About

Joshua I Glaser is an Assistant Professor of Neurology at Northwestern University Feinberg School of Medicine, specializing in Comprehensive Neurology. He is also affiliated with the McCormick School of Engineering. His professional profile indicates involvement in clinical and translational sciences, with a focus on neurology research. He is associated with the Northwestern University Clinical and Translational Sciences Institute (NUCATS). Further details about his specific research interests, background, or key contributions are not provided in the page text.

Research topics

• Computer Science
• Neuroscience
• Anatomy
• Biology
• Medicine
• Engineering
• Physics

Selected publications

• Flexible integration of corollary discharge and sensory feedback signals in somatosensory cortex

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-04-04

  articleOpen accessSenior authorCorresponding

  Motor control depends on the continuous integration of motor and sensory signals to maintain accurate estimates of body state, yet neural evidence for this integration remains elusive. Here, we investigated the interaction of motor corollary discharge and proprioceptive feedback signals in area 2 of monkey somatosensory cortex during voluntary and externally-perturbed reaching tasks. Though single neurons had mixed responses to corollary discharge and sensory feedback, we disentangled these signals at the population level to discover they occupy approximately orthogonal subspaces. Integrating information across these subspaces enabled accurate body state estimation prior to feedback arrival during voluntary movements. Moreover, the orthogonal population geometry of corollary discharge and sensory feedback enabled cancellation of movement-related signals to improve the decoding of external perturbations. Together, these results identified orthogonality as a population-level coding strategy for flexible integration of motor and sensory signals to support multiple distinct computations.

  PublisherOA PDFDOI

• Multi-area activity in mouse motor cortex associated with one- and two-handed oromanual dexterity

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-04-16

  articleOpen access

  Cortical dynamics during goal-directed dexterous hand movements are mainly understood from paradigms involving use of the contralateral hand. To study how movement-related activity changes when the ipsilateral or both hands are used, we exploited a natural form of rodent manual dexterity - food handling - that rodents can perform uni- or bimanually. We sampled kilohertz 3D kinematics as mice used either or both hands to manipulate food, while recording spiking activity in forelimb primary (fl-M1) and secondary (fl-M2) motor cortices, and in a lateral oral and manual (LOM) motor cortex area implicated in oromanual food handling. Unit- and population-level analyses showed that activity in fl-M1 and fl-M2 depended on both laterality (ipsi- vs contralateral) and "manuality" (uni- vs bimanual), with few differences between the two areas. By comparison, activity in LOM was largely laterality- and manuality-invariant. These results demonstrate how activity in multiple areas of mouse motor cortex varies as the same task is performed unimanually with either hand or bimanually with both. Our findings support a model in which fl-M1 and fl-M2 maintain separable information about both forelimbs for bimanual coordination, while LOM encodes ingestion-related forelimb parameters necessary for oromanual coordination.

  PublisherOA PDFDOI

• 487. Applying Mixture of Linear Regression Model for Identifying Depression Subtypes From Symptom Severity and MRI Brain Volumetric Data

  Biological Psychiatry · 2026-04-25

  article
  PublisherDOI

• Constraining inference of across-region interactions using neural activity perturbations

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-11-21

  preprintOpen accessSenior authorCorresponding

  Functional interactions between brain regions are often inferred from multi-region models fit to neural activity recorded in behaving animals. Here, we show that inference of across-region interactions is hindered by the wide breadth of model fits consistent with naturally occurring neural activity. In contrast, models fit to activity that includes region-wide activity perturbations provide well-constrained estimates of across-region interactions; simulations suggest these estimates can be accurate.

  PublisherDOI

• Active Dissociation of Intracortical Spiking and High Gamma Activity

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-07-11

  preprintOpen access

  Cortical high gamma activity (HGA) is used in many scientific investigations, yet its biophysical source is a matter of debate. Two leading hypotheses are that HGA predominantly represents summed postsynaptic potentials or-more commonly- predominantly represents summed local spikes. If the latter were true, the nearest neurons to an electrode should contribute most to HGA recorded on that electrode. We trained subjects to decouple spiking from HGA on a single electrode using a brain-machine interface. Their ability to decouple them indicated that HGA is not primarily generated by summed local spiking. Instead, HGA correlated with neuronal population co-firing of neurons that were widely distributed across millimeters. The neuronal spikes that contributed more to this co-firing also contributed more to, and preceded, spike-triggered HGA. These results suggest that HGA arises predominantly from summed postsynaptic potentials triggered by synchronous co-firing of widely distributed neurons.

