Biology subjects

Henderson, J. M.

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

At least 19 recordsLinked to original sources

bioRxiv2026

Neural decoding of speech using deep neural ensembles

Speech brain-computer interfaces (BCIs) can restore rapid communication to people with paralysis, but decoding errors still limit performance. In recent brain-to-text decoding competitions, deep ensemble methods, which combine predictions from multiple independently trained decoders, have delivered striking accuracy improvements and account for the largest gains over baseline approaches. However, these methods have not previously been tested in real-time, require substantial computational resources, and their performance under various clinically relevant constraints remains poorly understood. Here, we present the first closed-loop test of deep ensembles in a participant with bilateral intracortical microelectrode arrays, demonstrating a reduction in word error rate from 33.7% to 26.0% on a large-vocabulary task. Using additional data from three participants, we then assess how these gains depend on baseline error rate, training dataset size, and ensemble size, including the resource-accuracy tradeoffs most relevant for real-world deployment. Finally, we introduce a computationally efficient pseudoensembling approach based on test-time augmentation that improves decoding accuracy while requiring only a single base decoder, greatly reducing the computational burden of ensembling. Together, these results show that the benefits of deep ensembling can be realized in real time and under practical resource constraints, bringing speech BCIs closer to broader clinical adoption.

neuroscience↗

bioRxiv2026

Premotor cortex uses a compositional neural geometry to plan words

Speech requires precise serial ordering of words and phonemes into novel combinations. To accomplish this, the brain is believed to flexibly prepare utterances before producing them, even allowing pronunciation of never-before spoken words. To discover how neural populations achieve this, intracortical activity from premotor cortex was recorded while two speech neuroprosthesis pilot clinical trial participants attempted to speak factorially-balanced phoneme sequences. During preparation, activity encoded not only the next-phoneme, but multiple upcoming phoneme positions spanning whole words. We found that word-level plans were formed by compositionally combining phoneme representations, a mechanism that may enable efficient planning of novel sequences. When utterances contained more than one word, premotor cortex activity was largely limited to the first word, suggesting that articulatory planning is segmented by higher-order features. Together, these results reveal a compositional, hierarchically-segemented planning geometry, potentially a universal neural strategy for sequence organization across higher levels of language.

neuroscience↗

bioRxiv2026

Meaning-based guidance of attention in rhesus monkeys during naturalistic scene viewing

In humans and other primates, high-acuity vision is restricted to the fovea, requiring frequent saccadic eye movements to sample visual information, a process known as overt attention. Classical visual salience theory explains how low-level image features guide these movements, but recent human studies have shown that overt attention is also strongly guided by scene meaning--the spatial distribution of semantic informativeness. Whether this form of attentional guidance is uniquely human or shared across primate vision remains unknown. Here, we addressed this question by recording eye movements from two rhesus macaques freely viewing naturalistic indoor scenes. Fixation selection was modeled using meaning maps alongside image-based salience maps and center proximity. In both monkeys, meaning robustly predicted fixation selection after controlling for visual salience and center bias. Moreover, high-meaning regions captured attention independently of visual salience, whereas salience played an increasingly important role as meaning decreased. While this prioritization of meaningful regions remained robust across environments, familiarity broadened visual exploration by increasing the likelihood of fixating less meaningful areas. Finally, the influence of meaning on fixation selection strengthened with attentional engagement. These findings suggest that meaning-based attention is an evolutionarily conserved component of primate vision and establish a behavioral foundation for investigating its neural mechanisms.

animal behavior and cognition↗

bioRxiv2026

A patient derived xenograft repository capturing clinical and molecular heterogeneity of large B-cell lymphoma

