Biology subjects

Henderson, J. M.

Publications and source records attributed to Henderson, J. M..

32 records · Page 2Linked to original sources

bioRxiv2024

A real-time, high-performance brain-computer interface for finger decoding and quadcopter control

People with paralysis express unmet needs for peer support, leisure activities, and sporting activities. Many within the general population rely on social media and massively multiplayer video games to address these needs. We developed a high-performance finger brain-computer-interface system allowing continuous control of 3 independent finger groups with 2D thumb movements. The system was tested in a human research participant over sequential trials requiring fingers to reach and hold on targets, with an average acquisition rate of 76 targets/minute and completion time of 1.58 {+/-} 0.06 seconds. Performance compared favorably to previous animal studies, despite a 2-fold increase in the decoded degrees-of-freedom (DOF). Finger positions were then used for 4-DOF velocity control of a virtual quadcopter, demonstrating functionality over both fixed and random obstacle courses. This approach shows promise for controlling multiple-DOF end-effectors, such as robotic fingers or digital interfaces for work, entertainment, and socialization.

bioengineering↗

bioRxiv2023

Translating deep learning to neuroprosthetic control

Advances in deep learning have given rise to neural network models of the relationship between movement and brain activity that appear to far outperform prior approaches. Brain-computer interfaces (BCIs) that enable people with paralysis to control external devices, such as robotic arms or computer cursors, might stand to benefit greatly from these advances. We tested recurrent neural networks (RNNs) on a challenging nonlinear BCI problem: decoding continuous bimanual movement of two computer cursors. Surprisingly, we found that although RNNs appeared to perform well in offline settings, they did so by overfitting to the temporal structure of the training data and failed to generalize to real-time neuroprosthetic control. In response, we developed a method that alters the temporal structure of the training data by dilating/compressing it in time and re-ordering it, which we show helps RNNs successfully generalize to the online setting. With this method, we demonstrate that a person with paralysis can control two computer cursors simultaneously, far outperforming standard linear methods. Our results provide evidence that preventing models from overfitting to temporal structure in training data may, in principle, aid in translating deep learning advances to the BCI setting, unlocking improved performance for challenging applications.

neuroscience↗

bioRxiv2023

Long-term unsupervised recalibration of cursor BCIs

Intracortical brain-computer interfaces (iBCIs) require frequent recalibration to maintain robust performance due to changes in neural activity that accumulate over time. Compensating for this nonstationarity would enable consistently high performance without the need for supervised recalibration periods, where users cannot engage in free use of their device. Here we introduce a hidden Markov model (HMM) to infer what targets users are moving toward during iBCI use. We then retrain the system using these inferred targets, enabling unsupervised adaptation to changing neural activity. Our approach outperforms the state of the art in large-scale, closed-loop simulations over two months and in closed-loop with a human iBCI user over one month. Leveraging an offline dataset spanning five years of iBCI recordings, we further show how recently proposed data distribution-matching approaches to recalibration fail over long time scales; only target-inference methods appear capable of enabling long-term unsupervised recalibration. Our results demonstrate how task structure can be used to bootstrap a noisy decoder into a highly-performant one, thereby overcoming one of the major barriers to clinically translating BCIs.

neuroscience↗

bioRxiv2022

Arp2/3 inhibition switches Eps8's network associations to favour longer actin filament formation necessary for tunneling nanotubes

Tunneling nanotubes (TNTs) connect distant cells and mediate cargo transfer for intercellular communication in physiological and pathological contexts. How cells generate these actin-mediated protrusions to span lengths beyond those attainable by canonical filopodia remains unknown. Through a combination of micropatterning, microscopy and optical tweezer-based approaches, we demonstrate that TNTs forming through the outward extension of actin (not through cellular dislodgement) achieve distances greater than the mean length of filopodia, and that branched Arp2/3-dependent pathways attenuate the extent to which actin polymerizes in nanotubes, limiting TNT occurrence. Proteomic analysis using Epidermal growth factor receptor kinase substrate 8 (Eps8) as a positive effector of TNTs showed that upon Arp2/3 inhibition, proteins enhancing filament turnover and depolymerization were reduced and Eps8 instead exhibited heightened interactions with the inverted Bin/Amphiphysin/Rvs (I-BAR) domain protein IRSp53 that provides a direct connection with linear actin polymerases. Our data reveals how common protrusion players (Eps8 and IRSp53) form TNTs, and that when competing pathways overutilizing such proteins and monomeric actin in Arp2/3 networks are inhibited, processes promoting linear actin growth dominate to favour TNT formation. Thus, this work reinforces a general principle for actin network control for cellular protrusions where simple shifts in the balance between processes that inhibit actin growth versus those that promote growth dictate protrusion formation and the ultimate length scales protrusions achieve.

