Age-Related Differences in White Matter: Understanding Tensor-Based Results Using Fixel-Based Analysis.

Aging is associated with widespread alterations in cerebral white matter (WM). Most prior studies of age differences in WM have used diffusion tensor imaging (DTI), but typical DTI metrics (e.g., fractional anisotropy; FA) can reflect multiple neurobiological features, making interpretation challenging. Here, we used fixel-based analysis (FBA) to investigate age-related WM differences observed using DTI in a sample of 45 older and 25 younger healthy adults. Age-related FA differences were widespread but were strongly associated with differences in multi-fiber complexity (CX), suggesting that they reflected differences in crossing fibers in addition to structural differences in individual fiber segments. FBA also revealed a frontolimbic locus of age-related effects and provided insights into distinct microstructural changes underlying them. Specifically, age differences in fiber density were prominent in fornix, bilateral anterior internal capsule, forceps minor, body of the corpus callosum, and corticospinal tract, while age differences in fiber cross section were largest in cingulum bundle and forceps minor. These results provide novel insights into specific structural differences underlying major WM differences associated with aging.

Hippocampal Subfields and Limbic White Matter Jointly Predict Learning Rate in Older Adults.

Age-related memory impairments have been linked to differences in structural brain parameters, including cerebral white matter (WM) microstructure and hippocampal (HC) volume, but their combined influences are rarely investigated. In a population-based sample of 337 older participants aged 61-82 years (Mage = 69.66, SDage = 3.92 years), we modeled the independent and joint effects of limbic WM microstructure and HC subfield volumes on verbal learning. Participants completed a verbal learning task of recall over five repeated trials and underwent magnetic resonance imaging (MRI), including structural and diffusion scans. We segmented three HC subregions on high-resolution MRI data and sampled mean fractional anisotropy (FA) from bilateral limbic WM tracts identified via deterministic fiber tractography. Using structural equation modeling, we evaluated the associations between learning rate and latent factors representing FA sampled from limbic WM tracts, and HC subfield volumes, and their latent interaction. Results showed limbic WM and the interaction of HC and WM-but not HC volume alone-predicted verbal learning rates. Model decomposition revealed HC volume is only positively associated with learning rate in individuals with higher WM anisotropy. We conclude that the structural characteristics of limbic WM regions and HC volume jointly contribute to verbal learning in older adults.

Problematic Social Media Use and Perceived Social Isolation in Older Adults: A Cross-Sectional Study.

BACKGROUND: Social isolation in older adults is associated with numerous adverse health outcomes. In today's digital society, if individuals perceive themselves to be socially isolated, they can take steps to interact with others on social media platforms. Research with younger adults indicates that social media use is positively linked to social isolation. However, less is known about social media use and social isolation in older adults. OBJECTIVE: The objective of this study was to investigate the possible association between social isolation and degree of social media use in older adults. METHODS: Using Internet sources, we recruited 213 participants (79.8% female; mean age 62.6 years, SD 8.3) who responded to an online survey focusing on living situation, depression, social isolation, and 2 measures of social media use: estimated daily time on social media and problematic social media use. Next, using binary logistic regression, we assessed associations between social isolation and social media use. RESULTS: Our analyses failed to identify a relationship between perceived social isolation and estimated daily time on social media; however, higher problematic social media use was associated with higher perceived social isolation (OR 1.17). DISCUSSION AND CONCLUSION: Although no causal attribution can be made, our findings demonstrate an association between problematic social media use and perceived social isolation in older adults. Researchers conducting social media interventions in older adults should note this potential and monitor maladaptive use of these platforms. Overall, our results provide an important starting point for future studies on social media use and social isolation in older adults.

Hippocampal maturity promotes memory distinctiveness in childhood and adolescence.

