Waitt Advanced Biophotonics Center

Motor systems in animals are highly dependent on sensory information for optimal control and precision, with mechanosensory feedback from the somatosensory system playing a critical role. These mechanosensory pathways are woven into the descending feedforward pathways and local central pattern generator circuits that control and generate movement, respectively. Somatosensory feedback in mammals and insects, the two animal classes this review touches upon, is complex due to the increased demands that limbed locomotion, weight-bearing, and corrective movements place on sensorimotor control. In this review, we outline the salient features of the proprioceptive and exteroceptive sensory feedback pathways animals rely on for controlling movement and highlight some of the key principles of sensory feedback that are shared across the animal kingdom.

Optical recording of intricate molecular dynamics is becoming an indispensable technique for biological studies, accelerated by the development of new or improved biosensors and microscopy technology. This creates major computational challenges to extract and quantify biologically meaningful spatiotemporal patterns embedded within complex and rich data sources, many of which cannot be captured with existing methods. Here, we introduce activity quantification and analysis (AQuA2), a fast, accurate, and versatile data analysis platform built upon advanced machine-learning techniques. It decomposes complex live-imaging-based datasets into elementary signaling events, allowing accurate and unbiased quantification of molecular activities and identification of consensus functional units. We demonstrate applications across a wide range of biosensors, cell types, organs, animal models, microscopy techniques, and imaging approaches. As exemplar findings, we show how AQuA2 identified drug-dependent interactions between neurons and astroglia, as well as distinct sensorimotor signal propagation patterns in the mouse spinal cord.

Long-term potentiation (LTP) is a biochemical process that underlies learning in excitatory glutamatergic synapses in the Central Nervous System (CNS). A critical early driver of LTP is autophosphorylation of the abundant postsynaptic enzyme, Ca/calmodulin-dependent protein kinase II (CaMKII). Autophosphorylation is initiated by Ca flowing through NMDA receptors activated by strong synaptic activity. Its lifetime is ultimately determined by the balance of the rates of autophosphorylation and of dephosphorylation by protein phosphatase 1 (PP1). Here we have modeled the autophosphorylation and dephosphorylation of CaMKII during synaptic activity in a spine synapse using MCell4, an open source computer program for creating particle-based stochastic, and spatially realistic models of cellular microchemistry. The model integrates four earlier detailed models of separate aspects of regulation of spine Ca and CaMKII activity, each of which incorporate experimentally measured biochemical parameters and have been validated against experimental data. We validate the composite model by showing that it accurately predicts previous experimental measurements of effects of NMDA receptor activation, including high sensitivity of induction of LTP to phosphatase activity and persistence of autophosphorylation for a period of minutes after the end of synaptic stimulation. We then use the model to probe aspects of the mechanism of regulation of autophosphorylation of CaMKII that are difficult to measure . We examine the effects of "CaM-trapping," a process in which the affinity for Ca/CaM increases several hundred-fold after autophosphorylation. We find that CaM-trapping does not increase the proportion of autophosphorylated subunits in holoenzymes after a complex stimulus, as previously hypothesized. Instead, CaM-trapping may dramatically prolong the lifetime of autophosphorylated CaMKII through steric hindrance of dephosphorylation by protein phosphatase 1. The results provide motivation for experimental measurement of the extent of suppression of dephosphorylation of CaMKII by bound Ca/CaM. The composite MCell4 model of biochemical effects of complex stimuli in synaptic spines is a powerful new tool for realistic, detailed dissection of mechanisms of synaptic plasticity.

