Mapping of Structure-Function Age-Related Connectivity Changes on Cognition Using Multimodal MRI

Functional connectivity-based compensation in the brains of non-demented older adults and the influence of lifestyle: A longitudinal 7-year study

INTRODUCTION

The aging brain is characterized by structural decline and functional connectivity changes towards dedifferentiation, leading to cognitive decline. To some degree, the brain can compensate for structural deterioration. In this study, we aim to answer two questions: Where can we detect longitudinal functional connectivity-based compensation in the brains of cognitively healthy older adults? Can lifestyle predict the strength of this functional compensation?

METHODS

Using longitudinal data from 228 cognitively healthy older adults, we analyzed five measurement points over 7 years. Network-based statistics and latent growth modeling were employed to examine changes in structural and functional connectivity, as well as potential functional compensation for declines in processing speed and memory. Random forest and linear regression were used to predict the amplitude of compensation based on demographic, biological, and lifestyle factors.

RESULTS

Both functional and structural connectivity showed increases and decreases over time, depending on the specific connection and measure. Increased functional connectivity of 27 connections was linked to smaller declines in cognition. Five of those connections showed simultaneous decreases in fractional anisotropy, indicating direct compensation. The degree of compensation depended on the type of compensation and the cognitive ability, with demographic, biological, and lifestyle factors explaining 3.4-8.9% of the variance.

CONCLUSIONS

There are widespread changes in structural and functional connectivity in older adults. Despite the trend of dedifferentiation in functional connectivity, we detected both direct and indirect compensatory subnetworks that mitigated the decline in cognitive performance. The degree of compensation was influenced by demographic, biological, and lifestyle factors.

White matter correlates of cognition: A diffusion magnetic resonance imaging study

BACKGROUND

Our comprehension of the interplay of cognition and the brain remains constrained. While functional imaging studies have identified cognitive brain regions, structural correlates of cognitive functions remain underexplored. Advanced methods like Diffusion Magnetic Resonance Imaging (DMRI) facilitate the exploration of brain connectivity and White Matter (WM) tract microstructure. Therefore, we conducted connectometry method on DMRI data, to reveal WM tracts associated with cognition.

METHODS

125 healthy participants from the National Institute of Mental Health Intramural Healthy Volunteer Dataset were recruited. Multiple regression analyses were conducted between DMRI-derived Quantitative Anisotropy (QA) values within WM tracts and scores of participants in Flanker Inhibitory Control and Attention Test (attention), Dimensional Change Card Sort (executive function), Picture Sequence Memory Test (episodic memory), and List Sorting Working Memory Test (working memory) tasks from National Institute of Health toolbox. The significance level was set at False Discovery Rate (FDR)<0.05.

RESULTS

We identified significant positive correlations between the QA of WM tracts within the left cerebellum and bilateral fornix with attention, executive functioning, and episodic memory (FDR=0.018, 0.0002, and 0.0002, respectively), and a negative correlation between QA of WM tracts within bilateral cerebellum with attention (FDR=0.028). Working memory demonstrated positive correlations with QA of left inferior longitudinal and left inferior fronto-occipital fasciculi (FDR=0.0009), while it showed a negative correlation with QA of right cerebellar tracts (FDR=0.0005).

CONCLUSION

Our results underscore the intricate link between cognitive performance and WM integrity in frontal, temporal, and cerebellar regions, offering insights into early detection and targeted interventions for cognitive disorders.

Midlife dynamics of white matter architecture in lexical production

We aimed to examine the white matter changes associated with lexical production difficulties, beginning in midlife with increased naming latencies. To delay lexical production decline, middle-aged adults may rely on domain-general and language-specific compensatory mechanisms proposed by the LARA model (Lexical Access and Retrieval in Aging). However, the white matter changes supporting these mechanisms remains largely unknown. Using data from the CAMCAN cohort, we employed an unsupervised and data-driven methodology to examine the relationships between diffusion-weighted imaging and lexical production. Our findings indicate that midlife is marked by alterations in brain structure within distributed dorsal, ventral, and anterior cortico-subcortical networks, marking the onset of lexical production decline around ages 53-54. Middle-aged adults may initially adopt a "semantic strategy" to compensate for lexical production challenges, but this strategy seems compromised later (ages 55-60) as semantic control declines. These insights underscore the interplay between domain-general and language-specific processes in the trajectory of lexical production performance in healthy aging and hint at potential biomarkers for language-related neurodegenerative pathologies.

Reorganization of structural connectivity in the brain supports preservation of cognitive ability in healthy aging

The global population is aging rapidly, and a research question of critical importance is why some older adults suffer tremendous cognitive decline while others are mostly spared. Past aging research has shown that older adults with spared cognitive ability have better local short-range information processing while global long-range processing is less efficient. We took this research a step further to investigate whether the underlying structural connections, measured in vivo using diffusion magnetic resonance imaging (dMRI), show a similar shift to support cognitive ability. We analyzed the structural connectivity streamline probability (representing the probability of connection between regions) and nodal efficiency and local efficiency regional graph theory metrics to determine whether age and cognitive ability are related to structural network differences. We found that the relationship between structural connectivity and cognitive ability with age was nuanced, with some differences with age that were associated with poorer cognitive outcomes, but other reorganizations that were associated with spared cognitive ability. These positive changes included strengthened local intrahemispheric connectivity and increased nodal efficiency of the ventral occipital-temporal stream, nucleus accumbens, and hippocampus for older adults, and widespread local efficiency primarily for middle-aged individuals.

Structural and Functional Brain Connectivity Uniquely Contribute to Episodic Memory Performance in Older Adults

In this study, we examined the independent contributions of structural and functional connectivity markers to individual differences in episodic memory performance in 107 cognitively normal older adults from the BIOCARD study. Structural connectivity, defined by the diffusion tensor imaging (DTI) measure of radial diffusivity (RD), was obtained from two medial temporal lobe white matter tracts: the fornix and hippocampal cingulum, while functional connectivity markers were derived from network-based resting state functional magnetic resonance imaging (rsfMRI) of five large-scale brain networks: the control, default, limbic, dorsal attention, and salience/ventral attention networks. Hierarchical and stepwise linear regression methods were utilized to directly compare the relative contributions of the connectivity modalities to individual variability in a composite delayed episodic memory score, while also accounting for age, sex, cerebrospinal fluid (CSF) biomarkers of amyloid and tau pathology (i.e., Aβ₄₂/Aβ₄₀ and p-tau₁₈₁), and gray matter volumes of the entorhinal cortex and hippocampus. Results revealed that fornix RD, hippocampal cingulum RD, and salience network functional connectivity were each significant independent predictors of memory performance, while CSF markers and gray matter volumes were not. Moreover, in the stepwise model, the addition of sex, fornix RD, hippocampal cingulum RD, and salience network functional connectivity each significantly improved the overall predictive value of the model. These findings demonstrate that both DTI and rsfMRI connectivity measures uniquely contributed to the model and that the combination of structural and functional connectivity markers best accounted for individual variability in episodic memory function in cognitively normal older adults.