Large B-cell lymphomas (LBCLs) are a clinically and molecularly diverse group of malignancies with a rapidly evolving therapeutic landscape that has introduced new areas of clinical need such as post-CD19 chimeric antigen receptor T (CART19) progression. Patient derived xenograft (PDX) models are an important tool for mechanistic studies and preclinical evaluation of new therapies and can be generated from a variety of clinical contexts that capture tumor-intrinsic resistance mechanisms. We therefore undertook a comprehensive effort to generate PDX models that encompass the molecular landscape of LBCLs and include important clinical scenarios for new drug development. Here we describe the first 48 models within this publicly available repository, capturing the transcriptional and genetic subsets of LBCL. These models also include 23 generated from post-CART progression biopsies which reproduce patterns of progression driven by CD19 mutation or expression loss, as well as tumor cell-intrinsic CART19 resistance that we validate in vivo. STATEMENT OF SIGNIFICANCEYang et al. describe X-LYMPH (Xenografts of Lymphoma), a publicly available and molecularly annotated PDX repository that captures the heterogeneity of large B-cell lymphoma. X-LYMPH includes models of chimeric antigen receptor T cell resistance, providing a shared foundation for mechanistic research and therapeutic development for lymphomas.

cancer biology↗

bioRxiv2026

Cross-brain transfer of high-performance intracortical speech and handwriting BCIs

Intracortical brain-computer interfaces (BCIs) that decode complex movements, such as handwriting and speech, can require substantial training data to achieve high performance. We investigated whether leveraging the neural activity recordings of previous users could reduce this initial data collection burden for new BCI users (an approach we call "cross-brain transfer"). Using intracortical recordings from five BrainGate2 clinical trial participants, we tested cross-brain transfer for both speech and handwriting neural decoders trained and evaluated on general, unconstrained corpora of spoken and written English. We found that cross-brain transfer improved decoding performance when training data from the target user was limited (< 200 sentences), and that dataset-specific input layers to the decoder were critical for combining data across users. Without trainable input layers, transfer failed and performed worse than training from scratch on target user data only. Finally, we measured the effectiveness of cross-brain transfer relative to training with (1) more data from the same user and (2) more electrode-permuted data from the same user, which simulates sampling from another brain with identical neural latent structure. In some cases (T16 speech, T12 handwriting), cross-brain transfer appeared as effective as additional permuted data from the same user, while in others (T12 speech, T15 speech) electrode-permuted data was more beneficial. Our results successfully demonstrate and characterize cross-brain transfer learning between multiple intracortical BCI users, for both speech and handwriting, using a general open-ended dataset not restricted to small sets of words or phrases. This work highlights a promising path towards addressing a key barrier to the clinical translation of BCIs, while clarifying when cross-brain transfer may be most beneficial and the decoder design choices needed to realize those gains.

neuroscience↗

bioRxiv2025

Deep Learning Enables Automated Segmentation and Quantification of Ultrastructure from Transmission Electron Microscopy Images

Transmission electron microscopy (TEM) has become an essential technique for observing subcellular ultrastructure, and is widely used in both clinical diagnosis and biomedical research. However, analysis of TEM data remains extremely labor-intensive and often inconsistent across operators due to the lack of dedicated computational methods. Here, we present TEAMKidney, a deep learning framework for accurate and scalable measurement of ultrastructures in TEM images across species, magnifications, and instrument platforms. We collected 12,991 TEM images from patients with multiple kidney diseases and from different animal models. By combining a self-training-based semantic segmentation stage with a TEM-tailored panoptic segmentation model, we address two major challenges in TEM data analysis: the lack of accurately labeled training data and the difficulty of achieving high segmentation accuracy for complex ultrastructure. Application of TEAMKidney to both human and animal images successfully reveals disease-associated changes in two critical glomerular ultrastructures: the glomerular basement membrane and podocyte foot processes. In addition to significantly outperforming existing tools, TEAMKidney shows close agreement with pathological expert measurements used in clinical assessment protocols. By reducing dependence on manual tracing while preserving expert-level accuracy, TEAMKidney demonstrates that deep learning can substantially reduce the burden of image analysis in both clinical pathology and biomedical research settings.