Adaptive learning systems need to meet two complementary and partially conflicting goals: detecting regularities in the world versus remembering specific events. The hippocampus (HC) keeps a fine balance between computations that extract commonalities of incoming information (i.e., pattern completion) and computations that enable encoding of highly similar events into unique representations (i.e., pattern separation). Histological evidence from young rhesus monkeys suggests that HC development is characterized by the differential development of intrahippocampal subfields and associated networks. However, due to challenges in the in vivo investigation of such developmental organization, the ontogenetic timing of HC subfield maturation remains controversial. Delineating its course is important, as it directly influences the fine balance between pattern separation and pattern completion operations and, thus, developmental changes in learning and memory. Here, we relate in vivo, high-resolution structural magnetic resonance imaging data of HC subfields to behavioral memory performance in children aged 6-14 y and in young adults. We identify a multivariate profile of age-related differences in intrahippocampal structures and show that HC maturity as captured by this pattern is associated with age differences in the differential encoding of unique memory representations.

Optimization and validation of automated hippocampal subfield segmentation across the lifespan.

Automated segmentation of hippocampal (HC) subfields from magnetic resonance imaging (MRI) is gaining popularity, but automated procedures that afford high speed and reproducibility have yet to be extensively validated against the standard, manual morphometry. We evaluated the concurrent validity of an automated method for hippocampal subfields segmentation (automated segmentation of hippocampal subfields, ASHS; Yushkevich et al., ) using a customized atlas of the HC body, with manual morphometry as a standard. We built a series of customized atlases comprising the entorhinal cortex (ERC) and subfields of the HC body from manually segmented images, and evaluated the correspondence of automated segmentations with manual morphometry. In samples with age ranges of 6-24 and 62-79 years, 20 participants each, we obtained validity coefficients (intraclass correlations, ICC) and spatial overlap measures (dice similarity coefficient) that varied substantially across subfields. Anterior and posterior HC body evidenced the greatest discrepancies between automated and manual segmentations. Adding anterior and posterior slices for atlas creation and truncating automated output to the ranges manually defined by multiple neuroanatomical landmarks substantially improved the validity of automated segmentation, yielding ICC above 0.90 for all subfields and alleviating systematic bias. We cross-validated the developed atlas on an independent sample of 30 healthy adults (age 31-84) and obtained good to excellent agreement: ICC (2) = 0.70-0.92. Thus, with described customization steps implemented by experts trained in MRI neuroanatomy, ASHS shows excellent concurrent validity, and can become a promising method for studying age-related changes in HC subfield volumes.

A harmonized segmentation protocol for hippocampal and parahippocampal subregions: Why do we need one and what are the key goals?

The advent of high-resolution magnetic resonance imaging (MRI) has enabled in vivo research in a variety of populations and diseases on the structure and function of hippocampal subfields and subdivisions of the parahippocampal gyrus. Because of the many extant and highly discrepant segmentation protocols, comparing results across studies is difficult. To overcome this barrier, the Hippocampal Subfields Group was formed as an international collaboration with the aim of developing a harmonized protocol for manual segmentation of hippocampal and parahippocampal subregions on high-resolution MRI. In this commentary we discuss the goals for this protocol and the associated key challenges involved in its development. These include differences among existing anatomical reference materials, striking the right balance between reliability of measurements and anatomical validity, and the development of a versatile protocol that can be adopted for the study of populations varying in age and health. The commentary outlines these key challenges, as well as the proposed solution of each, with concrete examples from our working plan. Finally, with two examples, we illustrate how the harmonized protocol, once completed, is expected to impact the field by producing measurements that are quantitatively comparable across labs and by facilitating the synthesis of findings across different studies. © 2016 Wiley Periodicals, Inc.

Changes in Search Path Complexity and Length During Learning of a Virtual Water Maze: Age Differences and Differential Associations with Hippocampal Subfield Volumes.

Impairment of hippocampus-dependent cognitive processes has been proposed to underlie age-related deficits in navigation. Animal studies suggest a differential role of hippocampal subfields in various aspects of navigation, but that hypothesis has not been tested in humans. In this study, we examined the association between volume of hippocampal subfields and age differences in virtual spatial navigation. In a sample of 65 healthy adults (age 19-75 years), advanced age was associated with a slower rate of improvement operationalized as shortening of the search path over 25 learning trials on a virtual Morris water maze task. The deficits were partially explained by greater complexity of older adults' search paths. Larger subiculum and entorhinal cortex volumes were associated with a faster decrease in search path complexity, which in turn explained faster shortening of search distance. Larger Cornu Ammonis (CA)1-2 volume was associated with faster distance shortening, but not in path complexity reduction. Age differences in regional volumes collectively accounted for 23% of the age-related variance in navigation learning. Independent of subfield volumes, advanced age was associated with poorer performance across all trials, even after reaching the asymptote. Thus, subiculum and CA1-2 volumes were associated with speed of acquisition, but not magnitude of gains in virtual maze navigation.

