The virtual aging brain: Causal inference supports interhemispheric dedifferentiation in healthy aging.

The mechanisms of cognitive decline and its variability during healthy aging are not fully understood, but have been associated with reorganization of white matter tracts and functional brain networks. Here, we built a brain network modeling framework to infer the causal link between structural connectivity and functional architecture and the consequent cognitive decline in aging. By applying in-silico interhemispheric degradation of structural connectivity, we reproduced the process of functional dedifferentiation during aging. Thereby, we found the global modulation of brain dynamics by structural connectivity to increase with age, which was steeper in older adults with poor cognitive performance. We validated our causal hypothesis via a deep-learning Bayesian approach. Our results might be the first mechanistic demonstration of dedifferentiation during aging leading to cognitive decline.

Association of regional white matter hyperintensities with hypertension and cognition in the population-based 1000BRAINS study.

BACKGROUND: White matter hyperintensities of presumed vascular origin (WMH) are frequent in cerebral magnetic resonance imaging of older people. They are promoted by vascular risk factors, especially hypertension, and are associated with cognitive deficits at the group level. It has been suggested that not only the severity, but also the location, of lesions might critically influence cognitive deficits and represent different pathologies. METHODS: In 560 participants (65.2 ± 7.5 years, 51.4% males) of the population-based 1000BRAINS study, we analyzed the association of regional WMH using Fazekas scoring separately for cerebral lobes, with hypertension and cognition. RESULTS: WMH most often affected the frontal lobe (83.7% score >0), followed by the parietal (75.8%), temporal (32.7%), and occipital lobe (7.3%). Higher Fazekas scores in the frontal, parietal, and temporal lobe were associated with higher blood pressure and antihypertensive treatment in unadjusted ordinal regression models and in models adjusted for age, sex, and vascular risk factors (e.g., age- and sex-adjusted odds ratio = 1.14, 95% confidence interval = 1.03-1.25 for the association of frontal lobe WMH Fazekas score with systolic blood pressure [SBP] [per 10 mm Hg]; 1.13 [1.02-1.23] for the association of parietal lobe score with SBP; 1.72 [1.19-2.48] for the association of temporal lobe score with antihypertensive medications). In linear regressions, higher frontal lobe scores were associated with lower performance in executive function and non-verbal memory, and higher parietal lobe scores were associated with lower performance in executive function, verbal-, and non-verbal memory. CONCLUSIONS: Hypertension promotes WMH in the frontal, parietal, and temporal lobe. WMH in the frontal and parietal lobe are associated with reduced executive function and memory.

Interrelating differences in structural and functional connectivity in the older adult's brain.

In the normal aging process, the functional connectome restructures and shows a shift from more segregated to more integrated brain networks, which manifests itself in highly different cognitive performances in older adults. Underpinnings of this reorganization are not fully understood, but may be related to age-related differences in structural connectivity, the underlying scaffold for information exchange between regions. The structure-function relationship might be a promising factor to understand the neurobiological sources of interindividual cognitive variability, but remain unclear in older adults. Here, we used diffusion weighted and resting-state functional magnetic resonance imaging as well as cognitive performance data of 573 older subjects from the 1000BRAINS cohort (55-85 years, 287 males) and performed a partial least square regression on 400 regional functional and structural connectivity (FC and SC, respectively) estimates comprising seven resting-state networks. Our aim was to identify FC and SC patterns that are, together with cognitive performance, characteristic of the older adults aging process. Results revealed three different aging profiles prevalent in older adults. FC was found to behave differently depending on the severity of age-related SC deteriorations. A functionally highly interconnected system is associated with a structural connectome that shows only minor age-related decreases. Because this connectivity profile was associated with the most severe age-related cognitive decline, a more interconnected FC system in older adults points to a process of dedifferentiation. Thus, functional network integration appears to increase primarily when SC begins to decline, but this does not appear to mitigate the decline in cognitive performance.

Hippocampus co-atrophy pattern in dementia deviates from covariance patterns across the lifespan.

