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

Lucht, Sarah; Glaubitz, Lina; Moebus, Susanne; Schramm, Sara; Jockwitz, Christiane; Caspers, Svenja; Hoffmann, Barbara

doi:10.1016/j.ijheh.2021.113867

Cited by 5 publications

(2 citation statements)

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“…Recent neuroimaging studies report findings consistent with others, but are not able to resolve the ongoing inconsistencies in this area [63,64], despite further experimental mouse modelling [65 ▪ ].…”

Section: Epidemiological Associations Between Air Pollutants and Ment...mentioning

confidence: 58%

Air pollution and mental health: associations, mechanisms and methods

King

Zhang

Cohen

2021

Current Opinion in Psychiatry

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Purpose of reviewThere is increasing interest in the links between exposure to air pollution and a range of health outcomes. The association with mental health however is much less established. This article reviews developments in the field over the past 12 months, highlighting the evidence for causation, associations between multiple air pollutants and mental health outcomes, and assesses the challenges of researching this topic. Recent findingsIncreasingly rigorous methods are being applied to the investigation of a broader range of mental health outcomes. These methods include basic science, neuroimaging, and observational studies representing diverse geographical locations. Cohort studies with linked high-resolution air pollutant exposure data are common, facilitating advanced analytic methods. To date, meta-analyses have demonstrated small and significant positive associations between long-term exposure to fine particulate matter and depressive symptoms and cognitive decline. Methodological complexities in measuring exposure and outcome pose ongoing difficulties for the field.

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Section: Epidemiological Associations Between Air Pollutants and Ment...mentioning

confidence: 58%

Air pollution and mental health: associations, mechanisms and methods

King

Zhang

Cohen

2021

Current Opinion in Psychiatry

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“…Here, the factor ‘age’ alone is not able to explain large parts of the variance. Despite the consideration of other factors, such as lifestyle (Bittner et al 2019 ; Hamer and Batty 2019 ), sex (Jahanshad and Thompson 2017 ; Jockwitz et al 2021a , b ), genetic predispositions (Honea et al 2009 ; Caspers et al 2020 ), or environmental influences (de Prado Bert et al 2018 ; Nussbaum et al 2020 ; Lucht et al 2022 ), the high inter-individual heterogeneity remains only partly explained. In fact, to identify general brain–phenotype relations in the older adult population on the group level, where each factor might show small effect sizes, very large sample sizes are required (Button et al 2013 ).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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