Paroxysmal sympathetic hyperactivity, commonly abbreviated PSH, is a devastating autonomic disturbance that can follow traumatic brain injury, stroke, and other forms of acquired brain damage. In affected patients, the sympathetic nervous system, the branch of the autonomic system responsible for the fight-or-flight response, erupts into sudden, repeated storms of overactivity. During these paroxysms, patients may develop fever-like elevations in body temperature, rapid heart rate, surging blood pressure, profuse sweating, and abnormally fast breathing, along with episodes of muscle posturing and rigidity. The episodes can easily be mistaken for sepsis, pain, or other medical crises, delaying appropriate treatment and exposing patients to unnecessary interventions. Beyond the immediate danger, PSH has been linked to worse functional outcomes and a slower, more complicated rehabilitation course.

Diagnosing PSH in patients with prolonged disorders of consciousness, a population that includes people in unresponsive wakefulness and minimally conscious states, is particularly challenging. These patients cannot report symptoms, and their behavioral fluctuations make clinical scoring scales, such as the PSH Assessment Method used in this study, difficult to apply reliably. Prolonged disorders of consciousness already present a diagnostic and prognostic puzzle, and medical comorbidities, including autonomic dysregulation, can complicate both care and recovery. Clinicians have long sought biomarkers that could supplement behavioral observation, and the new study suggests that a well-established electrophysiological technique may fill this gap.

The sympathetic skin response is a noninvasive test that has been used for decades, originally described in the 1980s as a method for assessing unmyelinated axon dysfunction in peripheral neuropathies. When an unexpected stimulus is delivered, typically a mild electrical pulse, sympathetic cholinergic fibers trigger a change in the electrical conductance of the skin through activation of sweat glands. Electrodes placed on the palms and soles record this transient voltage shift. The resulting waveform has two principal characteristics that clinicians measure: the latency, or the time between the stimulus and the onset of the response, and the amplitude, which reflects the size of the electrical deflection and is thought to correlate with the intensity of sympathetic outflow. The response has previously been applied in research on diabetes-related autonomic neuropathy, Parkinson’s disease, multiple system atrophy, and outcome prediction after intracerebral hemorrhage, but its role in identifying PSH in disorders of consciousness had not been systematically examined.

To investigate this question, the research team conducted a retrospective observational study of 124 consecutive patients with prolonged disorders of consciousness treated between March 2022 and March 2024. Using the consensus-based PSH Assessment Method, which scores clinical features such as fever, tachycardia, hypertension, tachypnea, and sweating, the patients were divided into a PSH-positive group of 43 individuals and a PSH-negative group of 81 individuals. Each patient underwent sympathetic skin response testing, and the researchers compared elicitation rates, latencies, amplitudes, and the difference in amplitude between the two sides of the body.

The results were striking. The rate at which a response could be elicited did not differ significantly between the groups, and neither did the latency, suggesting that the basic sympathetic pathway remained intact in both. What set the groups apart was the magnitude of the response. Both the absolute amplitude and the amplitude difference between the left and right sides were significantly elevated in the PSH-positive group, with P values below 0.001. Among the 75 patients in whom a response was elicitable, the researchers built a multivariable logistic regression model that simultaneously accounted for age, score on the Coma Recovery Scale-Revised, right and left amplitudes, and the amplitude difference, confirming that the model suffered from no significant multicollinearity. In that analysis, only the amplitude difference remained independently associated with PSH, with an odds ratio of 2.08 for every 0.1 millivolt increase and a 95 percent confidence interval spanning 1.49 to 3.45.

The diagnostic performance was even more impressive. Receiver operating characteristic analysis, a standard method for evaluating how well a continuous measure separates two groups, showed that the amplitude difference alone achieved an area under the curve of 0.96, with a confidence interval of 0.93 to 0.998, a level the authors describe as excellent. In practical terms, a test with an area under the curve of 0.96 distinguishes affected from unaffected individuals with near-perfect accuracy, approaching the performance of an ideal classifier.

