The study’s design was straightforward but rigorous. The researchers collected clinical data and contrast-enhanced cranial MRI images from all patients admitted with intracranial tuberculosis between January 2018 and March 2026. Each patient underwent follow-up lasting at least 12 months, after which outcomes were graded using the modified Rankin Scale, a standard clinical measure of functional disability. Patients scoring 0 to 2, meaning little to no disability, formed the favorable prognosis group of 67 individuals, while those scoring 3 to 6, ranging from moderate disability to death, formed the poor prognosis group of 52 patients.

Contrast-enhanced MRI was central to the investigation because it reveals pathological changes that plain CT and non-contrast imaging often miss. Gadolinium-based contrast agents leak across disrupted blood-brain barriers, highlighting thickened, inflamed meninges, tuberculomas, abscesses, and the characteristic basal enhancement pattern of tuberculous meningitis. This technique allowed the team to classify patients into imaging subtypes and to document complications such as cerebral infarction, hydrocephalus, and cerebral arteritis with far greater sensitivity than conventional approaches.

When the two prognosis groups were compared, striking differences emerged. Patients in the poor prognosis group were older and more frequently male. They more often presented with meningeal-type or mixed-type intracranial tuberculosis rather than the parenchymal form, and they showed significantly higher rates of meningeal thickening, involvement of the basal and pericallosal cisterns, multi-site meningeal involvement, acute cerebral infarction, hydrocephalus, and cerebral arteritis. Conversely, parenchymal-type disease and tuberculomas were less common in the poor outcome group, suggesting that disease confined to brain tissue with discrete granulomas may carry a better outlook than diffuse meningeal inflammation.

To move beyond simple associations, the researchers performed multivariate logistic regression, a statistical technique that isolates the independent contribution of each variable while controlling for all others. Three factors emerged as independent risk factors for poor long-term prognosis: age, acute cerebral infarction, and hydrocephalus. This finding is clinically meaningful because it separates true prognostic drivers from markers that merely travel alongside them. Meningeal thickening and arteritis, for example, were more common in poor-outcome patients but did not retain independent predictive value once infarction and hydrocephalus were accounted for.

The predictive model built from these three variables performed impressively well. Evaluated with receiver operating characteristic analysis, it achieved an area under the curve of 0.861, sensitivity of 70.1 percent, specificity of 89.6 percent, and overall accuracy of 80.7 percent. In practical terms, the model correctly identified nearly nine out of ten patients who would ultimately do well, while capturing seven out of ten of those destined for poor outcomes. An AUC above 0.85 is generally considered strong discrimination for a clinical prediction model, particularly one built on routinely available imaging and demographic data.

Each of the three predictors tells a coherent biological story. Older patients have less physiological reserve, more comorbidities, and blunted immune responses that impair clearance of infection and repair of tissue damage. Acute cerebral infarction in tuberculous meningitis typically results from inflammatory occlusion of small perforating arteries at the base of the brain, where exudate accumulates in the subarachnoid space and triggers vasculitis. Once brain tissue is lost to infarction, recovery is limited, and the infarct itself serves as a proxy for both disease severity and vascular involvement. Hydrocephalus, meanwhile, arises when inflammatory debris obstructs cerebrospinal fluid flow through the ventricular system or impairs its resorption at the arachnoid granulations, raising intracranial pressure and compressing neural tissue.

The Evans index, a ratio comparing the width of the frontal horns of the lateral ventricles to the maximal internal diameter of the skull, was among the imaging variables recorded, providing a quantitative measure of ventricular enlargement. Its inclusion alongside direct assessments of hydrocephalus reflects the study’s effort to capture both qualitative radiological judgments and reproducible morphometric markers. Similarly, the initial Glasgow Coma Scale score was documented for each patient, anchoring the clinical assessment at admission and allowing the investigators to account for baseline neurological status.

The implications for clinical practice are substantial. Patients with intracranial tuberculosis who present with advanced age, evidence of acute infarction on contrast-enhanced MRI, or ventricular enlargement can now be flagged early as high risk, prompting intensified monitoring, earlier consideration of surgical intervention for hydrocephalus such as ventriculoperitoneal shunting, and closer attention to antitubercular therapy optimization. Conversely, patients with parenchymal-type disease and tuberculomas, who showed more favorable outcomes in this cohort, may be candidates for less intensive follow-up protocols, though the authors caution that individualized assessment remains essential.

