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In this video article, accompanying the paper “An approach to learning the hierarchical organization of the frontal lobe”, we discuss a data driven approach to learning brain connectivity. Hierarchical models of brain connectivity are useful to understand how the brain can process sensory information, make decisions, and perform other high-level tasks. Despite extensive research, understanding the structure of the prefrontal cortex (PFC) remains a crucial challenge. In this work, we propose an approach to studying brain signals and uncovering characteristics of the underlying neural circuity, based on the mathematics of Gaussian processes and causal strengths. For discovering causations, we propose a metric referred to as double-averaged differential causal effect, which is a variant of the recently proposed differential causal effect, and it can be used as a principled measure of the causal strength between time series. We applied this methodology to study local field potential data from the frontal lobe, where the interest was in finding the causal relationship between the medial and lateral PFC areas of the brain. Our results suggest that the medial PFC causally influences the lateral PFC. 

In neuroscience, hierarchical models of brain connectivity, particularly in the prefrontal cortex (PFC), are used to understand how the brain can process sensory information, make decisions and perform other high level tasks. Despite extensive research, understanding the structure of the PFC remains a crucial challenge. To this end, we propose a data-driven approach to studying brain signals based on Gaussian processes and causal strengths. For discovering causations, we propose a metric referred to as double-averaged differential causal effect. The differential causal effect has been proposed recently, and it can be used to quantify causal strengths in a principled way. We studied real multivariate time series data that represent local field potentials from the frontal lobe. The interest was in finding the causal relationship between the medial and lateral PFC areas of the brain. Our results suggest that the medial PFC causally influences the lateral PFC. 

Abstract The performance of different versions of the discrete random walk models in turbulent flows with nonuniform normal root-mean-square (RMS) velocity fluctuations and turbulence time scales were carefully investigated. The OpenFOAM v2−f low Reynolds number turbulence model was used for evaluating the fully developed streamwise velocity and the wall-normal RMS velocity fluctuations profiles in a turbulent channel flow. The results were then used in an in-house matlab particle tracking code, including the drag and Brownian forces, and the trajectories of randomly injected point-particles with diameters ranging from 10 nm to 30 μm were evaluated under the one-way coupling assumption. The distributions and deposition velocities of fluid-tracer and finite-size particles were evaluated using the conventional-discrete random walk (DRW) model, the modified-DRW model including the velocity gradient drift correction, and the new improved-DRW model including the velocity and time gradient drift terms. It was shown that the conventional-DRW model leads to superfluous migration of fluid-point particles toward the wall and erroneous particle deposition rate. The concentration profiles of tracer particles obtained by using the modified-DRW model still are not uniform. However, it was shown that the new improved-DRW model with the velocity and time scale drift corrections leads to uniform distributions for fluid-point particles and reasonable concentration profiles for finite-size heavy particles. In addition, good agreement was found between the estimated deposition velocities of different size particles by the new improved-DRW model with the available data. 

Abstract The significance of respiratory droplet transmission in spreading respiratory diseases such as COVID-19 has been identified by researchers. Although one cough or sneeze generates a large number of respiratory droplets, they are usually infrequent. In comparison, speaking and singing generate fewer droplets, but occur much more often, highlighting their potential as a vector for airborne transmission. However, the flow dynamics of speech and the transmission of speech droplets have not been fully investigated. To shed light on this topic, two-dimensional geometries of a vocal tract for a labiodental fricative [f] were generated based on real-time MRI of a subject during pronouncing [f]. In these models, two different curvatures were considered for the tip tongue shape and the lower lip to highlight the effects of the articulator geometries on transmission dynamics. The commercial ANSYS-Fluent CFD software was used to solve the complex expiratory speech airflow trajectories. Simultaneously, the discrete phase model of the software was used to track submicron and large size respiratory droplets exhaled during [f] utterance. The simulations were performed for high, normal, and low lung pressures to explore the influence of loud, normal, and soft utterances, respectively, on the airflow dynamics. The presented results demonstrate the variability of the airflow and droplet propagation as a function of the vocal tract geometrical characteristics and loudness. 

Recovery of consciousness after traumatic brain injury (TBI) is heterogeneous and difficult to predict. Structures such as the thalamus and prefrontal cortex are thought to be important in facilitating consciousness. We sought to investigate whether the integrity of thalamo-prefrontal circuits, assessed via diffusion tensor imaging (DTI), was associated with the return of goal-directed behavior after severe TBI. We classified a cohort of severe TBI patients ( N = 25, 20 males) into Early and Late/Never outcome groups based on their ability to follow commands within 30 days post-injury. We assessed connectivity between whole thalamus, and mediodorsal thalamus (MD), to prefrontal cortex (PFC) subregions including dorsolateral PFC (dlPFC), medial PFC (mPFC), anterior cingulate (ACC), and orbitofrontal (OFC) cortices. We found that the integrity of thalamic projections to PFC subregions (L OFC, L and R ACC, and R mPFC) was significantly associated with Early command-following. This association persisted when the analysis was restricted to prefrontal-mediodorsal (MD) thalamus connectivity. In contrast, dlPFC connectivity to thalamus was not significantly associated with command-following. Using the integrity of thalamo-prefrontal connections, we created a linear regression model that demonstrated 72% accuracy in predicting command-following after a leave-one-out analysis. Together, these data support a role for thalamo-prefrontal connectivity in the return of goal-directed behavior following TBI. 

Abstract The return of consciousness after traumatic brain injury (TBI) is associated with restoring complex cortical dynamics; however, it is unclear what interactions govern these complex dynamics. Here, we set out to uncover the mechanism underlying the return of consciousness by measuring local field potentials (LFP) using invasive electrophysiological recordings in patients recovering from TBI. We found that injury to the thalamus, and its efferent projections, on MRI were associated with repetitive and low complexity LFP signals from a highly structured phase space, resembling a low-dimensional ring attractor. But why do thalamic injuries in TBI patients result in a cortical attractor? We built a simplified thalamocortical model, which connotes that thalamic input facilitates the formation of cortical ensembles required for the return of cognitive function and the content of consciousness. These observations collectively support the view that thalamic input to the cortex enables rich cortical dynamics associated with consciousness.