White matter integrity, as measured by diffusion tensor imaging, distinguishes between impaired and unimpaired older adult decision-makers: A preliminary investigation

Association between allostatic load and accelerated white matter brain aging: findings from the UK Biobank

White matter (WM) brain age, a neuroimaging-derived biomarker indicating WM microstructural changes, helps predict dementia and neurodegenerative disorder risks. The cumulative effect of chronic stress on WM brain aging remains unknown. In this study, we assessed cumulative stress using a multi-system composite allostatic load (AL) index based on inflammatory, anthropometric, respiratory, lipidemia, and glucose metabolism measures, and investigated its association with WM brain age gap (BAG), computed from diffusion tensor imaging data using a machine learning model, among 22 951 European ancestries aged 40 to 69 (51.40% women) from UK Biobank. Linear regression, Mendelian randomization, along with inverse probability weighting and doubly robust methods, were used to evaluate the impact of AL on WM BAG adjusting for age, sex, socioeconomic, and lifestyle behaviors. We found increasing one AL score unit significantly increased WM BAG by 0.29 years in association analysis and by 0.33 years in Mendelian analysis. The age- and sex-stratified analysis showed consistent results among participants 45-54 and 55-64 years old, with no significant sex difference. This study demonstrated that higher chronic stress was significantly associated with accelerated brain aging, highlighting the importance of stress management in reducing dementia and neurodegenerative disease risks.

The Relationship of MRI-Derived Alzheimer's and Cerebrovascular-Related Signatures With Level of and Change in Health and Financial Literacy

OBJECTIVE

The cortical thickness "signature" of Alzheimer's disease (AD-CT) and white matter hyperintensity (WMH) burden have each been associated with cognitive aging and incident AD and related dementias. Less is known about how these structural neuroimaging markers associate with other critical behaviors. We investigated associations of AD-CT and WMH volumes with a composite index of health and financial literacy given that the ability to access, understand, and utilize health and financial information significantly influences older adults' health outcomes.

DESIGN, SETTING, PARTICIPANTS

Participants were 303 adults without dementia (age∼80 years; 74% women) from the Rush Memory and Aging Project.

MEASUREMENTS

Baseline 3T MRI T1-weighted structural and T2-weighted FLAIR data were used to assess AD-CT and WMH volumes, respectively. Literacy was measured using questions designed to assess comprehension of health and financial information and concepts, yielding a total literacy score. Multivariable linear mixed effects regression models determined the relationship of each neuroimaging marker, first separately and then combined, with the level of and change in literacy.

RESULTS

Reduced AD-CT and higher WMH at baseline were each associated with lower levels of literacy; only AD-CT was associated with the rate of decline in literacy over time. The association of AD-CT with change in literacy persisted when both neuroimaging markers were included in the same model.

CONCLUSIONS

The cortical thickness signature of AD predicts changes in health and financial literacy in nondemented older adults suggesting that the multidimensional construct of health and financial literacy relies on specific brain networks implicated in AD.

Elevated blood pressure accelerates white matter brain aging among late middle-aged women: a Mendelian Randomization study in the UK Biobank

BACKGROUND

Elevated blood pressure (BP) is a modifiable risk factor associated with cognitive impairment and cerebrovascular diseases. However, the causal effect of BP on white matter brain aging remains unclear.

METHODS

In this study, we focused on N  = 228 473 individuals of European ancestry who had genotype data and clinical BP measurements available (103 929 men and 124 544 women, mean age = 56.49, including 16 901 participants with neuroimaging data available) collected from UK Biobank (UKB). We first established a machine learning model to compute the outcome variable brain age gap (BAG) based on white matter microstructure integrity measured by fractional anisotropy derived from diffusion tensor imaging data. We then performed a two-sample Mendelian randomization analysis to estimate the causal effect of BP on white matter BAG in the whole population and subgroups stratified by sex and age brackets using two nonoverlapping data sets.

RESULTS

The hypertension group is on average 0.31 years (95% CI = 0.13-0.49; P  < 0.0001) older in white matter brain age than the nonhypertension group. Women are on average 0.81 years (95% CI = 0.68-0.95; P  < 0.0001) younger in white matter brain age than men. The Mendelian randomization analyses showed an overall significant positive causal effect of DBP on white matter BAG (0.37 years/10 mmHg, 95% CI 0.034-0.71, P  = 0.0311). In stratified analysis, the causal effect was found most prominent among women aged 50-59 and aged 60-69.

