Ulcerative colitis is a chronic inflammatory bowel disease that causes long-standing inflammation and ulceration of the inner lining of the large intestine and rectum. Patients typically experience abdominal cramping, bloating, diarrhea and irregular bowel movements, symptoms that can significantly impair quality of life. Modern therapies, including corticosteroids, immunomodulators and biologic agents, have become remarkably effective at suppressing the underlying inflammation, and many patients achieve what clinicians call remission. Yet a substantial fraction of these patients do not feel well even when their colonoscopies and biopsies suggest their disease is quiet.

The central question of the new study is deceptively simple: are these lingering symptoms a sign of disease activity that standard testing misses, or are they driven by an entirely different mechanism? The answer matters enormously for patients. If persistent symptoms reflect ongoing, subclinical inflammation, treatment should be intensified. If instead they arise from altered gut-brain signaling, escalating immunosuppressive therapy may expose patients to risks without any benefit, and approaches that target the nervous system may be more appropriate.

The scale of the problem is considerable. According to the source announcement, in roughly one in three patients with ulcerative colitis, symptoms resembling those of irritable bowel syndrome persist even when inflammation is minimal or absent. Irritable bowel syndrome is a functional gastrointestinal disorder, distinct from inflammatory bowel disease, in which symptoms occur without the tissue damage and inflammation characteristic of colitis. The overlap between the two conditions has long complicated clinical decision-making, because the same complaint, diarrhea or abdominal pain, can signal very different underlying processes depending on the patient.

Chang, who is vice chief of the Vatche and Tamar Manoukian Division of Digestive Diseases at UCLA, brings to the project an unusually broad portfolio of leadership roles that reflect the interdisciplinary nature of the research. She directs the Walter and Shirley Wang Center for Integrative Digestive Health, co-directs the G. Oppenheimer Center for Neurobiology of Stress and Resilience, directs the Clinical Studies and Database Core of the Goodman-Luskin Microbiome Center and serves as program director of the UCLA Gastroenterology Fellowship Program. Her co-investigator, Sands, leads the gastroenterology division at Mount Sinai, one of the country’s major centers for inflammatory bowel disease care and research.

Methodologically, the study is designed to interrogate both sides of the gut-brain axis, the bidirectional communication network linking the enteric nervous system of the digestive tract with the central nervous system. The researchers will deploy brain imaging to examine how the brains of patients with quiescent colitis and persistent symptoms differ in their processing of visceral signals, an approach that has previously revealed altered pain and arousal networks in functional gastrointestinal disorders. In parallel, participants will wear wearable technology capable of measuring autonomic nervous system function, providing continuous, real-world data on heart rate variability and other physiological markers that reflect the balance between sympathetic and parasympathetic activity.

The autonomic nervous system is a plausible culprit in this context. It regulates gut motility, secretion and sensitivity, and disruptions in its signaling have been implicated in both inflammatory bowel disease and irritable bowel syndrome. Chronic inflammation can remodel autonomic pathways, and conversely, altered autonomic tone can influence immune activity in the gut. By capturing these measurements longitudinally in patients whose inflammation has subsided, the team hopes to determine whether persistent symptoms correlate with measurable autonomic dysfunction, with residual inflammatory activity, or with some interaction between the two.

Perhaps the most clinically ambitious component of the study is its test of a potential intervention. The researchers will evaluate whether non-invasive vagal nerve stimulation can improve symptoms in affected patients. Vagal nerve stimulation, which delivers electrical impulses to the vagus nerve, the main conduit of parasympathetic signaling between the brain and the body, has been used for years in neurology and is increasingly explored for disorders involving gut-brain dysregulation. Because the vagus nerve modulates both immune responses and visceral sensory processing, stimulating it could in principle address the mechanisms the study is designed to uncover, offering a therapy that works through the nervous system rather than the immune system.

The ultimate goal, according to the announcement, is to support new personalized treatment approaches for ulcerative colitis patients who have little to no active inflammation yet continue to experience persistent IBS-like symptoms. Personalization is the key word. If the study succeeds in distinguishing symptom drivers at the level of individual patients, clinicians could one day match therapies to mechanisms, reserving anti-inflammatory escalation for those with hidden inflammation and offering neuromodulation or related approaches to those whose symptoms stem from altered gut-brain signaling. That would represent a meaningful shift in how gastroenterologists manage the growing population of patients in inflammatory bowel disease remission who still do not feel well.