bioinformatics↗

bioRxiv2025

Improved interpretability in LFADS models using a learned, context-dependent per-trial bias

The computation-through-dynamics perspective argues that biological neural circuits process information via the continuous evolution of their internal states. Inspired by this perspective, Latent Factor Activity using Dynamical systems (LFADS, [1]) identifies a generative model consistent with the neural activity recordings. LFADS models neural dynamics with a recurrent neural network (RNN) generator, which results in excellent fit to the data. However, it has been difficult to understand the dynamics of the LFADS generator. In this work, we show that this poor interpretability arises in part because the generator implements complex, multi-stable dynamics. We introduce a simple modification to LFADS that ameliorates issues with interpretability by providing an inferred per-trial bias (modeled as a constant input) to the RNN generator, enabling it to contextually adapt a simpler dynamical system to individual trials. In both simulated neural recordings from pendulum oscillations and real recordings during arm movements in nonhuman primates, we observed that the standard LFADS learned complex, multi-stable dynamics, whereas the modified LFADS learned easier-to-understand contextual dynamics. This enabled direct analysis of the generator, which reproduced at a single-trial level previous results shown only through more complex analyses at the trial average. Finally, we applied the per-trial inferred bias LFADS model to human intracortical brain computer interface recordings during attempted finger movements and speech. We show that modifying neural dynamics using linear operations of the per-trial bias addresses non-stationarity and identifies the extent of behavioral variability, problems known to plague BCI. We call our modification to LFADS as "contextual LFADS".

neuroscience↗

bioRxiv2025

Preparatory encoding of diverse features of intended movement in the human motor cortex

Over the course of a voluntary movement, motor cortical activity exhibits a transition from preparation to execution, with markedly different activity across these phases. Preparatory activity in particular might be used to improve brain-computer interfaces (BCIs) that harness brain activity to control external assistive devices, for example by anticipating a users intended movement trajectory for quick and fluid performance. However, to leverage preparatory activity for clinical BCIs, we must first understand which features of upcoming movements are encoded by preparatory activity in humans. In this work, we collected intracortical recordings from 3 research participants in the BrainGate2 clinical trial to investigate whether diverse features of movement, such as direction, curvature, and distance, are encoded by preparatory activity in the human motor cortex. We first show that preparatory activity is tuned to the direction of upcoming movements, and this tuning is largely preserved across movements with different effectors. Further investigation demonstrated this preparatory activity is also informative of initial and endpoint directions of curved movement trajectories, and encodes movement distance and speed independently. Finally, we present an online control paradigm that leverages preparatory activity to predict movements towards intended directions in advance, yielding rapid, self-paced control of a computer cursor by human participants. Altogether, these results demonstrate that preparatory activity in the human motor cortex encodes rich features of upcoming movement, highlighting its potential use for high performance brain-computer interface applications.

neuroscience↗

bioRxiv2025

Key moments in naturalistic events synchronize neural activity patterns and dominate memory reinstatement

Continuous experiences are experienced and remembered in terms of events that unfold over time. There is strong evidence that event boundaries segment experience during comprehension and that event representations are compressed in memory; however, this compression is poorly understood. We developed a novel storyboard paradigm to test the hypothesis that representations of continuous experiences are defined by a subset of key moments that capture the underlying narrative. Participants agreed on when key moments occurred; some key moments corresponded with event boundaries, but many did not. fMRI during encoding revealed that neural activity patterns throughout the default network synchronized across individuals at key moments. Further, comparing fMRI during encoding to fMRI during retrieval revealed that key moments are overrepresented in neural patterns that are reinstated during event recall in the posterior-medial cortex. These results suggest that continuous events are punctuated by a small subset of meaningful moments, which dominate neural representations during perception and memory.