Limitations of the study are worth noting. As a single-center retrospective analysis, its findings require validation in larger, multicenter cohorts before widespread adoption of the risk model. The retrospective design also means that imaging protocols and clinical management may have varied over the eight-year enrollment period. Nevertheless, the study demonstrates that contrast-enhanced cranial MRI, already a mainstay of diagnosis in intracranial tuberculosis, can be repurposed as a prognostic tool, transforming a routine diagnostic scan into a roadmap for risk stratification and early intervention. As tuberculosis continues to pose a global burden, particularly in regions with limited neurosurgical resources, simple imaging-based predictors of outcome could help clinicians allocate intensive care where it matters most.

Subject of Research: Risk stratification of intracranial tuberculosis patients using contrast-enhanced MRI predictors of long-term prognosis

Article Title: Risk stratification in intracranial tuberculosis patients based on contrast-enhanced MRI

Article References: Wang, Y.-J., Qiu, X.-W., Wang, A.-L., Hu, Y.-N., Li, W.-Q., & Huang, X.-S. (2026). Risk stratification in intracranial tuberculosis patients based on contrast-enhanced MRI. BMC Infectious Diseases. https://doi.org/10.1186/s12879-026-14479-w

Image Credits: AI Generated

DOI: 10.1186/s12879-026-14479-w

Keywords: intracranial tuberculosis, contrast-enhanced MRI, hydrocephalus, cerebral infarction, tuberculous meningitis, prognosis, risk stratification, neuroradiology, modified Rankin Scale, cerebral arteritis, BMC Infectious Diseases, Risk

The context for this work is enormous. Alzheimer’s disease accounts for roughly 60 to 80 percent of dementia cases worldwide, affecting an estimated 57 million people today, a figure projected to exceed 150 million by 2050. China alone carries nearly 17 million cases of Alzheimer’s and other dementias. For decades, treatment relied on symptomatic drugs such as acetylcholinesterase inhibitors and memantine, which ease symptoms but do nothing to halt the underlying neurodegeneration. Lecanemab changed that calculus. In the pivotal Phase III Clarity AD trial, involving 1,795 patients with early disease, the antibody slowed cognitive decline by 27 percent over 18 months and significantly reduced amyloid plaque burden on PET imaging. That success earned accelerated FDA approval in January 2023, full approval in July 2023, and Chinese regulatory approval in January 2024.

But clinical trials, however rigorous, are not real life. Trial populations are carefully selected, typically younger and healthier than the patients clinicians actually see, and rare adverse events or those with long latency periods often only surface after a drug reaches a broad population. That is precisely why the research team, led by investigators at the First Affiliated Hospital of Anhui Medical University and collaborating institutions, chose a dual approach. They prospectively followed every patient receiving lecanemab at their center from July 2024 to February 2026, and in parallel they ran a disproportionality analysis on FAERS, the FDA’s spontaneous reporting database, using two standard signal-detection metrics: the reporting odds ratio and the proportional reporting ratio.

The prospective cohort tells a relatively encouraging story. Ninety-one patients with mild-to-moderate Alzheimer’s disease were enrolled, with a median treatment duration of 185 days. Thirteen adverse events were recorded, corresponding to an exposure-adjusted incidence of 56.48 events per 100 patient-years. The most frequent were amyloid-related imaging abnormalities with hemosiderin deposition, known as ARIA-H, at 12.39 per 100 patient-years, and infusion-related reactions at 8.85 per 100 patient-years. All seven ARIA-H cases were graded mild or moderate, and none required permanent discontinuation of the drug. Notably, the cohort recorded zero cases of ARIA-E, the edema form of the abnormality, likely reflecting the team’s decision to exclude APOE ε4 homozygotes, the subgroup at highest risk, along with strict imaging-based exclusion criteria borrowed from the Clarity AD protocol.

