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

Choir singing is associated with enhanced structural connectivity across the adult lifespan

The global ageing of populations calls for effective, ecologically valid methods to support brain health across adult life. Previous evidence suggests that music can promote white matter (WM) microstructure and grey matter (GM) volume while supporting auditory and cognitive functioning and emotional well-being as well as counteracting age-related cognitive decline. Adding a social component to music training, choir singing is a popular leisure activity among older adults, but a systematic account of its potential to support healthy brain structure, especially with regard to ageing, is currently missing. The present study used quantitative anisotropy (QA)-based diffusion MRI connectometry and voxel-based morphometry to explore the relationship of lifetime choir singing experience and brain structure at the whole-brain level. Cross-sectional multiple regression analyses were carried out in a large, balanced sample (N = 95; age range 21-88) of healthy adults with varying levels of choir singing experience across the whole age range and within subgroups defined by age (young, middle-aged, and older adults). Independent of age, choir singing experience was associated with extensive increases in WM QA in commissural, association, and projection tracts across the brain. Corroborating previous work, these overlapped with language and limbic networks. Enhanced corpus callosum microstructure was associated with choir singing experience across all subgroups. In addition, choir singing experience was selectively associated with enhanced QA in the fornix in older participants. No associations between GM volume and choir singing were found. The present study offers the first systematic account of amateur-level choir singing on brain structure. While no evidence for counteracting GM atrophy was found, the present evidence of enhanced structural connectivity coheres well with age-typical structural changes. Corroborating previous behavioural studies, the present results suggest that regular choir singing holds great promise for supporting brain health across the adult life span.

Empirical assessment of the assumptions of ComBat with diffusion tensor imaging

Purpose

Diffusion tensor imaging (DTI) is a magnetic resonance imaging technique that provides unique information about white matter microstructure in the brain but is susceptible to confounding effects introduced by scanner or acquisition differences. ComBat is a leading approach for addressing these site biases. However, despite its frequent use for harmonization, ComBat's robustness toward site dissimilarities and overall cohort size have not yet been evaluated in terms of DTI.

Approach

As a baseline, we match N = 358 participants from two sites to create a "silver standard" that simulates a cohort for multi-site harmonization. Across sites, we harmonize mean fractional anisotropy and mean diffusivity, calculated using participant DTI data, for the regions of interest defined by the JHU EVE-Type III atlas. We bootstrap 10 iterations at 19 levels of total sample size, 10 levels of sample size imbalance between sites, and 6 levels of mean age difference between sites to quantify (i) β AGE , the linear regression coefficient of the relationship between FA and age; (ii) γ ^ s f * , the ComBat-estimated site-shift; and (iii) δ ^ s f * , the ComBat-estimated site-scaling. We characterize the reliability of ComBat by evaluating the root mean squared error in these three metrics and examine if there is a correlation between the reliability of ComBat and a violation of assumptions.

Results

ComBat remains well behaved for β AGE when N > 162 and when the mean age difference is less than 4 years. The assumptions of the ComBat model regarding the normality of residual distributions are not violated as the model becomes unstable.

Conclusion

Prior to harmonization of DTI data with ComBat, the input cohort should be examined for size and covariate distributions of each site. Direct assessment of residual distributions is less informative on stability than bootstrap analysis. We caution use ComBat of in situations that do not conform to the above thresholds.

Association of Poor Oral Health With Neuroimaging Markers of White Matter Injury in Middle-Aged Participants in the UK Biobank

BACKGROUND AND OBJECTIVES

Poor oral health is a modifiable risk factor that is associated with clinically observed cardiovascular disease. However, the relationship between oral and brain health is not well understood. We tested the hypothesis that poor oral health is associated with worse neuroimaging brain health profiles in middle-aged persons without stroke or dementia.

METHODS

We performed a 2-stage cross-sectional neuroimaging study using UK Biobank data. First, we tested for association between self-reported poor oral health and MRI neuroimaging markers of brain health. Second, we used Mendelian randomization (MR) analyses to test for association between genetically determined poor oral health and the same neuroimaging markers. Poor oral health was defined as the presence of dentures or loose teeth. As instruments for the MR analysis, we used 116 independent DNA sequence variants linked to increased composite risk of dentures or teeth that are decayed, missing, or filled. Neuroimaging markers of brain health included white matter hyperintensity (WMH) volume and aggregate measures of fractional anisotropy (FA) and mean diffusivity (MD), 2 metrics indicative of white matter tract disintegrity obtained through diffusion tensor imaging across 48 brain regions.