cell biology↗

bioRxiv2022

Activated I-BAR IRSp53 clustering controls the formation of VASP-actin-based membrane protrusions

Filopodia are actin-rich membrane protrusions essential for cell morphogenesis, motility, and cancer invasion. How cells control filopodia initiation on the plasma membrane remains elusive. We performed experiments in cellulo, in vitro and in silico to unravel the mechanism of filopodia initiation driven by the membrane curvature sensor IRSp53. We showed that full-length IRSp53 self-assembles into clusters on membranes depending on PIP2. Using well-controlled in vitro reconstitution systems, we demonstrated that IRSp53 clusters recruit the actin polymerase VASP to assemble actin filaments locally on membranes, leading to the generation of actin-filled membrane protrusions reminiscent of filopodia. By pulling membrane nanotubes from live cells, we observed that IRSp53 can only be enriched and trigger actin assembly in nanotubes at highly dynamic membrane regions. Our work supports a regulation mechanism of IRSp53 in its attributes of curvature sensation and partner recruitment to ensure a precise spatial-temporal control of filopodia initiation.

biophysics↗

bioRxiv2020

Effects of Peripheral Haptic Feedback on Intracortical Brain-Computer Interface Control and Associated Sensory Responses in Motor Cortex

Intracortical brain-computer interfaces (iBCIs) provide people with paralysis a means to control devices with signals decoded from brain activity. Despite recent impressive advances, these devices still cannot approach able-bodied levels of control. To achieve naturalistic control and improved performance of neural prostheses, iBCIs will likely need to include proprioceptive feedback. With the goal of providing proprioceptive feedback via mechanical haptic stimulation, we aim to understand how haptic stimulation affects motor cortical neurons and ultimately, iBCI control. We provided skin shear haptic stimulation as a substitute for proprioception to the back of the neck of a person with tetraplegia. The neck location was determined via assessment of touch sensitivity using a monofilament test kit. The participant was able to correctly report skin shear at the back of the neck in 8 unique directions with 65% accuracy. We found motor cortical units that exhibited sensory responses to shear stimuli, some of which were strongly tuned to the stimuli and well modeled by cosine-shaped functions. We also demonstrated online iBCI cursor control with continuous skin-shear feedback driven by decoded command signals. Cursor control performance increased slightly but significantly when the participant was given haptic feedback, compared to the purely visual feedback condition.

neuroscience↗

bioRxiv2020

High-performance brain-to-text communication via imagined handwriting

Brain-computer interfaces (BCIs) can restore communication to people who have lost the ability to move or speak. To date, a major focus of BCI research has been on restoring gross motor skills, such as reaching and grasping1-5 or point-and-click typing with a 2D computer cursor6,7. However, rapid sequences of highly dexterous behaviors, such as handwriting or touch typing, might enable faster communication rates. Here, we demonstrate an intracortical BCI that can decode imagined handwriting movements from neural activity in motor cortex and translate it to text in real-time, using a novel recurrent neural network decoding approach. With this BCI, our study participant (whose hand was paralyzed) achieved typing speeds that exceed those of any other BCI yet reported: 90 characters per minute at >99% accuracy with a general-purpose autocorrect. These speeds are comparable to able-bodied smartphone typing speeds in our participants age group (115 characters per minute)8 and significantly close the gap between BCI-enabled typing and able-bodied typing rates. Finally, new theoretical considerations explain why temporally complex movements, such as handwriting, may be fundamentally easier to decode than point-to-point movements. Our results open a new approach for BCIs and demonstrate the feasibility of accurately decoding rapid, dexterous movements years after paralysis.

neuroscience↗

bioRxiv2020

Decoding spoken English phonemes from intracortical electrode arrays in dorsal precentral gyrus