The FAERS analysis, drawing on 2,969 reports identifying lecanemab as the primary suspect drug, largely mirrored the cohort findings. The most frequently reported events were headache, ARIA-E, ARIA-H, and infusion-related reactions, all of which appear in the drug’s official prescribing information. The signal strengths for ARIA-E and ARIA-H were extraordinarily high, with reporting odds ratios exceeding 13,000, reflecting how tightly these imaging abnormalities are associated with anti-amyloid therapy in the pharmacovigilance record. Infusion-related reactions also showed a robust signal, with a reporting odds ratio of 45.71 across 263 cases, consistent with the known immunological profile of a large-molecule exogenous protein triggering fever, chills, and blood pressure fluctuations during intravenous administration.

Then came the finding that stands out. Cerebral infarction, or ischemic stroke, is not listed in lecanemab’s prescribing information, yet the FAERS analysis revealed a strong signal with a reporting odds ratio of 22.31 across 55 reported cases. The researchers dug deeper, mapping concomitant medications and medical histories for those 55 patients. They found that 17 of them, roughly 31 percent, had at least one identifiable traditional stroke risk factor or were taking antithrombotic drugs such as aspirin or clopidogrel. Hypertension was documented in 11 patients and diabetes in two. Strikingly, 91 percent of the cases listed concomitant medications as being used for an unknown indication, underscoring how poorly baseline comorbidities are captured in spontaneous reporting systems. The authors are careful to frame this as a hypothesis-generating signal rather than proof of causation, noting that underlying cerebral amyloid angiopathy, combined with antithrombotic use and preexisting cardiovascular disease, could plausibly explain many of these ischemic events without lecanemab being the direct trigger.

Timing emerged as another key theme. A time-to-onset analysis of 898 FAERS reports with valid onset dates showed that nearly 46 percent of adverse events occurred within the first one to two months of treatment. A Weibull shape parameter below 1 classified the temporal profile as an early-failure curve, meaning the hazard of experiencing an adverse event is highest early on and declines thereafter, though a persistent fraction, about 13.7 percent, occurred more than a year after initiation. The prospective cohort showed a qualitatively similar pattern, with the highest proportion of events also clustering in the first two months. Infusion-related reactions tended to strike during the first one or two infusions, while ARIA, both the edema and the hemorrhage forms, could appear at any point in the treatment course, reinforcing the need for ongoing MRI surveillance rather than a fixed early window of vigilance.

Demographics added further nuance. In the FAERS data, patients aged 65 to 85 accounted for nearly 65 percent of reports, and in the prospective cohort the same age group made up 77 percent of those experiencing adverse events. Women were overrepresented in the spontaneous reports, consistent with the higher prevalence of Alzheimer’s disease in females. The authors suggest that this age bracket represents both the bulk of the treated population and a particularly vulnerable group, warranting prioritized monitoring of safety parameters in clinical practice.

Some findings were quietly reassuring. Despite gastrointestinal disorders ranking fifth in report volume, the disproportionality analysis yielded a protective signal, with a reporting odds ratio of 0.65, suggesting lecanemab does not meaningfully elevate the risk of nausea or diarrhea beyond what would be expected by chance. Infections and infestations, ranking seventh, similarly showed a protective signal at 0.49, indicating no significant increase in infection risk. These results diverge from what one might expect from the prescribing information and likely reflect reporting biases inherent to spontaneous systems, where only unusual or unexpected events tend to be filed.

The study is not without limitations, and the authors are candid about them. The prospective cohort is single-center and modest in size, with a median exposure of just 185 days, which limits conclusions about long-term safety beyond one year. The FAERS analysis cannot calculate true incidence rates because spontaneous reporting lacks a denominator, and the time-to-onset subset, representing about 30 percent of reports, may be biased toward medically attended events with precise dates. Still, the convergence of two independent data sources, a carefully phenotyped real-world cohort and a large pharmacovigilance database, offers a more textured picture than either alone. The overall message is one of cautious optimism: lecanemab’s safety profile in routine practice appears broadly manageable and consistent with trial data, provided clinicians remain vigilant during the first two months of therapy, monitor for ARIA throughout the entire treatment course, and interpret the emerging stroke signal with the cardiovascular context of each individual patient firmly in mind. Larger, multicenter prospective studies will be needed to confirm these signals and refine risk-mitigation strategies as anti-amyloid therapy becomes a routine part of Alzheimer’s care.