The hippocampus is a plastic region and highly susceptible to ageing and dementia. Previous studies explicitly imposed a priori models of hippocampus when investigating ageing and dementia-specific atrophy but led to inconsistent results. Consequently, the basic question of whether macrostructural changes follow a cytoarchitectonic or functional organization across the adult lifespan and in age-related neurodegenerative disease remained open. The aim of this cross-sectional study was to identify the spatial pattern of hippocampus differentiation based on structural covariance with a data-driven approach across structural MRI data of large cohorts (n = 2594). We examined the pattern of structural covariance of hippocampus voxels in young, middle-aged, elderly, mild cognitive impairment and dementia disease samples by applying a clustering algorithm revealing differentiation in structural covariance within the hippocampus. In all the healthy and in the mild cognitive impaired participants, the hippocampus was robustly divided into anterior, lateral and medial subregions reminiscent of cytoarchitectonic division. In contrast, in dementia patients, the pattern of subdivision was closer to known functional differentiation into an anterior, body and tail subregions. These results not only contribute to a better understanding of co-plasticity and co-atrophy in the hippocampus across the lifespan and in dementia, but also provide robust data-driven spatial representations (i.e. maps) for structural studies.

Pathway-Specific Genetic Risk for Alzheimer's Disease Differentiates Regional Patterns of Cortical Atrophy in Older Adults.

Brain aging is highly variable and represents a challenge to delimit aging from disease processes. Moreover, genetic factors may influence both aging and disease. Here we focused on this issue and investigated effects of multiple genetic loci previously identified to be associated with late-onset Alzheimer's disease (AD) on brain structure of older adults from a population sample. We calculated a genetic risk score (GRS) using genome-wide significant single-nucleotide polymorphisms from genome-wide association studies of AD and tested its effect on cortical thickness (CT). We observed a common pattern of cortical thinning (right inferior frontal, left posterior temporal, medial occipital cortex). To identify CT changes by specific biological processes, we subdivided the GRS effect according to AD-associated pathways and performed follow-up analyses. The common pattern from the main analysis was further differentiated by pathway-specific effects yielding a more bilateral pattern. Further findings were located in the superior parietal and mid/anterior cingulate regions representing 2 unique pathway-specific patterns. All patterns, except the superior parietal pattern, were influenced by apolipoprotein E. Our step-wise approach revealed atrophy patterns that partially resembled imaging findings in early stages of AD. Our study provides evidence that genetic burden for AD contributes to structural brain variability in normal aging.

Functional network reorganization in older adults: Graph-theoretical analyses of age, cognition and sex.

Healthy aging has been associated with a decrease in functional network specialization. Importantly, variability of alterations of functional connectivity is especially high across older adults. Whole-brain functional network reorganization, though, and its impact on cognitive performance within particularly the older generation is still a matter of debate. We assessed resting state functional connectivity (RSFC) in 772 older adults (55-85 years, 421 males) using a graph-theoretical approach. Results show overall age-related increases of between- and decreases of within-network RSFC. With similar phenomena observed in young to middle-aged adults, i.e. that RSFC reorganizes towards more pronounced functional network integration, the current results amend such evidence for the old age. The results furthermore indicate that RSFC reorganization in older adults particularly pertain to early sensory networks (e.g. visual and sensorimotor network). Importantly, RSFC differences of these early sensory networks were found to be a relevant mediator in terms of the age-related cognitive performance differences. Further, we found systematic sex-related network differences with females showing patterns of more segregation (i.e. default mode and ventral attention network) and males showing a higher integrated network system (particularly for the sensorimotor network). These findings underpin the notion of sex-related connectivity differences, possibly facilitating sex-related behavioral functioning.

Generalizing age effects on brain structure and cognition: A two-study comparison approach.