The grant also reflects a broader trend in digestive disease research, in which the traditional boundary between organic and functional disorders is dissolving. Conditions once dismissed as purely psychological are now understood to involve measurable alterations in neural signaling, immune activity and the microbial environment of the gut. By combining neuroimaging, wearable physiology and a neuromodulation trial within a single five-year program, the UCLA and Mount Sinai team is positioned to generate exactly the kind of mechanistic evidence that could move persistent post-inflammatory symptoms from the margins of gastroenterology into the mainstream of precision medicine. For the roughly one in three patients whose colitis has quieted but whose symptoms have not, the study offers the prospect that their suffering will finally be explained, and treated, on its own terms.

Subject of Research: Persistent IBS-like symptoms in ulcerative colitis patients with resolved inflammation and gut-brain signaling mechanisms

Article Title: UCLA Health awarded $4 million NIH grant to study why ulcerative colitis symptoms persist after inflammation subsides

Article References: UCLA Health awarded $4 million NIH grant to study why ulcerative colitis symptoms persist after inflammation subsides. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: ulcerative colitis, irritable bowel syndrome, gut-brain axis, NIH grant, UCLA Health, Mount Sinai, vagal nerve stimulation, autonomic nervous system, brain imaging, inflammatory bowel disease, gastroenterology, Lin Chang

The measurement in question is the Evans index, a ratio that radiologists have used for more than eight decades. First described by William Evans in 1942 as a way to estimate ventricular enlargement on pneumoencephalograms, the index is calculated by dividing the width of the frontal horns of the brain’s lateral ventricles by the maximum internal width of the skull at the same level. A value above 0.3 has traditionally signaled enlarged ventricles, whether from hydrocephalus or cerebral atrophy. What is new, and what the latest research confirms, is that this same ratio carries powerful prognostic information for patients whose cancer has spread to the leptomeninges.

In a study published in the Journal of Neuro-Oncology, researchers at the Instituto Nacional de Cancerología in Mexico City analyzed records from 345 patients with neoplastic meningitis from solid tumors who were seen between January 2010 and December 2024 and had imaging studies available for review. The cohort, one of the largest of its kind reported to date, was dominated by breast cancer, which accounted for 66 percent of cases, followed by lung cancer at 12 percent, genitourinary cancers at 6 percent, and head and neck cancers at 5 percent. This distribution reflects the epidemiology of leptomeningeal disease in the modern era, where breast cancer patients, living longer with better systemic therapies, increasingly face this devastating complication.

The headline finding is stark. Across the entire cohort, median overall survival was 6.9 months, with a 95 percent confidence interval of 5.5 to 8.3 months, underscoring how lethal the condition remains even at a major cancer center. But when the researchers stratified patients by their Evans index, the survival curves separated dramatically. Patients with an index of 0.3 or below had a median survival of 7.36 months, while those whose index exceeded 0.3 survived a median of only 2.99 months, a difference that reached statistical significance with a P value of 0.020. In practical terms, a single ratio measured on a scan that every patient already undergoes more than doubled the expected survival gap.

What makes this finding scientifically credible rather than merely suggestive is the rigor of the statistical adjustment. Enlarged ventricles could simply be a marker of older age, worse neurological status, or more advanced disease, all of which independently shorten survival. To rule out such confounding, the investigators built a multivariable model that accounted for age, sex, primary tumor type, Karnofsky Performance Score, neurological symptoms, activity of extracranial disease, and cerebrospinal fluid levels of both protein and lactic dehydrogenase, a marker of tissue breakdown. Even after all of these adjustments, an Evans index above 0.3 remained independently associated with mortality, carrying a hazard ratio of 1.95 with a 95 percent confidence interval of 1.09 to 3.50 and a P value of 0.024. In other words, patients with enlarged ventricles faced nearly twice the risk of death at any given time compared with those whose ventricles were normal in size.

The biological logic behind this association is plausible. Neoplastic meningitis can impair the resorption of cerebrospinal fluid, leading to a form of communicating hydrocephalus in which the ventricles gradually swell. Ventricular enlargement on imaging may therefore be a visible signature of disrupted fluid dynamics, rising intracranial pressure, and accumulating tumor burden within the nervous system. It may also capture the cumulative damage that cancer inflicts on the brain’s ability to manage its own fluid environment. If so, the Evans index functions less as a generic measure of brain shrinkage and more as a window into the pathophysiology of the disease itself, explaining why it retains predictive power even after accounting for clinical variables.

