About

James J Gugger, MD, PharmD, is an Adjunct Assistant Professor of Neurology at the University of Pennsylvania's Perelman School of Medicine. He is affiliated with the Department of Neurology and the Penn Epilepsy Center. Dr. Gugger completed his PharmD at Rutgers University, Ernest Mario School of Pharmacy in 2006, and earned his MD from the State University of New York, Downstate Medical Center, College of Medicine in 2015. His research focuses on epilepsy, traumatic brain injury, neurodegeneration, and related neurological conditions. He has contributed to numerous publications in these fields, emphasizing the development of models and approaches for understanding and managing neurological disorders.

Research topics

• Medicine
• Internal medicine
• Anesthesia
• Neuroscience
• Cardiology

Selected publications

• Improving Standardization and Access to Care via Seizure Pathways in the Emergency Department

  Western Journal of Emergency Medicine · 2026-01-04

  articleOpen access

  INTRODUCTION: Seizures are one of the most common neurological presentations to an emergency department (ED), often as a first seizure of life or a breakthrough seizure. There is practice variation regarding the diagnostic workup and management for these patient populations. A standardized pathway for emergent evaluation of first seizure of life or breakthrough seizure currently does not exist, resulting in variability in evaluation and timing of outpatient care. METHODS: We created standardized pathways for evaluation and management of patients presenting to the ED with a first seizure of life or breakthrough seizure. These pathways, implemented at a large, quaternary-care hospital system, were utilized on 130 patients presenting with a seizure and compared with all patients with seizure on whom the pathway was not used, between May 2022-October 2023. Outcomes of interest included ED length of stay (LOS), proportion of patients admitted, time to outpatient follow-up, and difference in resource utilization. We compared categorical variables using chi-square test and continuous variables using the Wilcoxon rank-sum test. Equality of variance between the two cohorts was tested using the Levene test. RESULTS: There was no statistically significant difference between the percentage of male and female patients evaluated via standard-of-care model (45.6% and 49.5%) and those on the pathway (56.9% and 43.1%). The average age of patients was similar between standard-of-care and pathway groups (41 and 39 years, respectively). Median ED LOS was 5.0 (Interquartile range [IQR] 2.9-9.4) hours for standard of care and 4.8 (IQR 3.1-7.0) hours for pathway (P = .34), with a significant difference in variability in time for pathway group (P < .001). Fewer patients were admitted or observed with pathway use (P < .02). Median time to outpatient follow-up was 41.0 days (IQR 17.0-93.0) with standard of care and 23.5 days (IQR 8.0-57.0) with pathway use (P < .001). More urinalyses (P < .001), drug screens (P < .001), alcohol levels (P < .001) and computed tomography for first seizures (P < .001) were ordered for the pathway group. Fewer magnetic resonance imaging studies were ordered for patients in the breakthrough seizures group using the pathway (P < .001). CONCLUSION: Standardized pathways to approach seizure presentation in the ED can reduce variability in care, improve time to outpatient neurologic care, and standardize seizure-safety counseling.