Normal aging is accompanied by an interindividually variable decline in cognitive abilities and brain structure. This variability, in combination with methodical differences and differences in sample characteristics across studies, pose a major challenge for generalizability of results from different studies. Therefore, the current study aimed at cross-validating age-related differences in cognitive abilities and brain structure (measured using cortical thickness [CT]) in two large independent samples, each consisting of 228 healthy older adults aged between 65 and 85 years: the Longitudinal Healthy Aging Brain (LHAB) database (University of Zurich, Switzerland) and the 1000BRAINS (Research Centre Jülich, Germany). Participants from LHAB showed significantly higher education, physical well-being, and cognitive abilities (processing speed, concept shifting, reasoning, semantic verbal fluency, and vocabulary). In contrast, CT values were larger for participants of 1000BRAINS. Though, both samples showed highly similar age-related differences in both, cognitive abilities and CT. These effects were in accordance with functional aging theories, for example, posterior to anterior shift in aging as was shown for the default mode network. Thus, the current two-study approach provides evidence that independently on heterogeneous metrics of brain structure or cognition across studies, age-related effects on cognitive ability and brain structure can be generalized over different samples, assuming the same methodology is used.

Evaluation of non-negative matrix factorization of grey matter in age prediction.

The relationship between grey matter volume (GMV) patterns and age can be captured by multivariate pattern analysis, allowing prediction of individuals' age based on structural imaging. Raw data, voxel-wise GMV and non-sparse factorization (with Principal Component Analysis, PCA) show good performance but do not promote relatively localized brain components for post-hoc examinations. Here we evaluated a non-negative matrix factorization (NNMF) approach to provide a reduced, but also interpretable representation of GMV data in age prediction frameworks in healthy and clinical populations. This examination was performed using three datasets: a multi-site cohort of life-span healthy adults, a single site cohort of older adults and clinical samples from the ADNI dataset with healthy subjects, participants with Mild Cognitive Impairment and patients with Alzheimer's disease (AD) subsamples. T1-weighted images were preprocessed with VBM8 standard settings to compute GMV values after normalization, segmentation and modulation for non-linear transformations only. Non-negative matrix factorization was computed on the GM voxel-wise values for a range of granularities (50-690 components) and LASSO (Least Absolute Shrinkage and Selection Operator) regression were used for age prediction. First, we compared the performance of our data compression procedure (i.e., NNMF) to various other approaches (i.e., uncompressed VBM data, PCA-based factorization and parcellation-based compression). We then investigated the impact of the granularity on the accuracy of age prediction, as well as the transferability of the factorization and model generalization across datasets. We finally validated our framework by examining age prediction in ADNI samples. Our results showed that our framework favorably compares with other approaches. They also demonstrated that the NNMF based factorization derived from one dataset could be efficiently applied to compress VBM data of another dataset and that granularities between 300 and 500 components give an optimal representation for age prediction. In addition to the good performance in healthy subjects our framework provided relatively localized brain regions as the features contributing to the prediction, thereby offering further insights into structural changes due to brain aging. Finally, our validation in clinical populations showed that our framework is sensitive to deviance from normal structural variations in pathological aging.

On the integrity of functional brain networks in schizophrenia, Parkinson's disease, and advanced age: Evidence from connectivity-based single-subject classification.

Previous whole-brain functional connectivity studies achieved successful classifications of patients and healthy controls but only offered limited specificity as to affected brain systems. Here, we examined whether the connectivity patterns of functional systems affected in schizophrenia (SCZ), Parkinson's disease (PD), or normal aging equally translate into high classification accuracies for these conditions. We compared classification performance between pre-defined networks for each group and, for any given network, between groups. Separate support vector machine classifications of 86 SCZ patients, 80 PD patients, and 95 older adults relative to their matched healthy/young controls, respectively, were performed on functional connectivity in 12 task-based, meta-analytically defined networks using 25 replications of a nested 10-fold cross-validation scheme. Classification performance of the various networks clearly differed between conditions, as those networks that best classified one disease were usually non-informative for the other. For SCZ, but not PD, emotion-processing, empathy, and cognitive action control networks distinguished patients most accurately from controls. For PD, but not SCZ, networks subserving autobiographical or semantic memory, motor execution, and theory-of-mind cognition yielded the best classifications. In contrast, young-old classification was excellent based on all networks and outperformed both clinical classifications. Our pattern-classification approach captured associations between clinical and developmental conditions and functional network integrity with a higher level of specificity than did previous whole-brain analyses. Taken together, our results support resting-state connectivity as a marker of functional dysregulation in specific networks known to be affected by SCZ and PD, while suggesting that aging affects network integrity in a more global way. Hum Brain Mapp 38:5845-5858, 2017. © 2017 Wiley Periodicals, Inc.