The study is particularly valuable because it provides what scientists call external validation. The prognostic role of ventricular size in leptomeningeal metastasis had been proposed before, most notably in a large European study published in Neurology in 2024 that examined ventricular size and its dynamics in patients with solid tumors. But prognostic biomarkers in oncology have a notorious history of failing when tested outside the populations in which they were discovered. By independently confirming the association in a distinct cohort, drawn from a different continent and health system, and by demonstrating robustness in a fully adjusted model, the Mexican team has strengthened the case that the Evans index is a genuine and transferable prognostic tool rather than a statistical fluke.

The clinical implications could be substantial. Decisions in neoplastic meningitis are among the most difficult in neuro-oncology. Options range from intrathecal chemotherapy and focal radiation to novel systemic agents that penetrate the blood-brain barrier, yet all carry toxicity, and evidence for survival benefit is limited to selected patient groups. Guidelines from the European Association of Neuro-Oncology and the European Society for Medical Oncology emphasize that prognosis should guide treatment intensity, but the prognostic tools available to date, such as performance status and cerebrospinal fluid markers, are imperfect. A ratio that requires nothing more than calipers on an existing magnetic resonance or computed tomography scan could be integrated into clinical trials as a stratification variable, into prognostic scores alongside existing clinical factors, and into everyday conversations between oncologists and patients about what lies ahead.

There are, of course, caveats. The study was retrospective and conducted at a single institution, which raises the possibility of selection bias, and the data could not be made publicly available because they contain potentially identifiable patient information. The Evans index is also a static measurement, capturing ventricular size at a single moment, whereas some researchers argue that serial changes in ventricular caliber over time may be even more informative. Prospective studies that track the index dynamically, and that test whether it improves decision-making in real time, would be the natural next step. Still, the effect size observed here, nearly a doubling of mortality risk after adjustment, is large enough that few would dismiss it.

For a disease that has resisted decades of therapeutic progress, the emergence of a cheap, universally available prognostic marker is a meaningful advance. The Evans index was never designed for this purpose; it was conceived in an era of air encephalography as a crude gauge of ventricular dilation. That it now appears to forecast survival in patients with cancer invading their central nervous system is a reminder that sometimes the most powerful tools in modern medicine are the simplest ones, hiding in plain sight on scans that clinicians already order every day. For the thousands of patients diagnosed with neoplastic meningitis each year, and for the physicians wrestling with how best to treat them, this humble ratio may soon become part of the standard vocabulary of care.

Subject of Research: Prognostic value of the Evans index, a ventricular size ratio on brain imaging, in patients with neoplastic meningitis from solid tumors

Article Title: Prognostic value of the Evans index in neoplastic meningitis: a real-world cohort study

Article References: González-Vázquez, A., Lorenzana-Mendoza, N. A., Reyes Pérez, J. A., & Cacho-Díaz, B. (2026). Prognostic value of the Evans index in neoplastic meningitis: a real-world cohort study. Journal of Neuro-Oncology, 179(2), Article 71. https://doi.org/10.1007/s11060-026-05780-4

Image Credits: AI Generated

DOI: 10.1007/s11060-026-05780-4

Keywords: neoplastic meningitis, leptomeningeal metastasis, Evans index, prognostic biomarker, brain imaging, hydrocephalus, ventricular enlargement, overall survival, breast cancer, neuro-oncology, cerebrospinal fluid, validation study

The research, led by Valeriia Abramova of the Computer Vision and Robotics Institute at the University of Girona, together with neurologists at Hospital Universitari Dr Josep Trueta, addresses a gap that has limited existing AI stroke tools. Most current detection systems treat the problem as a simple yes-or-no question: is a large vessel occlusion present, or not? But clinicians need more than that. The choice of endovascular strategy differs depending on which vessel segment is occluded. Blockages in the posterior circulation, such as the basilar artery, are more often treated with balloon angioplasty and permanent stents, while occlusions in the anterior circulation typically call for stent thrombectomy or catheter aspiration. Clinical guidelines for posterior circulation occlusions are still evolving, and uncertainty persists over optimal anesthesia and treatment approaches. Knowing the precise vessel involved therefore shapes intervention planning, prognosis estimation, and clinical interpretation in ways a binary alarm cannot.

Commercial software packages such as RapidLVO, Viz-LVO, e-CTA, and StrokeViewer-LVO have brought automated detection into hospitals, but they remain constrained by reduced sensitivity for distal occlusions, vulnerability to imaging artifacts that trigger false positives, and dependence on standardized acquisition protocols that can hamper generalization across institutions. Recent research efforts have pushed toward explicit localization of occlusions, including two-stage convolutional neural network pipelines on four-dimensional CT angiography and self-configuring object detection frameworks applied to maximum intensity projection images. Yet most of these approaches stop at drawing a bounding box around the affected region without linking that region to a specific, named vessel segment. The Girona team’s contribution is to do both tasks at once, within a single unified framework.