RESULTS

We included 40,175 persons (mean age 55 years, female sex 53%) enrolled from 2006 to 2010, who underwent a dedicated research brain MRI between 2014 and 2016. Among participants, 5,470 (14%) had poor oral health. Poor oral health was associated with a 9% increase in WMH volume (β = 0.09, SD = 0.014, p < 0.001), 10% change in aggregate FA score (β = 0.10, SD = 0.013, p < 0.001), and 5% change in aggregate MD score (β = 0.05, SD = 0.013, p < 0.001). Genetically determined poor oral health was associated with a 30% increase in WMH volume (β = 0.30, SD = 0.06, p < 0.001), 43% change in aggregate FA score (β = 0.43, SD = 0.06, p < 0.001), and 10% change in aggregate MD score (β = 0.10, SD = 0.03, p < 0.01).

DISCUSSION

Among middle age Britons without stroke or dementia, poor oral health was associated with worse neuroimaging brain health profiles. Genetic analyses confirmed these associations, supporting a potentially causal association. Because the neuroimaging markers evaluated in this study precede and are established risk factors of stroke and dementia, our results suggest that oral health, an easily modifiable process, may be a promising target for very early interventions focused on improving brain health.

Disentangling in-vivo microstructural changes of white and gray matter in mild cognitive impairment and Alzheimer's disease: a systematic review and meta-analysis

The microstructural characteristics of white and gray matter in mild cognitive impairment (MCI) and the early-stage of Alzheimer's disease (AD) remain unclear. This study aimed to systematically identify the microstructural damages of MCI/AD in studies using neurite orientation dispersion and density imaging (NODDI), and explore their correlations with cognitive performance. Multiple databases were searched for eligible studies. The 10 eligible NODDI studies were finally included. Patients with MCI/AD showed overall significant reductions in neurite density index (NDI) of specific white matter structures in bilateral hemispheres (left hemisphere: -0.40 [-0.53, -0.27], P < 0.001; right: -0.33 [-0.47, -0.19], P < 0.001), involving the bilateral superior longitudinal fasciculus (SLF), uncinate fasciculus (UF), the left posterior thalamic radiation (PTR), and the left cingulum. White matter regions exhibited significant increased orientation dispersion index (ODI) (left: 0.25 [0.02, 0.48], P < 0.05; right: 0.27 [0.07, 0.46], P < 0.05), including the left cingulum, the right UF, and the bilateral parahippocampal cingulum (PHC), and PTR. Additionally, the ODI of gray matter showed significant reduction in bilateral hippocampi (left: -0.97 [-1.42, -0.51], P < 0.001; right: -0.90 [-1.35, -0.45], P < 0.001). The cognitive performance in MCI/AD was significantly associated with NDI (r = 0.50, P < 0.001). Our findings highlight the microstructural changes in MCI/AD were characterized by decreased fiber orientation dispersion in the hippocampus, and decreased neurite density and increased fiber orientation dispersion in specific white matter tracts, including the cingulum, UF, and PTR. Moreover, the decreased NDI may indicate the declined cognitive level of MCI/AD patients.

Subject classification and cross-time prediction based on functional connectivity and white matter microstructure features in a rat model of Alzheimer's using machine learning

BACKGROUND

The pathological process of Alzheimer's disease (AD) typically takes decades from onset to clinical symptoms. Early brain changes in AD include MRI-measurable features such as altered functional connectivity (FC) and white matter degeneration. The ability of these features to discriminate between subjects without a diagnosis, or their prognostic value, is however not established.

METHODS

The main trigger mechanism of AD is still debated, although impaired brain glucose metabolism is taking an increasingly central role. Here, we used a rat model of sporadic AD, based on impaired brain glucose metabolism induced by an intracerebroventricular injection of streptozotocin (STZ). We characterized alterations in FC and white matter microstructure longitudinally using functional and diffusion MRI. Those MRI-derived measures were used to classify STZ from control rats using machine learning, and the importance of each individual measure was quantified using explainable artificial intelligence methods.

RESULTS

Overall, combining all the FC and white matter metrics in an ensemble way was the best strategy to discriminate STZ rats, with a consistent accuracy over 0.85. However, the best accuracy early on was achieved using white matter microstructure features, and later on using FC. This suggests that consistent damage in white matter in the STZ group might precede FC. For cross-timepoint prediction, microstructure features also had the highest performance while, in contrast, that of FC was reduced by its dynamic pattern which shifted from early hyperconnectivity to late hypoconnectivity.

