Biology subjects

Miller, K. J.

Publications and source records attributed to Miller, K. J..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Feasibility of Stereo EEG Based Brain Computer Interfacing in An Adult and Pediatric Cohort

IntroductionStereoelectroencephalography (sEEG) is a mesoscale intracranial monitoring method which records from the brain volumetrically with depth electrodes. Implementation of sEEG in BCI has not been well-described across a diverse patient cohort. MethodsAcross eighteen subjects, channels with high frequency broadband (HFB, 65-115Hz) power increases during hand, tongue, or foot movements during a motor screening task were provided real-time feedback based on these HFB power changes to control a cursor on a screen. ResultsSeventeen subjects established successful control of the overt motor BCI, but only nine were able to control imagery BCI with[≥] 80% accuracy. In successful imagery BCI, HFB power in the two target conditions separated into distinct subpopulations, which appear to engage unique subnetworks of the motor cortex compared to cued movement or imagery alone. ConclusionsEEG-based motor BCI utilizing overt movement and kinesthetic imagery is robust across patient ages and cortical regions with substantial differences in learning proficiency between real or imagined movement.

bioengineering↗

bioRxiv2024

Bred for affection: The canine anterior ectosylvian gyrus responds selectively to social reinforcement

Studying mammalian brain function aids our understanding of human brain evolution. We implanted a beagle with a prototype human neuromodulation platform that measures activity from the brain surface. One year later, a set of simple sensory tasks was performed, finding visual and somatosensory representation in the canine homologs of the expected areas in humans. Surprisingly, the canine anterior ectosylvian gyrus, which is anatomically homologous to human receptive speech areas, was selectively active during independent social reinforcement tasks. This suggests that human speech understanding may have evolved from more general mammalian brain structures that are specialized for social reinforcement.

animal behavior and cognition↗

bioRxiv2024

Functional Mapping of Movement and Speech Using Task-Based Electrophysiological Changes in Stereoelectroencephalography

IntroductionStereoelectroencephalography (sEEG) has become the predominant method for intracranial seizure localization. When imaging, semiology, and scalp EEG are not in full agreement or definitively localizing, implanted sEEG recordings are used to test candidate seizure onset zones (SOZs). Discovered SOZs may then be targeted for resection, laser ablation, or neurostimulation. If a SOZ is eloquent, resection and ablation are both contraindicated, so identifying functional representation is crucial for therapeutic decision making. ObjectiveWe present a novel functional brain mapping technique that utilizes task-based electrophysiological changes in sEEG during behavioral tasks and test this in pediatric and adult patients. MethodssEEG was recorded in twenty patients with epilepsy, aged 6-39 (12 female, 18 of 20 patients < 21 years old), who underwent implanted monitoring to identify seizure onset. Each performed 1) visually cued simple repetitive movements of the hand, foot, or tongue while electromyography was recorded, and 2) simple picture naming or verb generation speech tasks while audio was recorded. Broadband changes in the power spectrum of the sEEG were compared between behavior and rest. ResultsElectrophysiological functional mapping of movement and/or speech areas was completed in all 20 patients. Eloquent representation was identified in both cortex and white matter, and generally corresponded to classically described functional anatomic organization as well as other clinical mapping results. Robust maps of brain activity were identified in healthy brain, regions of developmental or acquired structural abnormality, and SOZs. ConclusionTask based electrophysiological mapping using broadband changes in the sEEG signal reliably identifies movement and speech representation in pediatric and adult epilepsy patients.

neuroscience↗

bioRxiv2024

Dynamic reinforcement learning reveals time-dependent shifts in strategy during reward learning.

Different brain systems have been hypothesized to subserve multiple "experts" that compete to generate behavior. In reinforcement learning, two general processes, one model-free (MF) and one model-based (MB), are often modeled as a mixture of agents (MoA) and hypothesized to capture differences between automaticity vs. deliberation. However, shifts in strategy cannot be captured by a static MoA. To investigate such dynamics, we present the mixture-of-agents hidden Markov model (MoA-HMM), which simultaneously learns inferred action values from a set of agents and the temporal dynamics of underlying "hidden" states that capture shifts in agent contributions over time. Applying this model to a multi-step, reward-guided task in rats reveals a progression of within-session strategies: a shift from initial MB exploration to MB exploitation, and finally to reduced engagement. The inferred states predict changes in both response time and OFC neural encoding during the task, suggesting that these states are capturing real shifts in dynamics.