neuroscience↗

bioRxiv2025

Error encoding in human speech motor cortex

Humans monitor their actions, including detecting errors during speech production. This self-monitoring capability also enables speech neuroprosthesis users to recognize mistakes in decoded output upon receiving visual or auditory feedback. However, it remains unknown whether neural activity related to error detection is present in the speech motor cortex. In this study, we demonstrate the existence of neural error signals in speech motor cortex firing rates during intracortical brain-to-text speech neuroprosthesis use. This activity could be decoded to enable the neuroprosthesis to identify its own errors with up to 86% accuracy. Additionally, we observed distinct neural patterns associated with specific types of mistakes, such as phonemic or semantic differences between the persons intended and displayed words. These findings reveal how feedback errors are represented within the speech motor cortex, and suggest strategies for leveraging these additional cognitive signals to improve neuroprostheses.

neuroscience↗

bioRxiv2024

Representation of Verbal Thought in Motor Cortex and Implications for Speech Neuroprostheses

Speech brain-computer interfaces show great promise in restoring communication for people who can no longer speak1-3, but have also raised privacy concerns regarding their potential to decode private verbal thought4-6. Using multi-unit recordings in three participants with dysarthria, we studied the representation of inner speech in the motor cortex. We found a robust neural encoding of inner speech, such that individual words and continuously imagined sentences could be decoded in real-time This neural representation was highly correlated with overt and perceived speech. We investigated the possibility of "eavesdropping" on private verbal thought, and demonstrated that verbal memory can be decoded during a non-speech task. Nevertheless, we found a neural "overtness" dimension that can help to avoid any unintentional decoding. Together, these results demonstrate the strong representation of verbal thought in the motor cortex, and highlight important design considerations and risks that must be addressed as speech neuroprostheses become more widespread.

neuroscience↗

bioRxiv2024

A mosaic of whole-body representations in human motor cortex

Understanding how the body is represented in motor cortex is key to understanding how the brain controls movement. The precentral gyrus (PCG) has long been thought to contain largely distinct regions for the arm, leg and face (represented by the "motor homunculus"). However, mounting evidence has begun to reveal a more intermixed, interrelated and broadly tuned motor map. Here, we revisit the motor homunculus using microelectrode array recordings from 20 arrays that broadly sample PCG across 8 individuals, creating a comprehensive map of human motor cortex at single neuron resolution. We found whole-body representations throughout all sampled points of PCG, contradicting traditional leg/arm/face boundaries. We also found two speech-preferential areas with a broadly tuned, orofacial-dominant area in between them, previously unaccounted for by the homunculus. Throughout PCG, movement representations of the four limbs were interlinked, with homologous movements of different limbs (e.g., toe curl and hand close) having correlated representations. Our findings indicate that, while the classic homunculus aligns with each areas preferred body region at a coarse level, at a finer scale, PCG may be better described as a mosaic of functional zones, each with its own whole-body representation.

neuroscience↗

bioRxiv2024

Few-shot Algorithms for Consistent Neural Decoding (FALCON) Benchmark

Intracortical brain-computer interfaces (iBCIs) can restore movement and communication abilities to individuals with paralysis by decoding their intended behavior from neural activity recorded with an implanted device. While this activity yields high-performance decoding over short timescales, neural data are often nonstationary, which can lead to decoder failure if not accounted for. To maintain performance, users must frequently recalibrate decoders, which requires the arduous collection of new neural and behavioral data. Aiming to reduce this burden, several approaches have been developed that either limit recalibration data requirements (few-shot approaches) or eliminate explicit recalibration entirely (zero-shot approaches). However, progress is limited by a lack of standardized datasets and comparison metrics, causing methods to be compared in an ad hoc manner. Here we introduce the FALCON benchmark suite (Few-shot Algorithms for COnsistent Neural decoding) to standardize evaluation of iBCI robustness. FALCON curates five datasets of neural and behavioral data that span movement and communication tasks to focus on behaviors of interest to modern-day iBCIs. Each dataset includes calibration data, optional few-shot recalibration data, and private evaluation data. We implement a flexible evaluation platform which only requires user-submitted code to return behavioral predictions on unseen data. We also seed the benchmark by applying baseline methods spanning several classes of possible approaches. FALCON aims to provide rigorous selection criteria for robust iBCI decoders, easing their translation to real-world devices. https://snel-repo.github.io/falcon/