Subject of Research: Real-world safety of the Alzheimer's drug lecanemab assessed through a prospective patient cohort and FDA adverse event database signal analysis

Article Title: Safety of lecanemab in real-world patients with Alzheimer’s disease: a prospective, observational case series supported by FAERS signal analysis

Article References: Ji, H., Yang, Y., Wu, X., & Bao, E. (2026). Safety of lecanemab in real-world patients with Alzheimer’s disease: a prospective, observational case series supported by FAERS signal analysis. Frontiers in Neuroinformatics, 20, Article 1977226. https://doi.org/10.3389/fninf.2026.1977226

Image Credits: AI Generated

DOI: 10.3389/fninf.2026.1977226

Keywords: lecanemab, Alzheimer's disease, ARIA, FAERS, drug safety, pharmacovigilance, cerebral infarction, infusion-related reactions, amyloid-beta, monoclonal antibody, adverse events, real-world evidence

Moyamoya vasculopathy is an umbrella term covering two related but distinct conditions. Moyamoya disease is a chronic, progressive narrowing of the terminal internal carotid arteries and the proximal segments of the anterior and middle cerebral arteries, accompanied by a hazy network of tiny collateral vessels at the base of the brain that resembles a puff of smoke on angiography, the meaning of the Japanese word moyamoya. Moyamoya syndrome, by contrast, produces a similar vascular picture but arises secondarily, most often when intracranial atherosclerosis mimics the appearance of the idiopathic disease. Distinguishing the two matters enormously, because their underlying biology, prognosis, and treatment strategies diverge, yet conventional angiography alone often cannot separate them reliably.

The research team, led by investigators at a major Chinese hospital registry, enrolled 398 consecutive adults with radiologically confirmed moyamoya vasculopathy between September 2017 and April 2025. Every patient underwent imaging on the same 3.0-Tesla MR scanner using a standardized protocol that included T1-weighted, T2-weighted, FLAIR, diffusion-weighted, and time-of-flight angiographic sequences, along with high-resolution vessel wall imaging. By scanning all participants on a single machine, the investigators eliminated the interscanner variability that often plagues multicenter imaging studies, allowing vessel wall measurements to be compared directly across the cohort.

High-resolution MRI has emerged in recent years as the decisive tool for separating the two etiologies. In moyamoya disease, the affected arteries typically show concentric, uniform wall thickening, a reduction in the outer diameter of the vessel, and negative or constrictive remodeling, meaning the artery wall shrinks inward as the disease progresses rather than expanding outward. Atherosclerosis-associated moyamoya vasculopathy displays the opposite signature: irregular, eccentric thickening caused by lipid-rich or hemorrhagic plaques, and positive or expansive remodeling, in which the vessel enlarges to accommodate plaque growth. Applying these criteria, the researchers classified 279 patients as having moyamoya disease and 61 as having the atherosclerosis-associated form.

The clinical profiles of the two groups differed in telling ways. Patients with moyamoya disease were more frequently female, consistent with the well-established female predominance of the idiopathic condition, and were significantly more likely to report a family history of early-onset stroke in first-degree relatives, a hallmark of the genetic predisposition linked to the RNF213 susceptibility gene. They also showed more frequent involvement of the posterior cerebral arteries, reflecting the diffuse, whole-circle-of-Willis nature of the disease. The atherosclerosis-associated group, though similar in age, lacked these familial and posterior-circulation signatures, reinforcing the view that it represents a secondary, plaque-driven process rather than a genetically driven arteriopathy.

To determine which imaging features actually tracked with stroke, the team took an unusually granular approach: they analyzed each cerebral hemisphere separately. Of the 558 hemispheres in the moyamoya disease group, 169 had suffered middle cerebral artery territory infarcts, visible as acute or chronic lesions on MRI, while 389 remained asymptomatic. Because the two hemispheres of the same patient are statistically correlated, the researchers used generalized estimating equations, a regression framework that treats each patient as a cluster, adjusting for age, sex, hypertension, diabetes, hyperlipidemia, smoking, alcohol use, and family history of early stroke. This design allowed each hemisphere to serve, in effect, as its own internal comparison.