ObjectiveTo evaluate the potential of intracortical electrode array signals for brain-computer interfaces (BCIs) to restore lost speech, we measured the performance of classifiers trained to discriminate a comprehensive basis set for speech: 39 English phonemes. We classified neural correlates of spoken-out-loud words in the "hand knob" area of precentral gyrus, which we view as a step towards the eventual goal of decoding attempted speech from ventral speech areas in patients who are unable to speak. ApproachNeural and audio data were recorded while two BrainGate2 pilot clinical trial participants, each with two chronically-implanted 96-electrode arrays, spoke 420 different words that broadly sampled English phonemes. Phoneme onsets were identified from audio recordings, and their identities were then classified from neural features consisting of each electrodes binned action potential counts or high-frequency local field potential power. We also examined two potential confounds specific to decoding overt speech: acoustic contamination of neural signals and systematic differences in labeling different phonemes onset times. Main resultsA linear decoder achieved up to 29.3% classification accuracy (chance = 6%) across 39 phonemes, while a recurrent neural network classifier achieved 33.9% accuracy. Parameter sweeps indicated that performance did not saturate when adding more electrodes or more training data, and that accuracy improved when utilizing time-varying structure in the data. Microphonic contamination and phoneme onset differences modestly increased decoding accuracy, but could be mitigated by acoustic artifact subtraction and using a neural speech onset marker, respectively. SignificanceThe ability to decode a comprehensive set of phonemes using intracortical electrode array signals from a nontraditional speech area suggests that placing electrode arrays in ventral speech areas is a promising direction for speech BCIs.

neuroscience↗

bioRxiv2020

The neural representation of force across grasp types in motor cortex of humans with tetraplegia

Intracortical brain-computer interfaces (iBCIs) have the potential to restore hand grasping and object interaction to individuals with tetraplegia. Optimal grasping and object interaction require simultaneous production of both force and grasp outputs. However, since overlapping neural populations are modulated by both parameters, grasp type could affect how well forces are decoded from motor cortex in a closed-loop force iBCI. Therefore, this work quantified the neural representation and offline decoding performance of discrete hand grasps and force levels in two participants with tetraplegia. Participants attempted to produce three discrete forces (light, medium, hard) using up to five hand grasp configurations. A two-way Welch ANOVA was implemented on multiunit neural features to assess their modulation to force and grasp. Demixed principal component analysis was used to assess for population-level tuning to force and grasp and to predict these parameters from neural activity. Three major findings emerged from this work: 1) Force information was neurally represented and could be decoded across multiple hand grasps (and, in one participant, across attempted elbow extension as well); 2) Grasp type affected force representation within multi-unit neural features and offline force classification accuracy; and 3) Grasp was classified more accurately and had greater population-level representation than force. These findings suggest that force and grasp have both independent and interacting representations within cortex, and that incorporating force control into real-time iBCI systems is feasible across multiple hand grasps if the decoder also accounts for grasp type. Significance StatementIntracortical brain-computer interfaces (iBCIs) have emerged as a promising technology to potentially restore hand grasping and object interaction in people with tetraplegia. This study is among the first to quantify the degree to which hand grasp affects force-related - or kinetic - neural activity and decoding performance in individuals with tetraplegia. The study results enhance our overall understanding of how the brain encodes kinetic parameters across varying kinematic behaviors -- and in particular, the degree to which these parameters have independent versus interacting neural representations. Such investigations are a critical first step to incorporating force control into human-operated iBCI systems, which would move the technology towards restoring more functional and naturalistic tasks.

neuroscience↗

bioRxiv2019

Systematic assessment of commercially available low-input miRNA library preparation kits

High-throughput sequencing is increasingly favoured to assay the presence and abundance of micro RNAs (miRNAs) in biological samples, even from low RNA amounts, and a number of commercial vendors now offer kits that allow miRNA sequencing from sub-nanogram (ng) inputs. However, although biases introduced during library preparation have been documented, the relative performance of current reagent kits has not been investigated in detail. Here, six commercial kits capable of handling <100ng total RNA input were used for library preparation, performed by kit manufactures, on synthetic miRNAs of known quantities and human biological total RNA samples. We compared the performance of miRNA detection sensitivity, reliability, titration response and the ability to detect differentially expressed miRNAs. In addition, we assessed the use of unique molecular identifiers sequence (UMI) tags in one kit. We observed differences in detection sensitivity and ability to identify differentially expressed miRNAs between the kits, but none were able to detect the full repertoire of expected miRNAs. The reliability within the replicates of all kits was good, while larger differences were observed between the kits, although none could accurately quantify the majority of miRNAs. UMI tags, at least within the input ranges tested, offered little advantage to improve data utility. In conclusion, biases in miRNA abundance are heavily influenced by the kit used for library preparation, suggesting that comparisons of datasets prepared by different procedures should be made with caution. This article is intended to assist researchers select the most appropriate kit for their experimental conditions.

bioinformatics↗

bioRxiv2018

Neural ensemble dynamics in dorsal motor cortex during speech in people with paralysis