  PublisherOA PDFDOI

• Posttraumatic Epilepsy and Dementia Risk

  UNC Libraries · 2026-04-21

  articleOpen access1st authorCorresponding

  Importance: Although both head injury and epilepsy are associated with long-term dementia risk, posttraumatic epilepsy (PTE) has only been evaluated in association with short-term cognitive outcomes. Objective: To investigate associations of PTE with dementia risk. Design, Setting, and Participants: The Atherosclerosis Risk in Communities (ARIC) study initially enrolled participants from 1987 to 1989 and this prospective cohort study uses data through December 31, 2019, with a median follow-up of 25 years. Data were analyzed between March 14, 2023, and January 2, 2024. The study took place in 4 US communities in Minnesota, Maryland, North Carolina, and Mississippi. Of 15 792 ARIC study participants initially enrolled, 2061 were ineligible and 1173 were excluded for missing data, resulting in 12 558 included participants. Exposures: Head injury was defined by self-report and International Classification of Diseases (ICD) diagnostic codes. Seizure/epilepsy was defined using ICD codes. PTE was defined as a diagnosis of seizure/epilepsy occurring more than 7 days after head injury. Head injury, seizure/epilepsy, and PTE were analyzed as time-varying exposures. Main Outcomes and Measures: Dementia was defined using cognitive assessments, informant interviews, and ICD and death certificate codes. Adjusted Cox and Fine and Gray proportional hazards models were used to estimate dementia risk. Results: Participants had a mean (SD) age of 54.3 (5.8) years at baseline, 57.7% were female, 28.2% were of self-reported Black race, 14.4% were ultimately categorized as having head injury, 5.1% as having seizure/epilepsy, and 1.2% as having PTE. Over a median follow-up of 25 (25th to 75th percentile, 17-30) years, 19.9% developed dementia. In fully adjusted models, compared with no head injury and no seizure/epilepsy, PTE was associated with 4.56 (95% CI, 4.49-5.95) times the risk of dementia, while seizure/epilepsy was associated with 2.61 (95% CI, 2.21-3.07) times the risk and head injury with 1.63 (95% CI, 1.47-1.80) times the risk. The risk of dementia associated with PTE was significantly higher than the risk associated with head injury alone and with nontraumatic seizure/epilepsy alone. Results were slightly attenuated in models accounting for the competing risks of mortality and stroke, but patterns of association remained similar. In secondary analyses, the increased dementia risk associated with PTE occurring after first vs second head injury and after mild vs moderate/severe injury was similar. Conclusions and Relevance: In this community-based cohort, there was an increased risk of dementia associated with PTE that was significantly higher than the risk associated with head injury or seizure/epilepsy alone. These findings provide evidence that PTE is associated with long-term outcomes and supports both the prevention of head injuries via public health measures and further research into the underlying mechanisms and the risk factors for the development of PTE, so that efforts can also be focused on the prevention of PTE after a head injury.

  PublisherDOI

• Advancing Dry Electroencephalography With Scalable, Soft, and Transcranial Magnetic Stimulation‐Compatible Ti ₃ C ₂ T _x MXene Electrodes for Research and Clinical‐Grade Applications

  Advanced Science · 2026-02-15

  articleOpen access

  ABSTRACT Electroencephalography (EEG), essential for diagnosing and researching neurological disorders, utilizes gelled electrodes, which present limitations in safety, comfort, stability, and usability, particularly in long‐term applications. We introduce a novel dry EEG technology using soft, porous, low‐impedance, Ti 3 C 2 T x MXene electrodes. The 10 Hz impedance of these electrodes across scalp locations is 2.1 ± 1.8 kΩ, comparable to gelled Ag/AgCl electrodes and below clinical thresholds. Ti 3 C 2 T x electrodes maintain stable impedance over 4.5 h on agarose phantoms and retain structure after 50 cycles of 80% axial compression. These electrodes are suitable for simultaneous EEG and transcranial magnetic stimulation (TMS), exhibiting no significant displacement, heating, or unsafe charge densities under TMS fields. We benchmarked dry electrodes across recording scenarios and hair types against gelled electrodes. In full‐scalp steady‐state visual evoked potential (SSVEP) recordings, gelled and Ti 3 C 2 T x electrodes were highly correlated (R &gt; 0.89). Clinical EEG with Ti 3 C 2 T x electrodes captured all features observed with gelled electrodes (R &gt; 0.84) and was rated for clinical quality by neurologists. Furthermore, dry MXene EEG electrode recorded high‐quality EEG for over 4 h. In mobile EEG, Ti 3 C 2 T x electrodes did not induce signal distortions and enabled task‐specific feature detection with a comparable signal‐to‐noise ratio to gelled electrodes. These findings establish dry Ti 3 C 2 T x electrodes as an alternative to gel‐based systems, with broad potential in clinical diagnostics and research.

