AAV11 permits efficient retrograde targeting of projection neurons

Viral tracers that permit efficient retrograde targeting of projection neurons are powerful vehicles for structural and functional dissections of the neural circuit and for the treatment of brain diseases. Recombinant adeno-associated viruses (rAAVs) are the most potential candidates because they are low-toxic with high-level transgene expression and minimal host immune responses. Currently, some rAAVs based on capsid engineering for retrograde tracing have been widely used in the analysis and manipulation of neural circuits, but suffer from brain area selectivity and inefficient retrograde transduction in certain neural connections. Here, we discovered that the recombinant adeno-associated virus 11 (rAAV11) exhibits potent retrograde labeling of projection neurons with enhanced efficiency to rAAV2-retro in some neural connections. Combined with calcium recording technology, rAAV11 can be used to monitor neuronal activities by expressing Cre recombinase or calcium-sensitive functional probe. In addition, we further showed the suitability of rAAV11 for astrocyte targeting. These properties make rAAV11 a promising tool for the mapping and manipulation of neural circuits and gene therapy of some neurological and neurodegenerative disorders.

LRRTM3 regulates activity-dependent synchronization of synapse properties in topographically connected hippocampal neural circuits

Synaptic cell-adhesion molecules (CAMs) organize the architecture and properties of neural circuits. However, whether synaptic CAMs are involved in activity-dependent remodeling of specific neural circuits is incompletely understood. Leucine-rich repeat transmembrane protein 3 (LRRTM3) is required for the excitatory synapse development of hippocampal dentate gyrus (DG) granule neurons. Here, we report that Lrrtm3-deficient mice exhibit selective reductions in excitatory synapse density and synaptic strength in projections involving the medial entorhinal cortex (MEC) and DG granule neurons, accompanied by increased neurotransmitter release and decreased excitability of granule neurons. LRRTM3 deletion significantly reduced excitatory synaptic innervation of hippocampal mossy fibers (Mf) of DG granule neurons onto thorny excrescences in hippocampal CA3 neurons. Moreover, LRRTM3 loss in DG neurons significantly decreased mossy fiber long-term potentiation (Mf-LTP). Remarkably, silencing MEC–DG circuits protected against the decrease in the excitatory synaptic inputs onto DG and CA3 neurons, excitability of DG granule neurons, and Mf-LTP in Lrrtm3-deficient mice. These results suggest that LRRTM3 may be a critical factor in activity-dependent synchronization of the topography of MEC–DG–CA3 excitatory synaptic connections. Collectively, our data propose that LRRTM3 shapes the target-specific structural and functional properties of specific hippocampal circuits.

Neural Circuits and Some New Factors Involved in Hippocampal Memory

Humans and other primates have memory, and the hippocampus plays a critical role in this process. The neural circuitry is one of the structural foundations for the hippocampus in exerting memory function. To understand the relationship between the hippocampus and memory, we need to understand neural circuits. Past research has identified several classical neural circuits involved in memory. Although there are challenges with the study of hippocampal neural circuits, research on this topic has continued, and some progress has been made. Here, we discuss recent advances in our understanding of hippocampal neural circuit mechanisms and some of the newly discovered factors that affect memory. Substantial progress has been made regarding hippocampal memory circuits and Alzheimer’s disease. However, it is unclear whether these novel findings regarding hippocampal memory circuits hold promise for human memory studies. Additional research on this topic is needed.

Neural Circuits Underlying Behavioral Flexibility: Insights From Drosophila

Behavioral flexibility is critical to survival. Animals must adapt their behavioral responses based on changes in the environmental context, internal state, or experience. Studies in Drosophila melanogaster have provided insight into the neural circuit mechanisms underlying behavioral flexibility. Here we discuss how Drosophila behavior is modulated by internal and behavioral state, environmental context, and learning. We describe general principles of neural circuit organization and modulation that underlie behavioral flexibility, principles that are likely to extend to other species.

Drosophila Mechanical Nociceptors Preferentially Sense Localized Poking

Mechanical nociception is an evolutionarily conserved sensory process required for the survival of living organisms. Previous studies have revealed much about the neural circuits and key sensory molecules in mechanical nociception, but the cellular mechanisms adopted by nociceptors in force detection remain elusive. To address this issue, we study the mechanosensation of a fly larval nociceptor (class IV da neurons, c4da) using a customized mechanical device. We find that c4da are sensitive to mN-scale forces and make uniform responses to the forces applied at different dendritic regions. Moreover, c4da showed a greater sensitivity to more localized forces, consistent with them being able to sense the poking of sharp objects, such as wasp ovipositor. Further analysis reveals that high morphological complexity, mechanosensitivity to lateral tension and active signal propagation in the dendrites altogether facilitate the mechanosensitivity and sensory features of c4da. In particular, we discover that Piezo and Ppk1/Ppk26, two key mechanosensory molecules, make differential but additive contributions to the mechanosensation of c4da. In all, our results provide updates into understanding how c4da process mechanical signals at the cellular level and reveal the contributions of key molecules.

Mechanisms of Human Motor Learning Do Not Function Independently

Human motor learning is governed by a suite of interacting mechanisms each one of which modifies behavior in distinct ways and rely on different neural circuits. In recent years, much attention has been given to one type of motor learning, called motor adaptation. Here, the field has generally focused on the interactions of three mechanisms: sensory prediction error SPE-driven, explicit (strategy-based), and reinforcement learning. Studies of these mechanisms have largely treated them as modular, aiming to model how the outputs of each are combined in the production of overt behavior. However, when examined closely the results of some studies also suggest the existence of additional interactions between the sub-components of each learning mechanism. In this perspective, we propose that these sub-component interactions represent a critical means through which different motor learning mechanisms are combined to produce movement; understanding such interactions is critical to advancing our knowledge of how humans learn new behaviors. We review current literature studying interactions between SPE-driven, explicit, and reinforcement mechanisms of motor learning. We then present evidence of sub-component interactions between SPE-driven and reinforcement learning as well as between SPE-driven and explicit learning from studies of people with cerebellar degeneration. Finally, we discuss the implications of interactions between learning mechanism sub-components for future research in human motor learning.

Tractography of White Matter Fibers Using Diffusion MRI

To understand more about the human brain and how it works, it is vital to understand how the neural circuits connect different regions of the brain. The human brain is filled predominantly with water and the majority of the water molecules undergo diffusion which can be captured with the help of diffusion MRI. Diffusion weighted images enable us to reconstruct the neural circuits in a non-invasive manner, and this procedure is referred to as tractography. Tractography aids neurosurgeons to understand the neural connectivity of the patient. This chapter attempts to explain the procedure of tractography and different types of algorithms.