Differential aging of cerebral white matter in middle-aged and older adults: A seven-year follow-up.

The few extant reports of longitudinal white matter (WM) changes in healthy aging, using diffusion tensor imaging (DTI), reveal substantial differences in change across brain regions and DTI indices. According to the "last-in-first-out" hypothesis of brain aging late-developing WM tracts may be particularly vulnerable to advanced age. To test this hypothesis we compared age-related changes in association, commissural and projection WM fiber regions using a skeletonized, region of interest DTI approach. Using linear mixed effect models, we evaluated the influences of age and vascular risk at baseline on seven-year changes in three indices of WM integrity and organization (axial diffusivity, AD, radial diffusivity, RD, and fractional anisotropy, FA) in healthy middle-aged and older adults (mean age=65.4, SD=9.0years). Association fibers showed the most pronounced declines over time. Advanced age was associated with greater longitudinal changes in RD and FA, independent of fiber type. Furthermore, older age was associated with longitudinal RD increases in late-developing, but not early-developing projection fibers. These findings demonstrate the increased vulnerability of later developing WM regions and support the "last-in-first-out" hypothesis of brain aging.

White matter and memory in healthy adults: Coupled changes over two years.

Numerous cross-sectional studies have used diffusion tensor imaging (DTI) to link age-related differences in white matter (WM) anisotropy and concomitant decrements in cognitive ability. Due to a dearth of longitudinal evidence, the relationship between changes in diffusion properties of WM and cognitive performance remains unclear. Here we examine the relationship between two-year changes in WM organization and cognitive performance in healthy adults (N=96, age range at baseline=18-79 years). We used latent change score models (LCSM) to evaluate changes in age-sensitive cognitive abilities - fluid intelligence and associative memory. WM changes were assessed by fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD) in WM regions that are considered part of established memory networks and exhibited individual differences in change. In modeling change, we postulated reciprocal paths between baseline measures and change factors, within and between WM and cognition domains, and accounted for individual differences in baseline age. Although baseline cross-sectional memory performance was positively associated with FA and negatively with RD, longitudinal effects told an altogether different story. Independent of age, longitudinal improvements in associative memory were significantly associated with linear reductions in FA and increases in RD. The present findings demonstrate the sensitivity of DTI-derived indices to changes in the brain and cognition and affirm the importance of longitudinal models for evaluating brain-cognition relations.

Regional brain shrinkage and change in cognitive performance over two years: The bidirectional influences of the brain and cognitive reserve factors.

We examined relationships between regional brain shrinkage and changes in cognitive performance, while taking into account the influence of chronological age, vascular risk, Apolipoprotein E variant and socioeconomic status. Regional brain volumes and cognitive performance were assessed in 167 healthy adults (age 19-79 at baseline), 90 of whom returned for the follow-up after two years. Brain volumes were measured in six regions of interest (ROIs): lateral prefrontal cortex (LPFC), prefrontal white matter (PFw), hippocampus (Hc), parahippocampal gyrus (PhG), cerebellar hemispheres (CbH), and primary visual cortex (VC), and cognitive performance was evaluated in three domains: episodic memory (EM), fluid intelligence (Gf), and vocabulary (V). Average volume loss was observed in Hc, PhG and CbH, but reliable individual differences were noted in all examined ROIs. Average positive change was observed in EM and V performance but not in Gf scores, yet only the last evidenced individual differences in change. We observed reciprocal influences among neuroanatomical and cognitive variables. Larger brain volumes at baseline predicted greater individual gains in Gf, but differences in LPFC volume change were in part explained by baseline level of cognitive performance. In one region (PFw), individual change in volume was coupled with change in Gf. Larger initial brain volumes did not predict slower shrinkage. The results underscore the complex role of brain maintenance and cognitive reserve in adult development.

Age differences in hippocampal subfield volumes from childhood to late adulthood.

