Waitt Advanced Biophotonics Center

Pain is both a sensory and emotional experience caused by various harmful stimuli. While numerous studies have explored peripheral and central pain mechanisms, the specific neural circuits linking the spinal cord to the brain remain poorly defined. In this study, we demonstrate the involvement of calcitonin gene-related peptide (CGRP)-positive neurons in the parvicellular part of the subparafascicular nucleus (SPFp) in pain. Tracing revealed that CGRP neurons in the SPFp (CGRP) receive projections from the dorsal horn. Increased calcium activity was observed in CGRP neurons during mechanical, thermal, and inflammatory stimuli. Genetic silencing of these neurons resulted in reduced pain responses in animals. Furthermore, optogenetic activation of CGRP neurons induced aversive memory but did not alter mechanical or thermal pain thresholds. This study reveals a distinct neural circuit involving CGRP neurons that mediates pain, which differs from CGRP neurons in the parabrachial nucleus. Understanding these circuits could lead to better pain treatments with fewer side effects.

Electroencephalography (EEG) holds promise for brain-computer interface (BCI) devices as a non-invasive measure of neural activity. With increased attention to EEG-based BCI systems, publicly available datasets incorporating the complex stimuli found in naturalistic speech are necessary to establish a common standard of performance within the BCI community. Effective solutions must overcome noise in the EEG signal and remain reliable across sessions and stimuli that reflect types of real-world linguistic complexity without overfitting to a dataset or task. We present two validated datasets (N=8 and N=16) for classification at the phoneme and word level and by the articulatory properties of phonemes. EEG signals were recorded from 64 channels while subjects listened to and repeated six consonants and five vowels. Individual phonemes were combined in different phonetic environments to produce coarticulated variation in 40 consonant-vowel pairs, 20 real words, and 20 pseudowords. Phoneme pairs and words were presented during a control condition and during transcranial magnetic stimulation (TMS) to assess whether stimulation would augment the EEG signal associated with specific articulatory processes.

Rhythmic motor behaviors are generated by neural networks termed central pattern generators (CPGs). Although locomotor CPGs have been extensively characterized, it remains unknown how the neuronal populations composing them interact to generate adaptive rhythms in mammals. We explored the cooperation dynamics among the three main populations of ipsilaterally projecting spinal CPG neurons-V1, V2a, and V2b neurons-in scratch reflex rhythmogenesis. Individual ablation of the three neuronal populations reduced the oscillation frequency. Activation of excitatory V2a neurons enhanced the oscillation frequency, while activating inhibitory V1 neurons suppressed movement. Building on these findings, we developed a neuromechanical model made of self-oscillating flexor and extensor modules coupled via inhibition. Rhythm frequency is increased by strong intra-module inhibition and facilitation mechanisms in excitatory neurons and decreased by strong inter-module inhibition. In sum, we describe how genetically identified neuron types and the strengths of their synaptic connections drive scratch rhythmogenesis.

Biological visual systems are celebrated for their ability to reliably and precisely recognize objects. However, the specific neural mechanisms responsible for this capability remain largely elusive. In this study, we investigate neural responses in the visual areas V1, V2, and V4 of the brain to natural stimuli using a framework that includes quadratic computations in order to capture local recurrent interactions, both excitatory and suppressive. We find that these quadratic computations and specific coordination between their elements strongly increase both the predictive power of the model and the neural selectivity to natural stimuli. Particularly important were (i) coordination between excitatory and suppressive features to represent mutually exclusive hypotheses regarding incoming stimuli, such as orthogonal orientations or opposing motion directions in area V4, (ii) balance in the contribution of excitatory and suppressive components and its maintenance at similar levels across stages of processing, and (iii) refinement of feature selectivity between stages, with earlier stages representing broader category of inputs. Overall, this work describes how the brain could use multiple nonlinear mechanisms to increase selectivity of neural responses to natural stimuli.

Synaptotagmin-7 (Syt7) KO mice show diurnal fluctuations of mania- and depression-like behavioral abnormalities. Although GluN2B-NMDAR hypoactivity has been shown to be involved in the induction of mania-like behaviors of the Syt7 KO mice in the dark phase, the reasons for the depression-like behaviors in the light phase and behavioral fluctuation remain unknown. Here, we show that bipolar I disorder (BDI)-patient-induced pluripotent stem cell (iPSC)-derived islet-like organoids exhibited Syt7-dependent insulin secretion defects; moreover, Syt7-deficiency-induced insulin hyposecretion generated depression-like behaviors in Syt7 KO mice in the light phase. Furthermore, pancreatic insulin secretion and neuronal activity showed opposite diurnal patterns, in which the Syt7-deficiency-induced disequilibrium induced periodic antagonistic shifts in the mania- and depression-like behaviors. Finally, using RNA sequencing (RNA-seq) analysis, we explored downstream pathways that might underlie the diurnal fluctuation of behaviors. Therefore, Syt7-deficiency-induced insulin hypoactivity contributed to light-phase depression-like behaviors and diurnal behavioral fluctuations in the mice.