Technically, the researchers adapted the state-of-the-art nnDetection framework, a self-configuring three-dimensional object detection method built on the Retina U-Net architecture. This network fuses the RetinaNet one-stage detector with a U-Net encoder-decoder, extracting multi-level image features through convolutional layers with instance normalization and ReLU activation, while lateral and transposed convolutional connections build a feature pyramid. Two sub-networks operate on the pyramid levels: one for classification and one for bounding box regression, trained respectively with binary cross-entropy loss and a generalized intersection-over-union loss, with additional semantic segmentation supervision during training. The classification branch labels each detected occlusion as belonging to one of three clinically crucial segments: the basilar artery, the terminal internal carotid artery, or the M1 segment of the middle cerebral artery. Model training used a fixed patch size of 128 by 128 by 128 voxels, sixty epochs, and stochastic gradient descent with Nesterov momentum, implemented in PyTorch on an NVIDIA GeForce GTX 1080 Ti GPU.

The study’s most distinctive twist lies in how it constrains the search space anatomically. The Circle of Willis is the ring-shaped arterial network at the center of the brain that supplies blood to cerebral tissue, and it encompasses precisely the vessels most often affected by these occlusions. Because manual segmentation of this structure is laborious and ground truth vessel masks were unavailable for the datasets, the team trained a standard nnU-Net segmentation model on data from the TopCoW challenge, a competition dedicated to topology-aware Circle of Willis segmentation in CT and MR angiography spanning diverse anatomical variants. Their segmentation model achieved a Dice score of 0.944 plus or minus 0.026. Applied to the stroke scans, the automatic Circle of Willis segmentation was expanded by 50 voxels in all three dimensions, a margin chosen to guarantee that all occlusions in the dataset fell inside the resulting region of interest.

This anatomical prior enabled a head-to-head comparison of two strategies. In the global approach, the network trained and inferred on the full CT angiography volume. In the local approach, images were cropped to the Circle of Willis-derived region before training and inference. The development dataset comprised 179 CT angiography scans acquired on a Philips Ingenuity scanner at Hospital Dr. Josep Trueta, all containing occlusions annotated with three-dimensional bounding boxes by an expert neurologist. Of these, 143 scans were used for training with five-fold cross-validation, while 36 were held out for internal testing. Class distribution reflected the natural epidemiology of these strokes: the M1 segment dominated at 67 percent of cases, terminal internal carotid artery occlusions comprised 25 percent, and basilar artery occlusions were rare at 8 percent.

The results on the internal test set were strikingly strong for both variants. The global approach achieved a detection sensitivity of 0.92 at 0.08 false positives per image, rising to 0.97 at 0.20 false positives per image, while the local approach matched the same sensitivities at just 0.03 and 0.13 false positives per image respectively. A case-level paired bootstrap analysis at a fixed operating point of 0.1 false positives per image found a mean sensitivity difference of 0.000 with a 95 percent confidence interval spanning negative 0.081 to positive 0.081, confirming no systematic performance gap between the strategies. Each approach missed only a single occlusion. Localization accuracy was equally tight: the mean three-dimensional distance between predicted and ground truth bounding box centers was 1.76 millimeters for both strategies. Interestingly, the full-volume model generated more low-confidence false positives, whereas the region-restricted model produced fewer spurious detections overall.

Classification performance held up nearly as well. On the internal test set, overall accuracy reached 94 percent for the global approach and 91 percent for the local approach, with Cohen’s kappa statistics of 0.88 and 0.82, both indicating almost perfect agreement with expert ground truth. Every basilar artery occlusion was classified correctly by both approaches, a success the authors attribute to the basilar artery’s distinctive, isolated position at the base of the brain, which reduces ambiguity. The main classification shortfall appeared in the terminal internal carotid artery class, where accuracy fell from 89 percent globally to 78 percent locally. Counting detection and classification together, the occlusion was both found and correctly labeled in 33 of 36 cases for the global approach and 32 of 36 for the local one.

