Goldman, V. | Search bioRxiv

bioRxiv2025

Sex-specific Effects of Outdoor Air Pollution on Subcortical Microstructure and Weight Gain: Findings from the ABCD Study

Obesity is associated with structural alterations of brain regions that support eating behavior. Exposure to air pollutants might exacerbate this association through neurotoxic effects on the brain. This study evaluated whether air pollution exposure 9-10 years old children, coupled with brain microstructure development in appetite-regulating regions, is associated with body mass index (BMI) changes over two years, and whether these associations differ by sex. Data were gathered from the Adolescent Brain Cognitive Development Study(R) (nbaseline=4,802, ages=9-10, males=49.9%, nfollow-up=2,439, ages=11-12, males=51.1%). Annual average estimates of ambient fine particulate matter (PM2.5), nitrogen dioxide (NO2), ground-level ozone (O3), and redox-weighted oxidative capacity (Oxwt, a joint measure of NO2 and O3) were gathered from youths residential addresses. Brain microstructure in 16 subcortical regions was assessed using diffusion-weighted MRI, focusing on proxies of cellular and neurite density: restricted normalized isotropic (RNI) and directional (RND) diffusion, respectively. Linear mixed-effects models examined whether air pollution and brain microstructure are related to BMI changes over two years, and whether these associations differed by sex. Exposure to PM2.5 coupled with high RND estimates in right caudate nucleus, bilateral putamen, and pallidum were associated with higher BMI over time, with pronounced effects in males (all p<0.05). PM2.5 coupled with greater neurite density in regions involved in reward-processing and decision-making were associated with higher BMI over a 2-year follow-up, especially in males. This research highlights air pollution as a modifiable risk factor for how differences in basal ganglia neurite density map onto obesity risk, with important implications for public health policy. HIGHLIGHTSO_LIHigh PM2.5 exposure and subcortical neurite density is associated with weight gain C_LIO_LIPM2.5 and subcortical development associations with BMI are pronounced in males C_LIO_LISignificant associations with BMI were found in regions involved in food intake C_LI

neuroscience↗

bioRxiv2018

Focus on the breath: Brain decoding reveals internal states of attention during meditation

Meditation practices are used to cultivate internally-oriented attention to bodily sensations, which may improve health via cognitive and emotion regulation of bodily signals. However, it remains unclear how meditation impacts internal attention states due to lack of measurement tools that can objectively assess mental states during meditation practice itself, and produce time estimates of internal focus at individual or group levels. To address these measurement gaps, we tested the feasibility of applying multi-voxel pattern analysis (MVPA) to single-subject fMRI data to (1) learn and recognize internal attentional (IA) states relevant for meditation during a directed IA task, and (2) decode or estimate the presence of those IA states during an independent meditation session. Within a mixed sample of experienced meditators and novice controls (N=16), we first used MVPA to develop single-subject brain classifiers for 5 modes of attention during an IA task in which subjects were specifically instructed to engage in one of five states (i.e., meditation-related states: breath attention, mind wandering, and self-referential processing, and control states: attention to feet and sounds). Using standard cross-validation procedures, MVPA classifiers were trained in five of six IA blocks for each subject, and predictive accuracy was tested on the independent sixth block (iterated until all block volumes were tested, N=2160). Across participants, all five IA states were significantly recognized well above chance (>41% vs. 20% chance). At the individual level, IA states were recognized in most participants (87.5%), suggesting that recognition of IA neural patterns may be generalizable for most participants, particularly experienced meditators. Next, for those who showed accurate IA neural patterns, the originally trained classifiers were then applied to a separate meditation run (10-min) to make an inference about the percentage time engaged in each IA state (breath attention, mind wandering, or self-referential processing). Preliminary group-level analyses demonstrated that during meditation practice, participants spent more time attending to breath compared to mind wandering or self-referential processing. This paradigm established the feasibility of using MVPA classifiers to objectively assess mental states during meditation at the participant level, which holds promise for improved measurement of internal attention states cultivated by meditation.

neuroscience↗