The results in moyamoya disease were strikingly consistent. Ischemic hemispheres were independently associated with greater middle cerebral artery stenosis, more advanced Suzuki stage, and higher stenosis scores on the internal carotid, middle cerebral, and posterior cerebral arteries. The posterior cerebral artery score carried the strongest effect, with each one-point increase more than doubling the odds of ischemia. Intriguingly, the remodeling index of the middle cerebral artery showed the opposite relationship: hemispheres with more constrictive remodeling were more likely to be ischemic, suggesting that for a given degree of luminal narrowing, the inward shrinkage of the vessel wall compounds hemodynamic compromise, while better-preserved or compensating vessels maintain perfusion.

In the atherosclerosis-associated group, the picture inverted almost completely. Here, the degree of middle cerebral artery stenosis, the Suzuki stage, and the carotid and posterior circulation scores all lost their statistical significance after adjustment. What remained significant were the remodeling index of the middle cerebral artery, which was positively associated with ischemia, and involvement of the anterior cerebral artery. This pattern suggests that in plaque-driven moyamoya vasculopathy, it is not the raw percentage of narrowing that matters but how the vessel wall remodels around the plaque and whether the anterior circulation can still serve as a collateral pathway. Positive remodeling, long recognized as a marker of vulnerable atherosclerotic plaques, appears to signal danger rather than compensation in this group.

The authors are careful to frame these findings as cross-sectional associations rather than proven predictors of future stroke, noting that their definition of ischemic hemispheres included both acute and old infarcts, and that the retrospective, single-center design and the relatively small atherosclerotic subgroup impose limits on generalizability. They also acknowledge that a small fraction of patients lacked internal carotid artery involvement on angiography, a population that the revised 2022 diagnostic criteria would technically exclude, though sensitivity analyses excluding these patients left the results qualitatively unchanged. Even so, the hemisphere-level, cluster-adjusted analysis represents one of the most detailed noninvasive characterizations of moyamoya vasculopathy published to date.

The clinical implications are potentially far-reaching. For patients with moyamoya disease, quantitative measures such as stenosis severity, Suzuki stage, and combined arterial scores, together with evidence of constrictive remodeling, could help identify which hemisphere is at highest risk and guide the timing of revascularization surgery. For those with the atherosclerosis-associated form, attention should shift toward plaque behavior and anterior circulation collateral integrity rather than stenosis percentage alone, aligning management more closely with modern intracranial atherosclerosis care, where recent long-term evidence has questioned the value of stenting and pointed instead toward bypass options in selected cases. The study’s core lesson is that moyamoya vasculopathy is not one disease but two, and the artery wall itself, imaged at high resolution, tells clinicians which one they are treating.

Subject of Research: Etiology-specific associations between high-resolution MRI vessel wall features and cerebral infarction in adult moyamoya vasculopathy

Article Title: High‐Resolution MRI Revealed Different Etiology‐Specific Associations With Cerebral Infarction in Adult Moyamoya Vasculopathy

Article References: Han, G., Pan, J., Hong, Y., Fan, X., Yao, M., Zhou, L., Zhu, Y., Feng, F., & Ni, J. (2026). High‐Resolution MRI Revealed Different Etiology‐Specific Associations With Cerebral Infarction in Adult Moyamoya Vasculopathy. Annals of Clinical and Translational Neurology, 13(10), 2057-2067. https://doi.org/10.1002/acn3.70380

Image Credits: AI Generated

DOI: 10.1002/acn3.70380

Keywords: moyamoya disease, moyamoya vasculopathy, high-resolution MRI, vessel wall imaging, cerebral infarction, intracranial atherosclerosis, remodeling index, Suzuki stage, collateral circulation, stroke risk, RNF213, revascularization

The work addresses a persistent bottleneck in preclinical stroke research. The most widely used animal model of ischemic stroke, the transient middle cerebral artery occlusion (tMCAO) model, is typically assessed by staining brain sections with 2,3,5-triphenyltetrazolium chloride, or TTC. Living tissue stains a vivid red, while infarcted tissue remains pale white. For decades, researchers have measured the volume of the white region by manually tracing its boundary on images of each slice — a labor-intensive process whose accuracy depends on the training, fatigue, and judgment of the individual observer.