Differential predictability of cognitive profiles from brain structure in older males and females.

Structural brain imaging parameters may successfully predict cognitive performance in neurodegenerative diseases but mostly fail to predict cognitive abilities in healthy older adults. One important aspect contributing to this might be sex differences. Behaviorally, older males and females have been found to differ in terms of cognitive profiles, which cannot be captured by examining them as one homogenous group. In the current study, we examined whether the prediction of cognitive performance from brain structure, i.e. region-wise grey matter volume (GMV), would benefit from the investigation of sex-specific cognitive profiles in a large sample of older adults (1000BRAINS; N = 634; age range 55-85 years). Prediction performance was assessed using a machine learning (ML) approach. Targets represented a) a whole-sample cognitive component solution extracted from males and females, and b) sex-specific cognitive components. Results revealed a generally low predictability of cognitive profiles from region-wise GMV. In males, low predictability was observed across both, the whole sample as well as sex-specific cognitive components. In females, however, predictability differences across sex-specific cognitive components were observed, i.e. visual working memory (WM) and executive functions showed higher predictability than fluency and verbal WM. Hence, results accentuated that addressing sex-specific cognitive profiles allowed a more fine-grained investigation of predictability differences, which may not be observable in the prediction of the whole-sample solution. The current findings not only emphasize the need to further investigate the predictive power of each cognitive component, but they also emphasize the importance of sex-specific analyses in older adults.

Prediction of cognitive performance differences in older age from multimodal neuroimaging data.

Differences in brain structure and functional and structural network architecture have been found to partly explain cognitive performance differences in older ages. Thus, they may serve as potential markers for these differences. Initial unimodal studies, however, have reported mixed prediction results of selective cognitive variables based on these brain features using machine learning (ML). Thus, the aim of the current study was to investigate the general validity of cognitive performance prediction from imaging data in healthy older adults. In particular, the focus was with examining whether (1) multimodal information, i.e., region-wise grey matter volume (GMV), resting-state functional connectivity (RSFC), and structural connectivity (SC) estimates, may improve predictability of cognitive targets, (2) predictability differences arise for global cognition and distinct cognitive profiles, and (3) results generalize across different ML approaches in 594 healthy older adults (age range: 55-85 years) from the 1000BRAINS study. Prediction potential was examined for each modality and all multimodal combinations, with and without confound (i.e., age, education, and sex) regression across different analytic options, i.e., variations in algorithms, feature sets, and multimodal approaches (i.e., concatenation vs. stacking). Results showed that prediction performance differed considerably between deconfounding strategies. In the absence of demographic confounder control, successful prediction of cognitive performance could be observed across analytic choices. Combination of different modalities tended to marginally improve predictability of cognitive performance compared to single modalities. Importantly, all previously described effects vanished in the strict confounder control condition. Despite a small trend for a multimodal benefit, developing a biomarker for cognitive aging remains challenging.

Associations between antagonistic SNPs for neuropsychiatric disorders and human brain structure.

A previously published genome-wide association study (GWAS) meta-analysis across eight neuropsychiatric disorders identified antagonistic single-nucleotide polymorphisms (SNPs) at eleven genomic loci where the same allele was protective against one neuropsychiatric disorder and increased the risk for another. Until now, these antagonistic SNPs have not been further investigated regarding their link to brain structural phenotypes. Here, we explored their associations with cortical surface area and cortical thickness (in 34 brain regions and one global measure each) as well as the volumes of eight subcortical structures using summary statistics of large-scale GWAS of brain structural phenotypes. We assessed if significantly associated brain structural phenotypes were previously reported to be associated with major neuropsychiatric disorders in large-scale case-control imaging studies by the ENIGMA consortium. We further characterized the effects of the antagonistic SNPs on gene expression in brain tissue and their association with additional cognitive and behavioral phenotypes, and performed an exploratory voxel-based whole-brain analysis in the FOR2107 study (n = 754 patients with major depressive disorder and n = 847 controls). We found that eight antagonistic SNPs were significantly associated with brain structural phenotypes in regions such as anterior parts of the cingulate cortex, the insula, and the superior temporal gyrus. Case-control differences in implicated brain structural phenotypes have previously been reported for bipolar disorder, major depressive disorder, and schizophrenia. In addition, antagonistic SNPs were associated with gene expression changes in brain tissue and linked to several cognitive-behavioral traits. In our exploratory whole-brain analysis, we observed significant associations of gray matter volume in the left superior temporal pole and left superior parietal region with the variants rs301805 and rs1933802, respectively. Our results suggest that multiple antagonistic SNPs for neuropsychiatric disorders are linked to brain structural phenotypes. However, to further elucidate these findings, future case-control genomic imaging studies are required.

