Overall, the intraclass correlation coefficient was 0.88, with a 95% confidence interval of 0.74–0.94. SynthSR generates synthetic 1-mm isotropic structural brain images, allowing scans acquired under different conditions to be placed into a common processing framework. In this multicentre study, investigators processed MRI scans with SynthSR and quantified hippocampal volume using FastSurfer.

They examined 27 low-resolution counterpart scans and 76 contrast-enhanced scans; one patient contributed both types. The population and design therefore tested SynthSR in a clinical setting involving heterogeneous image inputs, although the supplied findings do not establish how the tool performs across every acquisition protocol, disease stage or demographic group. The analysis showed that SynthSR processing systematically increased hippocampal-volume estimates compared with the reference-standard scans.

Despite that bias, relative consistency between the SynthSR-processed and original reference-standard measurements remained high, with the intraclass correlation coefficient of 0.88 and 95% confidence interval of 0.74–0.94. Agreement for synthetic images derived from low-resolution scans was moderate, with an intraclass correlation coefficient of 0.75 and 95% confidence interval of 0.36–0.90. In comparison, agreement between low-resolution images processed with SynthSR and corresponding SynthSR-processed reference-standard scans was excellent, reaching an intraclass correlation coefficient of 0.90 with a 95% confidence interval of 0.76–0.96.

These comparisons describe degrees of agreement among processed measurements; they do not demonstrate that acquisition differences caused the discrepancies. Contrast enhancement did not substantially alter measured performance, but the investigators cautioned that including contrast-enhanced images may require caution. In the longitudinal analysis, no statistically significant difference in measured volumetric change was detected between SynthSR-processed scans and reference-standard counterparts.

The wide limits of agreement nevertheless indicated substantial individual-level variability in estimated changes. Taken together, the results indicate that SynthSR can support group-level volumetric analyses when images are handled through a consistent processing pipeline, but its systematic overestimation, variable individual agreement and broad longitudinal limits make it unreliable for determining hippocampal-volume change in an individual patient.

Subject of Research: Evaluation of SynthSR for MRI harmonization and hippocampal-volume analysis in autoimmune encephalitis and related disorders

Article Title: Evaluating SynthSR for MRI harmonization and quantitative analysis of hippocampal volume in autoimmune neurologic diseases

Article References: Seery, N., Rowsthorn, E., Zia, A., Beech, P., Wesselingh, R., Kavnoudias, H., Rushen, T., Law, M., Vivash, L., Sanfilippo, P., Butzkueven, H., Harding, I. H., & Monif, M. (2026). Evaluating SynthSR for MRI harmonization and quantitative analysis of hippocampal volume in autoimmune neurologic diseases. Scientific Reports. https://doi.org/10.1038/s41598-026-76158-9

Image Credits: AI Generated

DOI: 10.1038/s41598-026-76158-9

Keywords: SynthSR, MRI harmonization, hippocampal volume, autoimmune encephalitis, artificial intelligence, medical imaging, quantitative MRI, FastSurfer, clinical MRI, Evaluating, harmonization, quantitative

The challenge stems from how modern MRI works. Head receiver coils are made of many small detector elements whose sensitivity varies smoothly across space, modulating the raw signal in ways that can masquerade as genuine differences in tissue water. The established solution is to acquire matched images with both the phased-array head coil and the scanner’s body coil, then derive a smooth correction field unique to each participant. That extra acquisition takes only about 64 seconds, but it also requires participant-specific processing, and crucially, it cannot be recovered after the fact. Archived datasets that lack the matched pair are effectively locked out of the most rigorous water-content quantification.

The research team, led by Zaheer Abbas, tested whether a fixed, configuration-specific template could replace that individual reference. Using data from 25 healthy male volunteers aged 21 to 44, they built a degree-3 polynomial template of the effective relative receive field from 24 participants and deployed it on the 25th, rotating through the cohort so every participant was held out exactly once. The template was constructed in a brain-relative coordinate frame, with each participant’s brain bounding box normalized to a common grid, allowing approximately corresponding locations to be pooled without scanner-space registration.

