Coherence Metrics Research Articles

Transformer-Based Topic Modeling for Urdu Translations of the Holy Quran

Topic modeling enables the discovery of concealed themes and patterns in extensive text collections. It facilitates a thorough examination of the messages present in religious texts. Topic modeling for Quranic verses is a trending study area, with various translations already explored including Bahasa, English, and Arabic. Yet, there is a need for further research, particularly in Urdu translations of the Quran. In this study, we propose applying the BERTopic framework to Urdu translations of The Holy Quran. By leveraging the BERTopic approach, which incorporates a fine-tuned BERT model, we aim to capture the contextual nuances and linguistic complexities unique to the Quran. In this study, we utilized existing Urdu translations of the Quran from eight different translators sourced from Tanzil, a renowned resource for Quranic text and translations. We assessed the performance of our proposed BERTopic model compared to traditional techniques like LDA and NMF, using coherence and diversity metrics. The results indicate that our BERT-based approach outperforms these conventional methods, achieving an average coherence improvement of 0.03 and a diversity score of 0.83. These findings highlight the effectiveness of BERTopic in extracting meaningful topics from Urdu translations of The Holy Quran and contribute to the computational analysis of religious texts, supporting scholarly endeavours in comparative studies of Quranic translations in Urdu.

Topic-Centric Explanations for News Recommendation

News recommender systems (NRS) have been widely applied for online news websites to help users find relevant articles based on their interests. Recent methods have demonstrated considerable success in terms of recommendation performance. However, the lack of explanation for these recommendations can lead to mistrust among users and lack of acceptance of recommendations. To address this issue, we propose a new explainable news model to construct a topic-aware explainable recommendation approach that can both accurately identify relevant articles and explain why they have been recommended, using information from associated topics. Additionally, our model incorporates two coherence metrics applied to assess topic quality, providing a measure of the interpretability of these explanations. The results of our experiments on the MIND dataset indicate that the proposed explainable NRS outperforms several other baseline systems, while it is also capable of producing interpretable topics measured by coherence metrics. Furthermore, we present a case study through real-world examples showcasing the usefulness of our NRS for generating explanations.

Applying short text topic models to instant messaging communication of software developers

When modeling topics from chat messages of developer instant messaging communication, individual chat messages are short text documents. Our study aims at understanding how short text topic models perform with conversations from developer instant messaging. We applied four models to nine Gitter chat rooms (with sizes ranging from ≈100 to ≈160,000 messages). To assess the quality of topics and identify the best performing models, we compared topics based on four metrics for topic coherence. Furthermore, for a subset of Gitter chat rooms we used two human-based assessments: intrusion tasks with 18 experts analyzing 40 topics each, and topic naming (assigning a name to a topic that summarizes its main concept) with eight additional experts naming 60 topics each. Models performed differently in terms of coherence metrics and human assessment depending on the corpus (small, medium or large chat room). Our findings offer recommendations for the selection and use of short text topic models with developer chat messages based on characteristics of models and their performance with different sizes of corpora, and based on different strategies to assess topic quality.

Mapping job fitness and skill coherence into wages: an economic complexity analysis

Leveraging the discrete skill and knowledge worker requirements of each occupation provided by O*NET, our empirical approach employs network-based tools from the Economic Complexity framework to characterize the US occupational network. This approach provides insights into the interplay between wages and the complexity or relatedness of the skill sets within each occupation, complementing conventional human capital frameworks. Our empirical strategy is threefold. First, we construct the Job and Skill Progression Networks, where nodes represent jobs (skills) and a link between two jobs (skills) indicates statistically significant co-occurrence of skills required to carry out those two jobs, that can be useful tools to identify job-switching paths and skill complementarities Second, by harnessing the Fitness and Complexity algorithm, we define a data-driven skill-based complexity measure of jobs that positively maps, but with interesting deviations, into wages and in the bottom–up and broad abstract/manual and routine/non-routine job characterisations, however providing a continuous and endogenous metric to assess the degree of complexity of each occupational skill-set. Third, building on relatedness and corporate coherence metrics, we introduce a measure of each job’s skill coherence, that negatively maps into wages. Our findings may inform policymakers and employers on designing more effective labour market policies and training schemes, that, rather than fostering hyper-specialization, should favor the acquisition of complex and “uncoherent” skill sets, enabling workers to more easily move throughout the job and skill progression networks and make informed career choices decisions while unlocking higher wage opportunities.