Baseline MRI Examination in the NAKO Health Study­Findings on Feasibility, Participation and Dropout Rates, Comfort, and Image Quality.

BACKGROUND: Magnetic resonance imaging (MRI) yields important information on the development and current status of many different diseases. Whole-body MRI was accordingly made a part of the multicenter, population-based NAKO Health Study. The present analysis concerns the feasibility of the baseline MRI examination and various aspects of quality assurance over the period 2014-2019. METHODS: 32 252 participants in the NAKO Health Study, aged 20 to 74, who had no contraindication to MRI were invited to undergo scanning in one of five MRI study centers across Germany. The whole-body MRI scan took about one hour and consisted of sequences for the visualization of structural and functional features of the brain, musculoskeletal system, cardiovascular system, and thoracoabdominal system. A comprehensive quality-assurance assessment was carried out, with evaluation of adverse events, the completeness of the MRI protocols, the participants' subjective perceptions, and image quality. RESULTS: 31 578 participants (97.9%) were successfully included in the MRI study. They reported a high level of comfort and suffered no severe adverse events; mild adverse events occurred in only four participants. Depending on the imaging sequence, the image quality was rated as excellent in 80.2% to 96.8% of cases. Quality assessment with respect to structural features of the brain revealed high consistency across study centers, as well as with regard to age-and sex-based differences in brain volume (men, 1203.81 ± 102.06 cm³; women, 1068.10 ± 86.69 cm³). CONCLUSION: Whole-body MRI was successfully implemented in the NAKO baseline examination and was associated with high patient comfort and very good image quality. The imaging biomarkers of the brain confirmed previously observed differences based on age and sex, underscoring the feasibility of data pooling.

Cortical similarities in psychiatric and mood disorders identified in federated VBM analysis via COINSTAC.

Structural neuroimaging studies have identified a combination of shared and disorder-specific patterns of gray matter (GM) deficits across psychiatric disorders. Pooling large data allows for examination of a possible common neuroanatomical basis that may identify a certain vulnerability for mental illness. Large-scale collaborative research is already facilitated by data repositories, institutionally supported databases, and data archives. However, these data-sharing methodologies can suffer from significant barriers. Federated approaches augment these approaches by enabling access or more sophisticated, shareable and scaled-up analyses of large-scale data. We examined GM alterations using Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation, an open-source, decentralized analysis application. Through federated analysis of eight sites, we identified significant overlap in the GM patterns (n = 4,102) of individuals with schizophrenia, major depressive disorder, and autism spectrum disorder. These results show cortical and subcortical regions that may indicate a shared vulnerability to psychiatric disorders.

The influence of bilingualism on gray matter volume in the course of aging: a longitudinal study.

