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

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

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

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

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

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

Proof-of-concept evidence for trimodal simultaneous investigation of human brain function.

The link between spatial (where) and temporal (when) aspects of the neural correlates of most psychological phenomena is not clear. Elucidation of this relation, which is crucial to fully understand human brain function, requires integration across multiple brain imaging modalities and cognitive tasks that reliably modulate the engagement of the brain systems of interest. By overcoming the methodological challenges posed by simultaneous recordings, the present report provides proof-of-concept evidence for a novel approach using three complementary imaging modalities: functional magnetic resonance imaging (fMRI), event-related potentials (ERPs), and event-related optical signals (EROS). Using the emotional oddball task, a paradigm that taps into both cognitive and affective aspects of processing, we show the feasibility of capturing converging and complementary measures of brain function that are not currently attainable using traditional unimodal or other multimodal approaches. This opens up unprecedented possibilities to clarify spatiotemporal integration of brain function.

PreQual: An automated pipeline for integrated preprocessing and quality assurance of diffusion weighted MRI images.

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.

Integration of spatio-temporal dynamics in emotion-cognition interactions: A simultaneous fMRI-ERP investigation using the emotional oddball task.

Although a large corpus of evidence has identified brain regions and networks involved in emotion-cognition interactions, it remains unclear how spatial and temporal dynamics of the mechanisms by which emotion interfaces with cognition are integrated. Capitalizing on multi-modal brain imaging approaches, we used simultaneous functional magnetic resonance imaging (fMRI) and event-related potential (ERP) recordings, to investigate the link between spatial and temporal aspects of processing in an emotional oddball task, and in relation to personality measures reflecting basic affective responses and emotion control. First, fMRI captured expected dorso-ventral dissociations, with greater response to targets in regions of dorsal brain networks (e.g., dorsolateral prefrontal cortex) and to emotional distracters in regions of ventral networks (e.g., ventrolateral prefrontal cortex, vlPFC). Also, ERP responses to targets were associated with a prominent P300, and responses to distracters with the late positive potential (LPP). Second, providing evidence for spatio-temporal integration of brain signals, ERP-informed fMRI analyses showed a link between LPP amplitude at parietal electrodes and the fMRI signal in the vlPFC, to emotional distraction. Third, regarding the link to personality measures, increased emotional arousability and attentional impulsiveness was associated with greater LPP differences between negative distracters and targets and enhanced response to negative distracters in the amygdala, respectively. Furthermore, we identified opposing relations between responses to emotional distraction and individual scores for cognitive reappraisal and self-control impulsiveness in posterior vlPFC. This suggests a greater engagement of this region in participants with reduced tendencies to employ reappraisal as a coping strategy and those with reduced ability to control impulsive responses during emotional distraction. Together, supporting the feasibility of integrating multi-dimensional approaches to clarify neural mechanisms of emotion-cognition interactions, these results point to convergence and complementarity between measures that differentially capture spatio-temporal dynamics of brain activity, and their associations with measures of individual differences in affective responses and control.

Prefrontal Cortex Contributions to the Development of Memory Formation.

The development of the brain, particularly the protracted maturation of the prefrontal cortex (PFC), supports the development of episodic memory. Yet how different regions of the PFC functionally mature to support age-related increases in memory performance remains unclear. We investigated the PFC contribution to subsequent memory (SM) of encoded visual scenes in children, adolescents, and young adults (n = 83). We identified distinct patterns of PFC activations supporting SM: regions in the lateral PFC showed positive SM effects, whereas regions in the superior and medial PFC showed negative SM effects. Both positive and negative SM effects increased with age. The magnitude of negative SM effects in the superior PFC partially mediated the age-related increase in memory. Functional connectivity between lateral PFC and regions in the medial temporal lobe (MTL) increased with age during successful memory formation. In contrast, functional connectivity between the superior PFC and regions in the MTL decreased with age, suggesting an age-related increase in the anti-correlation between these regions. These findings highlight the differential involvement of regions within the PFC supporting memory formation.

Apolipoprotein E ε4 allele effects on longitudinal cognitive trajectories are sex and age dependent.

INTRODUCTION: Questions remain about whether apolipoprotein E (APOE)-ε4 effects on cognitive decline are similar in men and women and how APOE-ε4 and age interact to influence decline in different cognitive domains. METHODS: In sex-stratified analyses, baseline age-dependent associations between APOE-ε4 status and longitudinal cognitive trajectories were examined in cognitively normal Caucasian older adults (631 men, 561 women, baseline age range: 50-93, 6733 assessments). RESULTS: In men, older baseline age was associated with greater effects of APOE-ε4 on longitudinal decline in memory and executive function, detectible from baseline age of 64 and 68, respectively. In women, older baseline age was associated with greater APOE-ε4 effects on longitudinal decline in attention, detectible at baseline age of 66. No significant APOE-ε4 effects were found for language, visual-spatial ability, or processing speed. DISCUSSION: Results highlight the importance of considering sex and age when assessing APOE-ε4-associated vulnerability to cognitive decline.

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.

Amygdala subnuclei response and connectivity during emotional processing.

The involvement of the human amygdala in emotion-related processing has been studied using functional magnetic resonance imaging (fMRI) for many years. However, despite the amygdala being comprised of several subnuclei, most studies investigated the role of the entire amygdala in processing of emotions. Here we combined a novel anatomical tracing protocol with event-related high-resolution fMRI acquisition to study the responsiveness of the amygdala subnuclei to negative emotional stimuli and to examine intra-amygdala functional connectivity. The greatest sensitivity to the negative emotional stimuli was observed in the centromedial amygdala, where the hemodynamic response amplitude elicited by the negative emotional stimuli was greater and peaked later than for neutral stimuli. Connectivity patterns converge with extant findings in animals, such that the centromedial amygdala was more connected with the nuclei of the basal amygdala than with the lateral amygdala. Current findings provide evidence of functional specialization within the human amygdala.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Processing of emotional distraction is both automatic and modulated by attention: evidence from an event-related fMRI investigation.

Traditionally, emotional stimuli have been thought to be automatically processed via a bottom-up automatic "capture of attention" mechanism. Recently, this view has been challenged by evidence that emotion processing depends on the availability of attentional resources. Although these two views are not mutually exclusive, direct evidence reconciling them is lacking. One limitation of previous investigations supporting the traditional or competing views is that they have not systematically investigated the impact of emotional charge of task-irrelevant distraction in conjunction with manipulations of attentional demands. Using event-related fMRI, we investigated the nature of emotion-cognition interactions in a perceptual discrimination task with emotional distraction by manipulating both the emotional charge of the distracting information and the demands of the main task. Our findings show that emotion processing is both automatic and modulated by attention, but emotion and attention were only found to interact when finer assessments of emotional charge (comparison of most vs. least emotional conditions) were considered along with an effective manipulation of processing load (high vs. low). The study also identified brain regions reflecting the detrimental impact of emotional distraction on performance as well as regions involved in coping with such distraction. Activity in the dorsomedial pFC and ventrolateral pFC was linked to a detrimental impact of emotional distraction, whereas the dorsal ACC and lateral occipital cortex were involved in helping with emotional distraction. These findings demonstrate that task-irrelevant emotion processing is subjective to both the emotional content of distraction and the level of attentional demand.

Longitudinal changes of connectomes and graph theory measures in aging.

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