Aligning Human and Computational Coherence Evaluations

Abstract Automated coherence metrics constitute an efficient and popular way to evaluate topic models. Previous works present a mixed picture of their presumed correlation with human judgment. This work proposes a novel sampling approach to mine topic representations at a large-scale while seeking to mitigate bias from sampling, enabling the investigation of widely-used automated coherence metrics via large corpora. Additionally, this article proposes a novel user study design, an amalgamation of different proxy tasks, to derive a finer insight into the human decision-making processes. This design subsumes the purpose of simple rating and outlier-detection user studies. Similar to the sampling approach, the user study conducted is very extensive, comprising forty study participants split into eight different study groups tasked with evaluating their respective set of one hundred topic representations. Usually, when substantiating the use of these metrics, human responses are treated as the golden standard. This article further investigates the reliability of human judgment by flipping the comparison and conducting a novel extended analysis of human response at the group and individual level against a generic corpus. The investigation results show a moderate to good correlation between these metrics and human judgment, especially for generic corpora, and derive further insights into the human perception of coherence. Analysing inter-metric correlations across corpora shows moderate to good correlation amongst these metrics. As these metrics depend on corpus statistics, this article further investigates the topical differences between corpora revealing nuances in applications of these metrics.

Trigeminal nerve direct current stimulation causes sustained increase in neural activity in the rat hippocampus

BackgroundTranscranial direct current stimulation (tDCS) is a noninvasive neuromodulation method that can modulate many brain functions including learning and memory. Recent evidence suggests that tDCS memory effects may be caused by co-stimulation of scalp nerves such as the trigeminal nerve (TN), and not the electric field in the brain. The TN gives input to brainstem nuclei, including the locus coeruleus that controls noradrenaline release across brain regions, including hippocampus. However, the effects of TN direct current stimulation (TN-DCS) are currently not well understood. HypothesisIn this study we tested the hypothesis that stimulation of the trigeminal nerve with direct current manipulates hippocampal activity via an LC pathway. MethodsWe recorded neural activity in rat hippocampus using multichannel silicon probes. We applied 3 min of 0.25 mA or 1 mA TN-DCS, monitored hippocampal activity for up to 1 h and calculated spikes-rate and spike-field coherence metrics. Subcutaneous injections of xylocaine were used to block TN, while intraperitoneal and intracerebral injection of clonidine were used to block the LC pathway. ResultsWe found that 1 mA TN-DCS caused a significant increase in hippocampal spike-rate lasting 45 min in addition to significant changes in spike-field coherence, while 0.25 mA TN-DCS did not. TN blockage prevented spike-rate increases, confirming effects were not caused by the electric field in the brain. When 1 mA TN-DCS was delivered during clonidine blockage no increase in spike-rate was observed, suggesting an important role for the LC-noradrenergic pathway. ConclusionThese results support our hypothesis and provide a neural basis to understand the tDCS TN co-stimulation mechanism. TN-DCS emerges as an important tool to potentially modulate learning and memory.

Quantifying the role of submesoscale Lagrangian transport features in the concentration of phytoplankton in a coastal system

Abstract Food resources in the ocean are often found in low densities, and need to be concentrated for efficient consumption. This is done in part by oceanographic features transporting and locally concentrating plankton, creating a highly patchy resource. Lagrangian approaches applied to ocean dynamics can identify these transport features, linking Lagrangian transport and spatial ecology. However, little is known about how Lagrangian approaches perform in ageostrophic coastal flows. This study evaluates two Lagrangian Coherent Structure metrics against the distribution of phytoplankton; Finite Time Lyapunov Exponents (FTLE) and Relative Particle Density (RPD). FTLE and RPD are applied to High Frequency Radar (HFR) observed surface currents within a biological hotspot, Palmer Deep Canyon Antarctica. FTLE and RPD identify different transport patterns, with RPD mapping single particle trajectories and FTLE tracking relative motion of paired particles. Simultaneous measurements of circulation and phytoplankton were gathered through the integration of vessel and autonomous glider surveys within the HFR footprint. Results show FTLE better defined phytoplankton patches compared to RPD, with the strongest associations occurring in stratified conditions, suggesting that phytoplankton congregate along FTLE ridges in coastal flows. This quantified relationship between circulation and phytoplankton patches emphasizes the role of transport in the maintenance of coastal food webs.