The researchers also performed a sensitivity analysis designed to include all 124 patients rather than only the subset with elicitable responses, coding the absence of a response as an amplitude of zero. This more conservative analysis told a somewhat different but complementary story. In this broader cohort, younger age, lower Coma Recovery Scale-Revised scores, and the amplitude difference were each independently associated with PSH, with the amplitude difference carrying an odds ratio of 1.47 per 0.1 millivolt. The amplitude difference alone yielded good diagnostic accuracy, with an area under the curve of 0.77. Notably, combining age, consciousness scale score, and the amplitude difference produced the highest accuracy of any model tested, with an area under the curve of 0.888. This suggests that the electrical measurement is most powerful when integrated with established clinical variables, particularly in settings where the response cannot be reliably elicited.

The findings carry important implications for the management of this vulnerable population. Because PSH episodes drive metabolic demand, raise intracranial pressure, and can hamper neurorehabilitation, early identification matters. A tool that requires only surface electrodes, a stimulator, and a few minutes of recording time could be performed at the bedside without transporting patients or exposing them to radiation. It could also help resolve the frequent diagnostic uncertainty in which PSH is confused with infection or other complications, a confusion that previous studies have documented even in patients with brainstem stroke. Moreover, the involvement of hypothalamic and brainstem circuitry in PSH pathophysiology, supported by diffusion tensor imaging and lesion-mapping studies, fits with the idea that sympathetic outflow measured at the skin could serve as a window onto central autonomic dysregulation.

The authors caution, as their study design demands, that the work is retrospective and observational. Causality cannot be established, and the coding of absent responses as zero millivolts in the sensitivity analysis represents an assumption that future prospective studies should refine. The population studied was drawn from a single rehabilitation setting in Nanjing, and validation in independent, multi-center cohorts will be needed before the amplitude difference can be adopted as a routine diagnostic threshold. Questions also remain about whether the measure tracks PSH severity over time or predicts response to treatment, avenues the researchers and others may pursue next.

Nevertheless, the study adds a compelling piece to a growing body of evidence that peripheral electrophysiology can illuminate central autonomic dysfunction. For families and clinicians navigating the long and uncertain road of disorders of consciousness, a reproducible, inexpensive marker that flags paroxysmal sympathetic hyperactivity could translate into earlier treatment, better-controlled episodes, and potentially improved rehabilitation outcomes. The work was supported by the National Key Research and Development Program of China and several Jiangsu Province research programs, and the authors report no conflicts of interest. If validated prospectively, the sympathetic skin response amplitude difference may become a standard component of the autonomic assessment in patients who cannot speak for themselves but whose nervous systems, as this research shows, still broadcast unmistakable signals.

Parkinson’s disease is a progressive neurological disorder caused primarily by the gradual loss or dysfunction of dopamine-producing neurons in a region of the brain called the substantia nigra. Dopamine is essential for the smooth control of movement because it helps regulate circuits within the basal ganglia, a network involved in initiating and coordinating motor activity. As dopamine signaling weakens, people may develop slowness of movement, muscle rigidity, balance problems and tremor. A resting tremor typically appears when the affected limb is relaxed and may temporarily diminish during purposeful movement. Although tremor is often the symptom that first brings a person to medical attention, its relationship to long-term disability has remained surprisingly complex.

The study focuses on “drug-resistant rest tremor,” meaning tremor that persists despite pharmacological treatment. The term does not necessarily imply that all available therapies have failed or that the symptom is completely untreatable. Rather, it describes tremor that shows limited improvement in response to medications commonly used to enhance dopamine signaling or otherwise reduce Parkinsonian motor symptoms. This distinction matters because Parkinson’s symptoms do not all arise from identical changes in the brain. Slowness and rigidity often respond more predictably to dopaminergic medication, while tremor can be influenced by additional neural circuits, including pathways connecting the basal ganglia, thalamus and motor areas of the cerebral cortex.

