Biology subjects

Perkins, S. M.

Publications and source records attributed to Perkins, S. M..

7 recordsLinked to original sources

bioRxiv2025

SONIC: A Benchmarking Paradigm for Brain-Computer Interfaces

Brain-computer interfaces (BCIs) can restore function for individuals with neuro-logical disorders and have the potential to transform the way people interact with digital systems. However, the development of advanced BCI applications, such as fluent speech synthesis, is dependent on the underlying information transfer capacity of the physical neural interface employed. A significant barrier to progress has been the lack of standardized, application-agnostic methods for benchmarking BCI system performance prior to clinical trials. Here, we introduce SONIC, a novel preclinical benchmarking paradigm designed to evaluate the information transfer rate (ITR) of a BCI system. This paradigm treats the brain and BCI as a noisy communication channel, where information is sent into the brain via precisely controlled sensory stimuli and read out by the neural interface. We implemented this paradigm in an ovine model by presenting rapid sequences of pure tones while recording neural activity from the primary auditory cortex with the Paradromics Connexus(C) BCI, a fully implanted system utilizing high-density intracortical micro-electrode arrays with wireless power and data transmission. A convolutional neural network was used to decode tones based on neural features. Our results demonstrate an achieved ITR of over 200 bits per second (bps), which is the highest reported BCI ITR to date. For reference, this rate exceeds the linguistic information content of human speech. This ITR is achieved with a total neural interface, filtering, and data aggregation delay of 56 milliseconds. Further analysis demonstrated that ITR remains high (> 100 bps) for the lowest total delay tested (11 ms), supporting the needs of latency-sensitive applications (e.g., direct speech synthesis). This work establishes a new benchmark for BCI performance and demonstrates that the Connexus BCI possesses the bandwidth necessary to support highly advanced applications. This benchmark provides a robust framework for preclinical BCI evaluation, enabling principled system design optimization to accelerate the translation of next-generation neurotechnology.

bioengineering↗

bioRxiv2023

Simple decoding of behavior from a complicated neural manifold

Decoders for brain-computer interfaces (BCIs) assume constraints on neural activity, chosen to reflect scientific beliefs while yielding tractable computations. Recent scientific advances suggest that the true constraints on neural activity, especially its geometry, may be quite different from those assumed by most decoders. We designed a decoder, MINT, to embrace statistical constraints that are potentially more appropriate. If those constraints are accurate, MINT should outperform standard methods that explicitly make different assumptions. Additionally, MINT should be competitive with expressive machine learning methods that can implicitly learn constraints from data. MINT performed well across tasks, suggesting its assumptions are well-matched to the data. MINT outperformed other interpretable methods in every comparison we made. MINT outperformed expressive machine learning methods in 37 of 42 comparisons. MINTs computations are simple, scale favorably with increasing neuron counts, and yield interpretable quantities such as data likelihoods. MINTs performance and simplicity suggest it may be a strong candidate for many BCI applications.

bioengineering↗

bioRxiv2021

Flexible neural control of motor units

Voluntary movement requires communication from cortex to the spinal cord, where a dedicated pool of motor units (MUs) activates each muscle. The canonical description of MU function rests upon two foundational tenets. First, cortex cannot control MUs independently but supplies each pool with a common drive. Second, MUs are recruited in a rigid fashion that largely accords with Hennemans size principle. While this paradigm has considerable empirical support, a direct test requires simultaneous observations of many MUs across diverse force profiles. We developed an isometric task that allowed stable MU recordings even during rapidly changing forces. MU activity patterns were surprisingly behavior-dependent. MU activity could not be accurately described as reflecting common drive, but could be captured by assuming multiple drives. Neuropixels probe recordings revealed that, consistent with the requirements of flexible control, the motor cortex population response displays a great many degrees of freedom. Neighboring cortical sites recruited different MUs. Thus, MU activity is flexibly controlled to meet task demands, and cortex may contribute to this ability.

neuroscience↗

bioRxiv2020

Cognitive Deficits and Altered Functional Brain Network Organization in Pediatric Brain Tumor Patients

Pediatric brain tumor survivors experience significant cognitive sequelae from their diagnosis and treatment. The exact mechanisms of cognitive injury are poorly understood, and validated predictors of long-term cognitive outcome are lacking. Large-scale, distributed brain systems provide a window into brain organization and function that may yield insight into these mechanisms and outcomes. Here, we evaluated functional network architecture, cognitive performance, and brain-behavior relationships in pediatric brain tumor patients. Patients ages 4-18 years old with diagnosis of a brain tumor underwent awake resting state fMRI during regularly scheduled clinical visits and were tested with the NIH Toolbox Cognition Battery. We observed that functional network organization was significantly altered in patients compared to age- and sex-matched healthy controls, with the integrity of the dorsal attention network particularly affected. Moreover, patients demonstrated significant impairments in multiple domains of cognitive performance, including attention. Finally, a significant amount of variance of age-adjusted total composite scores from the Toolbox was explained by changes in segregation between the dorsal attention and default mode networks. Our results suggest that changes in functional network organization may provide insight into long-term changes in cognitive function in pediatric brain tumor patients.