How closely did the borrowed template reproduce each individual’s own receive field? The median relative mismatch averaged 2.273 percent, with a standard deviation of 0.568 percent and a range from 1.431 to 3.615 percent across the cohort. That figure sounds modest, but the authors are careful to note that receive-field fidelity and final water-content agreement are distinct questions. A single global fidelity metric cannot simply be propagated as a bound on the error in the final quantitative maps, particularly because the mismatch statistic is computed after independent median normalization and is therefore blind to differences in overall scale.

The real test came downstream. Each arm of the comparison—the explicit individual reference (Arm E) and the template substitution (Arm T)—underwent its own independently estimated residual calibration, including a spatially regularized intercept field and a ventricular cerebrospinal fluid calibration step. The primary outcomes were participant-level median water content in white and gray matter, expressed in percentage units relative to pure water. The results were striking: mean differences between the two arms were just −0.04 percentage units in white matter and −0.17 in gray matter, with mean absolute errors of 0.17 and 0.22 units and concordance correlation coefficients of 0.991 and 0.989 respectively. Whole-brain bias was −0.12 units, and all Bland–Altman agreement widths stayed below one percentage unit.

Perhaps the most mechanistically interesting finding concerns why the agreement was so good despite the template’s imperfection. Decomposing the processing chain revealed that the template substitution raised the pre-calibration proton-density proxy by roughly 6.5 percent on average—but the independently estimated CSF calibration scalar rose by a nearly matching 6.35 percent. Because that scalar enters the final water-content denominator, the two shifts largely cancelled, leaving only small residual differences. The authors stress this is a descriptive consistency with a calibration-coupling interpretation, not proof that CSF calibration uniquely causes the residual difference, and they caution that the same buffering may not occur on other scanners or configurations.

Two deliberately sabotaged control conditions sharpened the picture. When the receive correction was replaced with a uniform divisor of one—discarding receive information entirely—regional errors grew roughly five-fold in white matter and whole brain, and concordance dropped to the 0.81 to 0.89 range. A mirrored template, reflected along one normalized axis, performed worse than the correct template as well, though somewhat better than the no-receive control. The downstream pipeline is therefore not indifferent to receive information: the template carries spatial structure that genuinely matters, even as the calibration chain absorbs much of the error.

The agreement at the level of regional medians conceals larger local discrepancies. Dividing each brain into central and peripheral rim zones, the team found that disagreement grew toward the brain’s edge, most clearly in gray matter, where the rim 95th-percentile absolute difference reached 3.183 percentage units and the rim spatial standard deviation 1.375 units. Those figures brush up against the few-percentage-point changes that can accompany pathology, which is why the authors explicitly decline to claim suitability for voxel- or lesion-level measurement, particularly in cortical and peripheral regions. The small regional-median biases should not be read as evidence of spatial interchangeability.

The limitations are candidly enumerated. The cohort comprised 25 male participants at a single site, on one 3 Tesla MAGNETOM Prisma, with one receive configuration; there was no independent deployment cohort. The explicit individual reference strategy is itself a body-coil-referenced relative approach, not biological ground truth, so the results speak to agreement rather than accuracy. No equivalence or noninferiority margin was prespecified. External transfer across scanners, coils, vendors, or field strengths was never tested, and same-pipeline scan–rescan repeatability was not measured in this study. An exploratory image-derived geometry proxy showed a modest descriptive association with rim mismatch, hinting that head positioning relative to the coil may matter, though the confidence intervals were broad.

The practical payoff is real but bounded. For a previously established scanner and coil configuration, the template eliminates roughly 1.1 minutes of acquisition time per participant plus the associated receive-field derivation, opening the door to rigorous water-content quantification in retrospective datasets and large cohorts where the matched reference pair was never acquired. The authors frame their conclusion with appropriate restraint: the evidence supports technical feasibility within the evaluated configuration, not universal replacement of participant-specific referencing. Future work, they argue, should test prospectively acquired external cohorts, varied repositioning, and diverse populations, with any clinically meaningful agreement margin defined before outcomes are inspected. For now, the study offers a carefully quantified demonstration that a crowd-sourced calibration can nearly match an individual one—provided you stay on the scanner it was built for.