The researchers’ central observation is that persistent, medication-resistant resting tremor was associated with slower progression of Parkinson’s disease. In practical terms, individuals with this symptom pattern appeared less likely to develop rapid worsening across the broader range of motor and nonmotor features that define advancing disease. The result is notable because a tremor that remains visible and bothersome can create the impression that the illness is particularly severe. Yet the study indicates that the prominence or treatment resistance of tremor should not be interpreted on its own as a reliable forecast of accelerated decline. The symptom may be difficult to suppress while still coexisting with a comparatively slower evolution of other neurological impairments.

One possible explanation lies in the different neural mechanisms underlying tremor and other Parkinsonian symptoms. Rest tremor is thought to emerge from abnormal rhythmic activity within interconnected motor circuits rather than from dopamine loss alone. Electrical oscillations involving the basal ganglia and thalamocortical networks may become synchronized in a way that produces the characteristic shaking of a relaxed limb. These oscillations can sometimes persist even when medication has successfully improved movement speed or rigidity. If tremor reflects a circuit-level disturbance that is partly separable from the processes driving widespread neuronal degeneration, then drug-resistant tremor could identify a subgroup with a distinct form of disease biology rather than simply a more advanced stage.

The finding could influence how clinicians discuss prognosis with newly diagnosed patients. Parkinson’s disease is highly variable: some people remain relatively stable for many years, while others develop substantial mobility limitations, cognitive changes or autonomic symptoms over a shorter period. At present, doctors combine symptoms, examination findings, treatment response and other clinical information to estimate how a patient’s condition may evolve. The new association suggests that the behavior of tremor—including whether it responds to medication—could contribute to that assessment. It is not a standalone prognostic test, but it may become one piece of a more refined clinical profile that distinguishes tremor-dominant disease from forms characterized early by gait difficulty, postural instability or cognitive impairment.

The result also carries a message for drug development. If tremor-resistant Parkinson’s reflects abnormal network activity rather than only inadequate dopamine replacement, then treatments aimed exclusively at increasing dopamine may not fully address the symptom. Researchers may need to examine therapies that modulate pathological brain rhythms or target regions involved in tremor generation. Deep brain stimulation, for example, can influence activity in motor circuits and is already used for selected patients whose symptoms remain disabling despite medication. Future approaches might combine dopaminergic treatment with circuit-specific interventions, although the study itself does not establish that any particular therapy will alter the long-term course of the disease.

At the same time, the association should be interpreted carefully. A relationship between drug-resistant tremor and slower progression does not prove that persistent tremor protects the brain or causes Parkinson’s disease to advance more slowly. Clinical studies can reveal patterns between symptoms and outcomes, but those patterns may reflect underlying factors that are not directly measured. Differences in age at onset, disease subtype, medication exposure, genetics, coexisting conditions or the way progression is assessed could all influence the result. Tremor is also not a single uniform phenomenon: its frequency, distribution, severity and relationship to voluntary movement can vary considerably from one patient to another. Independent studies and longer-term follow-up will be important for determining how consistently the association appears across different populations.

The work nevertheless adds to a growing view of Parkinson’s disease as a collection of related but biologically diverse syndromes rather than one uniform illness. Two people may receive the same diagnosis while having different patterns of neuronal vulnerability, brain-network dysfunction and clinical progression. One patient may develop prominent tremor with relatively preserved walking and cognition, while another may experience early balance problems or cognitive symptoms with little tremor at all. Recognizing these differences is essential for precision medicine, in which prognosis and treatment are tailored to the biology of an individual’s disease. The study’s message is therefore both counterintuitive and potentially useful: the symptom that looks most dramatic may not be the symptom that best predicts future disability.

