Brain-state mediated modulation of inter-laminar dependencies in visual cortex.

Spatial attention is a quintessential example of adaptive information processing in the brain and is critical for recognizing behaviorally relevant objects in a cluttered environment. Object recognition is mediated by neural encoding along the ventral visual hierarchy. How the deployment of spatial attention aids these hierarchical computations is unclear. Prior studies point to two distinct mechanisms: an improvement in the efficacy of information directed from one encoding stage to another, and/or a suppression of shared information within encoding stages. To test these proposals, it is crucial to estimate the attentional modulation of unique information flow across and shared information within the encoding stages of the visual hierarchy. We investigated this in the multi-stage laminar network of visual area V4, an area strongly modulated by attention. Using network-based dependency estimation from multivariate data, we quantified the modulation of inter-layer information flow during a change detection task and found that deployment of attention indeed strengthened unique dependencies between the input and superficial layers. Using the partial information decomposition framework, we estimated the modulation of shared dependencies and found that they are reduced specifically in the putative excitatory subpopulations within a layer. Surprisingly, we found a strengthening of unique dependencies within the laminar populations, a finding not previously predicted. Crucially, these modulation patterns were also observed during successful behavioral outcomes (hits) that are thought to be mediated by endogenous brain state fluctuations, and not by experimentally imposed attentive states. Finally, phases of endogenous fluctuations that were optimal for 'hits' were associated with reduced neural excitability. A reduction in neural excitability, potentially mediated by diminished shared inputs, suggests a novel mechanism for enhancing unique information transmission during optimal states. By decomposing the modulation of multivariate information, and combined with prior theoretical work, our results suggest common computations of optimal sensory states that are attained by either task demands or endogenous fluctuations.

Brain-state mediated modulation of inter-laminar dependencies in visual cortex.

Spatial attention is critical for recognizing behaviorally relevant objects in a cluttered environment. How the deployment of spatial attention aids the hierarchical computations of object recognition remains unclear. We investigated this in the laminar cortical network of visual area V4, an area strongly modulated by attention. We found that deployment of attention strengthened unique dependencies in neural activity across cortical layers. On the other hand, shared dependencies were reduced within the excitatory population of a layer. Surprisingly, attention strengthened unique dependencies within a laminar population. Crucially, these modulation patterns were also observed during successful behavioral outcomes that are thought to be mediated by internal brain state fluctuations. Successful behavioral outcomes were also associated with phases of reduced neural excitability, suggesting a mechanism for enhanced information transfer during optimal states. Our results suggest common computation goals of optimal sensory states that are attained by either task demands or internal fluctuations.

Automated 3D analysis of social head-gaze behaviors in freely moving marmosets.

Social communication relies on the ability to perceive and interpret the direction of others' attention, which is commonly conveyed through head orientation and gaze direction in both humans and non-human primates. However, traditional social gaze experiments in non-human primates require restraining head movements, which significantly limit their natural behavioral repertoire. Here, we developed a novel framework for accurately tracking facial features and three-dimensional head gaze orientations of multiple freely moving common marmosets (Callithrix jacchus). To accurately track the facial features of marmoset dyads in an arena, we adapted computer vision tools using deep learning networks combined with triangulation algorithms applied to the detected facial features to generate dynamic geometric facial frames in 3D space, overcoming common occlusion challenges. Furthermore, we constructed a virtual cone, oriented perpendicular to the facial frame, to model the head gaze directions. Using this framework, we were able to detect different types of interactive social gaze events, including partner-directed gaze and jointly-directed gaze to a shared spatial location. We observed clear effects of sex and familiarity on both interpersonal distance and gaze dynamics in marmoset dyads. Unfamiliar pairs exhibited more stereotyped patterns of arena occupancy, more sustained levels of social gaze across inter-animal distance, and increased gaze monitoring. On the other hand, familiar pairs exhibited higher levels of joint gazes. Moreover, males displayed significantly elevated levels of gazes toward females' faces and the surrounding regions irrespective of familiarity. Our study lays the groundwork for a rigorous quantification of primate behaviors in naturalistic settings.