Subject of Research: Inter-subject receive-template substitution for quantitative brain water content mapping at 3 Tesla

Article Title: Inter-subject receive-template substitution for quantitative brain water content mapping at 3 T: a leave-one-out evaluation

Article References: Abbas, Z., Ullah, F., Gegechkori, S., Worthoff, W. A., & Shah, N. J. (2026). Inter-subject receive-template substitution for quantitative brain water content mapping at 3 T: a leave-one-out evaluation. Frontiers in Neuroscience, 20, Article 1986813. https://doi.org/10.3389/fnins.2026.1986813

Image Credits: AI Generated

DOI: 10.3389/fnins.2026.1986813

Keywords: quantitative MRI, brain water content, receive sensitivity correction, leave-one-out evaluation, proton density mapping, 3 Tesla, inter-subject template, Bland-Altman agreement, CSF calibration, Frontiers in Neuroscience, Inter-subject, receive-template

Limbic encephalitis is an inflammatory condition that targets the limbic system, a network of deep brain structures including the hippocampus and amygdala that are critical for memory and emotion. The disease typically produces subacute cognitive decline, personality changes, and seizures, and it is often triggered by the immune system mistakenly attacking brain tissue. In systemic sclerosis — the autoimmune connective tissue disease the patient had been living with since 2021 — neurological complications are usually attributed to blood vessel damage rather than direct inflammation of brain parenchyma, making this case particularly unusual and diagnostically challenging.

When the patient was admitted in January 2026 with progressive confusion, memory loss, and episodes of absent awareness, his clinical team faced a diagnostic puzzle. Prolonged video-electroencephalography monitoring captured abnormal electrical activity from the right temporal lobe, pointing toward a seizure focus in the brain’s temporal region. But his routine brain MRI showed only subtle, unilateral changes in the right mesial temporal structures — findings that fell short of the bilateral involvement required by current diagnostic criteria for definite autoimmune limbic encephalitis. Without those criteria met, the team turned to quantitative imaging methods that go beyond what the naked eye can see on standard scans.

The researchers deployed two complementary advanced techniques. The first, T2 relaxometry, measures how quickly protons in brain tissue return to their equilibrium state after being excited by radiofrequency pulses in the MRI scanner — a property that changes when tissue undergoes inflammation, edema, or cell damage. The second, proton magnetic resonance spectroscopy, measures the concentrations of specific brain metabolites such as N-acetylaspartate, a marker of neuronal health and density. Together, these methods provide objective, numbers-based measurements of tissue integrity rather than relying on visual interpretation alone.

The results were striking. While conventional MRI showed abnormalities confined to the right mesial temporal region, T2 relaxometry revealed significantly prolonged relaxation times bilaterally — meaning both the left and right mesial temporal structures showed evidence of tissue alteration, not just the right side. The spectroscopy data reinforced this picture: N-acetylaspartate levels were reduced on both sides, with a more pronounced decrease on the right. This pattern of bilateral, right-predominant involvement aligned with what neuropathological studies of limbic encephalitis have long suggested — that the inflammatory process often affects both temporal lobes even when only one appears abnormal on standard imaging.

The quantitative findings proved decisive for the patient’s clinical course. Based on the combined evidence from relaxometry, spectroscopy, electroencephalography, and the clinical picture, the team diagnosed antibody-negative probable autoimmune limbic encephalitis in the setting of systemic sclerosis. Treatment with the immunosuppressant cyclophosphamide — a chemotherapy agent that dampens immune system activity — was initiated alongside antiseizure medications. The patient responded well: his seizures came under control, his cognitive symptoms improved, and follow-up imaging showed stabilization of the previously identified abnormalities without progression.

The case carries broader implications for how suspected limbic encephalitis is diagnosed and classified. Current diagnostic criteria, established by international consensus panels, require bilateral mesial temporal abnormalities on conventional MRI for a diagnosis of definite autoimmune limbic encephalitis. Patients with unilateral findings are instead categorized as having possible autoimmune encephalitis — a distinction that affects treatment decisions, prognosis discussions, and eligibility for clinical trials. If quantitative techniques like relaxometry and spectroscopy can reliably detect bilateral involvement that conventional MRI misses, they could help reclassify patients who are currently labeled as having only possible disease, potentially opening the door to more aggressive immunotherapy.

