measures, maximize statistical power to track brain change. 3D maps, tracking rates of structural atrophy over time, were created from 1030 longitudinal brain MRI scans (1-year follow-up) of 104 AD patients (age: 75.7+/-7.2 years; MMSE: 23.3+/-1.8, at baseline), 254 amnestic MCI subjects (75.0+/-7.2 years; 27.0+/-1.8), and 157 healthy elderly subjects (75.9+/-5.1 years; 29.1+/-1.0), as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI). To determine which TBM designs gave greatest statistical power, we compared different linear and nonlinear registration parameters (including different regularization functions), and different numerical summary measures derived from the maps. Detection power was greatly enhanced by summarizing changes in a statistically-defined region-of-interest (ROI) derived from an independent training sample of 22 AD patients. Effect sizes were compared using cumulative distribution function (CDF) plots and false discovery rate methods. In power analyses, the best method required only 48 AD and 88 MCI subjects to give 80% power to detect a 25% reduction in the mean annual change using a two-sided test (at alpha=0.05). This is a drastic sample size reduction relative to using clinical scores as outcome measures (619 AD/6797 MCI for the ADAS-Cog, and 408 AD/796 MCI for the Clinical Dementia Rating sum-of-boxes scores). TBM offers high statistical power to track brain changes in large, multi-site neuroimaging studies and clinical trials of AD.

In this article, we introduce a multimodal multivariate network analysis to characterize the linkage between the patterns of information from the same individual's complementary brain images, and illustrate its potential by showing its ability to distinguish older from younger adults with greater power than several previously established methods. Our proposed method uses measurements from every brain voxel in each person's complementary co-registered images and uses the partial least square (PLS) algorithm to form a combined latent variable that maximizes the covariance among all of the combined variables. It represents a new way to calculate the singular value decomposition from the high-dimensional covariance matrix in a computationally feasible way. Analyzing fluorodeoxyglucose positron emission tomography (PET) and volumetric magnetic resonance imaging (MRI) images, this method distinguished 14 older adults from 15 younger adults (p=4e-12) with no overlap between groups, no need to correct for multiple comparisons, and greater power than the univariate Statistical Parametric Mapping (SPM), multimodal SPM or multivariate PLS analysis of either imaging modality alone. This technique has the potential to link patterns of information among any number of complementary images from an individual, to use other kinds of complementary complex datasets besides brain images, and to characterize individual state- or trait-dependent brain patterns in a more powerful way.

Human structural neuroimaging studies have supported the preferential effects of healthy aging on frontal cortex, but reductions in other brain regions have also been observed. We investigated the regional network pattern of gray matter using magnetic resonance imaging (MRI) in young adult and old rhesus macaques (RMs) to evaluate age effects throughout the brain in a nonhuman primate model of healthy aging in which the full complement of Alzheimer's disease (AD) pathology does not occur. Volumetric T1 MRI scans were spatially normalized and segmented for gray matter using statistical parametric mapping (SPM2) voxel-based morphometry. Multivariate network analysis using the scaled subprofile model identified a linear combination of two gray matter patterns that distinguished the young from old RMs. The combined pattern included reductions in bilateral dorsolateral and ventrolateral prefrontal and orbitofrontal and superior temporal sulcal regions with areas of relative preservation in vicinities of the cerebellum, globus pallidus, visual cortex, and parietal cortex in old compared with young RMs. Higher expression of this age-related gray matter pattern was associated with poorer performance in working memory. In the RM model of healthy aging, the major regionally distributed effects of advanced age on the brain involve reductions in prefrontal regions and in the vicinity of the superior temporal sulcus. The age-related differences in gray matter reflect the effects of healthy aging that cannot be attributed to AD pathology, providing support for the targeted effects of aging on the integrity of frontal lobe regions and selective temporal lobe areas and their associated cognitive functions.

Measures of structural brain change based on longitudinal MR imaging are increasingly important but can be degraded by intensity non-uniformity. This non-uniformity can be more pronounced at higher field strengths, or when using multichannel receiver coils. We assessed the ability of the non-parametric non-uniform intensity normalization (N3) technique to correct non-uniformity in 72 volumetric brain MR scans from the preparatory phase of the Alzheimer's Disease Neuroimaging Initiative (ADNI). Normal elderly subjects (n=18) were scanned on different 3-T scanners with a multichannel phased array receiver coil at baseline, using magnetization prepared rapid gradient echo (MP-RAGE) and spoiled gradient echo (SPGR) pulse sequences, and again 2 weeks later. When applying N3, we used five brain masks of varying accuracy and four spline smoothing distances (d=50, 100, 150 and 200 mm) to ascertain which combination of parameters optimally reduces the non-uniformity. We used the normalized white matter intensity variance (standard deviation/mean) to ascertain quantitatively the correction for a single scan; we used the variance of the normalized difference image to assess quantitatively the consistency of the correction over time from registered scan pairs. Our results showed statistically significant (p<0.01) improvement in uniformity for individual scans and reduction in the normalized difference image variance when using masks that identified distinct brain tissue classes, and when using smaller spline smoothing distances (e.g., 50-100 mm) for both MP-RAGE and SPGR pulse sequences. These optimized settings may assist future large-scale studies where 3-T scanners and phased array receiver coils are used, such as ADNI, so that intensity non-uniformity does not influence the power of MR imaging to detect disease progression and the factors that influence it.