neuroscience↗

bioRxiv2024

Gesture encoding in human left precentral gyrus neuronal ensembles

Understanding the cortical activity patterns driving dexterous upper limb motion has the potential to benefit a broad clinical population living with limited mobility through the development of novel brain-computer interface (BCI) technology. The present study examines the activity of ensembles of motor cortical neurons recorded using microelectrode arrays in the dominant hemisphere of two BrainGate clinical trial participants with cervical spinal cord injury as they attempted to perform a set of 48 different hand gestures. Although each participant displayed a unique organization of their respective neural latent spaces, it was possible to achieve classification accuracies of [~]70% for all 48 gestures (and [~]90% for sets of 10). Our results show that single unit ensemble activity recorded in a single hemisphere of human precentral gyrus has the potential to generate a wide range of gesture-related signals across both hands, providing an intuitive and diverse set of potential command signals for intracortical BCI use.

neuroscience↗

bioRxiv2024

Digital pathology assessment of kidney glomerular filtration barrier ultrastructure in an animal model of podocytopathy

BackgroundTransmission electron microscopy (TEM) images can visualize kidney glomerular filtration barrier ultrastructure, including the glomerular basement membrane (GBM) and podocyte foot processes (PFP). Podocytopathy is associated with glomerular filtration barrier morphological changes observed experimentally and clinically by measuring GBM or PFP width. However, these measurements are currently performed manually. This limits research on podocytopathy disease mechanisms and therapeutics due to labor intensiveness and inter-operator variability. MethodsWe developed a deep learning-based digital pathology computational method to measure GBM and PFP width in TEM images from the kidneys of Integrin-Linked Kinase (ILK) podocyte-specific conditional knockout (cKO) mouse, an animal model of podocytopathy, compared to wild-type (WT) control mouse. We obtained TEM images from WT and ILK cKO littermate mice at 4 weeks old. Our automated method was composed of two stages: a U-Net model for GBM segmentation, followed by an image processing algorithm for GBM and PFP width measurement. We evaluated its performance with a 4-fold cross-validation study on WT and ILK cKO mouse kidney pairs. ResultsMean (95% confidence interval) GBM segmentation accuracy, calculated as Jaccard index, was 0.73 (0.70-0.76) for WT and 0.85 (0.83-0.87) for ILK cKO TEM images. Automated and manual GBM width measurements were similar for both WT (p=0.49) and ILK cKO (p=0.06) specimens. While automated and manual PFP width measurements were similar for WT (p=0.89), they differed for ILK cKO (p<0.05) specimens. WT and ILK cKO specimens were morphologically distinguishable by manual GBM (p<0.05) and PFP (p<0.05) width measurements. This phenotypic difference was reflected in the automated GBM (p<0.05) more than PFP (p=0.06) widths. ConclusionsThese results suggest that certain automated measurements enabled via deep learning-based digital pathology tools could distinguish healthy kidneys from those with podocytopathy. Our proposed method provides high-throughput, objective morphological analysis and could facilitate podocytopathy research and translate into clinical diagnosis. Key pointsO_LIWe leveraged U-Net architecture in an algorithm to measure the widths of glomerular basement membrane and podocyte foot processes. C_LIO_LIDeep learning-based automated measurement of glomerular filtration barrier morphology has promise in podocytopathy research and diagnosis. C_LI

pathology↗

bioRxiv2024

A flexible intracortical brain-computer interface for typing using finger movements