Background: Bilingualism is associated with higher gray matter volume (GMV) as a form of brain reserve in brain regions such as the inferior frontal gyrus (IFG) and the inferior parietal lobule (IPL). A recent cross-sectional study reported the age-related GMV decline in the left IFG and IPL to be steeper for bilinguals than for monolinguals. The present study aimed at supporting this finding for the first time with longitudinal data. Methods: In the current study, 200 participants aged 19 to 79 years (87 monolinguals, 113 sequential bilinguals, mostly native German speakers with variable second language background) were included. Trajectories of GMV decline in the bilateral IFG and IPL were analyzed in mono- and bilinguals over two time points (mean time interval: 3.6 years). For four regions of interest (left/right IFG and left/right IPL), mixed Analyses of Covariance were conducted to assess (i) GMV changes over time, (ii) GMV differences for language groups (monolinguals/bilinguals), and (iii) the interaction between time point and language group. Corresponding analyses were conducted for the two factors of GMV, surface area (SA) and cortical thickness (CT). Results: There was higher GMV in bilinguals compared to monolinguals in the IPL, but not IFG. While the left and right IFG and the right IPL displayed a similar GMV change in mono- and bilinguals, GMV decline within the left IPL was significantly steeper in bilinguals. There was greater SA in bilinguals in the bilateral IPL and a steeper CT decline in bilinguals within in the left IPL. Conclusion: The cross-sectional observations of a steeper GMV decline in bilinguals could be confirmed for the left IPL. Additionally, the higher GMV in bilinguals in the bilateral IPL may indicate that bilingualism contributes to brain reserve especially in posterior brain regions. SA appeared to contribute to bilinguals' higher GMV in the bilateral IPL, while CT seemed to account for the steeper structural decline in bilinguals in the left IPL. The present findings demonstrate the importance of time as an additional factor when assessing the neuroprotective effects of bilingualism on structural features of the human brain.

Characterization of the angular gyrus in an older adult population: a multimodal multilevel approach.

The angular gyrus (AG) has been associated with multiple cognitive functions, such as language, spatial and memory functions. Since the AG is thought to be a cross-modal hub region suffering from significant age-related structural atrophy, it may also play a key role in age-related cognitive decline. However, the exact relation between structural atrophy of the AG and cognitive decline in older adults is not fully understood, which may be related to two aspects: First, the AG is cytoarchitectonically divided into two areas, PGa and PGp, potentially sub-serving different cognitive functions. Second, the older adult population is characterized by high between-subjects variability which requires targeting individual phenomena during the aging process. We therefore performed a multimodal (gray matter volume [GMV], resting-state functional connectivity [RSFC] and structural connectivity [SC]) characterization of AG subdivisions PGa and PGp in a large older adult population, together with relations to age, cognition and lifestyle on the group level. Afterwards, we switched the perspective to the individual, which is especially important when it comes to the assessment of individual patients. The AG can be considered a heterogeneous structure in of the older brain: we found the different AG parts to be associated with different patterns of whole-brain GMV associations as well as their associations with RSFC, and SC patterns. Similarly, differential effects of age, cognition and lifestyle on the GMV of AG subdivisions were observed. This suggests each region to be structurally and functionally differentially involved in the older adult's brain network architecture, which was supported by differential molecular and genetic patterns, derived from the EBRAINS multilevel atlas framework. Importantly, individual profiles deviated considerably from the global conclusion drawn from the group study. Hence, general observations within the older adult population need to be carefully considered, when addressing individual conditions in clinical practice.

Classification and prediction of cognitive performance differences in older age based on brain network patterns using a machine learning approach.

Age-related cognitive decline varies greatly in healthy older adults, which may partly be explained by differences in the functional architecture of brain networks. Resting-state functional connectivity (RSFC) derived network parameters as widely used markers describing this architecture have even been successfully used to support diagnosis of neurodegenerative diseases. The current study aimed at examining whether these parameters may also be useful in classifying and predicting cognitive performance differences in the normally aging brain by using machine learning (ML). Classifiability and predictability of global and domain-specific cognitive performance differences from nodal and network-level RSFC strength measures were examined in healthy older adults from the 1000BRAINS study (age range: 55-85 years). ML performance was systematically evaluated across different analytic choices in a robust cross-validation scheme. Across these analyses, classification performance did not exceed 60% accuracy for global and domain-specific cognition. Prediction performance was equally low with high mean absolute errors (MAEs ≥ 0.75) and low to none explained variance (R2 ≤ 0.07) for different cognitive targets, feature sets, and pipeline configurations. Current results highlight limited potential of functional network parameters to serve as sole biomarker for cognitive aging and emphasize that predicting cognition from functional network patterns may be challenging.