CONCLUSIONS

Our study highlights the MRI-derived measures that best discriminate STZ vs control rats early in the course of the disease, with potential translation to humans.

Identification of Clinically Relevant Brain Endothelial Cell Biomarkers in Plasma

BACKGROUND

Proteins expressed by brain endothelial cells (BECs), the primary cell type of the blood-brain barrier, may serve as sensitive plasma biomarkers for neurological and neurovascular conditions, including cerebral small vessel disease.

METHODS

Using data from the BLSA (Baltimore Longitudinal Study of Aging; n=886; 2009-2020), BEC-enriched proteins were identified among 7268 plasma proteins (measured with SomaScanv4.1) using an automated annotation algorithm that filtered endothelial cell transcripts followed by cross-referencing with BEC-specific transcripts reported in single-cell RNA-sequencing studies. To identify BEC-enriched proteins in plasma most relevant to the maintenance of neurological and neurovascular health, we selected proteins significantly associated with 3T magnetic resonance imaging-defined white matter lesion volumes. We then examined how these candidate BEC biomarkers related to white matter lesion volumes, cerebral microhemorrhages, and lacunar infarcts in the ARIC study (Atherosclerosis Risk in Communities; US multisite; 1990-2017). Finally, we determined whether these candidate BEC biomarkers, when measured during midlife, were related to dementia risk over a 25-year follow-up period.

RESULTS

Of the 28 proteins identified as BEC-enriched, 4 were significantly associated with white matter lesion volumes (CDH5 [cadherin 5], CD93 [cluster of differentiation 93], ICAM2 [intracellular adhesion molecule 2], GP1BB [glycoprotein 1b platelet subunit beta]), while another approached significance (RSPO3 [R-Spondin 3]). A composite score based on 3 of these BEC proteins accounted for 11% of variation in white matter lesion volumes in BLSA participants. We replicated the associations between the BEC composite score, CDH5, and RSPO3 with white matter lesion volumes in ARIC, and further demonstrated that the BEC composite score and RSPO3 were associated with the presence of ≥1 cerebral microhemorrhages. We also showed that the BEC composite score, CDH5, and RSPO3 were associated with 25-year dementia risk.

CONCLUSIONS

In addition to identifying BEC proteins in plasma that relate to cerebral small vessel disease and dementia risk, we developed a composite score of plasma BEC proteins that may be used to estimate blood-brain barrier integrity and risk for adverse neurovascular outcomes.

Maintaining brain health across the lifespan

Across the lifespan, the human body and brain endure the impact of a plethora of exogenous and endogenous factors that determine the health outcome in old age. The overwhelming inter-individual variance spans between progressive frailty with loss of autonomy to largely preserved physical, cognitive, and social functions. Understanding the mechanisms underlying the diverse aging trajectories can inform future strategies to maintain a healthy body and brain. Here we provide a comprehensive overview of the current literature on lifetime factors governing brain health. We present the growing body of evidence that unhealthy alimentary regime, sedentary behaviour, sleep pathologies, cardio-vascular risk factors, and chronic inflammation exert their harmful effects in a cumulative and gradual manner, and that timely and efficient intervention could promote healthy and successful aging. We discuss the main effects and interactions between these risk factors and the resulting brain health outcomes to follow with a description of current strategies aiming to eliminate, treat, or counteract the risk factors. We conclude that the detailed insights about modifiable risk factors could inform personalized multi-domain strategies for brain health maintenance on the background of increased longevity.

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.

Impact of transcranial direct current stimulation on white matter microstructure integrity in mild cognitive impairment patients according to effect modifiers as risk factors for Alzheimer's disease

Background

Little research exists on how individual risk factors for Alzheimer's disease (AD) affect the intermediate phenotype after transcranial direct current stimulation (tDCS), despite the importance of precision medicine-based therapeutic approaches.

Objective

To determine how an application of sequential tDCS (2 mA/day, left dorsolateral prefrontal cortex, 10 sessions) affects changes in white matter (WM) microstructure integrity in 63 mild cognitive impairment (MCI) patients with effect modifiers such as Aβ deposition, APOE ε4 carrier status, BDNF Val66Met polymorphism status, and sex.

Methods

We examined individual effect modifier-by-tDCS interactions and multiple effect modifiers-by-tDCS interactions for diffusion metrics. We also evaluated the association between baseline Aβ deposition and changes in WM microstructure integrity following tDCS.