neuroscience↗

bioRxiv2024

ER-detect: a pipeline for robust detection of early evoked responses in BIDS-iEEG electrical stimulation data

Human brain connectivity can be measured in different ways. Intracranial EEG (iEEG) measurements during single pulse electrical stimulation provide a unique way to assess the spread of electrical information with millisecond precision. To provide a robust workflow to process these cortico-cortical evoked potential (CCEP) data and detect early evoked responses in a fully automated and reproducible fashion, we developed Early Response (ER)-detect. ER-detect is an open-source Python package and Docker application to preprocess BIDS structured iEEG data and detect early evoked CCEP responses. ER-detect can use three response detection methods, which were validated against 14 manually annotated CCEP datasets from two different sites by four independent raters. Results showed that ER-detects automated detection performed on par with the inter-rater reliability (Cohens Kappa of [~]0.6). Moreover, ER-detect was optimized for processing large CCEP datasets, to be used in conjunction with other connectomic investigations. ER-detect provides a highly efficient standardized workflow such that iEEG-BIDS data can be processed in a consistent manner and enhance the reproducibility of CCEP based connectivity results.

neuroscience↗

bioRxiv2023

Cognitive Model Discovery via Disentangled RNNs

Computational cognitive models are a fundamental tool in behavioral neuroscience. They instantiate in software precise hypotheses about the cognitive mechanisms underlying a particular behavior. Constructing these models is typically a difficult iterative process that requires both inspiration from the literature and the creativity of an individual researcher. Here, we adopt an alternative approach to learn parsimonious cognitive models directly from data. We fit behavior data using a recurrent neural network that is penalized for carrying information forward in time, leading to sparse, interpretable representations and dynamics. When fitting synthetic behavioral data from known cognitive models, our method recovers the underlying form of those models. When fit to laboratory data from rats performing a reward learning task, our method recovers simple and interpretable models that make testable predictions about neural mechanisms.

neuroscience↗

bioRxiv2023

Predictive and Interpretable: Combining Artificial Neural Networks and Classic Cognitive Models to Understand Human Learning and Decision Making

Quantitative models of behavior are a fundamental tool in cognitive science. Typically, models are hand-crafted to implement specific cognitive mechanisms. Such "classic" models are interpretable by design, but may provide poor fit to experimental data. Artificial neural networks (ANNs), on the contrary, can fit arbitrary datasets at the cost of opaque mechanisms. Here, we adopt a hybrid approach, combining the predictive power of ANNs with the interpretability of classic models. We apply this approach to Reinforcement Learning (RL), beginning with classic RL models and replacing their components one-by-one with ANNs. We find that hybrid models can provide similar fit to fully-general ANNs, while retaining the interpretability of classic cognitive models: They reveal reward-based learning mechanisms in humans that are strikingly similar to classic RL. They also reveal mechanisms not contained in classic models, including separate rewardblind mechanisms, and the specific memory contents relevant to reward-based and reward-blind mechanisms.

neuroscience↗

bioRxiv2023

Flexible Multi-Step Hypothesis Testing of Human ECoG Data using Cluster-based Permutation Tests with GLMEs