Potential of dynamic ocean management strategies for western Pacific leatherback sea turtle bycatch mitigation in New Zealand

Western Pacific leatherback sea turtles (Dermochelys coriacea) are a priority bycatch mitigation concern due to the projected extinction of the population before the end of the 21st century. The species regularly occurs as bycatch in gillnet and surface longline fisheries. Here, we explore the potential for dynamic ocean management in an emerging hotspot of leatherback sea turtle bycatch in the New Zealand pelagic longline fishery. We compared spatial areas of different sizes built from single oceanographic covariates as well as built from a composite risk surface developed through ensemble random forests. We found that, individually, the Okubo–Weiss parameter, sea surface temperature (SST) anomaly, SST, moon phase, and distance to the SST front were important oceanographic covariates for leatherback sea turtle bycatch. However, the spatial areas built from the composite risk surface were the most effective at discriminating sets with and without bycatch across a range of risk cutoffs. When we also considered implementation metrics of spatial area and coherence as part of performance, the area derived from the composite risk surface with a risk of interaction per set greater than 52% performed best. This spatial area was ephemeral, occurring 1 or 2 weeks each year, and localized, occurring along the north coast of East Cape in the North Island of New Zealand. The apparent presence of discrete spatial areas with elevated risk may be useful to inform future management in the area. Considering implementation metrics in defining utility was useful for identifying tradeoffs between the total size and the underlying covariates delineating a spatial area. As such, we recommend these types of metrics to be included when designing spatial bycatch mitigation strategies elsewhere.

MIT: Mutual Information Topic Model for Diverse Topic Extraction.

To automatically mine structured semantic topics from text, neural topic modeling has arisen and made some progress. However, most existing work focuses on designing a mechanism to enhance topic coherence but sacrificing the diversity of the extracted topics. To address this limitation, we propose the first neural-based topic modeling approach purely based on mutual information maximization, called the mutual information topic (MIT) model, in this article. The proposed MIT significantly improves topic diversity by maximizing the mutual information between word distribution and topic distribution. Meanwhile, MIT also utilizes Dirichlet prior in latent topic space to ensure the quality of mined topics. The experimental results on three publicly benchmark text corpora show that MIT could extract topics with higher coherence values (considering four topic coherence metrics) than competitive approaches and has a significant improvement on topic diversity metric. Besides, our experiments prove that the proposed MIT converges faster and more stable than adversarial-neural topic models.

Stimulus-Informed Generalized Canonical Correlation Analysis for Group Analysis of Neural Responses to Natural Stimuli.

Various new brain-computer interface technologies or neuroscience applications require decoding stimulus-following neural responses to natural stimuli such as speech and video from, e.g., electroencephalography (EEG) signals. In this context, generalized canonical correlation analysis (GCCA) is often used as a group analysis technique, which allows the extraction of correlated signal components from the neural activity of multiple subjects attending to the same stimulus. GCCA can be used to improve the signal-to-noise ratio of the stimulus-following neural responses relative to all other irrelevant ( non-)neural activity, or to quantify the correlated neural activity across multiple subjects in a group- wise coherence metric. However, the traditional GCCA technique is stimulus-unaware: no information about the stimulus is used to estimate the correlated components from the neural data of several subjects. Therefore, the GCCA technique might fail to extract relevant correlated signal components in practical situations where the amount of information is limited, for example, because of a limited amount of training data or group size. This motivates a new stimulus-informed GCCA (SI-GCCA) framework that allows taking the stimulus into account to extract the correlated components. We show that SI-GCCA outperforms GCCA in various practical settings, for both auditory and visual stimuli. Moreover, we showcase how SI-GCCA can be used to steer the estimation of the components towards the stimulus. As such, SI-GCCA substantially improves upon GCCA for various purposes, ranging from preprocessing to quantifying attention.

Data Lake Management System based on Topic Modeling

In an environment full of competitiveness, data is a valuable asset for any company looking to grow. It represents a real competitive economic and strategic lever. The most reputable companies are not only concerned with collecting data from heterogeneous data sources, but also with analyzing and transforming these datasets into better decision-making. In this context, the data lake continues to be a powerful solution for storing large amounts of data and providing data analytics for decision support. In this paper, we examine the intelligent data lake management system that addresses the drawbacks of traditional business intelligence, which is no longer capable of handling data-driven demands. Data lakes are highly suitable for analyzing data from a variety of sources, particularly when data cleaning is time-consuming. However, ingesting heterogeneous data sources without any schema represents a major issue, and a data lake can easily turn into a data swamp. In this study, we implement the LDA topic model for managing the storage, processing, analysis, and visualization of big data. To assess the usefulness of our proposal, we evaluated its performance based on the topic coherence metric. The results of these experiments showed our approach to be more accurate on the tested datasets

Differences in Functional Connectivity between Patients with Depression with and without Nonsuicidal Self-injury.

