Biology subjects

Kamdar, F.

Publications and source records attributed to Kamdar, F..

12 recordsLinked to original sources

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↗

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

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

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

HCM-associated mutations in MYH6/7 drive pathologic expression of TGF-β1 in cardiomyocytes within weeks of developmental specification

Hypertrophic cardiomyopathy (HCM) is characterized by myocyte hypertrophy, sarcomere disarray, and myocardial fibrosis, leading to significant morbidity and mortality. As the most common inherited cardiomyopathy, HCM largely results from mutations in sarcomeric protein genes. Current treatments for HCM primarily focus on alleviating late-stage symptoms, with a critical gap in the detailed understanding of early-stage deficiencies that drive disease progression. We recently showed, in monolayers of cardiomyocytes derived from human induced pluripotent stem cells (hiPSCs) with MYH7 R723C and MYH6 R725C mutations, altered expression of several extracellular matrix (ECM)-related genes with associated defects in cardiomyocyte-ECM adhesion. To better evaluate the cardiomyocyte-ECM interface and pathological ECM dynamics in early-stage HCM, here we adopted a 3D engineered heart tissue (EHT) model containing both cardiomyocytes and fibroblasts, the primary contributor to ECM remodeling. Mutant EHTs showed aberrant cardiomyocyte distribution, augmented calcium handling, and force generation compared to controls. Altered proteoglycan deposition and increased phosphorylated focal adhesion kinase (pFAK) further indicated changes in ECM composition and connectivity. Elevated transforming growth factor beta-1 (TGF-{beta}1) secretion and a higher proportion of activated fibroblasts were identified in mutant EHTs, along with sustained TGF-{beta}1 transcription specifically in mutant cardiomyocytes. Remarkably, blocking TGF-{beta}1 receptor signaling reduced fibroblast activation and contraction force to control levels. This study underscores the early interplay of mutant hiPSC-CMs with fibroblasts, wherein mutant cardiomyocytes initiate fibroblast activation via TGF-{beta}1 overexpression, independent of the immune system. These findings provide a promising foundation for developing and implementing novel strategies to treat HCM well before the manifestation of clinically detectable fibrosis and cardiac dysfunction.

bioengineering↗

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↗

bioRxiv2023

Pseudo-linear Summation explains Neural Geometry of Multi-finger Movements in Human Premotor Cortex

How does the motor cortex combine simple movements (such as single finger flexion/extension) into complex movements (such hand gestures or playing piano)? Motor cortical activity was recorded using intracortical multi-electrode arrays in two people with tetraplegia as they attempted single, pairwise and higher order finger movements. Neural activity for simultaneous movements was largely aligned with linear summation of corresponding single finger movement activities, with two violations. First, the neural activity was normalized, preventing a large magnitude with an increasing number of moving fingers. Second, the neural tuning direction of weakly represented fingers (e.g. middle) changed significantly as a result of the movement of other fingers. These deviations from linearity resulted in non-linear methods outperforming linear methods for neural decoding. Overall, simultaneous finger movements are thus represented by the combination of individual finger movements by pseudo-linear summation.

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↗

bioRxiv2023

A high-performance speech neuroprosthesis

Speech brain-computer interfaces (BCIs) have the potential to restore rapid communication to people with paralysis by decoding neural activity evoked by attempted speaking movements into text1,2 or sound3,4. Early demonstrations, while promising, have not yet achieved accuracies high enough for communication of unconstrainted sentences from a large vocabulary1-7. Here, we demonstrate the first speech-to-text BCI that records spiking activity from intracortical microelectrode arrays. Enabled by these high-resolution recordings, our study participant, who can no longer speak intelligibly due amyotrophic lateral sclerosis (ALS), achieved a 9.1% word error rate on a 50 word vocabulary (2.7 times fewer errors than the prior state of the art speech BCI2) and a 23.8% word error rate on a 125,000 word vocabulary (the first successful demonstration of large-vocabulary decoding). Our BCI decoded speech at 62 words per minute, which is 3.4 times faster than the prior record for any kind of BCI8 and begins to approach the speed of natural conversation (160 words per minute9). Finally, we highlight two aspects of the neural code for speech that are encouraging for speech BCIs: spatially intermixed tuning to speech articulators that makes accurate decoding possible from only a small region of cortex, and a detailed articulatory representation of phonemes that persists years after paralysis. These results show a feasible path forward for using intracortical speech BCIs to restore rapid communication to people with paralysis who can no longer speak.

neuroscience↗

bioRxiv2022

Myosin Heavy Chain Converter Domain Mutations Drive Early-Stage Changes in Extracellular Matrix Dynamics in Hypertrophic Cardiomyopathy

More than 60% of hypertrophic cardiomyopathy (HCM)-causing mutations are found in the gene loci encoding cardiac myosin-associated proteins including myosin heavy chain (MHC) and myosin binding protein C (MyBP-C). Moreover, patients with more than one independent HCM mutation may be at increased risk for more severe disease expression and adverse outcomes. However detailed mechanistic understanding, especially at early stages of disease progression, is limited. To identify early-stage HCM triggers, we generated single (MYH7 c.2167C>T [R723C] with a known pathogenic significance in the MHC converter domain) and double (MYH7 c.2167C>T [R723C]; MYH6 c.2173C>T [R725C] with unknown significance) myosin gene mutations in human induced pluripotent stem cells (hiPSCs) using a base-editing strategy. Cardiomyocytes (CMs) derived from hiPSCs with either single or double mutation exhibited phenotypic characteristics consistent with later-stage HCM including hypertrophy, multinucleation, altered calcium handling, metabolism and arrhythmia. We then probed mutant CMs at time points prior to the detection of known HCM characteristics. We found MYH7/MYH6 dual mutation dysregulated extracellular matrix (ECM) remodeling, altered integrin expression, and interrupted cell-ECM adhesion by limiting the formation of focal adhesions. These results point to a new phenotypic feature of early-stage HCM and reveal novel therapeutic avenues aimed to delay or prohibit disease onset.

bioengineering↗