BackgroundTime series analysis is critical for understanding brain signals and their relationship to behavior and cognition. Cluster-based permutation tests (CBPT) are commonly used to analyze a variety of electrophysiological signals including EEG, MEG, ECoG, and sEEG data without a priori assumptions about specific temporal effects. However, two major limitations of CBPT include the inability to directly analyze experiments with multiple fixed effects and the inability to account for random effects (e.g. variability across subjects). Here, we propose a flexible multi-step hypothesis testing strategy using CBPT with Linear Mixed Effects Models (LMEs) and Generalized Linear Mixed Effects Models (GLMEs) that can be applied to a wide range of experimental designs and data types. MethodsWe first evaluate the statistical robustness of LMEs and GLMEs using simulated data distributions. Second, we apply a multi-step hypothesis testing strategy to analyze ERPs and broadband power signals extracted from human ECoG recordings collected during a simple image viewing experiment with image category and novelty as fixed effects. Third, we assess the statistical power differences between analyzing signals with CBPT using LMEs compared to CBPT using separate t-tests run on each fixed effect through simulations that emulate broadband power signals. Finally, we apply CBPT using GLMEs to high-gamma burst data to demonstrate the extension of the proposed method to the analysis of nonlinear data. ResultsFirst, we found that LMEs and GLMEs are robust statistical models. In simple simulations LMEs produced highly congruent results with other appropriately applied linear statistical models, but LMEs outperformed many linear statistical models in the analysis of "suboptimal" data and maintained power better than analyzing individual fixed effects with separate t-tests. GLMEs also performed similarly to other nonlinear statistical models. Second, in real world human ECoG data, LMEs performed at least as well as separate t-tests when applied to predefined time windows or when used in conjunction with CBPT. Additionally, fixed effects time courses extracted with CBPT using LMEs from group-level models of pseudo-populations replicated latency effects found in individual category-selective channels. Third, analysis of simulated broadband power signals demonstrated that CBPT using LMEs was superior to CBPT using separate t-tests in identifying time windows with significant fixed effects especially for small effect sizes. Lastly, the analysis of high-gamma burst data using CBPT with GLMEs produced results consistent with CBPT using LMEs applied to broadband power data. ConclusionsWe propose a general approach for statistical analysis of electrophysiological data using CBPT in conjunction with LMEs and GLMEs. We demonstrate that this method is robust for experiments with multiple fixed effects and applicable to the analysis of linear and nonlinear data. Our methodology maximizes the statistical power available in a dataset across multiple experimental variables while accounting for hierarchical random effects and controlling FWER across fixed effects. This approach substantially improves power and accuracy leading to better reproducibility. Additionally, CBPT using LMEs and GLMEs can be used to analyze individual channels or pseudo-population data for the comparison of functional or anatomical groups of data. Highlights[bullet] Combining CBPT with GLMEs allows statistical analysis to match experimental design. [bullet]CBPT with GLMEs accounts for subject variability and hierarchical random effects. [bullet]The proposed method maintains control of type I error, type II error, and FWER. [bullet]CBPT with GLMEs can be applied to individual channels and pseudo-population data.

neuroscience↗

bioRxiv2023

Distributed representations of prediction error signals across the cortical hierarchy are synergistic

An important question concerning inter-areal communication in the cortex is whether these interactions are synergistic, i.e. brain signals can either share common information (redundancy) or they can encode complementary information that is only available when both signals are considered together (synergy). Here, we dissociated cortical interactions sharing common information from those encoding complementary information during prediction error processing. To this end, we computed co-information, an information-theoretical measure that distinguishes redundant from synergistic information among brain signals. We analyzed auditory and frontal electrocorticography (ECoG) signals in five common awake marmosets performing two distinct auditory oddball tasks and investigated to what extent event-related potentials (ERP) and broadband (BB) dynamics encoded redundant and synergistic information during auditory prediction error processing. In both tasks, we observed multiple patterns of synergy across the entire cortical hierarchy with distinct dynamics. The information conveyed by ERPs and BB signals was highly synergistic even at lower stages of the hierarchy in the auditory cortex, as well as between auditory and frontal regions. Using a brain-constrained neural network, we simulated the spatio-temporal patterns of synergy and redundancy observed in the experimental results and further demonstrated that the emergence of synergy between auditory and frontal regions requires the presence of strong, long-distance, feedback and feedforward connections. These results indicate that the distributed representations of prediction error signals across the cortical hierarchy can be highly synergistic.

neuroscience↗

bioRxiv2022

Chronic modulation of human memory and thalamic-hippocampal theta activities.

Electrical stimulation is a powerful therapeutic tool for treating neurologic and neuropsychiatric disorders. Sensing and modulating electrophysiological biomarkers of memory over extended timescales is necessary for tracking and improving memory in humans. Here, we describe results from humans in their natural home environments using a novel, investigational system enabling chronic stimulation and multi-channel recording of anterior thalamic and hippocampal local field potentials during memory tasks. Four people with focal epilepsy performed a free recall verbal memory task repeatedly for up to fifty months on a touch-screen device with wireless signal acquisition with electrophysiology and behavioral data streaming to a cloud environment. Anterior thalamic-hippocampal spectral activities in the theta frequency range were found to correlate with memory processing, to predict task performance, and to be modulated by deep brain stimulation. Our results provide a new biomarker-based technology for chronic remote tracking of memory performance and modulation of the associated neural activities. One Sentence SummaryElectrical stimulation in the anterior thalamic nuclei modulates theta frequency activities and improves human verbal memory performance chronically.

neuroscience↗

bioRxiv2022

Signatures of electrical stimulation driven network interactions in the human limbic system