For patients and families, the findings offer neither a reason to dismiss persistent tremor nor a guarantee of a benign course. Drug-resistant tremor can remain frustrating, socially visible and functionally disruptive even when other aspects of Parkinson’s disease progress slowly. Its presence still deserves careful treatment and regular evaluation. What the research changes is the interpretation of that symptom. Rather than viewing medication-resistant resting tremor simply as evidence of more severe degeneration, clinicians may increasingly see it as a clue to a particular neurological phenotype. By separating symptom burden from disease speed, the study opens a more nuanced chapter in Parkinson’s research—one in which the brain’s most conspicuous signal may reveal not greater damage, but a different route through the disease.

Subject of Research: Parkinson’s disease progression and drug-resistant resting tremor

Article Title: Drug-resistant rest tremor is associated with slower disease progression in Parkinson’s disease

Article References: Xu, X., Liu, J., Ruan, Z. et al. Drug-resistant rest tremor is associated with slower disease progression in Parkinson’s disease. npj Parkinsons Dis. (2026). https://doi.org/10.1038/s41531-026-01524-3

Image Credits: AI Generated

DOI: 10.1038/s41531-026-01524-3

Keywords: Parkinson’s disease, resting tremor, drug-resistant tremor, disease progression, dopamine, basal ganglia, movement disorders, neurology

This novel approach hinges on the application of machine learning algorithms to decode the brain’s complex electrical activity. Similar to how a linguist learns a new language by identifying patterns and inferring meaning, the algorithm constructs a comprehensive “dictionary” of brain waveforms. By recognizing frequently occurring patterns in EEG data and interpreting them in context, the system unveils nuances that escape even the sharpest human observers. This technology promises to reveal the hidden electrical language of the brain, providing insights into neurological functions and dysfunctions.

The proof-of-concept exploration employed genetic mouse models harboring variations in the TSC1 gene, known to provoke epileptic conditions. Unlike traditional studies that require seizure occurrences during EEG monitoring, this investigation focused purely on “normal” brain activity, capturing data segments free from visible seizure episodes. The algorithm successfully identified subtle, strain-dependent EEG differences that correlated with the presence of the pathogenic gene mutation. This discerning capability demonstrated that neurological alterations manifest in baseline brain activity, even sans overt symptoms.

Notably, the research leveraged a diverse group of over 40 mice, encompassing three distinct genetic strains, which allowed the team to test the algorithm’s robustness across varied biological backgrounds. By analyzing EEG data collected over multiple days, the method demonstrated remarkable accuracy in differentiating seizure-prone mice from their healthy counterparts. These findings illuminate the possibility that epilepsy-related neural networks subtly alter brain rhythms, forming a detectable signature that could revolutionize diagnosis.

The University of Delaware collaborative effort stems from a synergistic partnership between the fields of computational neuroscience and biomedical engineering. Insights from Dr. Austin Brockmeier, an assistant professor specializing in electrical and computer engineering, melded with Dr. Amanda Hernan’s expertise in psychological and brain sciences, focusing on pediatric epilepsy. Their combined approach bridges computational rigor with clinical relevance, targeting tangible improvements in diagnostic precision and patient outcomes.

Looking forward, the research team is poised to translate these technical innovations from murine models to human clinical settings. Supported by funding from the Delaware Clinical and Translational Research ACCEL Program, ongoing studies aim to apply the AI algorithm to pediatric EEG recordings from children undergoing epilepsy evaluation at Nemours Children’s Health. Pediatric EEGs pose additional challenges due to their brevity and the heterogeneity of epilepsy manifestations, but the team remains hopeful that their refined analytical tools will uncover neural biomarkers predictive of disease onset.

A significant virtue of this AI-driven method lies in its capacity to detect brain activity changes long before seizures manifest, potentially enabling preemptive therapeutic interventions. By capturing subtle fluctuations in the brain’s electrical landscape, the system could provide neurologists with a real-time window into disease progression and treatment efficacy, circumventing the current trial-and-error approach. Such early detection would not only hasten diagnosis but also reduce the considerable psychological burden inflicted on families grappling with the uncertainty of epilepsy’s unpredictable cycles.

