Alfred F. Chao

Background: Attention problems are common transdiagnostic symptoms of psychiatric illness. Although environmental exposures and experiences influence attention during adolescent development, the underlying neural pathways by which they do so is unclear. Methods: We measured attention problems, attention-related brain networks, and multidimensional environmental experiences (the “exposome”) using data from the ABCD Study (N = 11,878). We tested whether the exposome is associated with 9-10-year-olds’ attention-related brain network strength and current and future attention problems. We further examined cross-sectional indirect pathways linking the exposome, brain network strength, and attention problems. Results: The exposome predicted youths’ current and future self-, caregiver-, and teacher-reported attention problems as well as their current attention-related brain network strength. This brain network signature of sustained attention also predicted attention problems from all three reporters. Indirect effects models revealed that the exposome was associated with current reported attention problems both directly and indirectly though this brain signature. Conversely, predictive brain network strength was related to attention problems both directly and indirectly through the exposome. Conclusion: Interactions between environmental exposures, experiences, and brain network organization are associated with attention problems in early adolescence. These findings support a bidirectional framework linking the environment and functional brain networks in the development of attention problems.

Every movement requires the nervous system to solve a complex biomechanical control problem, but this process is mostly veiled from one’s conscious awareness. Simultaneously, we also have conscious experience of controlling our movements—our sense of agency (SoA). Whether SoA corresponds to those neural representations that implement actual neuromuscular control is an open question with ethical, medical, and legal implications. If SoA is the conscious experience of control, this predicts that SoA can be decoded from the same brain structures that implement the so-called “inverse dynamics” computations for planning movement. We correlated human (male and female) fMRI measurements during hand movements with the internal representations of a deep neural network (DNN) performing the same hand control task in a biomechanical simulation– revealing detailed cortical encodings of sensorimotor states, idiosyncratic to each subject. We then manipulated SoA by usurping control of participants’ muscles via electrical stimulation, and found that the same voxels which were best explained by modeled inverse dynamics representations— which, strikingly, were located in canonically visual areas—also predicted SoA. Importantly, model-brain correspondences and robust SoA decoding could both be achieved within single subjects, enabling relationships between motor representations and awareness to be studied at the level of the individual. Significance Statement: The inherent complexity of biomechanical control problems is belied by the seeming simplicity of directing movements in our subjective experience. This aspect of our experience suggests we have limited conscious access to the neural and mental representations involved in controlling the body – but of which of the many possible representations are we, in fact, aware? Understanding which motor control representations percolate into awareness has taken on increasing importance as emerging neural interface technologies push the boundaries of human autonomy. In our study, we leverage machine learning models that have learned to control simulated bodies to localize biomechanical control representations in the brain. Then, we show that these brain regions predict perceived agency over the musculature during functional electrical stimulation.

Sleep is critical to a variety of cognitive functions and insufficient sleep can have negative consequences for mood and behavior across the lifespan. An important open question is how sleep duration is related to functional brain organization which may in turn impact cognition. To characterize the functional brain networks related to sleep across youth and young adulthood, we analyzed data from the publicly available Human Connectome Project (HCP) dataset, which includes n‐back task‐based and resting‐state fMRI data from adults aged 22–35 years (task n = 896; rest n = 898). We applied connectome‐based predictive modeling (CPM) to predict participants' mean sleep duration from their functional connectivity patterns. Models trained and tested using 10‐fold cross‐validation predicted self‐reported average sleep duration for the past month from n‐back task and resting‐state connectivity patterns. We replicated this finding in data from the 2‐year follow‐up study session of the Adolescent Brain Cognitive Development (ABCD) Study, which also includes n‐back task and resting‐state fMRI for adolescents aged 11–12 years (task n = 786; rest n = 1274) as well as Fitbit data reflecting average sleep duration per night over an average duration of 23.97 days. CPMs trained and tested with 10‐fold cross‐validation again predicted sleep duration from n‐back task and resting‐state functional connectivity patterns. Furthermore, demonstrating that predictive models are robust across independent datasets, CPMs trained on rest data from the HCP sample successfully generalized to predict sleep duration in the ABCD Study sample and vice versa. Thus, common resting‐state functional brain connectivity patterns reflect sleep duration in youth and young adults.

This article draws from prior work establishing the beneficial relationship between nature exposure and cognitive function to advocate for the incorporation of artificial nature stimuli in crewed spaceflight. Although whether and how long-duration sp...