2366
Symposium
Understanding the diversity of causal mechanisms underlying healthy brain function across contexts and demographics is increasingly urgent in light of global health disparities, aging populations, and the rise of neurodegenerative diseases. By integrating advanced computational modeling with multimodal neuroimaging, studies are revealing how systemic and environmental factors contribute to brain health and disease, and how these factors interact with specific demographics in the light of socio-economic inequality. This symposium capitalizes on cutting-edge machine learning methodologies to identify the effect of extra-cerebral factors on neuroimaging biomarkers of healthy and pathological aging, and will also introduce the use of dynamical system models to uncover mechanistic explanations linking these factors to whole-brain dynamics. We will also explore the synergy between approaches from computational neuroimaging and the broader interdisciplinary perspective required to identify and quantify contextual determinants of brain health from complex and heterogeneous data sources.
Attendees will gain a deeper understanding of (1) the role of computational modeling in elucidating causal mechanisms in brain function, focusing on modeling the effect of extra-cerebral factors on brain dynamics; (2) an interdisciplinary approach to quantify the effect of environmental and demographic variables on brain health biomarkers based on high-dimensional neuroimaging datasets; and (3) the potential of interventions addressing preventable risk factors, such as fostering creativity and sociocognitive engagement, among other policies aimed to mitigate accelerated brain aging and neurodegeneration.
The target audience for this symposium includes the neuroimaging community, computational neuroscientist, clinicians, and researchers in human brain mapping who are interested in exploring innovative methodologies, global perspectives, and collaborative approaches to brain research. It is particularly relevant to those seeking to understand how diverse cultural, regional, and institutional contexts shape neurobiological mechanisms and their applications to global health challenges.
Presentations
Brain aging is a complex process influenced by genetic, environmental, and social determinants, with variability often captured by the brain age gap (BAG), a metric quantifying the deviation between biological and chronological brain age. BAG has emerged as a valuable marker for cognitive decline and neurodegenerative disease (ND) progression, including Alzheimer’s disease (AD) and frontotemporal dementia (FTD). However, its biological underpinnings and links to social determinants of health (SDH) remain underexplored, particularly in underrepresented populations where socioeconomic disparities play a significant role. To address this, we developed a multimodal brain age prediction model, integrating structural and functional MRI data, to investigate BAG and its association with SDH. Leveraging a dataset of 1,084 cognitively normal (CN) individuals from Latin America (Latam) and the United States (USA), alongside individuals with subjective memory complaints (SMC), mild cognitive impairment (MCI), AD, and FTD (N=1,237), we aimed to elucidate the influence of SDH and regional disparities on brain aging. Our model employed support vector machine regressors (SVR) trained on gray matter volume (GMV) and functional connectivity (FC) features, combined through an ensemble stacking approach with ridge regression for enhanced predictive accuracy. The dynamic mean field (DMF) whole-brain model further investigated BAG-associated neural mechanisms, focusing on global coupling (G) and feedback inhibitory control (FIC) parameters linked to inter-areal connectivity and excitation/inhibition (E/I) balance. Findings revealed that Latam CN participants exhibited larger BAGs compared to their USA counterparts, indicating accelerated brain aging driven by socioeconomic adversity. BAG hierarchically increased across cognitive stages (CN < SMC < MCI < AD/FTD) and strongly correlated with Mini-Mental State Examination (MMSE) scores in patients, underscoring its specificity to disease-related cognitive decline. Higher SDH burdens correlated with larger BAGs in Latam groups, highlighting the role of adverse social exposomes in brain health disparities. Mechanistic analyses showed that larger BAGs were associated with reduced global connectivity and increased local inhibitory control, suggesting inter-areal disconnection and E/I imbalance as neurobiological markers of accelerated aging. These patterns were particularly pronounced in Latam cohorts, emphasizing the compounded effects of environmental and biological factors. This study underscores the utility of BAG as a biomarker for understanding brain aging and its socioeconomic determinants, advocating for its integration into public health strategies and clinical workflows. By addressing modifiable SDH factors, BAG models offer a pathway to mitigate brain aging disparities, improve early detection of cognitive decline, and guide targeted interventions, particularly in resource-limited settings. The findings highlight the need for future research to expand BAG modeling to diverse populations, incorporate additional neuroimaging modalities, and explore causal pathways linking SDH, brain aging, and neurodegeneration, paving the way for equitable and effective strategies in aging and dementia research.