Stimulation-evoked signals are starting to be used as biomarkers to indicate the state and health of brain networks. The human limbic network, often targeted for brain stimulation therapy, is involved in emotion and memory processing. Previous anatomical, neurophysiological and functional studies suggest distinct subsystems within the limbic network (Rolls, 2015). Previous studies using intracranial electrical stimulation, however, have emphasized the similarities of the evoked waveforms across the limbic network. We test whether these subsystems have distinct stimulation-driven signatures. In seven patients with drug-resistant epilepsy we stimulated the limbic system with single pulse electrical stimulation (SPES). Reliable cortico-cortical evoked potentials (CCEPs) were measured between hippocampus and the posterior cingulate cortex (PCC) and between the amygdala and the anterior cingulate cortex (ACC). However, the CCEP waveform in the PCC after hippocampal stimulation showed a unique and reliable morphology, which we term the limbic H-wave. This limbic H-wave was visually distinct and separately decoded from the amygdala to ACC waveform. Diffusion MRI data show that the measured endpoints in the PCC overlap with the endpoints of the parolfactory cingulum bundle rather than the parahippocampal cingulum, suggesting that the limbic H-wave may travel through fornix, mammillary bodies and the anterior nucleus of the thalamus (ANT). This was further confirmed by stimulating the ANT, which evoked the same limbic H-wave but with a shorter latency. Limbic subsystems have unique stimulation evoked signatures that may be used in the future to help develop stimulation therapies. Significance StatementThe limbic system is often compromised in diverse clinical conditions, such as epilepsy or Alzheimers disease, and it is important to characterize its typical circuit responses. Stimulation evoked waveforms have been used in the motor system to diagnose circuit pathology. We translate this framework to limbic subsystems using human intracranial stereo EEG (sEEG) recordings that measure deeper brain areas. Our sEEG recordings describe a stimulation evoked waveform characteristic to the memory and spatial subsystem of the limbic network that we term the limbic H-wave. The limbic H-wave follows anatomical white matter pathways from hippocampus to thalamus to the posterior cingulum and shows promise as a distinct biomarker of signaling in the human brain memory and spatial limbic network.

neuroscience↗

bioRxiv2022

Homunculus Interruptus: A motor association area in the depth of the central sulcus

Cells in the precentral gyrus of the human brain directly send signals to the periphery to generate movement and are topologically organized as a map of the body. We find that movement induced electrophysiological changes from implanted depth electrodes extend this map 3-dimensionally throughout the volume of the gyrus. Unexpectedly, this organization is interrupted by a motor association area in the depths of the central sulcus at its mid-lateral aspect that is active during many different types of movements from both sides of the body.

neuroscience↗

bioRxiv2022

Integrated human-machine interface for closed-loop stimulation using implanted and wearable devices

Recent development in implantable devices for electrical brain stimulation includes sensing and embedded computing capabilities that enable adaptive stimulation strategies. Applications include stimulation triggered by pathologic brain activity and endogenous rhythms, such as circadian rhythms. We developed and tested a system that integrates an electrical brain stimulation & sensing implantable device with embedded computing and uses a distributed system with commercial electronics, smartphone and smartwatch for patient annotations, extensive behavioral testing, and adaptive stimulation in subjects in their natural environments. The system enables precise time synchronization of the external components with the brain stimulating device and is coupled with automated analysis of continuous streaming electrophysiology synchronized with patient reports. The system leverages a real-time bi-directional interface between devices and patients with epilepsy living in their natural environment.

neuroscience↗

bioRxiv2022

Canonical Response Parameterization: Quantifying the structure of responses to single-pulse intracranial electrical brain stimulation