Speaking is a sensorimotor behavior whose neural basis is difficult to study at the resolution of single neurons due to the scarcity of human intracortical measurements and the lack of animal models. We recorded from electrode arrays in the hand knob area of motor cortex in people with tetraplegia. Neurons in this area, which have not previously been implicated in speech, modulated during speaking and during non-speaking movement of the tongue, lips, and jaw. This challenges whether the conventional model of a motor homunculus division by major body regions extends to the single-neuron scale. Spoken words and syllables could be decoded from single trials, demonstrating the potential utility of intracortical recordings for brain-computer interfaces (BCIs) to restore speech. Two neural population dynamics features previously reported for arm movements were also present during speaking: a large initial condition-invariant signal, followed by rotatory dynamics. This suggests that common neural dynamical motifs may underlie movement of arm and speech articulators.

neuroscience↗

bioRxiv2018

Segmentation of Glomeruli Within Trichrome Images Using Deep Learning

IntroductionThe number of glomeruli and glomerulosclerosis evaluated on kidney biopsy slides constitute as standard components of a renal pathology report. Prevailing methods for glomerular assessment remain manual, labor intensive and non-standardized. We developed a deep learning framework to accurately identify and segment glomeruli from digitized images of human kidney biopsies. MethodsTrichrome-stained images (n=275) from renal biopsies of 171 chronic kidney disease patients treated at the Boston Medical Center from 2009-12 were analyzed. A sliding window operation was defined to crop each original image to smaller images. Each cropped image was then evaluated by three experts into three categories: (a) No glomerulus, (b) Normal or partially sclerosed glomerulus and (c) Globally sclerosed glomerulus. This led to identification of 751 unique images representing non{-}glomerular regions, 611 images with either normal or partially sclerosed (NPS) glomeruli and 134 images with globally sclerosed (GS) glomeruli. A convolutional neural network (CNN) was trained with cropped images as inputs and corresponding labels as output. Using this model, an image processing routine was developed to scan the test data images to segment the GS glomeruli. ResultsThe CNN model was able to accurately discriminate non-glomerular images from NPS and GS images (Performance on test data - Accuracy: 92.67{+/-}2.02% and Kappa: 0.8681{+/-}0.0392). The segmentation model that was based on the CNN multi-label classifier accurately marked the GS glomeruli on the test data (Matthews correlation coefficient = 0.628). ConclusionThis work demonstrates the power of deep learning for assessing complex histologic structures from digitized human kidney biopsies.

pathology↗

bioRxiv2017

Meaning Guides Attention in Real-World Scenes: Evidence from Eye Movements and Meaning Maps

We compared the influences of meaning and salience on attentional guidance in scene images. Meaning was captured by \"meaning maps\" representing the spatial distribution of semantic information in scenes. Meaning maps were coded in a format that could be directly compared to maps of image salience generated from image features. We investigated the degree to which meaning versus image salience predicted human viewers spatial distribution of attention over scenes, with attention operationalized as duration-weighted fixation density. The results showed that both meaning and salience predicted the distribution of attention, but that when the correlation between meaning and salience was statistically controlled, meaning accounted for unique variance in attention but salience did not. This pattern was observed for early as well as late fixations, for fixations following short as well as long saccades, and for fixations including or excluding the centers of the scenes. The results strongly suggest that meaning guides attention in real world scenes. We discuss the results from the perspective of a cognitive relevance theory of attentional guidance in scenes.

neuroscience↗

bioRxiv2017

Inferring single-trial neural population dynamics using sequential auto-encoders

Neuroscience is experiencing a data revolution in which simultaneous recording of many hundreds or thousands of neurons is revealing structure in population activity that is not apparent from single-neuron responses. This structure is typically extracted from trial-averaged data. Single-trial analyses are challenging due to incomplete sampling of the neural population, trial-to-trial variability, and fluctuations in action potential timing. Here we introduce Latent Factor Analysis via Dynamical Systems (LFADS), a deep learning method to infer latent dynamics from single-trial neural spiking data. LFADS uses a nonlinear dynamical system (a recurrent neural network) to infer the dynamics underlying observed population activity and to extract de-noised single-trial firing rates from neural spiking data. We apply LFADS to a variety of monkey and human motor cortical datasets, demonstrating its ability to predict observed behavioral variables with unprecedented accuracy, extract precise estimates of neural dynamics on single trials, infer perturbations to those dynamics that correlate with behavioral choices, and combine data from non-overlapping recording sessions (spanning months) to improve inference of underlying dynamics. In summary, LFADS leverages all observations of a neural populations activity to accurately model its dynamics on single trials, opening the door to a detailed understanding of the role of dynamics in performing computation and ultimately driving behavior.

neuroscience↗