: Nonsuicidal self-injury (NSSI), which involves deliberate harm to body tissues without suicidal intent, represents an escalating clinical concern. We used electroencephalography (EEG) to investigate the differences in functional connectivity (FC) patterns in patients with depression with and without a history of NSSI. : Seventy-seven patients with mood disorders experiencing major depressive episodes were categorized into NSSI (Group A; n = 31) and non-NSSI (Group B; n = 46) groups on the basis of their NSSI history. EEG data were collected and FC was analyzed using coherence (Coh), imaginary coherence (iCoh), and phase-locking value (PLV) metrics. Network indices based on graph theory were calculated. Demographic and clinical characteristics and scale scores were compared between groups A and B. : While the two groups showed no significant differences in demographic characteristics such as age and diagnosis, the Beck Depression Inventory and Suicidal Ideation Questionnaire (SIQ) scores were higher in Group A. Binary logistic regression analyses revealed associations of NSSI with sex and the SIQ score. We examined the connectivity of 1,326 pairs of signals across six frequency bands, yielding 7,956 signal pairs. The two groups showed no significant differences in the Coh, iCoh, corrected PLV, or network indices but showed significant differences in all the frequency bands when an uncorrected t test was used. : In this study, FC differences in depression with and without NSSI were not observed. Further well-controlled research is expected to clarify neurobiological underpinnings and guide future interventions.

Depression, anxiety, and burnout in academia: topic modeling of PubMed abstracts

The problem of mental health in academia is increasingly discussed in literature, and to extract meaningful insights from the growing amount of scientific publications, text mining approaches are used. In this study, BERTopic, an advanced method of topic modeling, was applied to abstracts of 2,846 PubMed articles on depression, anxiety, and burnout in academia published in years 1975–2023. BERTopic is a modular technique comprising a text embedding method, a dimensionality reduction procedure, a clustering algorithm, and a weighing scheme for topic representation. A model was selected based on the proportion of outliers, the topic interpretability considerations, topic coherence and topic diversity metrics, and the inevitable subjectivity of the criteria was discussed. The selected model with 27 topics was explored and visualized. The topics evolved differently with time: research papers on students' pandemic-related anxiety and medical residents' burnout peaked in recent years, while publications on psychometric research or internet-related problems are yet to be presented more amply. The study demonstrates the use of BERTopic for analyzing literature on mental health in academia and sheds light on areas in the field to be addressed by further research.

Reduced corticospinal drive to antagonist muscles of upper and lower limbs during hands-and-knees crawling in infants with cerebral palsy: Evidence from intermuscular EMG-EMG coherence

BackgroundThere is growing interest in understanding the central control of hands-and-knees crawling, especially as a significant motor developmental milestone for early assessment of motor dysfunction in infants with cerebral palsy (CP) who have not yet acquired walking ability. In particular, CP is known to be associated with walking dysfunctions caused by early damage and incomplete maturation of the corticospinal tract. However, the extent of damage to the corticospinal connections during crawling in infants with CP has not been fully clarified. Therefore, this study aimed to investigate the disparities in intermuscular EMG-EMG coherence, which serve as indicators of corticospinal drives to antagonist muscles in the upper and lower limbs during crawling, between infants with and without CP. MethodsThis study involved 15 infants diagnosed with CP and 20 typically developing (TD) infants. Surface EMG recordings were obtained from two pairs of antagonist muscles in the upper limbs (triceps brachii (TB) and biceps brachii (BB)) and lower limbs (quadriceps femoris (QF) and hamstrings (HS)), while the infants performed hands-and-knees crawling at their self-selected velocity. Intermuscular EMG-EMG coherence was computed in two frequency bands, the beta band (15–30 Hz) and gamma band (30–60 Hz), which indicate corticospinal drive. Additionally, spatiotemporal parameters, including crawling velocity, cadence, duration, and the percentage of stance phase time, were calculated for comparison. Spearman rank correlations were conducted to assess the relationship between EMG-EMG coherence and crawling spatiotemporal parameters. ResultsInfants with CP exhibited significantly reduced crawling velocity, decreased cadence, longer cycle duration, and a higher percentage of stance phase time compared to TD infants. Furthermore, CP infants demonstrated decreased coherence in the beta and gamma frequency bands (indicators of corticospinal drive) in both upper and lower limb muscles. Regarding limb-related differences in the beta and gamma coherence, significant disparities were found between upper and lower limb muscles in TD infants (p < 0.05), but not in infants with CP (p > 0.05). Additionally, significant correlations between coherence metrics and crawling spatiotemporal parameters were identified in the TD group (p < 0.05), while such correlations were not evident in the CP group. ConclusionsOur findings suggest that the corticospinal drive may functionally influence the central control of antagonist muscles in the limbs during typical infant crawling. This functional role could be impaired by neurological conditions such as cerebral palsy. The neurophysiological markers of corticospinal drive, specifically intermuscular EMG-EMG coherence during crawling in infants with cerebral palsy, could potentially serve as a tool to assess developmental response to therapy.