Single-pulse electrical stimulation in the nervous system, often called cortico-cortical evoked potential (CCEP) measurement, is an important technique to understand how brain regions interact with one another. Voltages are measured from implanted electrodes in one brain area while stimulating another with brief current impulses separated by several seconds. Historically, researchers have tried to understand the significance of evoked voltage polyphasic deflections by visual inspection, but no general-purpose tool has emerged to understand their shapes or describe them mathematically. We describe and illustrate a new technique to parameterize brain stimulation data, where voltage response traces are projected into one another using a semi-normalized dot product. The length of timepoints from stimulation included in the dot product is varied to obtain a temporal profile of structural significance, and the peak of the profile uniquely identifies the duration of the response. Using linear kernel PCA, a canonical response shape is obtained over this duration, and then single-trial traces are parameterized as a projection of this canonical shape with a residual term. Such parameterization allows for dissimilar trace shapes from different brain areas to be directly compared by quantifying cross-projection magnitudes, response duration, canonical shape projection amplitudes, signal-to-noise ratios, explained variance, and statistical significance. Artifactual trials are automatically identified by outliers in sub-distributions of cross-projection magnitude, and rejected. This technique, which we call "Canonical Response Parameterization" (CRP) dramatically simplifies the study of CCEP shapes, and may also be applied in a wide range of other settings involving event-triggered data. Author summaryWe introduce a new machine learning technique for quantifying the structure of responses to single-pulse intracranial electrical brain stimulation. This approach allows voltage response traces of very different shape to be compared with one another. A tool like this has been needed to replace the status quo, where researchers may understand their data in terms of discovered structure rather than in terms of a pre-assigned, hand-picked, feature. The method compares single-trial responses pairwise to understand if there is a reproducible shape and how long it lasts. When significant structure is identified, the shape underlying it is isolated and each trial is parameterized in terms of this shape. This simple parameterization enables quantification of statistical significance, signal-to-noise ratio, explained variance, and average voltage of the response. Differently-shaped voltage traces from any setting can be compared with any other in a succinct mathematical framework. This versatile tool to quantify single-pulse stimulation data should facilitate a blossoming in the study of brain connectivity using implanted electrodes.

neuroscience↗

bioRxiv2022

Electrical stimulation of temporal and limbic circuitry produces distinct responses in human ventral temporal cortex

The human ventral temporal cortex (VTC) is highly connected to integrate visual perceptual inputs with feedback from cognitive and emotional networks. In this study, we used electrical brain stimulation to understand how different inputs from multiple brain regions drive unique electrophysiological responses in the VTC. We recorded intracranial EEG data in 6 patients implanted with intracranial electrodes for epilepsy surgery evaluation. Pairs of electrodes were stimulated with single pulse electrical stimulation, and corticocortical evoked potential (CCEP) responses were measured at electrodes in the collateral sulcus and lateral occipitotemporal sulcus of the VTC. Using a novel unsupervised machine learning method, we uncovered 2 to 4 distinct response shapes, termed basis profile curves (BPCs), at each recording electrode in the 11 to 500 ms post-stimulation interval. CCEPs of unique shape and high amplitude were elicited following stimulation of several regions and classified into a set of four consensus BPCs across subjects. One of the consensus BPCs was primarily elicited by stimulation of the hippocampus; another by stimulation of the amygdala; a third by stimulation of lateral cortical sites, such as the middle temporal gyrus; and the final one by stimulation of multiple distributed sites. Stimulation also produced sustained high frequency power decreases and low frequency power increases that spanned multiple BPC categories. Characterizing distinct shapes in stimulation responses provides a novel description of connectivity to the VTC and reveals significant differences in input from cortical and limbic structures. SIGNIFICANCE STATEMENTDisentangling the numerous input influences on highly connected areas in the brain is a critical step toward understanding how different brain networks work together to produce function. Single pulse electrical stimulation is an effective tool to accomplish this goal because the shapes and amplitudes of signals recorded from electrodes are informative of the synaptic physiology of the stimulation-driven inputs. We focused on targets in the ventral temporal cortex because it is an area strongly implicated in visual object perception. By using a data-driven clustering algorithm, we identified anatomical regions with distinct input connectivity profiles to the ventral temporal cortex. Examining high frequency power changes revealed possible modulation of excitability at the recording site induced by electrical stimulation of connected regions.

neuroscience↗

bioRxiv2022

Modulation of the gut microbiome by novel synthetic glycans for the production of propionate and the reduction of cardiometabolic risk factors