Keyboard typing with finger movements is a versatile digital interface for users with diverse skills, needs, and preferences. Currently, such an interface does not exist for people with paralysis. We developed an intracortical brain-computer interface (BCI) for typing with attempted flexion/extension movements of three finger groups on the right hand, or both hands, and demonstrated its flexibility in two dominant typing paradigms. The first paradigm is "point-and-click" typing, where a BCI user selects one key at a time using continuous real-time control, allowing selection of arbitrary sequences of symbols. During cued character selection with this paradigm, a human research participant with paralysis achieved 30-40 selections per minute with nearly 90% accuracy. The second paradigm is "keystroke" typing, where the BCI user selects each character by a discrete movement without real-time feedback, often giving a faster speed for natural language sentences. With 90 cued characters per minute, decoding attempted finger movements and correcting errors using a language model resulted in more than 90% accuracy. Notably, both paradigms matched the state-of-the-art for BCI performance and enabled further flexibility by the simultaneous selection of multiple characters as well as efficient decoder estimation across paradigms. Overall, the high-performance interface is a step towards the wider accessibility of BCI technology by addressing unmet user needs for flexibility.

neuroscience↗

bioRxiv2024

Measuring instability in chronic human intracortical neural recordings towards stable, long-term brain-computer interfaces

Intracortical brain-computer interfaces (iBCIs) enable people with tetraplegia to gain intuitive cursor control from movement intentions. To translate to practical use, iBCIs should provide reliable performance for extended periods of time. However, performance begins to degrade as the relationship between kinematic intention and recorded neural activity shifts compared to when the decoder was initially trained. In addition to developing decoders to better handle long-term instability, identifying when to recalibrate will also optimize performance. We propose a method to measure instability in neural data without needing to label user intentions. Longitudinal data were analyzed from two BrainGate2 participants with tetraplegia as they used fixed decoders to control a computer cursor spanning 142 days and 28 days, respectively. We demonstrate a measure of instability that correlates with changes in closed-loop cursor performance solely based on the recorded neural activity (Pearson r = 0.93 and 0.72, respectively). This result suggests a strategy to infer online iBCI performance from neural data alone and to determine when recalibration should take place for practical long-term use.

neuroscience↗

bioRxiv2024

Glomerular Elasticity and Gene Expression Patterns Define Two Phases of Alport Nephropathy

Alport syndrome (AS), caused by COL4A3,4,5 mutations, leads to progressive glomerular disease and eventual kidney failure. In Col43-/- mice (C57BL/6 background), we found that increased glomerular capillary deformability (reduced Youngs modulus, E) appears 2-3 months before detectable proteinuria or elevated serum creatinine. This early change indicates that podocyte injury precedes traditional clinical markers of disease and corresponds to reduced podocyte adhesion and loss. Bulk and podocyte-enriched RNA-seq data, obtained from deconvoluting bulk RNA sequencing data, showed that starting at 4 months, endoplasmic reticulum (ER) stress and the unfolded protein response (UPR) steadily rise while extracellular matrix remodeling, inflammation, epithelial-mesenchymal transition, and maladaptive repair begin. By 7 months, pathology shifts toward widespread parenchymal fibrosis, interleukin, cytokine, and chemokine signaling, cytoskeleton disruption, metabolic failure, and podocyte dedifferentiation. Notably, administration of the chemical chaperone Tauro-Urso-Deoxcycholic Acid (TUDCA) from weaning reduced ER stress, preserved glomerular stiffness, minimized podocyte detachment and loss, normalized inflammatory, injury, and fibrotic gene expression, and halved proteinuria and serum creatinine at 7 months, preserving kidney structure. Differentially expressed podocyte enriched genes from 4-month Col43-/- (vs WT) mice were mapped to human orthologs in the NEPTUNE cohort. Four genes (CRB2, GPC6, NKD1, STX11) were associated with End Stage Renal Disease or 40% loss of eGFR (ESRD40). Collectively, our results identify these four genes and ER stress/UPR as key and treatable drivers of podocyte injury and disease progression in Alport syndrome, well before overt proteinuria, and highlight UPR activation and several genes as promising targets for disease-modifying therapies.

cell biology↗