A machine learning approach towards the differentiation between interoceptive and exteroceptive attention.

Interoception, the representation of the body's internal state, plays a central role in emotion, motivation and wellbeing. Interoceptive sensibility, the ability to engage in sustained interoceptive awareness, is particularly relevant for mental health but is exclusively measured via self-report, without methods for objective measurement. We used machine learning to classify interoceptive sensibility by contrasting using data from a randomized control trial of interoceptive training, with functional magnetic resonance imaging assessment before and after an 8-week intervention (N = 44 scans). The neuroimaging paradigm manipulated attention targets (breath vs. visual stimuli) and reporting demands (active reporting vs. passive monitoring). Machine learning achieved high accuracy in distinguishing between interoceptive and exteroceptive attention, both for within-session classification (~80% accuracy) and out-of-sample classification (~70% accuracy), revealing the reliability of the predictions. We then explored the classifier potential for 'reading out' mental states in a 3-min sustained interoceptive attention task. Participants were classified as actively engaged about half of the time, during which interoceptive training enhanced their ability to sustain interoceptive attention. These findings demonstrate that interoceptive and exteroceptive attention is distinguishable at the neural level; these classifiers may help to demarcate periods of interoceptive focus, with implications for developing an objective marker for interoceptive sensibility in mental health research.

Interoceptive Awareness of the Breath Preserves Attention and Language Networks amidst Widespread Cortical Deactivation: A Within-Participant Neuroimaging Study.

Interoception, the representation of the body's internal state, serves as a foundation for emotion, motivation, and wellbeing. Yet despite its centrality in human experience, the neural mechanisms of interoceptive attention are poorly understood. The Interoceptive/Exteroceptive Attention Task (IEAT) is a novel neuroimaging paradigm that compares behavioral tracking of the respiratory cycle (Active Interoception) to tracking of a visual stimulus (Active Exteroception). Twenty-two healthy participants completed the IEAT during two separate scanning sessions (N = 44) as part of a randomized control trial of mindful awareness in body-oriented therapy (MABT). Compared with Active Exteroception, Active Interoception deactivated somatomotor and prefrontal regions. Greater self-reported interoceptive sensibility (MAIA scale) predicted sparing from deactivation within the anterior cingulate cortex (ACC) and left-lateralized language regions. The right insula, typically described as a primary interoceptive cortex, was only specifically implicated by its deactivation during an exogenously paced respiration condition (Active Matching) relative to self-paced Active Interoception. Psychophysiological interaction (PPI) analysis characterized Active Interoception as promoting greater ACC connectivity with lateral prefrontal and parietal regions commonly referred to as the dorsal attention network (DAN). In contrast to evidence relating accurate detection of liminal interoceptive signals such as the heartbeat to anterior insula activity, interoceptive attention toward salient signals such as the respiratory cycle may involve reduced cortical activity but greater ACC-DAN connectivity, with greater sensibility linked to reduced deactivation within the ACC and language-processing regions.

Influence of Mask Wearing during COVID-19 Surge and Non-Surge Time Periods in Two K-12 Public School Districts in Georgia, USA.

BACKGROUND: Into the third year of the COVID-19 pandemic and the second year of in-person learning for many K-12 schools in the United States, the benefits of mitigation strategies in this setting are still unclear. We compare COVID-19 cases in school-aged children and adolescents between a school district with a mandatory mask-wearing policy to one with an optional mask-wearing policy, during and after the peak period of the Delta variant wave of infection. METHODS: COVID-19 cases during the Delta variant wave (August 2021) and post the wave (October 2021) were obtained from public health records. Cases of K-12 students, stratified by grade level (elementary, middle, and high school) and school districts across two counties, were included in the statistical and spatial analyses. COVID-19 case rates were determined and spatially mapped. Regression was performed adjusting for specific covariates. RESULTS: Mask-wearing was associated with lower COVID-19 cases during the peak Delta variant period; overall, regardless of the Delta variant period, higher COVID-19 rates were seen in older aged students. CONCLUSION: This study highlights the need for more layered prevention strategies and policies that take into consideration local community transmission levels, age of students, and vaccination coverage to ensure that students remain safe at school while optimizing their learning environment.