BackgroundIncreasing evidence indicates that an altered gut microbiome participates in the development of cardiometabolic syndrome and associated risk factors, such as insulin resistance, dyslipidemia, and obesity, and that targeting the gut microbiome is a promising strategy to lower the risk for cardiometabolic diseases. Part of this reduction is mediated by specific metabolites generated by the gut microbiome. Propionate, a short-chain fatty acid (SCFA) produced from dietary glycans by certain gut microbes is known to exert multiple beneficial metabolic effects. Here, we identify KB39, a novel gut microbiome-targeting synthetic glycan selected for its strong propionate-producing capacity, and demonstrate its effects in vivo to reduce cardiometabolic disease using western diet-fed LDL receptor knock-out mice. MethodsEx vivo fermentation screening of a large library of synthetic glycan ensembles was performed using gut microbiome communities from healthy subjects and overweight patients with type 2 diabetes. A synthetic glycan identified for its high propionate-producing capacity (KB39) was then tested in vivo for effects on systemic, blood and cecal metabolic parameters in Ldlr-/- mice fed a western diet. ResultsEx vivo screening of ~600 synthetic glycans using human gut microbiota from healthy subjects and patients with type 2 diabetes identified a novel glycan (KB39) with high propionate-producing capacity that increased propionate contribution to total SCFA and propionate-producing bacterial taxa compared to negative control. In western diet-fed Ldlr-/- mice, KB39 treatment resulted in an enrichment in propiogenic bacteria and propionate biosynthetic genes in vivo and an increase in absolute and relative amounts of propionate in the cecum. This also resulted in significant decreases in serum total cholesterol, LDL-cholesterol, and insulin levels, as well as reduced hepatic triglycerides and cholesterol content compared to non-treated animals. Importantly, KB39 treatment significantly reduced atherosclerosis, liver steatosis and inflammation, upregulated hepatic expression of genes involved in fatty acid oxidation and downregulated transcriptional markers of inflammation, fibrosis and insulin resistance with only a mild lowering of body weight gain. ConclusionsOur data show that KB39, a novel synthetic glycan supporting a high propionate-producing microbiome, can reduce cardiometabolic risk factors and disease in mice and suggest this approach could be of benefit for the prevention or treatment of cardiometabolic diseases in humans. CLINICAL PERSPECTIVEWhat is new? O_LIA novel synthetic glycan, KB39, was selected from a library of compounds for its high propionate-producing capacity and beneficial effects on the human gut microbiome composition C_LIO_LIKB39 modulates the gut microbiome for high propionate production and significantly reduces cardiometabolic risk factors and disease in a murine model of cardiometabolic diseases C_LI What are the clinical implications? O_LIKB39, delivered orally, could be of benefit for the prevention or treatment of cardiometabolic diseases in humans C_LIO_LIThe efficacy of KB39 in mice compared to the clinical drug fenofibrate justifies further study in humans C_LI

microbiology↗

bioRxiv2022

The development of efficient communication in the human connectome.

The structure of the human connectome develops from childhood throughout adolescence to middle age, but how this affects the speed of neuronal signaling is not well described. To understand the maturation process of transmission speed across the connectome, we stimulated electrocorticographic (ECoG) electrode pairs in 75 human subjects of 4 to 51 years old. We then measured the latency of cortico-cortical evoked responses in other electrodes. Each year, responses got faster, until transmission speed reached a minimum latency at the age of 29-44 years old. Responses during middle age were about two times faster compared to childhood. These increases in speed were observed in both long- and short-range connections between and within frontal, central, parietal, and temporal areas. These findings indicate that long-range and short-range communication between brain regions increase in efficiency well throughout adolescence.

neuroscience↗

bioRxiv2021

The Effect of Minnelide against SARS-CoV-2 in a Murine Model

Severe acute respiratory syndrome coronavirus 2, SARS-CoV-2, is the causative agent of coronavirus disease 2019, COVID-19, and the current COVID-19 pandemic. Even as more vaccine candidates are released, more treatment options are critically needed. Here, we investigated the use of Minnelide, a water soluble pro-drug with anti-inflammatory properties, for the treatment of COVID-19. To do this, k18-hACE2 mice were infected with SARS-CoV-2 or given PBS control intranasally. The next day mice were either treated daily with low dose (0.0025mg/day) or high dose Minnelide (0.005mg/day), or given vehicle control intraperitoneal. Mice were weighed daily, and sacrificed at day 6 and 10 post-infection to analyze viral burden, cytokine response, and pathology. We observed a reduction in viral load in the lungs of Minnelide-treated mice infected with SARS-CoV-2 at day 10 post-infection compared to day 6 post-infection. All SARS-CoV-2 infected non-treated mice were moribund six days post-infection while treatment with Minnelide extended survival for both low (60% survival) and high (100% survival) dose treated mice ten days post-infection. Interestingly, cytokine analysis demonstrated a significant reduction in IL-6 (lung and heart) and D-dimer (serum) in high dose treated SARS-CoV-2 infected mice compared to mice infected with SARS-CoV-2 alone at day 6 post-infection. Additionally, histology analysis revealed that Minnelide treatment significantly improved lung pathology ten days post-infection with SARS-CoV-2 with all the mice exhibiting normal lung tissue with thin alveolar septa and no inflammatory cells. Overall, our study exhibits potential for the use of Minnelide to improve survival in COVID-19 patients.

microbiology↗