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Many animals possess mechanosensory neurons that fire when a limb nears the limit of its physical range, but the function of these proprioceptive limit detectors remains poorly understood. Here, we investigate a class of proprioceptors on the Drosophila leg called hair plates. Using calcium imaging in behaving flies, we find that a hair plate on the fly coxa (CxHP8) detects the limits of anterior leg movement. By reconstructing CxHP8 axons in an electron microscopy dataset, we found that they are wired to excite posterior leg movement and inhibit anterior leg movement. Consistent with this connectivity, optogenetic activation of CxHP8 neurons elicited posterior postural reflexes, while silencing altered the swing-to-stance transition during walking. Finally, we use comprehensive reconstruction of peripheral morphology and downstream connectivity to predict the function of other hair plates distributed across the fly leg. Our results suggest that each hair plate is specialized to control specific sensorimotor reflexes that are matched to the joint limit it detects. They also illustrate the feasibility of predicting sensorimotor reflexes from a connectome with identified proprioceptive inputs and motor outputs. The physiology and behavioral function of proprioceptors that detect joint limits are not fully understood. In this study, the authors used calcium imaging, optogenetics, behavioral genetics, and the connectome to demonstrate that hair plate proprioceptors on the fly leg detect joint limits and engage circuits to drive the leg away from those limits.

Walking animals must maintain stability in the presence of external perturbations, despite significant temporal delays in neural signaling and muscle actuation. Here, we develop a 3D kinematic model with a layered control architecture to investigate how sensorimotor delays constrain the robustness of walking behavior in the fruit fly, Drosophila . Motivated by the anatomical architecture of insect locomotor control circuits, our model consists of three component layers: a neural network that generates realistic 3D joint kinematics for each leg, an optimal controller that executes the joint kinematics while accounting for delays, and an inter-leg coordinator. The model generates realistic simulated walking that resembles real fly walking kinematics and sustains walking even when subjected to unexpected perturbations, generalizing beyond its training data. However, we found that the model’s robustness to perturbations deteriorates when sensorimotor delay parameters exceed the physiological range. These results suggest that fly sensorimotor control circuits operate close to the temporal limit at which they can detect and respond to external perturbations. More broadly, we show how a modular, layered model architecture can be used to investigate physiological constraints on animal behavior.

Walking animals must maintain stability in the presence of external perturbations, despite significant temporal delays in neural signaling and muscle actuation. Here, we develop a 3D kinematic model with a layered control architecture to investigate how sensorimotor delays constrain the robustness of walking behavior in the fruit fly, Drosophila . Motivated by the anatomical architecture of insect locomotor control circuits, our model consists of three component layers: a neural network that generates realistic 3D joint kinematics for each leg, an optimal controller that executes the joint kinematics while accounting for delays, and an inter-leg coordinator. The model generates realistic simulated walking that resembles real fly walking kinematics and sustains walking even when subjected to unexpected perturbations, generalizing beyond its training data. However, we found that the model’s robustness to perturbations deteriorates when sensorimotor delay parameters exceed the physiological range. These results suggest that fly sensorimotor control circuits operate close to the temporal limit at which they can detect and respond to external perturbations. More broadly, we show how a modular, layered model architecture can be used to investigate physiological constraints on animal behavior.

Alzheimer's disease (AD) is clinically characterized by a progressive cognitive decline associated with stereotyped accumulation of amyloid-beta (Aβ) plaques and hyperphosphorylated Tau (pTau) tangles across brain regions. While histopathology has revealed patterns of regional involvement, the molecular and cellular events that accompany and potentially drive this progression remain incompletely understood. We applied single nucleus RNA sequencing (RNAseq), ATAC-seq, and Multiome profiling to over 7 million high-quality nuclei from 10 brain regions-spanning medial and lateral entorhinal cortices, hippocampus, multiple temporal and frontal cortical areas, and primary visual cortex-sampled from the same cohort of 84 aged human donors across the AD spectrum (3 regions from all donors, 7 from those without severe co-morbidities). We predicted the cell-type for each nucleus by mapping to an expanded BRAIN initiative cell-type taxonomy, which included AD-associated non-neuronal states and ∼70 brain region-specific neuronal types. These datasets were paired with regional quantitative measurements of Aβ (6e10), pTau (AT8), and other protein pathologies, as well as cellular stains for neurons, microglia, and astrocytes. We inferred two distinct protein pathology accumulation patterns across brain regions: neocortical areas accumulated Aβ prior to pTau, whereas hippocampus and entorhinal cortex had early and, in some cases, substantial pTau burden independent of Aβ. Among neocortical regions, accumulation of AT8 beyond the temporal medial lobe strongly associated with dementia. In analyzing cell-type abundance differences associated with higher levels of pTau pathology, we identified shared motifs of selective neuronal loss consistent with our previous observations from the middle temporal gyrus. These included loss of L2/3 and L5 intratelencephalic excitatory neurons and several types of inhibitory interneurons (e.g., Vip, Sst, Pvalb). These same vulnerable inhibitory types were also reduced in hippocampal and entorhinal regions, when present, and we observed parallel increases in astrocyte, microglial, and oligodendrocyte precursor cells. Additionally, we observed a decrease in specific, regionally specialized neuron subtypes in the hippocampus, entorhinal cortex, and visual cortex. Our multimodal, multi-region single-cell atlas reveals common and region-specific patterns of cellular vulnerability in AD. These cell-types, particularly those commonly affected in distinct neural circuits, could serve as candidate therapeutic targets and biomarkers.