The researchers also explored whether the spectroscopy findings could distinguish limbic encephalitis from other conditions that affect the temporal lobes, such as mesial temporal sclerosis or low-grade tumors. While the reduced N-acetylaspartate pattern in this patient was consistent with inflammatory injury, the team acknowledged that larger studies comparing spectroscopy profiles across different temporal lobe pathologies would be needed to establish specific diagnostic thresholds. The case report, they noted, represents a single observation that requires validation in prospective cohorts.

For patients with systemic sclerosis who develop neurological symptoms, the case suggests that quantitative brain imaging may deserve a place in the diagnostic workup alongside conventional MRI. Because neurological complications of systemic sclerosis are typically attributed to vascular rather than inflammatory mechanisms, clinicians may not immediately consider limbic encephalitis — and standard imaging may not reveal the characteristic bilateral pattern needed to support the diagnosis. Advanced MRI techniques could help bridge this gap by providing objective evidence of inflammatory tissue changes even when conventional scans appear normal or show only subtle unilateral findings.

The Frankfurt team, led by Dr. Florian Stolz and colleagues, emphasized that their multimodal approach — combining conventional MRI, relaxometry, spectroscopy, FDG-PET, and electroencephalography — offers a template for evaluating diagnostically complex cases of suspected autoimmune brain disease. As quantitative imaging becomes more widely available on clinical MRI scanners, the boundary between what is visible and what is measurable continues to shift, and with it, the threshold at which rare and elusive conditions like limbic encephalitis can be confidently identified and treated.

Subject of Research: Quantitative MRI detection of bilateral limbic encephalitis in systemic sclerosis

Article Title: Case Report: Value of MR relaxometry and spectroscopy in multimodal imaging characterization of limbic encephalitis in systemic sclerosis

Article References: Stolz, F. A., Manzhurtsev, A., Bönstrup, M., Groener, D., Melzer, N., Rosenow, F., Hattingen, E., & Arendt, C. T. (2026). Case Report: Value of MR relaxometry and spectroscopy in multimodal imaging characterization of limbic encephalitis in systemic sclerosis. Frontiers in Radiology, 6, Article 1945816. https://doi.org/10.3389/fradi.2026.1945816

Image Credits: AI Generated

DOI: 10.3389/fradi.2026.1945816

Keywords: limbic encephalitis, systemic sclerosis, quantitative MRI, T2 relaxometry, magnetic resonance spectroscopy, autoimmune encephalitis, N-acetylaspartate, FDG-PET, cyclophosphamide, diagnostic criteria, hippocampus, amygdala

The conventional workhorse of breast diffusion MRI is the apparent diffusion coefficient, or ADC, a value derived from diffusion-weighted imaging that reflects how freely water molecules move within tissue. Malignant tumors, packed with dense cells and restrictive extracellular space, typically show lower ADC values than benign lesions. But radiologists have long relied on mean ADC within a manually drawn region of interest, a strategy that flattens a tumor’s internal complexity into one number. The new study argues that this averaging is precisely where diagnostic power is lost, because breast tumors are notoriously heterogeneous, mixing proliferative zones, necrotic cores, and benign-appearing regions within the same lesion.

MR cytometry attempts to recover that lost complexity by pairing multi-diffusion-time acquisition with quantitative biophysical modeling. In the study, patients underwent 3T MRI that included both pulsed gradient spin-echo and oscillating gradient spin-echo diffusion sequences. These acquisitions probe water diffusion across different length and time scales, and biophysical models then translate the signals into microstructural parameters that characterize tissue architecture in a more specific way than ADC alone. The result is a set of parameter maps—rather than a single scalar—capturing how cellular density and organization vary across the tumor.

To test whether these maps could be exploited more fully, the team enrolled 221 patients with pathologically confirmed breast lesions, 47 benign and 174 malignant, across two centers. Radiologists manually delineated each lesion, and the researchers extracted radiomic features from multiple parameter mappings, including time-dependent ADC metrics and MR-cytometry-derived metrics. Radiomics converts an image region into a high-dimensional feature vector describing shape, first-order intensity statistics, and texture patterns, allowing machine learning classifiers to learn which combinations of features separate benign from malignant tissue.