neuroscience↗

bioRxiv2019

Robust neural control of virtual locomotion enabled by a novel decoding strategy

Brain-machine interfaces (BMIs) for reaching have enjoyed continued performance improvements, yet there remains significant need for BMIs that control other movement classes. Recent scientific findings suggest that the intrinsic covariance structure of neural activity depends strongly on movement class, potentially necessitating different decode algorithms across classes. To address this, we developed a self-motion BMI based on cortical activity as monkeys performed non-reaching arm movements: cycling a hand-held pedal to progress along a virtual track. Unlike during reaching, we found no high-variance dimensions that directly correlated with to-be-decoded variables. Yet we could decode a single variable - self-motion - by non-linearly leveraging structure that spanned many high-variance neural dimensions. Resulting online BMI-control success rates approached those during manual control. These findings make two broad points regarding how to build decode algorithms that harmonize with the empirical structure of neural activity in motor cortex. First, even when decoding from the same cortical region (e.g., arm-related motor cortex) different movement classes may need to employ very different strategies. Although correlations between neural activity and hand velocity are prominent during reaching tasks, they are not a fundamental property of motor cortex and cannot be counted on to be present in general. Second, although one generally desires a low-dimensional readout, it can be beneficial to leverage a multi-dimensional high-variance subspace. Fully embracing this approach requires highly non-linear approaches tailored to the task at hand, but can produce near-native levels of performance. Significance StatementMany BMI decoders have been constructed for controlling movements normally performed with the arm. Yet it is unclear how these will function beyond the reach-like scenarios where they were developed. Existing decoders implicitly assume that neural covariance structure, and correlations with to-be-decoded kinematic variables, will be largely preserved across tasks. We find that the correlation between neural activity and hand kinematics, a feature typically exploited when decoding reach-like movements, is essentially absent during another task performed with the arm: cycling through a virtual environment. Nevertheless, the use of a different strategy, one focused on leveraging the highest-variance neural signals, supported high performance real-time BMI control.

neuroscience↗

bioRxiv2019

Neural trajectories in the supplementary motor area and primary motor cortex exhibit distinct geometries, compatible with different classes of computation

The supplementary motor area (SMA) is believed to contribute to higher-order aspects of motor control. To examine this contribution, we employed a novel cycling task and leveraged an emerging strategy: testing whether population trajectories possess properties necessary for a hypothesized class of computations. We found that, at the single-neuron level, SMA exhibited multiple response features absent in M1. We hypothesized that these diverse features might contribute, at the population level, to avoidance of population trajectory divergence - ensuring that two trajectories never followed the same path before separating. Trajectory divergence was indeed avoided in SMA but not in M1. Network simulations confirmed that low trajectory divergence is necessary when guidance of future action depends upon internally tracking contextual factors. Furthermore, the empirical trajectory geometry - helical in SMA versus elliptical in M1 - was naturally reproduced by networks that did, versus did not, internally track context.

neuroscience↗

bioRxiv2018

Identification of circulating protein biomarkers for pancreatic cancer cachexia

BackgroundOver 80% of patients with pancreatic ductal adenocarcinoma (PDAC) suffer from cachexia, characterized by severe muscle and fat loss. Although various model systems have improved our understanding of cachexia, translating the findings to human cachexia has remained a challenge. In this study, our objectives were to i) identify circulating protein biomarkers using serum for human PDAC cachexia, (ii) identify the ontological functions of the identified biomarkers and (iii) identify new pathways associated with human PDAC cachexia by performing protein co-expression analysis.\n\nMethodsSerum from 30 patients with PDAC was collected. Body composition measurements of skeletal muscle index (SMI), skeletal muscle density (SMD), total adipose index (TAI) were obtained from computed tomography scans (CT). Cancer associated weight loss (CAWL), an ordinal classification of history of weight loss and body mass index (BMI) was obtained from medical record. Serum protein profiles and concentrations were generated using SOMAscan, a quantitative aptamer-based assay. Ontological analysis of the proteins correlated with clinical variables (r[≥] 0.5 and p<0.05) was performed using DAVID Bioinformatics. Protein co-expression analysis was determined using pairwise Spearmans correlation.\n\nResultsOverall, 111 proteins of 1298 correlated with these clinical measures, 48 proteins for CAWL, 19 for SMI, 14 for SMD, and 30 for TAI. LYVE1, a homolog of CD44 implicated in tumor metastasis, was the top CAWL-associated protein (r= 0.67, p=0.0001). Other proteins such as INHBA, MSTN/GDF11, and PIK3R1 strongly correlated with CAWL. Proteins correlated with cachexia included those associated with proteolysis, acute inflammatory response, as well as B cell and T cell activation. Protein co-expression analysis identified networks such as activation of immune related pathways such as B-cell signaling, Th1 and Th2 pathways, natural killer cell signaling, IL6 signaling, and mitochondrial dysfunction.\n\nConclusionTaken together, these data both identify immune system molecules and additional secreted factors and pathways not previously associated with PDAC and confirm the activation of previously identified pathways. Identifying altered secreted factors in serum of PDAC patients may assist in developing minimally invasive laboratory tests for clinical cachexia as well as identifying new mediators.

cancer biology↗