Nociception - the detection of harmful stimuli by the nervous system - contributes to both rapid escape and long-term avoidance behaviors. larvae detect damaging heat, mechanical, and chemical stimuli with specialized multidendritic (md) neurons, and these cells are among the only sensory neurons that survive metamorphosis. However, it remains unknown which somatosensory neurons contribute to nociception in adult flies. In an optogenetic screen, we found that abdominal md neurons were the only somatosensory class to induce rapid escape and sustained place avoidance. Calcium imaging from abdominal md axons revealed that they are activated by thermal nociceptive stimuli (>40°C). Connectomic reconstruction showed that md axons form their strongest synaptic connections with ascending interneurons that project to the brain. Among these, we identified two classes of ascending neurons that mediate rapid escape responses and a third that supports sustained avoidance. Our findings reveal that adult meet several core criteria commonly used to define pain: dedicated nociceptors, ascending pathways connecting peripheral sensors to integrative brain centers, and a capacity for sustained avoidance of noxious stimuli.

Many animals possess mechanosensory neurons that fire when a limb nears the limit of its physical range, but the function of these proprioceptive limit detectors remains poorly understood. Here, we investigate a class of proprioceptors on the leg called hair plates. Using calcium imaging in behaving flies, we find that a hair plate on the fly coxa (CxHP8) detects the limits of anterior leg movement. Reconstructing CxHP8 axons in the connectome, we found that they are wired to excite posterior leg movement and inhibit anterior leg movement. Consistent with this connectivity, optogenetic activation of CxHP8 neurons elicited posterior postural reflexes, while silencing altered the swing-to-stance transition during walking. Finally, we use comprehensive reconstruction of peripheral morphology and downstream connectivity to predict the function of other hair plates distributed across the fly leg. Our results suggest that each hair plate is specialized to control specific sensorimotor reflexes that are matched to the joint limit it detects. They also illustrate the feasibility of predicting sensorimotor reflexes from a connectome with identified proprioceptive inputs and motor outputs.

Walking animals must maintain stability in the presence of external perturbations, despite significant temporal delays in neural signaling and muscle actuation. Here, we develop a 3D kinematic model with a layered control architecture to investigate how sensorimotor delays constrain robustness of walking behavior in the fruit fly, . Motivated by the anatomical architecture of insect locomotor control circuits, our model consists of three component layers: a neural network that generates realistic 3D joint kinematics for each leg, an optimal controller that executes the joint kinematics while accounting for delays, and an inter-leg coordinator. The model generates realistic simulated walking that resembles real fly walking kinematics and sustains walking even when subjected to unexpected perturbations, generalizing beyond its training data. However, we found that the model's robustness to perturbations deteriorates when sensorimotor delay parameters exceed the physiological range. These results suggest that fly sensorimotor control circuits operate close to the temporal limit at which they can detect and respond to external perturbations. More broadly, we show how a modular, layered model architecture can be used to investigate physiological constraints on animal behavior.

Molecularly defined cortical cell types have recently been linked to whole neuronal morphology (WNM), particularly those characterized by whole-brain-wide projections, such as intratelencephalic (IT), extratelencephalic (ET), and corticothalamic (CT) neurons. In contrast, classical morphological classifications (e.g., tufted TPC, small tufted SPC, and stellate SSC) are based primarily on local dendrosomatic and axonal structures, especially apical dendrites. This study bridges these perspectives by establishing a new neuronal taxonomy, analyzing the connectomes of defined cortical cell types, and comparing them with those obtained from bulk anterograde injections. Neurons were sparsely labeled via tamoxifen-inducible Cre lines with GFP reporters, and 1,419 WNM cells were comprehensively reconstructed with Vaa3D-TeraVR from ~15 areas across six functional regions of molecularly labeled brains imaged with 2p-fMOST. These cells were newly classified by integrating current molecular-WNM and classical morphological perspectives, with sample size augmented by 1,455 publicly available WNM cells reconstructed from the Mouse-Light project and CEBSIT. This effort defined ten combined molecular-WNM-classical morphological cell types: L5ET_TPC, L6CT_NPC, L6b_HPC, and seven IT types-L2/3IT_TPC, L4IT_SSC, L4IT_UPC, L4IT_TPC, L5IT_SPC, L6IT_IPC, and L6IT_car3PC. Clustering, quantitative analyses and random Forest classifier objectively validated these types and revealed their distinct connectomes, along with convergent, topographic, and hierarchical organizations across their projection brain regions. At the single-cell level, multiple organizational principles governing cortico-cortical (C-C) and cortico-subcortical (C-subC) connectomes emerged with unprecedented detail, offering a precise GPS-like tool for recordings and robust datasets for neuronal network modeling. Comparisons with bulk anterograde injection data underscored the limitations of traditional methods in identifying projection targets. Overall, our approach provides significant insights into cortical circuitry and elucidates the complex interplay between neuronal molecular identity, whole morphology, and classical morphological classification.

Quantifying movement is critical for understanding animal behavior. Advances in computer vision now enable markerless tracking from 2D video, but most animals live and move in 3D. Here, we introduce Anipose, a Python toolkit for robust markerless 3D pose estimation. Anipose is built on the popular 2D tracking method DeepLabCut, so users can easily expand their existing experimental setups to obtain accurate 3D tracking. It consists of four components: (1) a 3D calibration module, (2) filters to resolve 2D tracking errors, (3) a triangulation module that integrates temporal and spatial regularization, and (4) a pipeline to structure processing of large numbers of videos. We evaluate Anipose on four datasets: a moving calibration board, fruit flies walking on a treadmill, mice reaching for a pellet, and humans performing various actions. By analyzing 3D leg kinematics tracked with Anipose, we identify a key role for joint rotation in motor control of fly walking. We believe this open-source software and accompanying tutorials (anipose.org) will facilitate the analysis of 3D animal behavior and the biology that underlies it.