Balancing act of small GTPases downstream of plexin-A4 signaling motifs promotes dendrite elaboration in mammalian cortical neurons.

The precise development of neuronal morphologies is crucial to the establishment of synaptic circuits and, ultimately, proper brain function. Signaling by the axon guidance cue semaphorin 3A (Sema3A) and its receptor complex of neuropilin-1 and plexin-A4 has multifunctional outcomes in neuronal morphogenesis. Downstream activation of the RhoGEF FARP2 through interaction with the lysine-arginine-lysine motif of plexin-A4 and consequent activation of the small GTPase Rac1 promotes dendrite arborization, but this pathway is dispensable for axon repulsion. Here, we investigated the interplay of small GTPase signaling mechanisms underlying Sema3A-mediated dendritic elaboration in mouse layer V cortical neurons in vitro and in vivo. Sema3A promoted the binding of the small GTPase Rnd1 to the amino acid motif lysine-valine-serine (LVS) in the cytoplasmic domain of plexin-A4. Rnd1 inhibited the activity of the small GTPase RhoA and the kinase ROCK, thus supporting the activity of the GTPase Rac1, which permitted the growth and branching of dendrites. Overexpression of a dominant-negative RhoA, a constitutively active Rac1, or the pharmacological inhibition of ROCK activity rescued defects in dendritic elaboration in neurons expressing a plexin-A4 mutant lacking the LVS motif. Our findings provide insights into the previously unappreciated balancing act between Rho and Rac signaling downstream of specific motifs in plexin-A4 to mediate Sema3A-dependent dendritic elaboration in mammalian cortical neuron development.

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.

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.