Healthy aging has been associated with brain volume reductions preferentially affecting the frontal cortex, but also involving other regions. We used a network model of regional covariance, the Scaled Subprofile Model, with magnetic resonance imaging voxel-based morphometry to identify the regional distribution of gray matter associated with aging in 26 healthy adults, 22-77 years old. Scaled Subprofile Model analysis identified a pattern that was highly correlated with age (R2=0.66, P

This article introduces an automated method for the computation of changes in brain volume from sequential magnetic resonance images (MRIs) using an iterative principal component analysis (IPCA) and demonstrates its ability to characterize whole-brain atrophy rates in patients with Alzheimer's disease (AD). The IPCA considers the voxel intensity pairs from coregistered MRIs and identifies those pairs a sufficiently large distance away from the iteratively determined PCA major axis. Analyses of simulated and real MRI data support the underlying assumption of a linear relationship in paired voxel intensities, identify an outlier distance threshold that optimizes the trade-off between sensitivity and specificity in the detection of small volume changes while accounting for global intensity changes, and demonstrate an ability to detect changes as small as 0.04% of brain volume without confounding effects of between-scan shifts in voxel intensity. In eight patients with probable AD and eight age-matched normal control subjects, the IPCA was comparable to the established but partly manual digital subtraction (DS) method in characterizing annual rates of whole-brain atrophy: resulting rates were correlated (Spearman rank correlation = 0.94, P < 0.0005) and comparable in distinguishing probable AD from normal aging (IPCA-detected atrophy rates: 2.17 +/- 0.52% per year in the patients vs. 0.41 +/- 0.22% per year in the controls [Wilcoxon-Mann-Whitney test P = 7.8 x 10(-4)]; DS-detected atrophy rates: 3.51 +/- 1.31% per year in the patients vs. 0.48 +/- 0.29% per year in the controls [P = 7.8 x 10(-4)]). The IPCA could be used in tracking the progression of AD, evaluating the disease-modifying effects of putative treatments, and investigating the course of other normal and pathological changes in brain morphology.

Objective: Aging in Down's syndrome is accompanied by amyloid and neurofibrillary pathology, the regional and laminar distribution of which resembles pathological changes seen in Alzheimer's disease. Previous studies using magnetic resonance imaging (MRI) demonstrated age-related atrophy of medial temporal lobe structures in nondemented older subjects with Down's syndrome, reflecting early allocortical pathology. Corpus callosum atrophy has been established as a marker of neocortical neuronal loss in Alzheimer's disease. This study investigated whether atrophy of the corpus callosum and hippocampus occurs in nondemented subjects with Down's syndrome and compared the degree of age-related atrophy between these structures., Method: Hippocampus and corpus callosum measures were obtained from volumetric T(1)-weighted MRI scans of 34 nondemented Down's syndrome adults (mean age=41.6 years, 17 women) and 31 healthy comparison subjects (mean age=41.8 years, 14 women)., Results: Down's syndrome subjects had smaller corpus callosum areas and hippocampal volumes relative to age-matched healthy comparison subjects, even after age and total intracranial volume were controlled. There was an age-related decrease of corpus callosum area (most prominent in posterior regions) and hippocampal volume in the Down's syndrome group. The degree of the age effect was comparable between the total corpus callosum and hippocampus, and corpus callosum size was correlated with cognitive performance in the Down's syndrome subjects. There was no correlation between age and corpus callosum or hippocampal size in the comparison group., Conclusions: Comparable decrease of corpus callosum and hippocampal size with age in nondemented subjects with Down's syndrome suggests that neocortical neuronal alterations accompany allocortical changes in the predementia phase of Down's syndrome.

Objective: The specificity of magnetic resonance imaging (MRI)-based hippocampal measurements in detecting Alzheimer's disease (AD) pathology is reduced by an age-related reduction of the hippocampus volume. We propose an adjustment for this age effect to increase the diagnostic accuracy of hippocampal volumes in AD., Method: Using an orthogonal rotational transformation of the coordinate system, values of MRI-determined volumes of hippocampus-amygdala formation (HAF) were transformed according to the age effect in 27 AD patients and 28 age- and sex-matched healthy control subjects., Results: The age transformation increased the diagnostic accuracy of HAF volumes in the study sample and in an independent sample from the literature. The age-transformed HAF volume predicted AD in a subject with mild cognitive impairment (MCI) with later biopsy-confirmed AD., Conclusion: Age transformation may provide an easily applicable method to increase the clinical diagnostic accuracy of hippocampal measurements by considering the effect of aging on hippocampus volume.

Objective: In Down's syndrome (trisomy 21), a dementia syndrome occurs that is phenotypically similar to Alzheimer's disease; the initial phase is characterized by memory loss. The authors used an in vivo structural technique in the predementia stage of Alzheimer's disease in adults with Down's syndrome to investigate whether atrophy of medial temporal lobe structures occurs in these subjects and whether volumes of these structures correlate specifically with performance on memory tests., Method: The subjects were 34 nondemented Down's syndrome adults (mean age=41.6 years, 17 women and 17 men) and 33 healthy comparison subjects (mean age=41.3, 15 women and 18 men). By using T(1)-weighted magnetic resonance imaging slices taken perpendicular to the Sylvian fissure, volumes of the hippocampus, amygdala, anterior and posterior parahippocampal gyrus, and temporal pole CSF were measured in both hemispheres. These data were normalized to the total intracranial volume., Results: For Down's syndrome, smaller volumes of the right and left amygdala, hippocampus, and posterior parahippocampal gyrus were significantly associated with greater age; this association was not seen in the anterior parahippocampal gyrus. The amygdala and hippocampus volumes were positively correlated with memory measures. For the comparison group, there was no relationship between volume and age in any region., Conclusions: In the predementia phase of Down's syndrome, significant volume changes in medial temporal lobe structures occur with age and are related to memory. These structures are affected early in Alzheimer's disease in Down's syndrome, and their evaluation may help identify people in the preclinical stages of Alzheimer's disease.