The hippocampus is composed of distinct subfields: the four cornu ammonis areas (CA1-CA4), dentate gyrus (DG), and subiculum. The few in vivo studies of human hippocampal subfields suggest that the extent of age differences in volume varies across subfields during healthy childhood development and aging. However, the associations between age and subfield volumes across the entire lifespan are unknown. Here, we used a high-resolution imaging technique and manually measured hippocampal subfield and entorhinal cortex volumes in a healthy lifespan sample (N = 202), ages 8-82 yrs. The magnitude of age differences in volume varied among the regions. Combined CA1-2 volume evidenced a negative linear association with age. In contrast, the associations between age and volumes of CA3-DG and the entorhinal cortex were negative in mid-childhood and attenuated in later adulthood. Volume of the subiculum was unrelated to age. The different magnitudes and patterns of age differences in subfield volumes may reflect dynamic microstructural factors and have implications for cognitive functions across the lifespan. © 2015 Wiley Periodicals, Inc.

Path Complexity in Virtual Water Maze Navigation: Differential Associations with Age, Sex, and Regional Brain Volume.

Studies of human navigation in virtual maze environments have consistently linked advanced age with greater distance traveled between the start and the goal and longer duration of the search. Observations of search path geometry suggest that routes taken by older adults may be unnecessarily complex and that excessive path complexity may be an indicator of cognitive difficulties experienced by older navigators. In a sample of healthy adults, we quantify search path complexity in a virtual Morris water maze with a novel method based on fractal dimensionality. In a two-level hierarchical linear model, we estimated improvement in navigation performance across trials by a decline in route length, shortening of search time, and reduction in fractal dimensionality of the path. While replicating commonly reported age and sex differences in time and distance indices, a reduction in fractal dimension of the path accounted for improvement across trials, independent of age or sex. The volumes of brain regions associated with the establishment of cognitive maps (parahippocampal gyrus and hippocampus) were related to path dimensionality, but not to the total distance and time. Thus, fractal dimensionality of a navigational path may present a useful complementary method of quantifying performance in navigation.

Quantitative comparison of 21 protocols for labeling hippocampal subfields and parahippocampal subregions in in vivo MRI: towards a harmonized segmentation protocol.

OBJECTIVE: An increasing number of human in vivo magnetic resonance imaging (MRI) studies have focused on examining the structure and function of the subfields of the hippocampal formation (the dentate gyrus, CA fields 1-3, and the subiculum) and subregions of the parahippocampal gyrus (entorhinal, perirhinal, and parahippocampal cortices). The ability to interpret the results of such studies and to relate them to each other would be improved if a common standard existed for labeling hippocampal subfields and parahippocampal subregions. Currently, research groups label different subsets of structures and use different rules, landmarks, and cues to define their anatomical extents. This paper characterizes, both qualitatively and quantitatively, the variability in the existing manual segmentation protocols for labeling hippocampal and parahippocampal substructures in MRI, with the goal of guiding subsequent work on developing a harmonized substructure segmentation protocol. METHOD: MRI scans of a single healthy adult human subject were acquired both at 3 T and 7 T. Representatives from 21 research groups applied their respective manual segmentation protocols to the MRI modalities of their choice. The resulting set of 21 segmentations was analyzed in a common anatomical space to quantify similarity and identify areas of agreement. RESULTS: The differences between the 21 protocols include the region within which segmentation is performed, the set of anatomical labels used, and the extents of specific anatomical labels. The greatest overall disagreement among the protocols is at the CA1/subiculum boundary, and disagreement across all structures is greatest in the anterior portion of the hippocampal formation relative to the body and tail. CONCLUSIONS: The combined examination of the 21 protocols in the same dataset suggests possible strategies towards developing a harmonized subfield segmentation protocol and facilitates comparison between published studies.

Classifying Alzheimer's Disease Neuropathology Using Clinical and MRI Measurements.

