Search for: All records

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 kinematic computations for planning movement. We correlated human 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 kinematic 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. 

Abstract 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 includesn‐back task‐based and resting‐state fMRI data from adults aged 22–35 years (taskn = 896; restn = 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 fromn‐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 includesn‐back task and resting‐state fMRI for adolescents aged 11–12 years (taskn = 786; restn = 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 fromn‐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.