Six machine learning models were trained using five-fold cross-validation on a training cohort and then evaluated on both the training data and an external test cohort, a design intended to guard against overfitting and to estimate how the approach generalizes beyond a single institution. Performance was assessed with a battery of metrics: accuracy, sensitivity, specificity, F1 score, Brier score, and the area under the receiver operating characteristic curve, or AUC, which summarizes diagnostic discrimination across all decision thresholds.

The results were consistent across every comparison. On the training set, radiomics-based models built on ADC measurements achieved a mean AUC of 0.868 across the six classifiers, versus 0.768 for models using mean ADC values alone. When built on MR cytometry parameters, radiomics models reached a mean AUC of 0.914 compared with 0.857 for mean-value-based models. Combining ADC and MR cytometry features pushed the mean AUC to 0.935, against 0.879 for the corresponding mean-value approach. The pattern held on the external test set: 0.801 versus 0.717 for ADC-based models, 0.869 versus 0.799 for MR cytometry models, and 0.901 versus 0.841 for the combined features.

The single best result came from a logistic regression classifier, which achieved an AUC of 0.943 on the external evaluation, with a confidence interval of 0.901 to 0.986. That a relatively simple, interpretable model topped the field underscores a recurring lesson in radiomics research: the quality of the input features often matters more than the sophistication of the classifier. Rich, physically grounded maps from MR cytometry appear to give even linear models enough signal to separate benign from malignant lesions with high reliability.

Beyond raw performance, the authors emphasize a clinical dimension. MR cytometry is non-invasive and, according to the study, better reflects intratumoral heterogeneity than limited biopsy sampling, which by definition interrogates only a small fraction of a tumor. A biopsy needle samples one location; a parameter map samples the whole lesion. If validated further, MR cytometry-based radiomics could serve as a complementary tool for triaging lesions detected on screening, potentially reducing unnecessary biopsies of benign findings while flagging malignant ones that a mean ADC value might obscure.

The study also carries methodological implications for the broader radiomics field. Feature dimensionality is a known pitfall, and the researchers employed dimensionality reduction and regularization techniques, including principal component analysis and least absolute shrinkage and selection operator, or LASSO, feature selection, to keep models from memorizing noise. The use of an external test cohort, rather than a random split within a single dataset, strengthens the claim that the reported gains reflect genuine biological signal rather than site-specific imaging quirks. Reported confidence intervals for the AUC comparisons indicate that the radiomics advantage was substantial, though the imbalance between benign and malignant cases—47 versus 174—means benign-lesion representation remains a limitation worth monitoring in future validation work.

The research, supported by the National Natural Science Foundation of China Youth Fund and the China Postdoctoral Science Foundation, arrives amid a wider push to make quantitative MRI clinically actionable. Diffusion MRI is fast, contrast-free, and already part of standard breast protocols, so adding radiomic analysis of cytometry maps requires no new contrast agents or ionizing radiation. The authors conclude that radiomics improves diagnostic performance over current mean-value-based analysis, and that MR cytometry, by capturing heterogeneity that averages erase, shows promising potential for non-invasive breast tumor diagnosis. Larger multi-center validations will determine whether this combination earns a place in routine clinical workflows, but the message of the study is clear: tumors are maps, not means, and reading them as such may catch cancers that a single number misses.

Subject of Research: Radiomic analysis of MR cytometry mappings for differentiating benign and malignant breast tumors on diffusion MRI

Article Title: Radiomic analysis of MR cytometry mappings for breast tumor diagnosis

Article References: Luo, X., Ding, J., Mu, J., Wu, L., Liu, F., Bao, H., Gu, Y., Chen, L., & Shi, D. (2026). Radiomic analysis of MR cytometry mappings for breast tumor diagnosis. BMC Medical Imaging. https://doi.org/10.1186/s12880-026-02852-9