Background: Computer-aided machine learning models are being actively developed with clinically available biomarkers to diagnose Alzheimer's disease (AD) in living persons. Despite considerable work with cross-sectional in vivo data, many models lack validation against postmortem AD neuropathological data. Objective: Train machine learning models to classify the presence or absence of autopsy-confirmed severe AD neuropathology using clinically available features. Methods: AD neuropathological status are assessed at postmortem for participants from the National Alzheimer's Coordinating Center (NACC). Clinically available features are utilized, including demographics, Apolipoprotein E(APOE) genotype, and cortical thicknesses derived from ante-mortem MRI scans encompassing AD meta regions of interest (meta-ROI). Both logistic regression and random forest models are trained to identify linearly and nonlinearly separable features between participants with the presence (N = 91, age-at-MRI = 73.6±9.24, 38 women) or absence (N = 53, age-at-MRI = 68.93±19.69, 24 women) of severe AD neuropathology. The trained models are further validated in an external data set against in vivo amyloid biomarkers derived from PET imaging (amyloid-positive: N = 71, age-at-MRI = 74.17±6.37, 26 women; amyloid-negative: N = 73, age-at-MRI = 71.59±6.80, 41 women). Results: Our models achieve a cross-validation accuracy of 84.03% in classifying the presence or absence of severe AD neuropathology, and an external-validation accuracy of 70.14% in classifying in vivo amyloid positivity status. Conclusions: Our models show that clinically accessible features, including APOE genotype and cortical thinning encompassing AD meta-ROIs, are able to classify both postmortem confirmed AD neuropathological status and in vivo amyloid status with reasonable accuracies. These results suggest the potential utility of AD meta-ROIs in determining AD neuropathological status in living persons.

Vascular risk moderates associations between hippocampal subfield volumes and memory.

Advanced age and vascular risk negatively affect episodic memory. The hippocampus (HC) is a complex structure, and little is known about the roles of different HC regions in age-related memory declines. Using data from an ongoing longitudinal study, we investigated whether memory functions are related to volumes of specific HC subregions (CA1-2, CA3-4/dentate gyrus, and subiculum). Furthermore, we inquired if arterial hypertension, a common age-related vascular risk factor, modifies age-related differences in HC regional volumes, concurrent memory performance, and improvement in memory over multiple administrations. Healthy adults (n = 49, 52-82 years old) completed associative recognition and free recall tasks. In grouped path models, covariance structures differed between hypertensive and normotensive participants. Whereas larger CA3-4/dentate gyrus volumes predicted greater improvement in associative memory over repeated tests regardless of vascular risk, CA1-2 volumes were associated with improvement in noun recall only in hypertensive participants. Only among hypertensive participants, CA1-2 volumes negatively related to age and CA3-4/dentate gyrus and CA1-2 volumes were associated with performance at the last measurement occasion. These findings suggest that relatively small regions of the HC may play a role in age-related memory declines and that vascular risk factors associated with advanced age may modify that relationship.

Instructing Use of an Effective Strategy Improves Recognition Memory in Healthy Adults.

OBJECTIVES: Age-related memory decrements correlate with metacognitive declines, including knowledge and deployment of effective mnemonic encoding strategies. However, whether imparting such strategy suffices for mitigating memory differences is unclear. METHOD: In a longitudinal study of 276 healthy adults aged 18-79 years, we tested associative and working memory, and assessed beliefs regarding mnemonic strategies. Testing was repeated every 2 years, 5 times. Starting with the third occasion, we instructed participants to use an effective mnemonic strategy (sentence generation). Using continuous-time dynamic modeling, we assessed changes in the item and associative recognition, intervention effects, and their relations with age, sex, meta-memory beliefs, working memory, and metabolic health. RESULTS: Younger age, better working memory, and stronger belief in effective mnemonic strategies predicted better recognition, whereas instructional intervention attenuated associative memory deficits, with some persistence over time. DISCUSSION: The present findings show merely imparting effective strategies holds promise for mitigating age-related associative memory deficits.

Longitudinal developmental trajectories do not follow cross-sectional age associations in hippocampal subfield and memory development.