  PublisherOA PDFDOI

• Decoding speech intent from non-frontal cortical areas

  Journal of Neural Engineering · 2025-01-14 · 2 citations

  articleOpen access

  Abstract Objective . Brain machine interfaces (BMIs) that can restore speech have predominantly focused on decoding speech signals from the speech motor cortices. A few studies have shown some information outside the speech motor cortices, such as in parietal and temporal lobes, that also may be useful for BMIs. The ability to use information from outside the frontal lobe could be useful not only for people with locked-in syndrome, but also to people with frontal lobe damage, which can cause nonfluent aphasia or apraxia of speech. However, temporal and parietal lobes are predominantly involved in perceptive speech processing and comprehension. Therefore, to be able to use signals from these areas in a speech BMI, it is important to ascertain that they are related to production. Here, using intracranial recordings, we sought evidence for whether, when and where neural information related to speech intent could be found in the temporal and parietal cortices Approach . Using intracranial recordings, we examined neural activity across temporal and parietal cortices to identify signals associated with speech intent. We employed causal information to distinguish speech intent from resting states and other language-related processes, such as comprehension and working memory. Neural signals were analyzed for their spatial distribution and temporal dynamics to determine their relevance to speech production. Main results . Causal information enabled us to distinguish speech intent from resting state and other processes involved in language processing or working memory. Information related to speech intent was distributed widely across the temporal and parietal lobes, including superior temporal, medial temporal, angular, and supramarginal gyri. Significance . Loss of communication due to neurological diseases can be devastating. While speech BMIs have made strides in decoding speech from frontal lobe signals, our study reveals that the temporal and parietal cortices contain information about speech production intent that can be causally decoded prior to the onset of voice. This information is distributed across a large network. This information can be used to improve current speech BMIs and potentially expand the patient population for speech BMIs to include people with frontal lobe damage from stroke or traumatic brain injury.

  PublisherDOI

• Identifying Interpretable Latent Factors with Sparse Component Analysis

  bioRxiv (Cold Spring Harbor Laboratory) · 2024-02-06 · 7 citations

  preprintOpen accessSenior authorCorresponding

  Abstract In many neural populations, the computationally relevant signals are posited to be a set of ‘latent factors’ – signals shared across many individual neurons. Understanding the relationship between neural activity and behavior requires the identification of factors that reflect distinct computational roles. Methods for identifying such factors typically require supervision, which can be suboptimal if one is unsure how (or whether) factors can be grouped into distinct, meaningful sets. Here, we introduce Sparse Component Analysis (SCA), an unsupervised method that identifies interpretable latent factors. SCA seeks factors that are sparse in time and occupy orthogonal dimensions. With these simple constraints, SCA facilitates surprisingly clear parcellations of neural activity across a range of behaviors. We applied SCA to motor cortex activity from reaching and cycling monkeys, single-trial imaging data from C. elegans , and activity from a multitask artificial network. SCA consistently identified sets of factors that were useful in describing network computations.

  PublisherOA PDFDOI

• Neural dynamics hierarchy in motor cortex and striatum across naturalistic behaviors

  bioRxiv (Cold Spring Harbor Laboratory) · 2024-12-17 · 1 citations

  preprintOpen access

  Abstract Mammals perform a wide range of movements actuated by diverse patterns of muscle activity. Primary motor cortex (M1) and striatum are implicated in controlling these movements, but how their activity dynamics are organized to accommodate such diversity is poorly understood. We developed a paradigm that enabled us to investigate neural dynamics across diverse motor behaviors in mice. In contrast to existing views, we found neither behavior-specific nor behavior- invariant organization in single-neuron activity, population-level covariation, and muscle activity correlation. Instead, the similarity of activity dynamics between behaviors varied differentially across behavior pairs, forming a hierarchical organization. The same hierarchical organization was shared between M1 and striatum, despite stronger muscle activity correlation in M1 and greater behavior specificity in striatum. Network modeling indicated that striatal activity is sufficient to drive hierarchical dynamics in muscle pattern-generating circuits. These hierarchical dynamics may reflect a tradeoff between behavioral specialization and generalization in motor system function.

  PublisherOA PDFDOI

• Switching Autoregressive Low-rank Tensor Models

  2023-01-01

  article1st authorCorresponding
  PublisherDOI

• Switching Autoregressive Low-rank Tensor Models

  arXiv (Cornell University) · 2023-06-05 · 1 citations

  preprintOpen access

  An important problem in time-series analysis is modeling systems with time-varying dynamics. Probabilistic models with joint continuous and discrete latent states offer interpretable, efficient, and experimentally useful descriptions of such data. Commonly used models include autoregressive hidden Markov models (ARHMMs) and switching linear dynamical systems (SLDSs), each with its own advantages and disadvantages. ARHMMs permit exact inference and easy parameter estimation, but are parameter intensive when modeling long dependencies, and hence are prone to overfitting. In contrast, SLDSs can capture long-range dependencies in a parameter efficient way through Markovian latent dynamics, but present an intractable likelihood and a challenging parameter estimation task. In this paper, we propose switching autoregressive low-rank tensor (SALT) models, which retain the advantages of both approaches while ameliorating the weaknesses. SALT parameterizes the tensor of an ARHMM with a low-rank factorization to control the number of parameters and allow longer range dependencies without overfitting. We prove theoretical and discuss practical connections between SALT, linear dynamical systems, and SLDSs. We empirically demonstrate quantitative advantages of SALT models on a range of simulated and real prediction tasks, including behavioral and neural datasets. Furthermore, the learned low-rank tensor provides novel insights into temporal dependencies within each discrete state.

  PublisherOA PDFDOI

Recent grants

• NIH Grant F31EY025532

  NIH · $109k · 2018

Frequent coauthors

• Konrad P. Körding

  100 shared

• Patrick N. Lawlor

  Children's Hospital of Philadelphia

  32 shared

• Edward S. Boyden

  McGovern Institute for Brain Research

  27 shared

• Lee E. Miller

  Shirley Ryan AbilityLab

  23 shared

• Pavan Ramkumar

  16 shared

• George M. Church

  Harvard–MIT Division of Health Sciences and Technology

  16 shared

• John P. Cunningham

  Columbia University

  14 shared

• Daniel K. Wood

  University of the South Pacific

  14 shared

Education

• PhD, Interdepartmental Neuroscience Program

  Northwestern University - Chicago

  2018

• BS, Physics, Mathematics

  University of Illinois at Urbana-Champaign

  2011