Aging Influence on Gray Matter Structural Associations within the Default Mode Network Utilizing Bayesian Network Modeling

Changes in the Functional and Structural Default Mode Network across the Adult Lifespan Based on Partial Least Squares

The default mode network (DMN) has been extensively investigated in the literature. However, previous studies have mainly focused on age-related changes in the DMN between old and young participants. Age-dependent changes in specific regions within the DMN have not been adequately explored across the entire adult lifespan. Thus, in the present study, we performed a seed partial least squares (PLS) analysis to investigate lifespan-wide changes in the regions of the functional and structural DMNs using resting-state functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (MRI) data from healthy subjects aged 16-85 years. The posterior cingulate area was selected as the seed region based on prior fMRI studies. The single-group functional connectivity analysis showed a stable connection between the seed and the posterior cingulate cortex (PCC), middle temporal gyrus (MTG) and inferior temporal gyrus (ITG); a decreased connection between the seed and the medial prefrontal cortex (MPFC), anterior cingulate cortex (ACC) and superior frontal gyrus (SFG); and an increased connection between the seed and the precuneus (PreC), inferior parietal lobule (IPL) and middle frontal gyrus (MFG) across the entire lifespan. In contrast, in the single-group structural covariance analysis, the covariance connections of the seed to the DMN regions demonstrated a stable covariance trend to the PCC, MTG, superior temporal gyrus (STG) and ITG; an inverted U-shaped covariance trend to the MPFC, ACC, SFG, MFG and inferior frontal gyrus (IFG); and a U-shaped covariance trend to the PreC with age. Full-group analyses found significant linear decreases in functional and structural DMN integrity. Our findings provide crucial information regarding the influence of age on the function and structure of the DMN and may contribute to the understanding of the underlying mechanism of age-related changes in the DMN over the lifespan.

Effect of Ibuprofen on BrainAGE: A Randomized, Placebo-Controlled, Dose-Response Exploratory Study

BACKGROUND

The age of a person's brain can be estimated from structural brain images using an aggregate measure of variation in morphology across the whole brain. The brain age gap estimation (BrainAGE) score is computed as the difference between kernel-estimated brain age and chronological age. In this exploratory study, we investigated the application of the BrainAGE measure to identify potential novel effects of pharmacological agents on brain morphology.

METHODS

Twenty healthy participants (23-47 years of age) completed three structural magnetic resonance imaging scans 45 minutes after administration of placebo or 200 or 600 mg of ibuprofen in a double-blind, crossover study. An externally derived BrainAGE model from a sample of 480 healthy participants was used to examine the acute effect of ibuprofen on temporary neuroanatomical changes in healthy individuals.

RESULTS

The BrainAGE model produced age prediction for each participant with a mean absolute error of 6.7 years between the estimated and chronological age. The intraclass correlation coefficient for BrainAGE was 0.96. Relative to placebo, 200 and 600 mg of ibuprofen significantly decreased BrainAGE by 1.18 and 1.15 years, respectively (p < .05). The trained BrainAGE model identified the medial prefrontal cortex to be the strongest age predictor.

CONCLUSIONS

BrainAGE is a potentially useful construct to examine neurological effects of therapeutic drugs. Ibuprofen temporarily reduces BrainAGE by approximately 1 year, which is likely due to its acute anti-inflammatory effects.

The association between stress and mood across the adult lifespan on default mode network

Aging of brain structure and function is a complex process characterized by high inter- and intra-individual variability. Such variability may arise from the interaction of multiple factors, including exposure to stressful experience and mood variation, across the lifespan. Using a multimodal neuroimaging and neurocognitive approach, we investigated the association of stress, mood and their interaction, in the structure and function of the default mode network (DMN), both during rest and task-induced deactivation, throughout the adult lifespan. Data confirmed a decreased functional connectivity (FC) and task-induced deactivation of the DMN during the aging process and in subjects with lower mood; on the contrary, an increased FC was observed in subjects with higher perceived stress. Surprisingly, the association of aging with DMN was altered by stress and mood in specific regions. An increased difficulty to deactivate the DMN was noted in older participants with lower mood, contrasting with an increased deactivation in individuals presenting high stress, independently of their mood levels, with aging. Interestingly, this constant interaction across aging was globally most significant in the combination of high stress levels with a more depressed mood state, both during resting state and task-induced deactivations. The present results contribute to characterize the spectrum of FC and deactivation patterns of the DMN, highlighting the crucial association of stress and mood levels, during the adult aging process. These combinatorial approaches may help to understand the heterogeneity of the aging process in brain structure and function and several states that may lead to neuropsychiatric disorders.

Unraveling the mechanistic complexity of Alzheimer's disease through systems biology

Alzheimer's disease (AD) is a complex, multifactorial disease that has reached global epidemic proportions. The challenge remains to fully identify its underlying molecular mechanisms that will enable development of accurate diagnostic tools and therapeutics. Conventional experimental approaches that target individual or small sets of genes or proteins may overlook important parts of the regulatory network, which limits the opportunity of identifying multitarget interventions. Our perspective is that a more complete insight into potential treatment options for AD will only be made possible through studying the disease as a system. We propose an integrative systems biology approach that we argue has been largely untapped in AD research. We present key publications to demonstrate the value of this approach and discuss the potential to intensify research efforts in AD through transdisciplinary collaboration. We highlight challenges and opportunities for significant breakthroughs that could be made if a systems biology approach is fully exploited.

Characterizing structural association alterations within brain networks in normal aging using Gaussian Bayesian networks

Recent multivariate neuroimaging studies have revealed aging-related alterations in brain structural networks. However, the sensory/motor networks such as the auditory, visual and motor networks, have obtained much less attention in normal aging research. In this study, we used Gaussian Bayesian networks (BN), an approach investigating possible inter-regional directed relationship, to characterize aging effects on structural associations between core brain regions within each of these structural sensory/motor networks using volumetric MRI data. We then further examined the discriminability of BN models for the young (N = 109; mean age =22.73 years, range 20-28) and old (N = 82; mean age =74.37 years, range 60-90) groups. The results of the BN modeling demonstrated that structural associations exist between two homotopic brain regions from the left and right hemispheres in each of the three networks. In particular, compared with the young group, the old group had significant connection reductions in each of the three networks and lesser connection numbers in the visual network. Moreover, it was found that the aging-related BN models could distinguish the young and old individuals with 90.05, 73.82, and 88.48% accuracy for the auditory, visual, and motor networks, respectively. Our findings suggest that BN models can be used to investigate the normal aging process with reliable statistical power. Moreover, these differences in structural inter-regional interactions may help elucidate the neuronal mechanism of anatomical changes in normal aging.