Generalization was tested on the independent CODEC-IV benchmark, consisting of 48 CT perfusion-derived CT angiography scans from different scanners and hospitals. Detection sensitivity dropped to 0.71, but much of that decline traced to a mismatch in annotation conventions: the benchmark’s ground truth boxes were uniformly small and pinpointed the occlusion site, while the Girona training boxes captured the entire clot extent. When the true positive threshold was relaxed to 2 percent intersection over union, sensitivity climbed to 0.90 for the global approach and 0.88 for the local one, with mean center-to-center distances of roughly 2.4 millimeters confirming that the models were accurately placing their predictions even when box dimensions diverged. On a supplementary inference-only evaluation of the IACTA-EST challenge dataset, which included 51 occlusion-negative cases, the global model achieved an area under the curve of 0.95 in separating positive from negative cases, against 0.84 for the local model, hinting that a well-chosen confidence threshold could suppress false alarms in clinical triage.

Perhaps the most consequential number is temporal. Restricting inference to the Circle of Willis region made the local approach approximately 3.3 times faster than the global one, cutting mean processing time per case from about 227 seconds to roughly 89 seconds. In acute stroke care, where treatment delays of minutes measurably worsen neurological outcomes, that speedup matters even before accounting for the reduced computational cost of running on modest hospital hardware. The authors acknowledge limitations, including a single-center training cohort from one scanner, a small number of basilar artery cases, and class imbalance mirroring natural disease prevalence. Nonetheless, by coupling a deep object detection framework to an automatically derived vascular landmark, the study demonstrates that anatomical knowledge can be baked directly into machine learning pipelines, delivering detection and vessel-level diagnosis in one pass and pointing toward AI assistants that fit realistically into the breakneck rhythm of a stroke unit.

Subject of Research: Deep learning detection and vessel-level classification of large vessel occlusions in brain CT angiography guided by Circle of Willis localization

Article Title: Circle of Willis-Guided Localization for Simultaneous Detection and Classification of Large Vessel Occlusions in Brain CTA

Article References: Abramova, V., Oliver, A., Lal-Trehan Estrada, U. M., Hamadache, R. E., Martínez Arias, P., Freixenet, J., Terceño, M., Silva, Y., & Lladó, X. (2026). Circle of Willis-Guided Localization for Simultaneous Detection and Classification of Large Vessel Occlusions in Brain CTA. Neuroinformatics, 24(4), Article 62. https://doi.org/10.1007/s12021-026-09817-x

Image Credits: AI Generated

DOI: 10.1007/s12021-026-09817-x

Keywords: large vessel occlusion, stroke, CT angiography, Circle of Willis, deep learning, nnDetection, neuroinformatics, medical imaging, endovascular treatment, brain imaging, artificial intelligence, stroke diagnosis

A team at the Montréal Neurological Institute of McGill University has now published a detailed account of how they have managed this complexity, both inside their own laboratory and for the wider community. Writing in Nature Protocols, Zhen-Qi Liu, Vincent Bazinet and colleagues, led by Bratislav Misic, describe netneurotools, an open-source Python toolkit that has been continuously developed and maintained by the laboratory’s trainees since its inception. The paper is both a practical protocol for carrying out network neuroscience analyses and a manifesto for a different way of building scientific software, one in which the informal, ad hoc scripts that every laboratory accumulates are treated as a legitimate, shareable scientific resource.

The philosophy behind the toolkit is disarmingly simple. The authors describe netneurotools as the Swiss army knife of the laboratory: a collection of functions and routines that the group uses constantly but that belong to no established pipeline or package. Where large neuroimaging platforms excel at well-defined tasks such as preprocessing functional magnetic resonance imaging or reconstructing diffusion data, they are not designed to interoperate with one another. The gaps between them, the authors argue, are precisely where trainees are forced to improvise isolated heuristics and workarounds. netneurotools formalizes those improvisations, turning scattered personal scripts into documented, tested, reusable code that anyone can pick up.

Technically, the toolkit is built on the familiar foundations of the scientific Python ecosystem, drawing on array programming libraries such as NumPy, the algorithms of SciPy, machine learning utilities from scikit-learn, graph structures from NetworkX, and file-reading capabilities from nibabel and nilearn. It extends these foundations with capabilities that are specific to network neuroscience. These include utilities for handling cortical surface meshes and transforming data between the many parcellation schemes that fragment the field, from volumetric atlases to multi-resolution cortical subdivisions. Because a brain map computed on one parcellation cannot be directly compared with a map on another, robust surface-based resampling and interpolation are among the most valuable functions the package provides, sparing researchers from the error-prone manual conversions that have long been a rite of passage in the field.