Results

We found that APOE ε4 carrier status and sex had a significant interaction with tDCS, resulting in increased fractional anisotropy (FA) in the right uncinate fasciculus (UF) after stimulation. Additionally, we observed multiple effect modifiers-by-tDCS interactions on WM integrity of the right UF, leading to a more pronounced increase in FA values in APOE ε4 carriers and females with Val66 homozygotes. Finally, baseline Aβ deposition was positively associated with a difference in FA of the left cingulum in the hippocampal area, which showed a positive association with the changes in the score for delayed memory.

Conclusion

Our study shows the differential impact of individual AD risk factors on changes in the early intermediate phenotype after sequential tDCS in MCI patients. This research emphasizes the importance of precision medicine approaches in tDCS for the prodromal stages of AD.

Overnight olfactory enrichment using an odorant diffuser improves memory and modifies the uncinate fasciculus in older adults

OBJECTIVE

Cognitive loss in older adults is a growing issue in our society, and there is a need to develop inexpensive, simple, effective in-home treatments. This study was conducted to explore the use of olfactory enrichment at night to improve cognitive ability in healthy older adults.

METHODS

Male and female older adults (N = 43), age 60-85, were enrolled in the study and randomly assigned to an Olfactory Enriched or Control group. Individuals in the enriched group were exposed to 7 different odorants a week, one per night, for 2 h, using an odorant diffuser. Individuals in the control group had the same experience with de minimis amounts of odorant. Neuropsychological assessments and fMRI scans were administered at the beginning of the study and after 6 months.

RESULTS

A statistically significant 226% improvement was observed in the enriched group compared to the control group on the Rey Auditory Verbal Learning Test and improved functioning was observed in the left uncinate fasciculus, as assessed by mean diffusivity.

CONCLUSION

Minimal olfactory enrichment administered at night produces improvements in both cognitive and neural functioning. Thus, olfactory enrichment may provide an effective and low-effort pathway to improved brain health.

Lower myelin content is associated with more rapid cognitive decline among cognitively unimpaired individuals

INTRODUCTION

The influence of myelination on longitudinal changes in cognitive performance remains unclear.

METHODS

For each participant (N = 123), longitudinal cognitive scores were calculated. Myelin content was probed using myelin water fraction (MWF) or longitudinal relaxation rate (R1 ); both are MRI measures sensitive to myelin, with MWF being specific.

RESULTS

Lower MWF was associated with steeper declines in executive function (p < .02 in all regions) and lower R1 was associated with steeper declines in verbal fluency (p < .03 in all regions). Additionally, lower R1 was associated with steeper declines in executive function (p < .02 in all regions) and memory (p < .04 in occipital and cerebral white matter) but did not survive Bonferroni correction.

DISCUSSION

We demonstrate significant relationships between myelin content and the rates of change in cognitive performance among cognitively normal individuals. These findings highlight the importance of myelin in cognitive functioning and suggest MWF and R1 as imaging biomarkers to predict cognitive changes.

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.

Superficial white matter across development, young adulthood, and aging: volume, thickness, and relationship with cortical features

Superficial white matter (SWM) represents a significantly understudied part of the human brain, despite comprising a large portion of brain volume and making up a majority of cortico-cortical white matter connections. Using multiple, high-quality datasets with large sample sizes (N = 2421, age range 5-100) in combination with methodological advances in tractography, we quantified features of SWM volume and thickness across the brain and across development, young adulthood, and aging. We had four primary aims: (1) characterize SWM thickness across brain regions (2) describe associations between SWM volume and age (3) describe associations between SWM thickness and age, and (4) quantify relationships between SWM thickness and cortical features. Our main findings are that (1) SWM thickness varies across the brain, with patterns robust across individuals and across the population at the region-level and vertex-level; (2) SWM volume shows unique volumetric trajectories with age that are distinct from gray matter and other white matter trajectories; (3) SWM thickness shows nonlinear cross-sectional changes across the lifespan that vary across regions; and (4) SWM thickness is associated with features of cortical thickness and curvature. For the first time, we show that SWM volume follows a similar trend as overall white matter volume, peaking at a similar time in adolescence, leveling off throughout adulthood, and decreasing with age thereafter. Notably, the relative fraction of total brain volume of SWM continuously increases with age, and consequently takes up a larger proportion of total white matter volume, unlike the other tissue types that decrease with respect to total brain volume. This study represents the first characterization of SWM features across the large portion of the lifespan and provides the background for characterizing normal aging and insight into the mechanisms associated with SWM development and decline.

