Biology subjects

Brunton, B. W.

Publications and source records attributed to Brunton, B. W..

26 records · Page 2Linked to original sources

bioRxiv2021

Wing structure and neural encoding jointly determine sensing strategies in insect flight

Animals rely on sensory feedback to generate accurate, reliable movements. In many flying insects, strain-sensitive neurons on the wings provide rapid feedback that enables stable flight control. While the impacts of wing structure on aerodynamic performance have been widely studied, the impacts of wing structure on sensing remain unexplored. In this paper, we show how the structural properties of the wing and encoding by mechanosensory neurons interact to jointly determine optimal sensing strategies and performance. Specifically, we examine how neural sensors can be placed effectively over a flapping wing to detect body rotation about different axes, using a computational wing model with varying flexural stiffness inspired by the hawkmoth Manduca sexta. A small set of mechanosensors, conveying strain information at key locations with a single action potential per wingbeat, permit accurate detection of body rotation. Optimal sensor locations are concentrated at either the wing base or the wing tip, and they transition sharply as a function of both wing stiffness and neural threshold. Moreover, the sensing strategy and performance is robust to both external disturbances and sensor loss. Typically, only five sensors are needed to achieve near-peak accuracy, with a single sensor often providing accuracy well above chance. Our results show that small-amplitude, dynamic signals can be extracted efficiently with spatially and temporally sparse sensors in the context of flight. The demonstrated interaction of wing structure and neural encoding properties points to the importance of their joint evolution.

neuroscience↗

bioRxiv2020

Generalized neural decoders for transfer learning across participants and recording modalities

ObjectiveAdvances in neural decoding have enabled brain-computer interfaces to perform increasingly complex and clinically-relevant tasks. However, such decoders are often tailored to specific participants, days, and recording sites, limiting their practical long-term usage. Therefore, a fundamental challenge is to develop neural decoders that can robustly train on pooled, multi-participant data and generalize to new participants. ApproachWe introduce a new decoder, HTNet, which uses a convolutional neural network with two innovations: (1) a Hilbert transform that computes spectral power at data-driven frequencies and (2) a layer that projects electrode-level data onto predefined brain regions. The projection layer critically enables applications with intracranial electrocorticography (ECoG), where electrode locations are not standardized and vary widely across participants. We trained HTNet to decode arm movements using pooled ECoG data from 11 of 12 participants and tested performance on unseen ECoG or electroencephalography (EEG) participants; these pretrained models were also subsequently fine-tuned to each test participant. Main resultsHTNet outperformed state-of-the-art decoders when tested on unseen participants, even when a different recording modality was used. By fine-tuning these generalized HTNet decoders, we achieved performance approaching the best tailored decoders with as few as 50 ECoG or 20 EEG events. We were also able to interpret HTNets trained weights and demonstrate its ability to extract physiologically-relevant features. SignificanceBy generalizing to new participants and recording modalities, robustly handling variations in electrode placement, and allowing participant-specific fine-tuning with minimal data, HTNet is applicable across a broader range of neural decoding applications compared to current state-of-the-art decoders.

neuroscience↗

bioRxiv2020

Sleep Identification Enabled by Supervised Training Algorithms (SIESTA): An open-source platform for automatic sleep staging of rodent polysomnographic data

The temporal distribution of sleep stages is critical for the study of sleep function, regulation, and disorders in higher vertebrates. This temporal distribution is typically determined polysomnographically. In laboratory rodents, scoring of electrocorticography (ECoG) and electromyography (EMG) recordings is usually performed manually, where 5-10 second epochs are categorized as one of three specific stages: wakefulness, rapid-eye-movement (REM) sleep and non-REM (NREM) sleep. This process is laborious, time-consuming, and particularly impractical for large experimental cohorts with recordings lasting longer than 24 hours. To circumvent this problem, we developed an open-source Python toolkit, Sleep Identification Enabled by Supervised Training Algorithms (SIESTA), that automates the detection of these three main behavioral stages in mice. Our supervised machine learning algorithm extracts features from the ECoG and EMG signals, then automatically scores recordings with a hierarchical classifier based on Bagging Random Forest approaches. We evaluated this approach on data collected from wild-type mice housed under both normal and different lighting conditions, as well as from a mutant mouse line with abnormal sleep phenotypes. To validate its performance on test data, we compared SIESTA with manually scored data and obtained F1 scores of 0.92 for wakefulness, 0.81 for REM, and 0.93 for NREM. SIESTA has a user-friendly interface that can be used without coding expertise. To our knowledge, this is the first time that such a strategy has been developed using all open-source and freely available resources, and our aim is that SIESTA becomes a useful tool that facilitates further research of sleep in rodent models.

neuroscience↗

bioRxiv2020

Tactile active sensing in insect-plant pollination

The interaction between insects and the flowers they pollinate has driven the evolutionary diversity of both insects and flowering plants, two groups with the most numerous species on earth. Insects use vision and olfaction to localize their host plants, but to feed from the flower, they must find the tiny nectary opening, which can be well beyond their visual resolution. When vision is limited, the sense of touch becomes especially crucial, yet very few studies have investigated the role of rapid and precise tactile feedback in successful feeding and pollination interactions. In this paper, we study the remarkable feeding behavior of flying insects that use their proboscis, a flexible mouthpart often longer than their entire body length when unfurled, to expertly explore floral surfaces. Specifically, we observed how the crepuscular hawkmoth Manduca sexta interacts with artificial, 3D-printed flowers of varying shapes. We found that moths actively explore the flower for tactile features, systematically sweeping their proboscis from edge to center repeatedly until they locate the nectary. Moreover, naive moths rapidly learn to exploit flowers, and they adopt a tactile search strategy to more directly locate the nectary in as few as three to five consecutive visits. We suggest moths wield their proboscis to extract salient tactile features, such as floral edges and corolla curvature. Our results highlight the proboscis as a unique sensory structure and emphasize the central role of touch in insect-plant pollination interactions.