Beyond diagnosis, the research anticipates broader clinical impacts, including enhanced treatment management. Clinicians frequently face difficulties in assessing medication effectiveness because seizures naturally wax and wane over time. Advanced AI tools capable of continuous EEG pattern recognition could disentangle medication effects from natural seizure-free intervals, guiding data-driven decisions for optimized care.

Further horizons envision wearable EEG technologies integrated with AI analytics, permitting continuous monitoring of high-risk individuals in real-world environments. This real-time vigilance could transform patient care, offering timely alerts and personalized intervention windows. Moreover, analogous machine learning frameworks might be adapted for other complex neurological disorders, including autism spectrum disorders and attention deficit hyperactivity disorder (ADHD), underscoring the versatility and transformative potential of AI in neuroscience.

In essence, this research innovates at the nexus of neuroengineering and precision medicine. Brain-wave typing offers a novel frontier for understanding individualized neural signatures and tailoring interventions that align with each patient’s unique profile. The promise of such advances extends beyond technological novelty, holding the potential to improve lives by delivering clarity, reducing uncertainty, and ultimately guiding more effective treatments in epilepsy and beyond.

The journey from dissecting mouse brain waves to deploying AI-powered clinical diagnostics reflects a powerful example of translational neuroscience. University of Delaware’s interdisciplinary approach showcases how integrating computational algorithms with clinical neuroscience can pave the way for next-generation diagnostic tools. As the technology evolves, it will be critical to ensure robust validation, ethical data use, and seamless integration into healthcare settings to maximize benefit for patients.

Epilepsy’s characteristic unpredictability has long frustrated patients and physicians alike. By transforming the chaotic and complex electrical patterns of the brain into intelligible data, this AI approach offers hope for a future where epilepsy is diagnosed earlier, managed more effectively, and understood more deeply. The implications for reducing the emotional toll on patients and families could be profound, underscoring the vital role of technological innovation in human health.

Subject of Research: Animals

Article Title: Interpretable EEG biomarkers for neurological disease models in mice using bag-of-waves classifiers

News Publication Date: 20-May-2026

Web References:
https://iopscience.iop.org/article/10.1088/1741-2552/ae4d8c

References:
Journal of Neural Engineering, DOI: 10.1088/1741-2552/ae4d8c

Image Credits: Courtesy of The University of Delaware

Keywords: Neurological disorders, Seizures, Epilepsy, EEG, Artificial Intelligence, Machine Learning, Computational Neuroscience, Pediatric Epilepsy, Brain-wave Analysis, Precision Medicine, Biomarkers

COX-2, short for cyclooxygenase-2, is an enzyme known to play a significant role in inflammatory processes and neuroexcitation within the brain. Unlike traditional inflammatory markers that are challenging to observe in vivo within the central nervous system, COX-2’s upregulation in response to inflammatory stimuli makes it a promising candidate for studying inflammation-related neurological disorders. Researchers speculate that alterations in COX-2 levels could serve as biomarkers, linking inflammation to various neurological and psychiatric conditions.

The research team, led by Dr. Robert B. Innis from the National Institute of Mental Health, sought to develop a non-invasive imaging method to quantify COX-2 in the living human brain. This innovative approach aims to facilitate earlier detection of diseases, monitor disease progression, and assess the effectiveness of anti-inflammatory treatments. The findings may revolutionize how scientists and clinicians understand neuroinflammation’s role in disorders like Alzheimer’s disease, major depressive disorder, and Parkinson’s disease, potentially enhancing personalized medicine strategies.

The team commenced their study by evaluating the affinity of a newly developed radiotracer, ^11C-MC1, specifically targeting human COX-2. Initial experiments conducted on animal models, including PET imaging in rats and transgenic COX-2 mice, effectively confirmed the specific binding of ^11C-MC1 to COX-2, establishing a robust foundation for its application in humans. The subsequent phase involved imaging 27 healthy adult volunteers, carefully designed to validate the efficacy of this new radiotracer.