Network analysis itself forms a second major pillar. The toolkit implements routines for generating group-representative structural brain networks using distance-dependent consensus thresholding, an approach designed to respect the fact that anatomical connection probability falls with physical distance in the brain. It provides algorithms for randomizing weighted networks while preserving key topological properties, a crucial step in any null-model-based analysis, including a simulated annealing method developed by the same group for rigorously controlling network structure. It also implements a library of network communication models, which ask how signals could theoretically travel along the wiring of the brain, from classical shortest-path routing inspired by the Floyd, Roy and Warshall algorithms to navigation strategies and diffusion-style models that better capture the biology of neural signaling.

Statistical machinery rounds out the package. Network neuroscience increasingly relies on spatial statistics, because brain measures are arranged in space and neighboring regions are not independent. netneurotools includes implementations of spatial autocorrelation measures such as Moran’s I and Geary’s C, along with bivariate extensions that quantify spatial associations between two brain maps. It offers null models that preserve the spatial autocorrelation of data before statistical testing, a safeguard against the inflated significance that naive permutation schemes can produce. Dominance analysis, a technique from psychology for assessing the relative importance of correlated predictors in regression, is also available, addressing a common challenge when multiple brain properties compete to explain a neural phenomenon.

The protocol paper walks readers through complete workflows that chain these functions together to answer neurobiologically meaningful questions. Example analyses include relating brain network organization to microarchitectural features such as receptor distributions and cell-type composition, examining how strongly the brain’s structural wiring constrains its functional dynamics across different imaging modalities, and generating spatially informed null models for testing whether an observed pattern of structure-function coupling is unusual. Workflow diagrams in the paper show how data flow from raw parcellated imaging outputs, through the toolkit’s conversion, modeling and statistical layers, to interpretable figures, giving trainees a template they can adapt to their own projects rather than a black box they must trust blindly.

Beyond its technical content, the article makes a cultural argument that is likely to resonate far beyond one laboratory. The authors position netneurotools as a necessary counterweight to out-of-the-box software packages, arguing that smaller, ad hoc functions deserve recognition as real scientific contributions. By opening a window into the inner workings of a laboratory, the toolkit invites a new kind of discourse among research groups, one in which the unglamorous glue code that actually holds a project together is shared, critiqued and improved collectively. The package has been open to contributions from neuroscientists across the globe since its inception, and its development by trainees reflects a deliberate pedagogical choice: writing and maintaining shared infrastructure is itself a form of scientific training.

The timing of this publication is significant. A recent assessment of open-source neuroscience software described the field’s dependence on volunteer-maintained tools as precarious, and the proliferation of analysis pipelines has made reproducibility a persistent concern. By documenting their toolkit in a peer-reviewed protocols journal, the Misic laboratory is making a case that sustainability in computational neuroscience depends not only on large, polished platforms but also on transparent, community-maintained collections of mid-sized tools that bridge the gaps between them. The approach aligns with the FAIR principles for research software, which call for software to be findable, accessible, interoperable and reusable.

For a field whose data keep multiplying in modality and scale, the message is practical and quietly radical at once. The connectome may be the most complicated object ever mapped, but the daily work of studying it is made of thousands of small, concrete operations: converting a file, resampling a surface, rewiring a network, testing a spatial statistic. netneurotools gathers those operations into one open, living toolbox, and in doing so suggests that the health of network neuroscience may depend as much on how generously its practitioners share their everyday tools as on any single breakthrough analysis.

Subject of Research: An open-source, trainee-developed Python toolkit for network neuroscience analysis and brain imaging data integration

Article Title: netneurotools: a trainee-oriented approach to network neuroscience

Article References: Liu, Z.-Q., Bazinet, V., Hansen, J. Y., Milisav, F., Luppi, A. I., Ceballos, E. G., Farahani, A., Suarez, L. E., Shafiei, G., Markello, R. D., & Misic, B. (2026). netneurotools: a trainee-oriented approach to network neuroscience. Nature Protocols. https://doi.org/10.1038/s41596-026-01446-7

Image Credits: AI Generated

DOI: 10.1038/s41596-026-01446-7

Keywords: netneurotools, network neuroscience, Python toolkit, brain imaging, connectomics, open-source software, brain networks, spatial statistics, null models, FAIR principles, McGill University, Nature Protocols

Long COVID affects approximately five percent of the global population, manifesting as a constellation of persistent symptoms—many neurological—lasting months beyond the initial SARS-CoV-2 infection. Until now, the pathophysiological mechanisms underlying these chronic neuropsychiatric symptoms remained poorly understood, hindering therapeutic development. This study marks a pivotal advance by characterizing clear dopaminergic neuronal injury in affected individuals.