In the adult dentate gyrus of the hippocampus there are neuronal stem cells that give rise to immature neurons and subsequently to mature functional granule neurons. The rate of proliferation, differentiation, and survival is regulated intrinsically and extrinsically. For example, Wnt, BMP, TLX, and BDNF all regulate adult neurogenesis intrinsically, while exercise, environmental enrichment, stress, and epilepsy are some of the extrinsic factors that regulate adult neurogenesis. A clearer picture is emerging for the functional role of these newly born neurons in behavior, demonstrating that adult neurogenesis plays a role in recognizing events, places, objects, or people as unique when comparing options that are very similar, but that these newly born cells play little role in recognition when differences are greater. Most of the research on adult neurogenesis is conducted in experimental mammals, including mice and rats. The first evidence for adult neurogenesis in humans was reported in 1998, when postmortem brains from cancer patients injected with bromodeoxyuridine (BrdU) were examined and cells were found that had divided and differentiated into mature neurons. Subsequently, additional evidence using other techniques has confirmed human adult neurogenesis. Additional in vivo live reports will be needed to monitor the effects of changes in human adult neurogenesis with age and disease.

Recurrent neural networks (RNNs) based on model neurons that communicate via continuous signals have been widely used to study how cortical neural circuits perform cognitive tasks. Training such networks to perform tasks that require information maintenance over a brief period (i.e., working memory tasks) remains a challenge. Inspired by the robust information maintenance observed in higher cortical areas such as the prefrontal cortex, despite substantial inherent noise, we investigated the effects of random noise on RNNs across different cognitive functions, including working memory. Our findings reveal that random noise not only speeds up training but also enhances the stability and performance of RNNs on working memory tasks. Importantly, this robust working memory performance induced by random noise during training is attributed to an increase in synaptic decay time constants of inhibitory units, resulting in slower decay of stimulus-specific activity critical for memory maintenance. Our study reveals the critical role of noise in shaping neural dynamics and cognitive functions, suggesting that inherent variability may be a fundamental feature driving the specialization of inhibitory neurons to support stable information processing in higher cortical regions.

Corticocortical (CC) projections in the visual system facilitate hierarchical processing of sensory information. In addition to direct CC connections, indirect cortico-thalamo-cortical (CTC) pathways through the pulvinar nucleus of the thalamus can relay sensory signals and mediate cortical interactions according to behavioral demands. While the pulvinar connects extensively to the entire visual cortex, it is unknown whether transthalamic pathways link all cortical areas or whether they follow systematic organizational rules. Because mouse pulvinar neurons projecting to different areas are spatially intermingled, their input/output relationships have been difficult to characterize using traditional anatomical methods. To determine the organization of CTC circuits, we mapped the higher visual areas (HVAs) of male and female mice with intrinsic signal imaging and targeted five pulvinar→HVA pathways for projection-specific rabies tracing. We aligned post-mortem cortical tissue to in vivo maps for precise quantification of the areas and cell types projecting to each pulvinar→HVA population. Layer 5 corticothalamic (L5CT) "driver" inputs to the pulvinar originate predominantly from primary visual cortex (V1), consistent with the CC hierarchy. L5CT inputs from lateral HVAs specifically avoid driving reciprocal connections, consistent with the "no-strong-loops" hypothesis. Conversely, layer 6 corticothalamic (L6CT) "modulator" inputs are distributed across areas and are biased toward reciprocal connections. Unlike previous studies in primates, we find that every HVA receives disynaptic input from the superior colliculus. CTC circuits in the pulvinar thus depend on both target HVA and input cell type, such that driving and modulating higher-order pathways follow complementary connection rules similar to those governing first-order CT circuits. Understanding the function of the pulvinar will require knowledge of its anatomical connections. Using state-of-the-art rabies tracing, we establish a map of brain-wide and CTC pulvinar connections. While the primate tectopulvinar pathway selectively targets dorsal visual areas, we find ubiquitous SC input in the mouse. This extrageniculate projection supports unconscious visually-guided behavior, suggesting that all visual cortical areas contribute to such functions in mice. Our results also unify longstanding anatomical hypotheses. Namely, "driver" CTC inputs are feedforward relays and adhere to the "no-strong-loops" hypothesis. "Modulator" L6CT inputs are overrepresented in the target HVA, reflecting the reciprocal connectivity described in previous bulk tracing studies. Together, these findings constitute a comprehensive map to guide future experimental and theoretical studies of pulvinar function.