Correction: Prospective prediction of childhood body mass index trajectories using multi-task Gaussian processes
Related Articles
Self-reports map the landscape of task states derived from brain imaging
Psychological states influence our happiness and productivity; however, estimates of their impact have historically been assumed to be limited by the accuracy with which introspection can quantify them. Over the last two decades, studies have shown that introspective descriptions of psychological states correlate with objective indicators of cognition, including task performance and metrics of brain function, using techniques like functional magnetic resonance imaging (fMRI). Such evidence suggests it may be possible to quantify the mapping between self-reports of experience and objective representations of those states (e.g., those inferred from measures of brain activity). Here, we used machine learning to show that self-reported descriptions of experiences across tasks can reliably map the objective landscape of task states derived from brain activity. In our study, 194 participants provided descriptions of their psychological states while performing tasks for which the contribution of different brain systems was available from prior fMRI studies. We used machine learning to combine these reports with descriptions of brain function to form a ‘state-space’ that reliably predicted patterns of brain activity based solely on unseen descriptions of experience (N = 101). Our study demonstrates that introspective reports can share information with the objective task landscape inferred from brain activity.
Genetic architectures of childhood maltreatment and causal influence of childhood maltreatment on health outcomes in adulthood
Childhood maltreatment is increasingly recognized as a pivotal risk factor for adverse health outcomes. However, comprehensive analyses of its long-term impact are scarce. This study aims to fill this gap by examining the genetic architectures of childhood maltreatment and its influence on adult health and socioeconomic outcomes. Utilizing data from the UK Biobank (N = 129,017), we conducted sex-combined and sex-stratified genome-wide association studies to identify genomic loci associated with five childhood maltreatment subtypes. We then performed genetic correlation and Mendelian randomization (MR) analyses to assess the effects of childhood maltreatment on high-burden diseases, healthcare costs, lifespan, and educational attainment. We identified several novel loci for childhood maltreatment, including one locus for sexual abuse in sex-combined analysis, one novel locus for sexual abuse in males, one locus for emotional neglect in females, and one locus for sexual abuse in females. The pairwise genetic correlations between subtypes of childhood maltreatment were moderate to high, and similar patterns of genetic correlations between childhood maltreatment subtypes were observed in males and females. Childhood maltreatment was genetically correlated with ten out of 16 high-burden diseases significantly after multiple testing correction. Moreover, MR analyses suggest childhood maltreatment may increase the risk of age-related and other hearing loss, low back pain, major depressive disorder, and migraine in adulthood, and reduce the lifespan. Our study elucidates the genetic architecture of specific childhood maltreatment subtypes and the influence of childhood maltreatment on health outcomes in adulthood, highlighting the enduring influence of childhood maltreatment on lifelong health consequences. It is important to develop prevention strategies to lower the incidence of childhood maltreatment and provide support and care for victims of childhood maltreatment for better long-term health outcomes in the population.
Emotions and individual differences shape human foraging under threat
A common behavior in natural environments is foraging for rewards. However, this is often in the presence of predators. Therefore, one of the most fundamental decisions for humans, as for other animals, is how to apportion time between reward-motivated pursuit behavior and threat-motivated checking behavior. To understand what affects how people strike this balance, we developed an ecologically inspired task and looked at both within-participant dynamics (moods) and between-participant individual differences (questionnaires about real-life behaviors) in two large internet samples (n = 374 and n = 702) in a cross-sectional design. For the within-participant dynamics, we found that people regulate task-evoked stress homeostatically by changing behavior (increasing foraging and hiding). Individual differences, even in superficially related traits (apathy–anhedonia and anxiety–compulsive checking) reliably mapped onto unique behaviors. Worse task performance, due to maladaptive checking, was linked to gender (women checked excessively) and specific anxiety-related traits: somatic anxiety (reduced self-reported checking due to worry) and compulsivity (self-reported disorganized checking). While anhedonia decreased self-reported task engagement, apathy, strikingly, improved overall task performance by reducing excessive checking. In summary, we provide a multifaceted paradigm for assessment of checking for threat in a naturalistic task that is sensitive to both moods as they change throughout the task and clinical dimensions. Thus, it could serve as an objective measurement tool for future clinical studies interested in threat, vigilance or behavior–emotion interactions in contexts requiring both reward seeking and threat avoidance.
Preserving and combining knowledge in robotic lifelong reinforcement learning
Humans can continually accumulate knowledge and develop increasingly complex behaviours and skills throughout their lives, which is a capability known as ‘lifelong learning’. Although this lifelong learning capability is considered an essential mechanism that makes up general intelligence, recent advancements in artificial intelligence predominantly excel in narrow, specialized domains and generally lack this lifelong learning capability. Here we introduce a robotic lifelong reinforcement learning framework that addresses this gap by developing a knowledge space inspired by the Bayesian non-parametric domain. In addition, we enhance the agent’s semantic understanding of tasks by integrating language embeddings into the framework. Our proposed embodied agent can consistently accumulate knowledge from a continuous stream of one-time feeding tasks. Furthermore, our agent can tackle challenging real-world long-horizon tasks by combining and reapplying its acquired knowledge from the original tasks stream. The proposed framework advances our understanding of the robotic lifelong learning process and may inspire the development of more broadly applicable intelligence.
Dopaminergic modulation and dosage effects on brain state dynamics and working memory component processes in Parkinson’s disease
Parkinson’s disease (PD) is primarily diagnosed through its characteristic motor deficits, yet it also encompasses progressive cognitive impairments that profoundly affect quality of life. While dopaminergic medications are routinely prescribed to manage motor symptoms in PD, their influence extends to cognitive functions as well. Here we investigate how dopaminergic medication influences aberrant brain circuit dynamics associated with encoding, maintenance and retrieval working memory (WM) task-phases processes. PD participants, both on and off dopaminergic medication, and healthy controls, performed a Sternberg WM task during fMRI scanning. We employ a Bayesian state-space computational model to delineate brain state dynamics related to different task phases. Importantly, a within-subject design allows us to examine individual differences in the effects of dopaminergic medication on brain circuit dynamics and task performance. We find that dopaminergic medication alters connectivity within prefrontal-basal ganglia-thalamic circuits, with changes correlating with enhanced task performance. Dopaminergic medication also restores engagement of task-phase-specific brain states, enhancing task performance. Critically, we identify an “inverted-U-shaped” relationship between medication dosage, brain state dynamics, and task performance. Our study provides valuable insights into the dynamic neural mechanisms underlying individual differences in dopamine treatment response in PD, paving the way for more personalized therapeutic strategies.
Responses