Association between Long-Term Air Pollution, Chronic Traffic Noise, and Resting-State Functional Connectivity in the 1000BRAINS Study.

BACKGROUND: Older adults show a high variability in cognitive performance that cannot be explained by aging alone. Although research has linked air pollution and noise to cognitive impairment and structural brain alterations, the potential impact of air pollution and noise on functional brain organization is unknown. OBJECTIVE: This study examined the associations between long-term air pollution and traffic noise with measures of functional brain organization in older adults. We hypothesize that exposures to high air pollution and noise levels are associated with age-like changes in functional brain organization, shown by less segregated brain networks. METHODS: Data from 574 participants (44.1% female, 56-85 years of age) in the German 1000BRAINS study (2011-2015) were analyzed. Exposure to particulate matter (PM10, PM2.5, and PM2.5 absorbance), accumulation mode particle number (PNAM), and nitrogen dioxide (NO2) was estimated applying land-use regression and chemistry transport models. Noise exposures were assessed as weighted 24-h (Lden) and nighttime (Lnight) means. Functional brain organization of seven established brain networks (visual, sensorimotor, dorsal and ventral attention, limbic, frontoparietal and default network) was assessed using resting-state functional brain imaging data. To assess functional brain organization, we determined the degree of segregation between networks by comparing the strength of functional connections within and between networks. We estimated associations between air pollution and noise exposure with network segregation, applying multiple linear regression models adjusted for age, sex, socioeconomic status, and lifestyle variables. RESULTS: Overall, small associations of high exposures with lesser segregated networks were visible. For the sensorimotor networks, we observed small associations between high air pollution and noise and lower network segregation, which had a similar effect size as a 1-y increase in age [e.g., in sensorimotor network, -0.006 (95% CI: -0.021, 0.009) per 0.3 ×10-5/m increase in PM2.5 absorbance and -0.004 (95% CI: -0.006, -0.002) per 1-y age increase]. CONCLUSION: High exposure to air pollution and noise was associated with less segregated functional brain networks. https://doi.org/10.1289/EHP9737.

Long-term air pollution, noise, and structural measures of the Default Mode Network in the brain: Results from the 1000BRAINS cohort.

BACKGROUND: While evidence suggests that long-term air pollution (AP) and noise may adversely affect cognitive function, little is known about whether environmental exposures also promote structural changes in underlying brain networks. We therefore investigated the associations between AP, traffic noise, and structural measures of the Default Mode Network (DMN), a functional brain network known to undergo specific changes with age. METHODS: We analyzed data from 579 participants (mean age at imaging: 66.5 years) of the German 1000BRAINS study. Long-term residential exposure to particulate matter (diameter ≤10 µm [PM10]; diameter ≤2.5 µm [PM2.5]), PM2.5 absorbance (PM2.5abs), nitrogen dioxide (NO2), and accumulation mode particulate number concentration (PNAM) was estimated using validated land use regression and chemistry transport models. Long-term outdoor traffic noise was modeled at participants' homes based on a European Union's Environmental Noise Directive. As measures of brain structure, cortical thickness and local gyrification index (lGI) values were calculated for DMN regions from T1-weighted structural brain images collected between 2011 and 2015. Associations between environmental exposures and brain structure measures were estimated using linear regression models, adjusting for demographic and lifestyle characteristics. RESULTS: AP exposures were below European Union standards but above World Health Organization guidelines (e.g., PM10 mean: 27.5 µg/m3). A third of participants experienced outdoor 24-h noise above European recommendations. Exposures were not consistently associated with lGI values in the DMN. We observed weak inverse associations between AP and cortical thickness in the right anterior DMN (e.g., -0.010 mm [-0.022, 0.002] per 0.3 unit increase in PM2.5abs) and lateral part of the posterior DMN. CONCLUSION: Long-term AP and noise were not consistently associated with structural parameters of the DMN in the brain. While weak associations were present between AP exposure and cortical thinning of right hemispheric DMN regions, it remains unclear whether AP might influence DMN brain structure in a similar way as aging.