neuroscience↗

bioRxiv2020

Anipose: a toolkit for robust markerless 3D pose estimation

AO_SCPLOWBSTRACTC_SCPLOWQuantifying 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.

neuroscience↗

bioRxiv2020

Behavioral and neural variability of naturalistic arm movements

AO_SCPLOWBSTRACTC_SCPLOWMotor behaviors are central to many functions and dysfunctions of the brain, and understanding their neural basis has consequently been a major focus in neuroscience. However, most studies of motor behaviors have been restricted to artificial, repetitive paradigms, far removed from natural movements performed "in the wild." Here, we leveraged recent advances in machine learning and computer vision to analyze intracranial recordings from 12 human subjects during thousands of spontaneous, unstructured arm reach movements, observed over several days for each subject. These naturalistic movements elicited cortical spectral power patterns consistent with findings from controlled paradigms, but with considerable neural variability across subjects and events. We modeled inter-event variability using ten behavioral and environmental features; the most important features explaining this variability were reach angle and day of recording. Our work is among the first studies connecting behavioral and neural variability across cortex in humans during unstructured movements and contributes to our understanding of long-term naturalistic behavior.

neuroscience↗

bioRxiv2020

The balance hypothesis for the avian lumbosacral organ and an exploration of its morphological variation

ABSTRACTBirds (Aves) exhibit exceptional and diverse locomotor behaviors, including the exquisite ability to balance on two feet. How birds so precisely control their movements may be partly explained by a set of intriguing modifications in their lower spine. These modifications are collectively known as the lumbosacral organ (LSO) and are found in the fused lumbosacral vertebrae called the synsacrum. They include a set of transverse canal-like recesses in the synsacrum that align with lateral lobes of the spinal cord, as well as a dorsal groove in the spinal cord that houses an egg-shaped glycogen body. Based on compelling but primarily observational data, the most recent functional hypotheses for the LSO consider it to be a secondary balance organ, in which the transverse canals are analogous to the semicircular canals of the inner ear. If correct, this hypothesis would reshape our understanding of avian locomotion, yet the LSO has been largely overlooked in the recent literature. Here, we review the current evidence for this hypothesis and then explore a possible relationship between the LSO and balance-intensive locomotor ecologies. Our comparative morphological dataset consists of micro-computed tomography (μ-CT) scans of synsacra from ecologically diverse species. We find that birds that perch tend to have more prominent transverse canals, suggesting that the LSO is useful for balance-intensive behaviors. We then identify the crucial outstanding questions about LSO structure and function. The LSO may be a key innovation that allows independent but coordinated motion of the head and the body, and a full understanding of its function and evolution will require multiple interdisciplinary research efforts.Jargon-Free Birds have an uncanny ability to move their heads independently of their bodies. They can keep their heads remarkably still to focus on their prey while twisting in flight or perched on a bouncing branch. How are they able to do this? Like us, birds have balance organs in their inner ears that act like gyroscopes. Surprisingly, birds may have an additional balance organ known as the “lumbosacral organ” in their spine, right above their legs, which might help them sense the movement of their body separately from their head. This second balance organ may have played a very important role in bird evolution and how birds move, but it has seldom been considered in recent scientific studies. This intriguing hypothesis is based in part on a series of fluid-filled, canal-like recesses in the bone surrounding the spinal cord, which resemble the semicircular canals of the inner ear. We looked for evidence of these canal-like recesses in many different bird species, and we found it in every bird we examined. We also found that birds that perch often have deeper recesses than birds that do not perch, suggesting these canals help maintain balance. This paper presents those findings, reviews the existing research, and identifies some key questions that need to be asked to advance our understanding of this fascinating and mysterious part of the bird spinal cord.View Full Text

zoology↗

bioRxiv2018

Extracting Reproducible Time-Resolved Resting State Networks using Dynamic Mode Decomposition

Resting state networks (RSNs) extracted from functional magnetic resonance imaging (fMRI) scans are believed to reflect the intrinsic organization and network structure of brain regions. Most traditional methods for computing RSNs typically assume these functional networks are static throughout the duration of a scan lasting 5-15 minutes. However, they are known to vary on timescales ranging from seconds to years; in addition, the dynamic properties of RSNs are affected in a wide variety of neurological disorders. Recently, there has been a proliferation of methods for characterizing RSN dynamics, yet it remains a challenge to extract reproducible time-resolved networks. In this paper, we develop a novel method based on dynamic mode decomposition (DMD) to extract networks from short windows of noisy, high-dimensional fMRI data, allowing RSNs from single scans to be resolved robustly at a temporal resolution of seconds. We demonstrate this method on data from 120 individuals from the Human Connectome Project and show that unsupervised clustering of DMD modes discovers RSNs at both the group (gDMD) and the single subject (sDMD) levels. The gDMD modes closely resemble canonical RSNs. Compared to established methods, sDMD modes capture individualized RSN structure that both better resembles the population RSN and better captures subject-level variation. We further leverage this time-resolved sDMD analysis to infer occupancy and transitions among RSNs with high reproducibility. This automated DMD-based method is a powerful tool to characterize spatial and temporal structures of RSNs in individual subjects.

neuroscience↗