Dopamine neurons, crucial for motivation, movement, and cognition, predominantly reside in the striatum, a deep brain region. Using PET imaging with a highly specific marker for vesicular monoamine transporter 2 (VMAT2), the researchers quantified dopamine neuron integrity in long COVID patients versus healthy controls. The findings revealed reduced VMAT2 binding across the striatum’s ventral and dorsal components, correlating tightly with clinical symptom severity. Depletion in the ventral striatum aligned with diminished motivation, while loss in the dorsal putamen corresponded to bradykinesia, and reductions in the caudate putamen mirrored memory deficits.

Dr. Jeffrey Meyer, senior author and senior scientist at CAMH’s Brain Health Imaging Centre, emphasized that this is the strongest evidence to date implicating dopaminergic neurodegeneration in long COVID. Such neuronal loss has been extensively documented in other neurological disorders associated with overlapping symptoms, providing a pathophysiological framework for understanding the enduring neurological impairments reported by long COVID patients.

This research builds decisively on previous findings by the same group demonstrating elevated neuroinflammation within dopaminergic brain regions in long COVID. Inflammation is known to mediate dopamine neuron injury, and this study’s direct evidence of reduced dopamine transporter density offers a mechanistic link tying inflammation to neuronal dysfunction and symptomatology.

The implications for treatment are profound. Current long COVID therapeutic strategies largely neglect the dopaminergic system, focusing instead on inflammation and immune modulation. The CAMH team suggests that repurposing drugs aimed at boosting dopamine neurotransmission—such as dopamine precursors or inhibitors of dopamine metabolism—could represent a rational and promising intervention strategy.

Importantly, the study sets the stage for clinical trials to test dopamine-targeted therapies in long COVID. With collaboration from the University Health Network, trials will soon explore whether enhancing dopamine signaling can alleviate long COVID’s neuropsychiatric sequelae including fatigue, memory impairment, and motivational deficits.

For millions suffering worldwide, these findings offer both validation and hope—affirming that their symptoms have a biological basis and opening new avenues for evidence-based treatments. As research progresses, understanding the dopaminergic basis of long COVID may reshape how this multifaceted syndrome is approached clinically and scientifically.

Subject of Research: People
Article Title: Loss of vesicular monoamine transporter 2 in striatum of long COVID and relationship to neuropsychiatric symptoms
News Publication Date: July 10, 2026
Web References: https://www.thelancet.com/journals/EBIOM/article/PIIS2352-3964(26)00252-5/fulltext
References: DOI: 10.1016/j.ebiom.2026.106339
Keywords: Long COVID, dopamine neurons, striatum, PET imaging, neuroinflammation, neuropsychiatric symptoms

Mental health disorders frequently present overlapping symptoms and high rates of comorbidity, making clinical diagnosis and targeted intervention challenging. Until now, the extent to which these common disorders share underlying neurobiological mechanisms remained insufficiently understood. By leveraging the power of large sample sizes and advanced neuroimaging techniques, the study aimed to dissect the intricate circuitry involved in insomnia, depression, and anxiety—three conditions that collectively pose a significant public health burden worldwide.

Central to the findings is a consistent association between more severe symptoms across all three disorders and reductions in global brain metrics, specifically a smaller total cortical surface area and decreased thalamic volume. The cortex, responsible for higher-order cognitive functions and sensory processing, alongside the thalamus, which acts as a critical relay hub for sensory and motor signals, appear to play pivotal roles in the pathophysiology that spans these mental illnesses. These morphological reductions might reflect shared vulnerabilities that impair neural communication and processing, contributing to symptom exacerbation.

Moreover, functional connectivity analyses revealed weakened neural network interactions corresponding to greater symptom severity across insomnia, depression, and anxiety. Connectivity within large-scale brain systems, including those responsible for regulation of emotions and cognitive control, showed compromised integrity, potentially underpinning the common behavioral and psychological manifestations observed in these disorders. This cross-disorder neural signature hints at a transdiagnostic mechanism, suggesting that interventions enhancing network coherence might yield broad therapeutic benefits.

Beyond these cross-cutting markers, the study delineated nuanced, disorder-specific brain alterations that map onto distinct symptomatology. In insomnia, for example, the researchers identified smaller volumes in subcortical areas linked to reward processing. These regions—often overlooked in sleep research—may mediate dysregulated motivational and arousal states that perpetuate sleep disturbances, offering novel targets for insomnia therapeutics.

