Striatum-projecting prefrontal cortex neurons support working memory maintenance.

Neurons in the medial prefrontal cortex (mPFC) are functionally linked to working memory (WM) but how distinct projection pathways contribute to WM remains unclear. Based on optical recordings, optogenetic perturbations, and pharmacological interventions in male mice, we report here that dorsomedial striatum (dmStr)-projecting mPFC neurons are essential for WM maintenance, but not encoding or retrieval, in a T-maze spatial memory task. Fiber photometry of GCaMP6m-labeled mPFC→dmStr neurons revealed strongest activity during the maintenance period, and optogenetic inhibition of these neurons impaired performance only when applied during this period. Conversely, enhancing mPFC→dmStr pathway activity-via pharmacological suppression of HCN1 or by optogenetic activation during the maintenance period-alleviated WM impairment induced by NMDA receptor blockade. Moreover, cellular-resolution miniscope imaging revealed that >50% of mPFC→dmStr neurons are active during WM maintenance and that this subpopulation is distinct from neurons active during encoding and retrieval. In all task periods, neuronal sequences were evident. Striatum-projecting mPFC neurons thus critically contribute to spatial WM maintenance.

Dynamic reorganization of the cortico-basal ganglia-thalamo-cortical network during task learning.

Adaptive behavior is coordinated by neuronal networks that are distributed across multiple brain regions such as in the cortico-basal ganglia-thalamo-cortical (CBGTC) network. Here, we ask how cross-regional interactions within such mesoscale circuits reorganize when an animal learns a new task. We apply multi-fiber photometry to chronically record simultaneous activity in 12 or 48 brain regions of mice trained in a tactile discrimination task. With improving task performance, most regions shift their peak activity from the time of reward-related action to the reward-predicting stimulus. By estimating cross-regional interactions using transfer entropy, we reveal that functional networks encompassing basal ganglia, thalamus, neocortex, and hippocampus grow and stabilize upon learning, especially at stimulus presentation time. The internal globus pallidus, ventromedial thalamus, and several regions in the frontal cortex emerge as salient hub regions. Our results highlight the learning-related dynamic reorganization that brain networks undergo when task-appropriate mesoscale network dynamics are established for goal-oriented behavior.

Conservative significance testing of tripartite statistical relations in multivariate neural data.

An important goal in systems neuroscience is to understand the structure of neuronal interactions, frequently approached by studying functional relations between recorded neuronal signals. Commonly used pairwise measures (e.g., correlation coefficient) offer limited insight, neither addressing the specificity of estimated neuronal interactions nor potential synergistic coupling between neuronal signals. Tripartite measures, such as partial correlation, variance partitioning, and partial information decomposition, address these questions by disentangling functional relations into interpretable information atoms (unique, redundant, and synergistic). Here, we apply these tripartite measures to simulated neuronal recordings to investigate their sensitivity to noise. We find that the considered measures are mostly accurate and specific for signals with noiseless sources but experience significant bias for noisy sources.We show that permutation testing of such measures results in high false positive rates even for small noise fractions and large data sizes. We present a conservative null hypothesis for significance testing of tripartite measures, which significantly decreases false positive rate at a tolerable expense of increasing false negative rate. We hope our study raises awareness about the potential pitfalls of significance testing and of interpretation of functional relations, offering both conceptual and practical advice.

Tripartite functional relation measures enable the study of interesting effects in neural recordings, such as redundancy, functional connection specificity, and synergistic coupling. However, estimators of such relations are commonly validated using noiseless signals, whereas neural recordings typically contain noise. Here we systematically study the performance of tripartite estimators using simulated noisy neural signals. We demonstrate that permutation testing is not a robust procedure for inferring ground truth statistical relations from commonly used tripartite relation estimators. We develop an adjusted conservative testing procedure, reducing false positive rates of the studied estimators when applied to noisy data. Besides addressing significance testing, our results should aid in accurate interpretation of tripartite functional relations and functional connectivity.

Modelling the formation of atmospheric dust in brown dwarfs and planetary atmospheres.

Atmospheric dust from volcanoes, sand storms and biogenic products provides condensation seeds for water cloud formation on the Earth. Extrasolar planetary objects such as brown dwarfs and extrasolar giant planets have no comparable sources of condensation seeds. Hence, understanding cloud formation and further its implications for the climate requires a modelling effort that includes the treatment of seed formation (nucleation), growth and evaporation, in addition to rain-out, mixing and gas-phase depletion. This paper discusses nucleation in the ultra-cool atmospheres of brown dwarfs and extrasolar giant planets whose chemical gas-phase composition differs largely from the terrestrial atmosphere. A kinetic model for atmospheric dust formation is described, which, in recent work, has become part of a cloud-formation model. For the first time, diffusive replenishment of the upper atmosphere is introduced as a source term into our model equations. This paper further aims to show how experimental and computational chemistry work links into our dust-formation model, which is driven by applications in extraterrestrial environments.