  PublisherDOI

• Neuroimaging Correlates of Traumatic Brain Injury in an Older Community-Dwelling Population

  Neurology · 2025-04-04 · 1 citations

  articleOpen access

  BACKGROUND AND OBJECTIVES: Neuroimaging correlates of remote traumatic brain injury (TBI) are not well understood. Our objective was to examine associations of TBI with brain MRI markers of degeneration and vascular disease. METHODS: -score cut-point of -1.5 for volumetrics, cortical thickness, and fractional anisotropy (FA) and above +1.5 for mean diffusivity (MD). RESULTS: -score cut-point only for FA and MD. DISCUSSION: In this community-dwelling cohort of older adults, TBI was associated with smaller brain volumes, lower cortical thickness, lower FA, and higher MD. Further work is needed in the chronic postinjury period to elucidate the mechanisms underlying the observed structural changes after TBI.

  PublisherOA PDFDOI

• Evaluation of limbic microstructural abnormalities in temporal lobe epilepsy: A neurite orientation distribution and density imaging study

  Epilepsia · 2025-06-04 · 2 citations

  articleOpen access1st authorCorresponding

  OBJECTIVE: Widespread structural pathology in the limbic system is a hallmark of temporal lobe epilepsy (TLE). In this work, we sought to describe a comprehensive readout of limbic abnormalities in TLE using neurite orientation distribution and density imaging (NODDI). METHODS: This is a retrospective study of patients with drug-resistant TLE and healthy controls who underwent research magnetic resonance imaging. We estimated the degree of deviation of the NODDI parameters neurite density index (NDI) and orientation dispersion index (ODI) from healthy controls in limbic regions in the form of univariate z-scores. We calculated a multivariate deviation score combining both NDI and ODI (Mahalanobis distance). A summary score representing the overall level of deviation across limbic regions was then computed using the sum of regional deviation scores. We next assessed the diagnostic performance of summary scores in lateralizing TLE as well as associations with neuropsychological deficits and 12-month surgical outcome. RESULTS: The Mahalanobis distance revealed unique patterns of abnormalities between TLE participants (n = 74) and controls (n = 42), with only four of 18 (22%) areas displaying overlapping univariate and multivariate deviations. The multivariate summary score achieved the highest diagnostic accuracy in clinical lateralization of nonlesional TLE (area under the curve [AUC] = .95, 95% confidence interval [CI] = .77-1). Among surgical patients (n = 30), summary scores corresponding to the hemisphere ipsilateral and contralateral to surgery were predictive of seizure freedom at 12 months (AUC = .84, 95% CI = .76-.93). SIGNIFICANCE: We demonstrate unique patterns of abnormalities in neurite density and coherence in limbic microstructure in TLE. A summary score accounting for deviations in both neurite density and coherence achieved high diagnostic accuracy in clinical lateralization of TLE and was associated with surgical outcomes, warranting further study as a putative biomarker in TLE to be used alongside clinical data.

  PublisherOA PDFDOI

• From acute injury to chronic comorbidity: Interrupted time series modeling of traumatic brain injury impact among post-9/11 veterans

  medRxiv · 2025-05-18

  preprintOpen access

  Abstract Traumatic brain injury (TBI) is associated with a variety of adverse health outcomes that display complex behavior over time. The objective of this study was to investigate both the early and late health impacts of TBI within a single framework. This study evaluated TBI associations among a cohort of post-9/11 Veterans with TBI documented between 2008 and 2017 in Veteran Health Administration (VHA) records. The cohort included 108,408 post-9/11 Veterans with any history of TBI documentation between 2008-2017 who were demographically matched with 108,408 TBI negative controls. Interrupted time series (ITS) models were used to fit the prevalence of comorbidities over time (±6 years from index date, i.e. date of first TBI). Three ITS measures were modeled for each comorbidity: 1) The incidence rate (IR) in the month of TBI index date, 2) The incidence rate ratio (IRR) between TBI and control groups in the month of index date, and 3) Long-term changes in year-over-year diagnosis rates, i.e. the annual incidence rate difference (IRD) before vs. after index date. Overall, TBI was associated with conditions related to somatic, cognitive, and psychological outcomes including headache, cognitive dysfunction, and PTSD. Neurological events were found to be elevated within the month of TBI documentation. Conditions with the largest IR were post-traumatic stress disorder (PTSD) (+29%, p&lt;0.001), headache (+22%, p&lt;0.001), and adjustment disorder (+22%, p&lt;0.001). Conditions with the highest IRR across TBI and control groups were cognitive dysfunction (474, p&lt;0.001), vestibular dysfunction (137, p&lt;0.001), and stroke (72, p&lt;0.001). Long term, the conditions with the highest IRD were substance use disorders (p&lt;0.001) and mental health conditions (p&lt;0.001). This work demonstrates how ITS modeling can help bridge traditional divides between early and late paradigms of TBI investigation to help inform research and care for Veterans living with TBI.