Risk-averse receding horizon motion planning for obstacle avoidance using coherent risk measures

This paper studies the problem of risk-averse receding horizon motion planning for agents with uncertain dynamics, in the presence of stochastic, dynamic obstacles. We propose a model predictive control (MPC) scheme that formulates the obstacle avoidance constraint using coherent risk measures. To handle disturbances, or process noise, in the state dynamics, the state constraints are tightened in a risk-aware manner to provide a disturbance feedback policy. We also propose a waypoint following algorithm that uses the proposed MPC scheme for discrete distributions and prove its risk-sensitive recursive feasibility while guaranteeing finite-time task completion. We further investigate some commonly used coherent risk metrics, namely, conditional value-at-risk (CVaR), entropic value-at-risk (EVaR), and g-entropic risk measures, and propose a tractable incorporation within MPC. We illustrate our framework via simulation studies.

Integrating Text Classification into Topic Discovery Using Semantic Embedding Models

Topic discovery involves identifying the main ideas within large volumes of textual data. It indicates recurring topics in documents, providing an overview of the text. Current topic discovery models receive the text, with or without pre-processing, including stop word removal, text cleaning, and normalization (lowercase conversion). A topic discovery process that receives general domain text with or without processing generates general topics. General topics do not offer detailed overviews of the input text, and manual text categorization is tedious and time-consuming. Extracting topics from text with an automatic classification task is necessary to generate specific topics enriched with top words that maintain semantic relationships among them. Therefore, this paper presents an approach that integrates text classification for topic discovery from large amounts of English textual data, such as 20-Newsgroups and Reuters Corpora. We rely on integrating automatic text classification before the topic discovery process to obtain specific topics for each class with relevant semantic relationships between top words. Text classification performs a word analysis that makes up a document to decide what class or category to identify; then, the proposed integration provides latent and specific topics depicted by top words with high coherence from each obtained class. Text classification accomplishes this with a convolutional neural network (CNN), incorporating an embedding model based on semantic relationships. Topic discovery over categorized text is realized with latent Dirichlet analysis (LDA), probabilistic latent semantic analysis (PLSA), and latent semantic analysis (LSA) algorithms. An evaluation process for topic discovery over categorized text was performed based on the normalized topic coherence metric. The 20-Newsgroups corpus was classified, and twenty topics with the ten top words were identified for each class. The normalized topic coherence obtained was 0.1723 with LDA, 0.1622 with LSA, and 0.1716 with PLSA. The Reuters Corpus was also classified, and twenty and fifty topics were identified. A normalized topic coherence of 0.1441 was achieved when applying the LDA algorithm, obtaining 20 topics for each class; with LSA, the coherence was 0.1360, and with PLSA, it was 0.1436.

Multivariate prediction of cognitive performance from the sleep electroencephalogram

Human cognitive performance is a key function whose biological foundations have been partially revealed by genetic and brain imaging studies. The sleep electroencephalogram (EEG) is tightly linked to structural and functional features of the central nervous system and serves as another promising biomarker. We used data from MrOS, a large cohort of older men and cross-validated regularized regression to link sleep EEG features to cognitive performance in cross-sectional analyses. In independent validation samples 2.5–10% of variance in cognitive performance can be accounted for by sleep EEG features, depending on the covariates used. Demographic characteristics account for more covariance between sleep EEG and cognition than health variables, and consequently reduce this association by a greater degree, but even with the strictest covariate sets a statistically significant association is present. Sigma power in NREM and beta power in REM sleep were associated with better cognitive performance, while theta power in REM sleep was associated with worse performance, with no substantial effect of coherence and other sleep EEG metrics. Our findings show that cognitive performance is associated with the sleep EEG (r = 0.283), with the strongest effect ascribed to spindle-frequency activity. This association becomes weaker after adjusting for demographic (r = 0.186) and health variables (r = 0.155), but its resilience to covariate inclusion suggest that it also partially reflects trait-like differences in cognitive ability.