The new system, developed by Ziyuan Tang, Meng Li, and colleagues in the laboratories of Chang’an A. Zhan and Fengxian Li, takes a fundamentally different approach: it lets the pixel values of the image do the work. The algorithm decomposes each TTC-stained slice image into its separate RGB and HSV color channels and analyzes the histogram of pixel values in each channel. From these distributions, the software establishes thresholds that distinguish not just one damaged region but two: the ischemic core, where tissue has died outright, and the ischemic penumbra — the salvageable zone of endangered tissue surrounding the core that most existing analyses simply ignore.

The ischemic penumbra is far from a technical footnote. It is the region that neuroprotective therapies aim to rescue, and its extent shapes a patient’s prognosis. Yet in both manual tracing and conventional automated methods, the penumbra tends to be lumped together with the core or lost entirely, because the pale gradient between healthy red tissue and dead white tissue is difficult to delineate by eye or by simple thresholding. By quantifying each region separately, and weighting the score by the mean pixel value of the penumbra, the algorithm captures gradations of damage that volume measurements alone miss.

To determine which color channel carried the most reliable information, the team correlated each channel-based score with measurements made by experienced human observers. The blue channel emerged as the clear winner, showing the strongest correlation with manual assessment and the most accurate recognition of the boundary between penumbra and core. The resulting severity metric correlated with manual measurement with a Pearson coefficient of 0.9392, and with automated thresholding performed in the popular open-source Fiji software with a coefficient of 0.8885 — figures that place the algorithm comfortably in the range of agreement between trained humans themselves.

Crucially, the team did not stop at comparing pixels with tracings. They asked whether the new severity score actually reflected how sick the animals were. Mice underwent standardized neurological behavioral testing before their brains were collected 24 hours after the stroke surgery. The algorithm’s severity output correlated with the neurological deficit score at Spearman r = 0.7492, beating both manual measurement (r = 0.7206) and conventional automated measurement (r = 0.6528). In other words, the color-derived score predicted real-world motor impairment better than the methods it was designed to replace.

The researchers then tested whether the system would generalize beyond the tMCAO model, which blocks a major cerebral artery by briefly inserting a filament. In a photothrombotic stroke model — a fundamentally different technique in which a photosensitive dye and laser light trigger clotting in targeted surface vessels — the severity score again correlated significantly with manual measurement, suggesting the algorithm is not tuned to the quirks of a single experimental paradigm but captures something general about how infarcted tissue looks under TTC staining.

The implications for the field could be substantial. Preclinical stroke research has been shadowed by a reproducibility crisis, with hundreds of seemingly promising therapies failing to translate from mice to humans. Sources of measurement variability, including subjective lesion assessment, are frequently cited as contributors to that failure. An objective, high-throughput scoring method that removes observer bias, quantifies the penumbra, and correlates strongly with functional outcomes offers a way to standardize endpoints across laboratories — and potentially to detect subtler treatment effects in drug screens than manual scoring can resolve.

The system is also strikingly accessible. Unlike deep-learning approaches that require large annotated training datasets and specialized computing hardware, this algorithm relies on classical image analysis: channel decomposition, histogram analysis, and threshold-based labeling that can run on ordinary slice images. The authors describe it as a rapid, objective, and accurate tool for high-throughput evaluation of stroke outcomes in basic research, and the study was supported by the National Natural Science Foundation of China. While the work remains at the preclinical stage, it points toward a future in which the color of a stained brain section — read by software rather than by eye — becomes a common currency for comparing stroke severity across experiments, models, and laboratories.

Subject of Research: An automated image color difference algorithm for scoring cerebral infarct severity in mouse stroke models

Article Title: A mouse cerebral infarct severity scoring system based on image color difference algorithm

Article References: Tang, Z., Li, M., Wang, T., Zhang, H., Zhan, C. A., & Li, F. (2026). A mouse cerebral infarct severity scoring system based on image color difference algorithm. BMC Neuroscience. https://doi.org/10.1186/s12868-026-01047-w

Image Credits: AI Generated

DOI: 10.1186/s12868-026-01047-w

Keywords: ischemic stroke, cerebral infarction, TTC staining, image analysis, tMCAO model, ischemic penumbra, mouse model, neurological score, photothrombotic stroke, computer-assisted image processing, BMC Neuroscience, stroke research