  PublisherOA PDFDOI

• Annotating neurophysiologic data at scale with optimized human input

  Journal of Neural Engineering · 2025-06-12 · 7 citations

  articleOpen accessCorresponding

  Abstract Objective. Neuroscience experiments and devices are generating unprecedented volumes of data, but analyzing and validating them presents practical challenges, particularly in annotation. While expert annotation remains the gold standard, it is time consuming to obtain and often poorly reproducible. Although automated annotation approaches exist, they rely on labeled data first to train machine learning algorithms, which limits their scalability. A semi-automated annotation approach that integrates human expertise while optimizing efficiency at scale is critically needed. To address this, we present Annotation Co-pilot, a human-in-the-loop solution that leverages deep active learning (AL) and self-supervised learning (SSL) to improve intracranial EEG (iEEG) annotation, significantly reducing the amount of human annotations. Approach. We automatically annotated iEEG recordings from 28 humans and 4 dogs with epilepsy implanted with two neurodevices that telemetered data to the cloud for analysis. We processed 1500 h of unlabeled iEEG recordings to train a deep neural network using a SSL method Swapping Assignments between View to generate robust, dataset-specific feature embeddings for the purpose of seizure detection. AL was used to select only the most informative data epochs for expert review. We benchmarked this strategy against standard methods. Main result. Over 80 000 iEEG clips, totaling 1176 h of recordings were analyzed. The algorithm matched the best published seizure detectors on two datasets (NeuroVista and NeuroPace responsive neurostimulation) but required, on average, only 1/6 of the human annotations to achieve similar accuracy (area under the ROC curve of 0.9628 ± 0.015) and demonstrated better consistency than human annotators (Cohen’s Kappa of 0.95 ± 0.04). Significance . ‘Annotation Co-pilot’ demonstrated expert-level performance, robustness, and generalizability across two disparate iEEG datasets while reducing annotation time by an average of 83%. This method holds great promise for accelerating basic and translational research in electrophysiology, and potentially accelerating the pathway to clinical translation for AI-based algorithms and devices.

  PublisherDOI

• From acute injury to chronic comorbidity: Interrupted time series modeling of traumatic brain injury impact among post-9/11 veterans

  PLoS ONE · 2025-11-06

  articleOpen accessCorresponding

  Traumatic brain injury (TBI) is associated with a variety of adverse health outcomes that display complex behavior over time. The objective of this study was to investigate both the early and late health impacts of TBI within a single framework. This study evaluated TBI associations among a cohort of post-9/11 Veterans with TBI documented between 2008 and 2017 in Veteran Health Administration (VHA) records. The cohort included 108,408 post-9/11 Veterans with any history of TBI documentation between 2008-2017 who were demographically matched with 108,408 TBI negative controls. Interrupted time series (ITS) models were used to fit the prevalence of comorbidities over time (±6 years from index date, i.e., date of first TBI). Three ITS measures were modeled for each comorbidity: 1) The incidence rate (IR) in the month of TBI index date, 2) The incidence rate ratio (IRR) between TBI and control groups in the month of index date, and 3) Long-term changes in year-over-year diagnosis rates, i.e., the annual incidence rate difference (IRD) before vs. after index date. Overall, TBI was associated with conditions related to somatic, cognitive, and psychological outcomes including headache, cognitive dysfunction, and PTSD. Neurological events were found to be elevated within the month of TBI documentation. Conditions with the largest IR were post-traumatic stress disorder (PTSD) (+29%, p < 0.001), headache (+22%, p < 0.001), and adjustment disorder (+22%, p < 0.001). Conditions with the highest IRR across TBI and control groups were cognitive dysfunction (474, p < 0.001), vestibular dysfunction (137, p < 0.001), and stroke (72, p < 0.001). Long term, the conditions with the highest IRD were substance use disorders (p < 0.001) and mental health conditions (p < 0.001). This work demonstrates how ITS modeling can help bridge traditional divides between early and late paradigms of TBI investigation to help inform research and care for Veterans living with TBI.

