Brain basis and neural circuitry of habit-like attention in infants and adults

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Project Summary/ Abstract The brain has the ability to filter and prioritize a cacophony of often extraneous visual stimuli via a mechanism called attention. Attentional control is present even at birth and is tuned throughout development. Traditionally, attention is thought to be guided by either a goal-driven mechanism (top-down, supported by fronto-parietal brain networks) or a stimulus-driven mechanism (bottom-up, supported by visual networks). These mechanisms also follow distinct developmental trajectories where top-down attention reaches maturity slower than bottom-up attention due to the delay of frontal cortex development. However, recent work has highlighted a type of attention that does not fit within this dichotomy: one driven by prior experience (i.e., selection history) that can be characterized as a habit. This habit-like attention demonstrates behavioral inflexibility, deficits in attentional and basal ganglia disorders, and is present throughout the lifespan from infancy to old age. Despite extensive behavioral research, the underlying neural circuitry is debated in adults, and unknown in infants and children. Here, we aim to characterize the neural underpinnings of habit-like attention to elucidate how prior experience can bias attention as early as infancy. We hypothesize that this newly identified mechanism of attention is driven by subcortical structures. We will utilize neuroimaging—task-based (using a contextual cueing paradigm), structural (diffusion-weighted imaging), and functional (resting state) connectivity—in both healthy adults (18+) and infants (6—10 months). We will utilize state-of-the-art neuroimaging techniques with specialized protocols for infants and for adults and hierarchical Bayesian modeling to ensure robust individual estimation of neural activity to Aim 1: Identify the neural structures that support habitual attention. Further, we will relate predict individual neural organization from structural and functional connectivity to individual behavior using machine learning to Aim 2: Characterize the prototypical infant and adult dynamics between cortico-striatal circuitry and relate each individual’s connectivity fingerprints to the strength of their attentional habits a) within their age group b) between their age groups. Elucidating the neural underpinnings of habit-like attention may allow us to better understand, identify, and treat cognitive deficits in attention related-disorders in children, and Huntington’s and Parkinson’s diseases later in life. Moreover, this work will characterize individual differences in attentional habits from early in development and may reveal ways to improve attentional habits in both children and adults. This proposal has the necessary resources and well-matched team to uncover the neural basis of habit-like attention, which has exciting implications for early prediction and potential interventions for future attentional deficits.