Neuropilin 2/Plexin-A3 Receptors Regulate the Functional Connectivity and the Excitability in the Layers 4 and 5 of the Cerebral Cortex.

The functions of cortical networks are progressively established during development by series of events shaping the neuronal connectivity. Synaptic elimination, which consists of removing the supernumerary connections generated during the earlier stages of cortical development, is one of the latest stages in neuronal network maturation. The semaphorin 3F coreceptors neuropilin 2 (Nrp2) and plexin-A3 (PlxnA3) may play an important role in the functional maturation of the cerebral cortex by regulating the excess dendritic spines on cortical excitatory neurons. Yet, the identity of the connections eliminated under the control of Nrp2/PlxnA3 signaling is debated, and the importance of this synaptic refinement for cortical functions remains poorly understood. Here, we show that Nrp2/PlxnA3 controls the spine densities in layer 4 (L4) and on the apical dendrite of L5 neurons of the sensory and motor cortices. Using a combination of neuroanatomical, ex vivo electrophysiology, and in vivo functional imaging techniques in Nrp2 and PlxnA3 KO mice of both sexes, we disprove the hypothesis that Nrp2/PlxnA3 signaling is required to maintain the ectopic thalamocortical connections observed during embryonic development. We also show that the absence of Nrp2/PlxnA3 signaling leads to the hyperexcitability and excessive synchronization of the neuronal activity in L5 and L4 neuronal networks, suggesting that this system could participate in the refinement of the recurrent corticocortical connectivity in those layers. Altogether, our results argue for a role of semaphorin-Nrp2/PlxnA3 signaling in the proper maturation and functional connectivity of the cerebral cortex, likely by controlling the refinement of recurrent corticocortical connections.SIGNIFICANCE STATEMENT The function of a neuronal circuit is mainly determined by the connections that neurons establish with one another during development. Understanding the mechanisms underlying the establishment of the functional connectivity is fundamental to comprehend how network functions are implemented, and to design treatments aiming at restoring damaged neuronal circuits. Here, we show that the cell surface receptors for the family of semaphorin guidance cues neuropilin 2 (Nrp2) and plexin-A3 (PlxnA3) play an important role in shaping the functional connectivity of the cerebral cortex likely by trimming the recurrent connections in layers 4 and 5. By removing the supernumerary inputs generated during early development, Nrp2/PlxnA3 signaling reduces the neuronal excitability and participates in the maturation of the cortical network functions.

Investigating Principal Working Memory Features in Generalized, Panic, and Social Anxiety Spectrum Disorders.

Anxiety spectrum disorders are characterized by excessive and uncontrollable worrying about potential negative events in the short- and long-term future. Various reports linked anxiety spectrum disorders with working memory (WM) deficits despite conflicting results stemming from different study approaches. It remains unclear, however, how different anxiety spectrum disorders such as generalized anxiety disorder (GAD), social anxiety disorder (SAD), and panic disorder (PD), differ in WM function. In this study, we utilized verbal, numerical, and sequential evaluations of WM to cover most possible facets of the WM data space. We used principal component analysis to extract the uncorrelated/whitened components of WM based on these measures. We evaluated medication-free patients with GAD, SAD, and PD patients as well as matched healthy individuals using a battery that measures WM duration and load. We found that patients with GAD and SAD, but not PD, exhibited poor performance only in the WM principal component that represents maintenance. There were no other significant differences between the four groups. Further, different WM components significantly predicted the severity of anxiety symptoms in the groups. We explored the clinical utility of WM components for differentiating patients with anxiety spectrum disorders from healthy individuals. By only using the WM components that represent maintenance and encoding, we managed to differentiate patients from controls in 84% of cases. For the first time, we present multiple novel approaches to examine cognitive function and design cognitive screening, and potentially diagnostics, for psychiatric disorders.

Depression Reduces Accuracy While Parkinsonism Slows Response Time for Processing Positive Feedback in Patients with Parkinson's Disease with Comorbid Major Depressive Disorder Tested on a Probabilistic Category-Learning Task.

Major depressive disorder (MDD) is the most common non-motor manifestation of Parkinson's disease (PD) affecting 50% of patients. However, little is known about the cognitive correlates of MDD in PD. Using a computer-based cognitive task that dissociates learning from positive and negative feedback, we tested four groups of subjects: (1) patients with PD with comorbid MDD, (2) patients with PD without comorbid MDD, (3) matched patients with MDD alone (without PD), and (4) matched healthy control subjects. Furthermore, we used a mathematical model of decision-making to fit both choice and response time data, allowing us to detect and characterize differences between the groups that are not revealed by cognitive results. The groups did not differ in learning accuracy from negative feedback, but the MDD groups (PD patients with MDD and patients with MDD alone) exhibited a selective impairment in learning accuracy from positive feedback when compared to the non-MDD groups (PD patients without MDD and healthy subjects). However, response time in positive feedback trials in the PD groups (both with and without MDD) was significantly slower than the non-PD groups (MDD and healthy groups). While faster response time usually correlates with poor learning accuracy, it was paradoxical in PD groups, with PD patients with MDD having impaired learning accuracy and PD patients without MDD having intact learning accuracy. Mathematical modeling showed that both MDD groups (PD with MDD and MDD alone) were significantly slower than non-MDD groups in the rate of accumulation of information for stimuli trained by positive feedback, which can lead to lower response accuracy. Conversely, modeling revealed that both PD groups (PD with MDD and PD alone) required more evidence than other groups to make responses, thus leading to slower response times. These results suggest that PD patients with MDD exhibit cognitive profiles with mixed traits characteristic of both MDD and PD, furthering our understanding of both PD and MDD and their often-complex comorbidity. To the best of our knowledge, this is the first study to examine feedback-based learning in PD with MDD while controlling for the effects of PD and MDD.

Generalized Anxiety Disorder and Social Anxiety Disorder, but Not Panic Anxiety Disorder, Are Associated with Higher Sensitivity to Learning from Negative Feedback: Behavioral and Computational Investigation.

Anxiety disorders, including generalized anxiety disorder (GAD), social anxiety disorder (SAD), and panic anxiety disorder (PAD), are a group of common psychiatric conditions. They are characterized by excessive worrying, uneasiness, and fear of future events, such that they affect social and occupational functioning. Anxiety disorders can alter behavior and cognition as well, yet little is known about the particular domains they affect. In this study, we tested the cognitive correlates of medication-free patients with GAD, SAD, and PAD, along with matched healthy participants using a probabilistic category-learning task that allows the dissociation between positive and negative feedback learning. We also fitted all participants' data to a Q-learning model and various actor-critic models that examine learning rate parameters from positive and negative feedback to investigate effects of valence vs. action on performance. SAD and GAD patients were more sensitive to negative feedback than either PAD patients or healthy participants. PAD, SAD, and GAD patients did not differ in positive-feedback learning compared to healthy participants. We found that Q-learning models provide the simplest fit of the data in comparison to other models. However, computational analysis revealed that groups did not differ in terms of learning rate or exploration values. These findings argue that (a) not all anxiety spectrum disorders share similar cognitive correlates, but are rather different in ways that do not link them to the hallmark of anxiety (higher sensitivity to negative feedback); and (b) perception of negative consequences is the core feature of GAD and SAD, but not PAD. Further research is needed to examine the similarities and differences between anxiety spectrum disorders in other cognitive domains and potential implementation of behavioral therapy to remediate cognitive deficits.