  PublisherDOI

• Connectivity of the Piriform Cortex and its Implications in Temporal Lobe Epilepsy

  medRxiv · 2024-07-22 · 2 citations

  preprintOpen access

  Background: The piriform cortex has been implicated in the initiation, spread and termination of epileptic seizures. This understanding has extended to surgical management of epilepsy, where it has been shown that resection or ablation of the piriform cortex can result in better outcomes. How and why the piriform cortex may play such a crucial role in seizure networks is not well understood. To answer these questions, we investigated the functional and structural connectivity of the piriform cortex in both healthy controls and temporal lobe epilepsy (TLE) patients. Methods: We studied a retrospective cohort of 55 drug-resistant unilateral TLE patients and 26 healthy controls who received structural and functional neuroimaging. Using seed-to-voxel connectivity we compared the normative whole-brain connectivity of the piriform to that of the hippocampus, a region commonly involved in epilepsy, to understand the differential contribution of the piriform to the epileptogenic network. We subsequently measured the inter-piriform coupling (IPC) to quantify similarities in the inter-hemispheric cortical functional connectivity profile between the two piriform cortices. We related differences in IPC in TLE back to aberrations in normative piriform connectivity, whole brain functional properties, and structural connectivity. Results: We find that relative to the hippocampus, the piriform is functionally connected to the anterior insula and the rest of the salience ventral attention network (SAN). We also find that low IPC is a sensitive metric of poor surgical outcome (sensitivity: 85.71%, 95% CI: [19.12%, 99.64%]); and differences in IPC within TLE were related to disconnectivity and hyperconnectivity to the anterior insula and the SAN. More globally, we find that low IPC is associated with whole-brain functional and structural segregation, marked by decreased functional small-worldness and fractional anisotropy. Conclusions: Our study presents novel insights into the functional and structural neural network alterations associated with this structure, laying the foundation for future work to carefully consider its connectivity during the presurgical management of epilepsy.

  PublisherOA PDFDOI

• Remote Network Effects of Post-traumatic Lesions and Risk of Post-traumatic Epilepsy (P5-1.010)

  Neurology · 2024-04-09

  articleSenior author

  To assess epilepsy risk in a cohort of patients with post-traumatic contusions identified on MRI two-weeks post-injury using functional MRI to map the remote network effects of lesions.

  PublisherDOI

Frequent coauthors

• Ramon Diaz‐Arrastia

  120 shared

• Juebin Huang

  University of Mississippi Medical Center

  68 shared

• Emily L. Johnson

  Johns Hopkins University

  68 shared

• Andrea L.C. Schneider

  University of Pennsylvania

  68 shared

• Gregory L. Krauss

  Johns Hopkins Medicine

  67 shared

• Anna Kucharska‐Newton

  University of Kentucky

  67 shared

• Rebecca F. Gottesman

  66 shared

• Mary Jo Pugh

  University of Utah

  42 shared

Labs

• Neurology - Penn Epilepsy CenterPI

Education

• MD

  SUNY Downstate Medical Center College of Medicine

  2015

• PharmD

  Rutgers University Ernest Mario School of Pharmacy

  2006