Neurons represent changes in external and internal environments by altering their activity patterns. While coherent brain-wide patterns of neural activity have been observed in neuronal populations, very little is known about their dimensionality, geometry, and how they are correlated with sensory inputs. Here, we recorded the activity of most head neurons in experiencing changes in bacterial or control buffer stimuli around their nose. We first classified active neurons into six functional clusters: two sensory neuron clusters (ON and OFF responding to addition and removal of stimuli, respectively) and four motor/command neuron clusters (AVA, RME, SMDD and SMDV). Next, we estimated stimulus selectivity for each cluster and found that while sensory neurons exhibit their maximal responses within 15 seconds, changes in bacterial stimuli drive maximal responses in command and motor neuron clusters after tens of seconds. Furthermore, we show that bacterial stimuli induce neural dynamics that are best described by a hyperbolic, not Euclidean, space, of dimensionality eight. The hyperbolic space provided a better description of neural activity than the standard Euclidean space. This space can be separated into three components - one sensory, and two motor directions (forward-backward and dorsal-ventral). Collectively, we show that neural activity can be effectively represented in low-dimensional hyperbolic space to describe a sensorimotor transformation.

Long-term potentiation (LTP) induces presynaptic bouton enlargement and a reduction in the number of synaptic vesicles. To understand the relationship between these events, we performed 3D analysis of serial section electron micrographs in rat hippocampal area CA1, 2 hours after LTP induction. We observed a high vesicle packing density in control boutons, contrasting with a lower density in most LTP boutons. Notably, the summed membrane area of the vesicles lost in low-density LTP boutons is comparable to the surface membrane required for the observed bouton enlargement when compared to high-density control boutons. These novel findings suggest that presynaptic vesicle density provides a new structural indicator of LTP that supports a local mechanism of bouton enlargement.

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.

The human protein kinase CK2 has long been of interest as a target in oncology, but new evidence is emerging of its role in central nervous system (CNS) disorders. The CK2 catalytic subunit paralog CK2A2 is enriched in the CNS relative to other tissues; however, the catalytic subunit paralog CK2A1 is expressed at similar levels across most tissues. Current treatment modalities centered on CK2 inhibition under clinical study lack evidence of CNS activity, kinome-wide selectivity, or CK2A2 paralog selectivity. Therefore, a brain-penetrant inhibitor selective for CK2A2 over CK2A1 may enable the further elucidation of the role of this paralog in CNS pathologies. In this work, we describe a series of flavone-inspired inhibitors of CK2A1 and CK2A2 designed and synthesized in a structure-based drug-design campaign. Multiple candidates demonstrating promising CK2A2 potency, kinome-wide selectivity, and CNS permeability/activity were identified, with compound 65 representing the best confluence of these properties. Although these lead candidates exhibited only up to three-fold preferential inhibition of CK2A2, they represent a starting point for the study of CK2A2 and the disorders it may mediate in the CNS.

There is a morphodynamic component to synaptic learning by which changes in dendritic (postsynaptic) spine head size are associated with the strengthening or weakening of the synaptic connection between two neurons, in response to the temporal correlation of local presynaptic and postsynaptic signals. These morphological factors are in turn sculpted by the dynamics of the actin cytoskeleton. In this paper, we use Dynamical Graph Grammars (DGGs) implemented within a computer algebra system to model how networks of actin filaments can dynamically grow or shrink, reshaping the spine head. Dynamical Graph Grammars (DGGs) provide a well-defined way to accommodate dynamically changing system structure such as active cytoskeleton represented using dynamic graphs, within nonequilibrium statistical physics under the master equation. We show that DGGs can also incorporate biophysical forces between graph-connected objects at a finer time scale, with specialized DGG kinetic rules obeying biophysical constraints of Galilean invariance, conservation of momentum, and dissipation of conserved global energy. We use graph-local energy functions for cytoskeleton networks interacting with membranes, and derive DGG rules from the specialization of dissipative stochastic dynamics - separated into dissipative and thermal noise rule types - to a mutually exclusive and exhaustive collection of graph-local neighborhood types for the rule left hand sides. The dissipative rules comprise a stochastic version of gradient descent dynamics. The thermal noise rules use a Gaussian approximation of each position coordinate to sample jitter-like displacements. For the spine head model we designed and implemented DGG grammar mathematical sub-models including actin network growth, non-equilibrium statistical mechanics, and filament-membrane mechanical interaction to regulate the re-writing of graph objects. We simulate emergent biophysics of simplified networks of actin polymers and their interactions with membranes. From a biological perspective, we observe regulatory effects of three actin-binding proteins (ABPs) on the membrane size and find evidence supporting mechanisms of membrane growth.

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.