CONCLUSION

High BP can accelerate white matter brain aging among late middle-aged women, providing insights on planning effective control of BP for women in this age group.

Deciphering the causal relationship between blood pressure and regional white matter integrity: A two-sample Mendelian randomization study

Elevated arterial blood pressure (BP) is a common risk factor for cerebrovascular and cardiovascular diseases, but no causal relationship has been established between BP and cerebral white matter (WM) integrity. In this study, we performed a two-sample Mendelian randomization (MR) analysis with individual-level data by defining two nonoverlapping sets of European ancestry individuals (genetics-exposure set: N = 203,111; mean age = 56.71 years, genetics-outcome set: N = 16,156; mean age = 54.61 years) from UK Biobank to evaluate the causal effects of BP on regional WM integrity, measured by fractional anisotropy of diffusion tensor imaging. Two BP traits: systolic and diastolic blood pressure were used as exposures. Genetic variant was carefully selected as instrumental variable (IV) under the MR analysis assumptions. We existing large-scale genome-wide association study summary data for validation. The main method used was a generalized version of inverse-variance weight method while other MR methods were also applied for consistent findings. Two additional MR analyses were performed to exclude the possibility of reverse causality. We found significantly negative causal effects (FDR-adjusted p < .05; every 10 mmHg increase in BP leads to a decrease in FA value by .4% ~ 2%) of BP traits on a union set of 17 WM tracts, including brain regions related to cognitive function and memory. Our study extended the previous findings of association to causation for regional WM integrity, providing insights into the pathological processes of elevated BP that might chronically alter the brain microstructure in different regions.

Regional Vulnerability of the Corpus Callosum in the Context of Cardiovascular Risk

Many factors outside of cardiovascular health can impact the structure of white matter. Identification of reliable and clinically meaningful biomarkers of the neural effects of systemic and cardiovascular health are needed to refine etiologic predictions. We examined whether the corpus callosum demonstrates regional vulnerability to systemic cardiovascular risk factors. Three hundred and ninety-four older adults without dementia completed brain MRI, neurobehavioral evaluations, and blood draws. A subset (n = 126, n = 128) of individuals had blood plasma analyzed for inflammatory markers of interest (IL-6 and TNF-alpha). Considering diffusion tensor imaging (DTI) is a particularly reliable measure of white matter integrity, we utilized DTI to examine fractional anisotropy (FA) of anterior and posterior regions of the corpus callosum. Using multiple linear regression models, we simultaneously examined FA of the genu and the splenium to compare their associations with systemic and cardiovascular risk factors. Lower FA of the genu but not splenium was associated with greater systemic and cardiovascular risk, including higher systolic blood pressure (β = -0.17, p = .020), hemoglobin A1C (β = -0.21, p = .016) and IL-6 (β = -0.34, p = .005). FA of the genu was uniquely associated with cognitive processing speed (β = 0.20, p = .0015) and executive functioning (β = 0.15, p = .012), but not memory performances (β = 0.05, p = .357). Our results demonstrated differential vulnerability of the corpus callosum, such that frontal regions showed stronger, independent associations with biomarkers of systemic and cardiovascular health in comparison to posterior regions. Posterior white matter integrity may not reflect cardiovascular health. Clinically, these findings support the utility of examining the anterior corpus callosum as an indicator of cerebrovascular health.

Functional Connectivity and Microstructural Network Correlates of Interindividual Variability in Distinct Executive Functions of Healthy Older Adults

Executive functions, essential for daily life, are known to be impaired in older age. Some executive functions, including working memory updating and value-based decision-making, are specifically sensitive to age-related deterioration. While their neural correlates in young adults are well-described, a comprehensive delineation of the underlying brain substrates in older populations, relevant to identify targets for modulation against cognitive decline, is missing. Here, we assessed letter updating and Markov decision-making task performance to operationalize these trainable functions in 48 older adults. Resting-state functional magnetic resonance imaging was acquired to quantify functional connectivity (FC) in task-relevant frontoparietal and default mode networks. Microstructure in white matter pathways mediating executive functions was assessed with diffusion tensor imaging and quantified by tract-based fractional anisotropy (FA). Superior letter updating performance correlated with higher FC between dorsolateral prefrontal cortex and left frontoparietal and hippocampal areas, while superior Markov decision-making performance correlated with decreased FC between basal ganglia and right angular gyrus. Furthermore, better working memory updating performance was related to higher FA in the cingulum bundle and the superior longitudinal fasciculus. Stepwise linear regression showed that cingulum bundle FA added significant incremental contribution to the variance explained by fronto-angular FC alone. Our findings provide a characterization of distinct functional and structural connectivity correlates associated with performance of specific executive functions. Thereby, this study contributes to the understanding of the neural correlates of updating and decision-making functions in older adults, paving the way for targeted modulation of specific networks by modulatory techniques such as behavioral interventions and non-invasive brain stimulation.