Recovery performance improvement of image compressive sensing using complex‐valued Vandermonde matrix

AbstractHere, a novel image‐based quantized compressive sensing (QCS) framework based on complex‐valued Vandermonde (Vander) matrix is proposed. In the proposed QCS framework, a discrete wavelet transform (DWT) serves as a sparse basis and a partial complex‐valued Vander matrix serves as a measurement matrix. The theoretical analysis based on mutual coherence metric of compressive sensing (CS) theory shows that the proposed Vander measurement matrix has the best reconstruction performance among other conventional measurement matrices. The simulation results also show that the recovery quality using the proposed measurement matrix can be greatly improved compared with the other existing real‐valued measurement matrices. In particular, the experiment results also show that under the same measurement matrix, the reconstruction performance of Smoothed l0 norm (SL0) algorithm is better than that of Orthogonal Matching Pursuit (OMP) algorithm, sparsity adaptive matching pursuit (SAMP) algorithm and approximate message passing (AMP) algorithm. In addition, a sparse measurement matrix scheme is further proposed to achieve a trade‐off between recovery performance and computational complexity. The theoretical analysis and simulation results both show the proposed image‐based QCS is efficient.

“One Metric to Rule Them All”: A Common Metric for Symptoms of Depression and Generalized Anxiety in Adolescent Samples

There is a significant degree of heterogeneity in scales that purport to measure depression and generalized anxiety in adolescent samples, which makes it difficult to directly compare scores across studies. The aim of the current study is to develop a common metric that facilitates the comparison of scores from different but related scales of depression and generalized anxiety using a large adolescent sample. A nonequivalent-anchor-test design in conjunction with simultaneous calibration was used to equate and develop a common metric for six different scales. The common metric provided scores with acceptable levels of precision across the −1 to 3 range on θ, which represents the more severe and often clinical end of the spectrum. Thus, in the current study, we identified a coherent common metric that is closely aligned with the “distress” subfactor of the Hierarchical Taxonomy of Psychopathology model. Additional validation testing in independent samples is now required.

Development of polarization-sensitive optical coherence tomography imaging platform and metrics to quantify electrostimulation-induced peripheral nerve injury in vivo in a small animal model.

Neuromodulation devices are rapidly evolving for the treatment of neurological diseases and conditions. Injury from implantation or long-term use without obvious functional losses is often only detectable through terminal histology. New technologies are needed that assess the peripheral nervous system (PNS) under normal and diseased or injured conditions. We aim to demonstrate an imaging and stimulation platform that can elucidate the biological mechanisms and impacts of neurostimulation in the PNS and apply it to the sciatic nerve to extract imaging metrics indicating electrical overstimulation. A sciatic nerve injury model in a 15-rat cohort was observed using a newly developed imaging and stimulation platform that can detect electrical overstimulation effects with polarization-sensitive optical coherence tomography. The sciatic nerve was electrically stimulated using a custom-developed nerve holder with embedded electrodes for 1h, followed by a 1-h recovery period, delivered at above-threshold Shannon model -values in experimental groups: sham control (SC, , ), stimulation level 1 (SL1, , , and ), and stimulation level 2 (SL2, , , and ). The stimulation and imaging system successfully captured study data across the cohort. When compared to a SC after a 1-week recovery, the fascicle closest to the stimulation lead showed an average change of (SL1/SL2) in phase retardation and in optical attenuation relative to SC. Analysis of immunohistochemistry (IHC) shows a difference in myelin pixel counts and difference in axon pixel counts, and an overall increase in cell nuclei pixel count of . These metrics were consistent with IHC and hematoxylin/eosin tissue section analysis. The poststimulation changes observed in our study are manifestations of nerve injury and repair, specifically degeneration and angiogenesis. Optical imaging metrics quantify these processes and may help evaluate the safety and efficacy of neuromodulation devices.