Depressive symptoms displayed a unique profile characterized by notable cortical thinning in regions implicated in language, reward, and limbic processing. The thinning observed in these areas potentially mirrors neurodegenerative or neuroplastic changes driven by chronic mood dysregulation and sustained emotional distress, elucidating why individuals with depression often present cognitive and affective impairments alongside mood symptoms.

Anxiety symptom severity correlated with weakened amygdalar reactivity and diminished functional connectivity in regions rich in dopamine, glutamate, and histamine neurotransmitters. The amygdala’s centrality in threat detection and fear processing is well established, and the modulation of its activity through neurotransmitter systems further refines anxiety’s neurochemical landscape. Discovering these neurotransmitter-specific connectivity disruptions underscores the importance of targeting molecular pathways in anxiety disorder treatments.

A particularly intriguing aspect of the study was the frequent involvement of circuits connecting the amygdala, hippocampus, and medial prefrontal cortex across the symptom-specific associations. This triad is critical for emotional regulation, memory processing, and executive functions—domains frequently impaired across insomnia, depression, and anxiety. The anatomical and functional integrity of this circuit likely governs the nuanced interplay between these disorders, supporting the notion that they exist on a spectrum rather than as isolated entities.

Methodologically, the research harnessed multimodal MRI, combining structural scans that resolve fine-grained anatomy with functional imaging capturing real-time neural activity. This integrative approach allowed scientists to simultaneously assess volumetric, cortical thickness, and connectivity parameters, painting a comprehensive picture of brain alterations while controlling for confounding variables inherent in observational imaging studies. The immense sample size enhanced statistical power and generalizability, surmounting limitations that have traditionally hampered psychiatric neuroimaging research.

Clinically, these insights open avenues for refining diagnostic frameworks and personalizing treatment regimens. Transdiagnostic neurobiological markers might inform the development of biomarker-driven interventions that address overlapping brain dysfunctions, while symptom-specific neural signatures could guide precision medicine approaches targeting distinct pathological processes within each disorder. For instance, therapies augmenting thalamic volume or cortical surface area could mitigate symptom severity broadly, whereas pharmacological modulation of neurotransmitter circuits may better alleviate anxiety-specific disturbances.

The implications extend to the design of future studies as well. By establishing the shared and exclusive neural substrates of these intertwined disorders, researchers can prioritize mechanistic investigations focusing on the amygdala–hippocampal–prefrontal circuitry and neurotransmitter systems. This prioritization may accelerate the translation of neuroscientific discoveries into novel pharmacotherapies and neuromodulatory interventions, such as deep brain stimulation or transcranial magnetic stimulation, refined according to individual symptom profiles.

Furthermore, the work underscores the importance of large-scale biobanks and population-based neuroimaging repositories in advancing psychiatric neuroscience. The unprecedented scale of this study was crucial not only for detecting subtle structural brain changes but also for teasing apart complex transdiagnostic patterns that smaller cohorts might obscure. This paradigm exemplifies how big data collaborations can illuminate nuanced biological networks underpinning mental health disorders.

While offering substantial progress, the study also highlights ongoing challenges. The causal relationships between brain alterations and psychiatric symptoms remain to be elucidated, necessitating longitudinal designs and interventional studies. Additionally, genetic and environmental contributions to the observed brain changes warrant deeper exploration, potentially via integrating genomics, epigenetics, and lifestyle factors to construct comprehensive etiological models.

In summary, this multimodal neuroimaging investigation redefines our understanding of insomnia, depression, and anxiety by pinpointing both shared cerebral vulnerabilities and disorder-specific neural fingerprints. Its findings advocate for a dimensional, circuit-based conceptualization of mental health disorders—an approach poised to revolutionize diagnostics and therapeutic innovation. As psychiatric research continues to pivot toward precision neuroscience, such landmark studies will be instrumental in unraveling the tangled web of brain-behavior relationships that define human mental well-being.

Subject of Research: Neurobiological correlates of symptom severity in insomnia disorder, major depressive disorder, and anxiety disorders.

Article Title: Multimodal brain imaging of insomnia, depression and anxiety symptoms indicates transdiagnostic commonalities and differences.

Article References:
de Lange, S.C., Tissink, E., Bresser, T. et al. Multimodal brain imaging of insomnia, depression and anxiety symptoms indicates transdiagnostic commonalities and differences. Nat. Mental Health (2025). https://doi.org/10.1038/s44220-025-00412-8

Image Credits: AI Generated