Deep Learning Classifies Low- and High-Grade Glioma Patients with High Accuracy, Sensitivity, and Specificity Based on Their Brain White Matter Networks Derived from Diffusion Tensor Imaging

Classifying low-grade glioma (LGG) patients from high-grade glioma (HGG) is one of the most challenging tasks in planning treatment strategies for brain tumor patients. Previous studies derived several handcrafted features based on the tumor's texture and volume from magnetic resonance images (MRI) to classify LGG and HGG patients. The accuracy of classification was moderate. We aimed to classify LGG from HGG with high accuracy using the brain white matter (WM) network connectivity matrix constructed using diffusion tensor tractography. We obtained diffusion tensor images (DTI) of 44 LGG and 48 HGG patients using routine clinical imaging. Fiber tractography and brain parcellation were performed for each patient to obtain the fractional anisotropy, axial diffusivity, radial diffusivity, and mean diffusivity weighted connectivity matrices. We used a deep convolutional neural network (DNN) for classification and the gradient class activation map (GRAD-CAM) technique to identify the neural connectivity features focused on by the DNN. DNN could classify both LGG and HGG with 98% accuracy. The sensitivity and specificity values were above 0.98. GRAD-CAM analysis revealed a distinct WM network pattern between LGG and HGG patients in the frontal, temporal, and parietal lobes. Our results demonstrate that glioma affects the WM network in LGG and HGG patients differently.

Electrophysiological evidence for adult age-related sparing and decrements in emotion perception and attention

The present study examined adult age differences in processing emotional faces using a psychological refractory period paradigm. We used both behavioral and event-related potential (P1 component) measures. Task 1 was tone discrimination (fuzzy vs. pure tones) and Task 2 was emotional facial discrimination (“happy” vs. “angry” faces). The stimulus onset asynchrony (SOA) between the two tasks was 100, 300, and 900 ms. Earlier research observed larger age deficits in emotional facial discrimination for negative (angry) than for positive (happy) faces (Baena et al., 2010). Thus, we predicted that older adults would show decreased attentional efficiency in carrying out dual-task processing on the P1 (a component linked to amygdalar modulation of visual perception; Rotshtein et al., 2010). Both younger and older groups showed significantly higher P1 amplitudes at 100- and 300-ms SOAs than at the 900-ms SOA, and this suggests that both age groups could process Task 2 faces without central attention. Also, younger adults showed significantly higher P1 activations for angry than for happy faces, but older adults showed no difference. These results are consistent with the idea that younger adults exhibited amygdalar modulation of visual perception, but that older adults did not.

Neural correlates of emotion-cognition interactions: A review of evidence from brain imaging investigations

Complex dynamic behaviour involves reciprocal influences between emotion and cognition. On the one hand, emotion is a “double-edged sword” that may affect various aspects of our cognition and behaviour, by enhancing or hindering them and exerting both transient and long-term influences. On the other hand, emotion processing is also susceptible to cognitive influences, typically exerted in the form of emotion regulation. Noteworthy, both of these reciprocal influences are subjective to individual differences that may affect the way we perceive, experience, and eventually remember emotional experiences, or respond to emotionally challenging situations. Understanding these relationships is critical, as unbalanced emotion–cognition interactions may lead to devastating effects, such as those observed in mood and anxiety disorders. The present review analyses the reciprocal relationships between emotion and cognition, based on evidence derived from brain imaging investigations focusing on three main topics: (1) the impact of emotion on cognition, (2) the impact of cognition on emotion, and (3) the role of individual differences in emotion–cognition interactions. This evidence will be discussed in the context of identifying aspects that are fundamental to understanding the mechanisms underlying emotion–cognition interactions in healthy functioning, and to understanding changes associated with affective disorders.