Poor Oral Health Is Associated with Worse Brain Imaging Profiles

Importance

Poor oral health is a modifiable risk factor that is associated with a variety of health outcomes. However, the relationship between oral and brain health is not well understood.

Objective

To test the hypothesis that poor oral health is associated with worse neuroimaging brain health profiles in persons without stroke or dementia.

Design

We conducted a 2-stage cross-sectional neuroimaging study using data from the UK Biobank (UKB). First, we tested for association between self-reported poor oral health and MRI neuroimaging markers of brain health. Second, we used Mendelian Randomization (MR) analyses to test for association between genetically-determined poor oral health and the same neuroimaging markers.

Setting

Ongoing population study in the United Kingdom. The UKB enrolled participants between 2006 and 2010. Data analysis was performed from September 1, 2022, to January 10, 2023.

Participants

40,175 persons aged 40 to 70 enrolled between 2006 to 2010 who underwent a dedicated research brain MRI between 2012 and 2013.

Exposures

During MRI assessment, poor oral health was defined as the presence of dentures or loose teeth. As instruments for the MR analysis, we used 116 independent DNA sequence variants known to significantly increase the composite risk of decayed, missing, or filled teeth and dentures.

Main Outcomes and Measures

As neuroimaging markers of brain health, we assessed the volume of white matter hyperintensities (WMH), as well as aggregate measures of fractional anisotropy (FA) and mean diffusivity (MD), two metrics indicative of white matter tract disintegrity obtained through diffusion tensor imaging. These measurements were evaluated across 48 distinct brain regions, with FA and MD values for each region also considered as individual outcomes for the MR method.

Results

Among study participants, 5,470 (14%) had poor oral health. We found that poor oral health was associated with a 9% increase in WMH volume (beta = 0.09, standard deviation (SD) = 0.014, p P< 0.001), a 10% change in the aggregate FA score (beta = 0.10, SD = 0.013, P < 0.001), and a 5% change in the aggregate MD score (beta = 0.05, SD = 0.013, P < 0.001). Genetically-determined poor oral health was associated with a 30% increase in WMH volume (beta = 0.30, SD = 0.06, P < 0.001), a 43% change in aggregate FA score (beta = 0.42, SD = 0.06, P < 0.001), and an 10% change in aggregate MD score (beta = 0.10, SD = 0.03, P = 0.01).

Conclusions and Relevance

Among middle age Britons without stroke or dementia enrolled in a large population study, poor oral health was associated with worse neuroimaging brain health profiles. Genetic analyses confirmed these associations, supporting a potential causal association. Because the neuroimaging markers evaluated in the current study are established risk factors for stroke and dementia, our results suggest that oral health may be a promising target for interventions focused on improving brain health.

MRI and fluid biomarkers reveal determinants of myelin and axonal loss with aging

OBJECTIVE

White matter damage is a feature of Alzheimer's disease, yet little is known about how facets of the Alzheimer's disease process relate to key features of white matter structure. We examined the association of Alzheimer's disease (Aß_42/40 ratio; pTau181), neuronal injury (NfL), and reactive astrogliosis (GFAP) biomarkers with MRI measures of myelin content and axonal density.

METHODS

Among cognitively normal participants in the BLSA and GESTALT studies who received MRI measures of myelin content (defined by myelin water fraction [MWF]) and axonal density (defined by neurite density index [NDI]), we quantified plasma levels of Aβ₄₂ , Aβ₄₀ , pTau181, NfL, and GFAP. Linear regression models adjusted for demographic variables were used to relate these plasma biomarker levels to the MRI measures.

RESULTS

In total, 119 participants received MWF imaging (age: 56 [SD 21]), of which 43 received NDI imaging (age: 50 [SD 18]). We found no relationship between plasma biomarkers and total brain myelin content. However, secondary analysis found higher GFAP was associated with lower MWF in the temporal lobes (ß = -0.13; P = 0.049). Further, higher levels of NfL (ß = -0.22; P = 0.009) and GFAP (ß = -0.29; P = 0.002) were associated with lower total brain axonal density. Secondary analyses found lower Aβ_42/40 ratio and higher pTau181 were also associated with lower axonal density, but only in select brain regions. These results remained similar after additionally adjusting for cardiovascular risk factors.

INTERPRETATION

Plasma biomarkers of neuronal injury and astrogliosis are associated with reduced axonal density and region-specific myelin content. Axonal loss and demyelination may co-occur with neurodegeneration and astrogliosis ahead of clinically meaningful cognitive decline.