Brain clocks, which measure deviations between chronological age and predicted brain age (brain age gaps, or BAGs), reveal whether an individual's brain appears older or younger than expected. These models can characterize individual aging trajectories and identify pathological deviations in conditions like Alzheimer's Disease (AD) and behavioral variant frontotemporal dementia (bvFTD). However, challenges such as heterogeneous findings, limited computational methods, and a lack of diverse representation hinder progress in the field. Emerging biophysical whole-brain models offer a promising solution, enabling robust analyses with moderate sample sizes and shedding light on unexplored causal mechanisms. In parallel, creative and artistic experiences have been proposed as interventions to support brain health across the lifespan. Here we combine source space connectivity (EEG) with generative brain modeling in healthy controls (HCs) from both the global south and north, alongside Alzheimer’s disease (AD) and behavioral variant frontotemporal dementia (bvFTD) patients (N=1,399). Additionally, we analyzed how different types of creative experiences can protect the brain from accelerated brain aging via neural plasticity mechanisms, including a subsample of expert and matched non-expert participants in dance, music, visual arts, and video games, along with a pre/post-learning study (N=227). Using source-space EEG connectivity and Support Vector Machine (SVM) regression models, we examined how BAGs reflect disparities at both aggregate (e.g., geographic and income differences), individual levels (e.g., education and sex), and how can be used to characterize the effects of creativity on brain health (expertise and learning). The BAGs in aging were modulated by diversity-related factors inducing accelerated aging, including geography (south>north), income (GPD, low > high), sex (female>male), and education (low > high). A larger BAG was observed in patients, with sex further increasing the effects in AD (female>male). In creative experiences, we observed delayed brain age across all domains, and scalable effects (expertise>learning). The higher the level of expertise and performance, the greater the delay in brain age. Biophysical modeling shows that BAGs are related to specific mechanisms: global hyperexcitability and reduced connectivity (structural disintegration) were implicated in aging’s BAGs. Hypoexcitability and severe disintegration were related to dementia. On the other hand, creativity is associated with reduced hyperexcitability, increased network efficiency, and increased structural integrity. Our work sheds light on biophysical mechanisms of accelerated aging in diverse and underserved populations and provides domain-independent evidence of creativity’s positive impact on brain health.
Allostatic-interoception, the anticipation and sensing of bodily signals, is linked to sociocognitive function and cardiovascular health. Impairments in this process may arise in behavioral-variant frontotemporal dementia (bvFTD) and Alzheimer’s disease (AD) due to structural and functional disruptions in the allostatic-interoception network (AIN). Here, we integrate three complementary studies to investigate the AIN's role in sociocognitive processes and cardiovascular risk in neurodegeneration. In Study 1, we conducted PRISMA Activated Likelihood Estimate (ALE) meta-analyses on 170 studies (N=7,032), including structural and functional neuroimaging data. We identified key AIN regions (e.g., insula, amygdala, orbitofrontal cortex, anterior cingulate cortex, thalamus, hippocampus) across interoception, emotion, and social cognition domains in all neurodegenerative diseases combined, with more pronounced involvement in bvFTD in patient specific analyses. Study 2 examined multimodal neuroimaging in 1,501 participants (bvFTD: N=304; AD: N=512; controls: N=685) and found that higher cardiovascular risk (e.g., BMI, hypertension, diabetes, smoking) correlated with reduced structural integrity and functional connectivity within the AIN. Functional disruptions were widespread in bvFTD (bilateral insula, cingulate, orbitofrontal cortex, thalamus, hippocampus) and localized in AD (hippocampus, parahippocampus). In Study 3, we analyzed intrinsic neural timescales of interoception using EEG (N=112; bvFTD: N=31; AD: N=35; controls: N=46). Longer autocorrelation windows (ACW) during interoception were observed in bvFTD (frontotemporal/parietal clusters) and AD (central/occipital-parietal clusters). Longer ACW correlated with poorer sociocognitive performance and AIN structural changes in bvFTD. Our findings reveal consistent AIN involvement in bvFTD and partial involvement in AD, supporting predictive coding theories of allostatic-interoception. Increased cardiovascular risk exacerbates AIN impairments, highlighting the need to address systemic health factors in dementia care. This work underscores the potential benefits of interventions targeting allostatic-interoception to mitigate behavioral impairments in neurodegenerative diseases, particularly in bvFTD.
Brain function changes during our lifespan. So far, only pairwise functional interactions between brain regions have been employed to predict brain age. However, high-order interactions (HOI) can capture complex non-linear associations between pairwise regions and the rest of the brain, detecting key synergistic and redundant couplings. To bridge this gap, we employed cutting-edge deep learning techniques to analyze HOI and predict brain age from functional MRI (fMRI) and electroencephalography (EEG) data, encompassing over 5,000 participants from 15 countries. Our brain-age gap models revealed a progressive acceleration of brain aging from healthy individuals to those with neurocognitive disorders. We observed increasing brain-age gaps from healthy controls to individuals with mild cognitive impairment (MCI), and further increases in those with Alzheimer’s disease (AD) and frontotemporal dementia (FTD). We found that individual clinical conditions, country-level socioeconomic factors, gender disparities, and environmental exposomes influence brain-age gaps. Individuals in countries with more significant socioeconomic inequalities, higher pollution levels, and limited healthcare access exhibit older brain ages. Our research advances our understanding of brain aging. It highlights the urgent need for global health policies that address the social and environmental factors contributing to accelerated brain aging, particularly in regions facing greater socioeconomic challenges.