Results from the human study revealed that ^11C-MC1 efficiently crossed the blood-brain barrier, binding specifically to its established target, demonstrating a strong specificity for COX-2 in cortical regions. The findings also indicated a favorable ratio between specific COX-2 binding and background noise, highlighting the potential of this radiotracer for future clinical investigations of neuroinflammation.

Dr. Innis emphasized the implications of the findings, highlighting that neuroinflammation can exacerbate various neurological conditions, transforming the landscape of treatment and diagnosis in psychiatry and neurology. The ability to visualize COX-2 levels non-invasively in the brain signifies a substantial leap in understanding the complex interplay between inflammation and neurodegeneration, paving the way for developing targeted therapies that could eventually improve patient outcomes.

Moreover, the potential of ^11C-MC1 as a reliable tool for studying neuroinflammation raises intriguing prospects for advancing PET imaging technology. This research not only underscores the significance of COX-2 as a biomarker but also sets a precedent for exploring additional PET tracers that could further elucidate the nuances of neuroinflammatory processes.

The study aligns seamlessly with ongoing research aimed at refining imaging techniques that significantly enhance diagnostic capabilities in neurology and psychiatry. As researchers and clinicians continue to characterize the intricacies of brain disorders, the introduction of non-invasive imaging modalities becomes increasingly critical. This research represents a vital step toward developing personalized treatment plans tailored to individual patients’ unique inflammatory profiles, fostering a new era in the management of neurological conditions.

This innovative approach is supported by the National Institute of Mental Health, reflecting the dedication and investment in enhancing molecular imaging techniques. The potential of COX-2 PET imaging to integrate into clinical practice could serve as a catalyst for improving diagnostic accuracy and therapeutic monitoring, reinforcing the importance of continued exploration in this area of medical research.

In conclusion, the research heralds an exciting frontier in understanding and treating neuroinflammatory conditions, allowing for more detailed insights into COX-2’s role within the brain’s complex network. The implications of these findings extend far beyond the realm of academia, poised to influence clinical practices, enhance patient care, and advance the field of nuclear medicine.

As research progresses, the scientific community eagerly anticipates further developments in PET imaging related to neuroinflammation and its implications for various neurological and psychiatric disorders. The impact of this pioneering study is poised to resonate across the fields of neuroscience, radiology, and mental health for years to come, exemplifying the power of innovative imaging technology in unraveling the complexity of neurobiology.

Understanding the intricate relationship between neuroinflammation, disease progression, and patient outcomes is vital for developing effective therapeutic interventions. As ongoing studies expand upon these findings, the horizon for personalized medicine, focused on specific neuroinflammatory pathways, becomes increasingly attainable, reinforcing the integration of advanced imaging techniques into everyday clinical practice.

Continued collaboration and funding in this area will undoubtedly drive the future of molecular imaging and therapeutic development, ensuring researchers remain at the forefront of addressing the challenges associated with neuroinflammatory diseases and other pressing health concerns. The pursuit of knowledge in this domain serves as a critical reminder of the necessity for innovation in medical research to enhance our collective understanding of the human brain and improve patient lives.

Subject of Research: COX-2 PET imaging as a quantifier of neuroinflammation
Article Title: PET Quantification in Healthy Humans of Cyclooxygenase-2, a Potential Biomarker of Neuroinflammation
News Publication Date: March 28, 2025
Web References: https://doi.org/10.2967/jnumed.124.268525
References: N/A
Image Credits: Martin Noergaard, Intramural Research Program, National Institute of Mental Health, Bethesda, MD, USA; Department of Computer Science, University of Copenhagen, Copenhagen, Denmark.

Keywords: Neuroinflammation, COX-2, PET imaging, biomarkers, neurological disorders, inflammation, molecular imaging, positron emission tomography, personalized medicine.