Cross-sectional findings suggest that volumes of specific hippocampal subfields increase in middle childhood and early adolescence. In contrast, a small number of available longitudinal studies reported decreased volumes in most subfields over this age range. Further, it remains unknown whether structural changes in development are associated with corresponding gains in children's memory. Here we report cross-sectional age differences in children's hippocampal subfield volumes together with longitudinal developmental trajectories and their relationships with memory performance. In two waves, 109 participants aged 6-10 years (wave 1: MAge=7.25, wave 2: MAge=9.27) underwent high-resolution magnetic resonance imaging to assess hippocampal subfield volumes (imaging data available at both waves for 65 participants) and completed tasks assessing hippocampus dependent memory processes. We found that cross-sectional age-associations and longitudinal developmental trends in hippocampal subfield volumes were discrepant, both by subfields and in direction. Further, volumetric changes were largely unrelated to changes in memory, with the exception that increase in subiculum volume was associated with gains in spatial memory. Longitudinal and cross-sectional patterns of brain-cognition couplings were also discrepant. We discuss potential sources of these discrepancies. This study underscores that children's structural brain development and its relationship to cognition cannot be inferred from cross-sectional age comparisons.

Dynamic modeling of practice effects across the healthy aging-Alzheimer's disease continuum.

Standardized tests of learning and memory are sensitive to changes associated with both aging and superimposed neurodegenerative diseases. Unfortunately, repeated behavioral test administration can be confounded by practice effects (PE), which may obscure declines in level of abilities and contribute to misdiagnoses. Growing evidence, however, suggests PE over successive longitudinal measurements may differentially predict cognitive status and risk for progressive decline associated with aging, mild cognitive impairment (MCI), and dementia. Thus, when viewed as a reflection of neurocognitive plasticity, PE may reveal residual abilities that can add to our understanding of age- and disease-related changes in learning and memory. The present study sought to evaluate differences in PE and verbal recall in a clinically characterized aging cohort assessed on multiple occasions over 3 years. Participants included 256 older adults recently diagnosed as cognitively unimpaired (CU; n = 126), or with MCI of amnestic (n = 65) or non-amnestic MCI (n = 2085), and multi-domain amnestic dementia of the Alzheimer's type (DAT; n = 45). We applied a continuous time structural equation modeling (ctsem) approach to verbal recall performance on the Hopkins Verbal Learning Test in order to distinguish PE from individual occasion performance, coupled random changes, age trends, and differing measurement quality. Diagnoses of MCI and dementia were associated with lower recall performance on all trials, reduced PE gain per occasion, and differences in non-linear dynamic parameters. Practice self-feedback is a dynamic measure of the decay or acceleration in PE process changes over longitudinal occasions. As with PE and mean recall, estimated practice self-feedback followed a gradient from positive in CU participants to null in participants with diagnosed MCI and negative for those with dementia diagnoses. Evaluation of sensitivity models showed this pattern of variation in PE was largely unmodified by differences in age, sex, or educational attainment. These results show dynamic modeling of PE from longitudinal performance on standardized learning and memory tests can capture multiple aspects of behavioral changes in MCI and dementia. The present study provides a new perspective for modeling longitudinal change in verbal learning in clinical and cognitive aging research.

A Role for Prior Knowledge in Statistical Classification of the Transition from Mild Cognitive Impairment to Alzheimer's Disease.

BACKGROUND: The transition from mild cognitive impairment (MCI) to dementia is of great interest to clinical research on Alzheimer's disease and related dementias. This phenomenon also serves as a valuable data source for quantitative methodological researchers developing new approaches for classification. However, the growth of machine learning (ML) approaches for classification may falsely lead many clinical researchers to underestimate the value of logistic regression (LR), which often demonstrates classification accuracy equivalent or superior to other ML methods. Further, when faced with many potential features that could be used for classifying the transition, clinical researchers are often unaware of the relative value of different approaches for variable selection. OBJECTIVE: The present study sought to compare different methods for statistical classification and for automated and theoretically guided feature selection techniques in the context of predicting conversion from MCI to dementia. METHODS: We used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to evaluate different influences of automated feature preselection on LR and support vector machine (SVM) classification methods, in classifying conversion from MCI to dementia. RESULTS: The present findings demonstrate how similar performance can be achieved using user-guided, clinically informed pre-selection versus algorithmic feature selection techniques. CONCLUSION: These results show that although SVM and other ML techniques are capable of relatively accurate classification, similar or higher accuracy can often be achieved by LR, mitigating SVM's necessity or value for many clinical researchers.