Image Credits: AI Generated

DOI: 10.1186/s12880-026-02852-9

Keywords: radiomics, MR cytometry, breast cancer, diffusion MRI, ADC, machine learning, BMC Medical Imaging, tumor heterogeneity, logistic regression, quantitative MRI, breast tumor diagnosis, LASSO

The pathological story began with post-mortem studies in the late 1990s, notably Kidd and colleagues’ landmark 1999 paper in Brain, which established that demyelination in the cerebral cortex is widespread in multiple sclerosis. Subsequent work revealed a striking heterogeneity: leukocortical lesions straddling the white and grey matter boundary, purely intracortical lesions, and subpial lesions that extend along the outer surface of the brain. Subpial lesions proved particularly devastating, often spanning entire gyri and representing the dominant form of cortical pathology in progressive disease. Crucially, these lesions are linked to inflammatory clusters, including B-cell follicle-like structures, in the meninges — the membranes bathing the brain in cerebrospinal fluid.

That link between meningeal inflammation and cortical damage was cemented by Magliozzi and colleagues in 2010, who described a gradient of neuronal loss radiating inward from the pial surface. Neurons closest to the inflamed meninges die first, creating an outside-in pattern of destruction that has since been confirmed in thalamic and cortical compartments, even in children with multiple sclerosis. The review argues that this compartmentalized inflammation — largely segregated from the blood and resistant to many peripherally acting therapies — is a key biological driver of progression independent of relapse activity, a form of worsening that defies the classic inflammatory relapse model.

Seeing this pathology in living patients has been the field’s great technical challenge. Conventional magnetic resonance imaging dramatically underestimates cortical lesion burden, a fact established when Geurts and colleagues combined post-mortem MRI with histopathology in 2005. The breakthrough came with double inversion recovery sequences, which suppress cerebrospinal fluid and white matter signals to isolate the cortex, and with consensus scoring standards published in 2011. Ultrahigh-field 7 Tesla MRI then pushed detection further, revealing cortical lesions as dynamic entities that expand, stabilize and accrue tissue damage over time, and enabling longitudinal characterization of lesion evolution that earlier studies could only infer.

The clinical payoff has been substantial. Cortical lesions are highly specific to multiple sclerosis, appearing in radiologically isolated syndromes, clinically isolated syndromes and early relapsing disease, while remaining essentially absent in migraine and neuromyelitis optica spectrum disorders. Their formal incorporation into the 2017 revision of the McDonald criteria as a valid site for dissemination in space marked a turning point, and more recent work shows that combining cortical lesions with the central vein sign and paramagnetic rim lesions sharpens diagnostic accuracy further.

Prognostically, cortical lesion burden has emerged as one of the most powerful imaging predictors available. Studies spanning decades of follow-up show that cortical lesions detected at diagnosis independently forecast physical disability progression, conversion to secondary progressive disease, cognitive impairment and even seizure susceptibility, given the established association between temporal lobe cortical pathology and epilepsy in multiple sclerosis. A 20-year study by Ziccardi and colleagues demonstrated that lesions seen at diagnosis predict long-term cognitive decline, while large cohort analyses have linked cortical damage to earlier transition to the progressive phase. Newer research from 2024 to 2026 extends this picture, showing that intrathecal inflammatory profiles combined with grey matter damage predict early progression independent of relapse activity, and that choroid plexus enlargement on 7 Tesla MRI correlates with cortical and paramagnetic rim lesions to flag the same risk.

Treatment effects on the cortical compartment are now being systematically mapped. Disease-modifying therapies show differential impacts: glatiramer acetate, dimethyl fumarate, natalizumab, fingolimod, ocrelizumab and cladribine have all been associated with reductions or stabilization in cortical lesion accumulation or grey matter atrophy in various studies, while sphingosine-1-phosphate modulators such as siponimod address progression in secondary progressive disease. Yet the review emphasizes that compartmentalized meningeal inflammation remains a therapeutic blind spot, and emerging data on Bruton’s tyrosine kinase inhibitors and lymphotoxin-driven meningeal injury pathways suggest that targeting this compartment could finally offer true neuroprotection.

The next frontier, the authors argue, lies in integrating single-cell RNA sequencing, spatial transcriptomics and quantitative MRI to define biologically meaningful cortical endotypes. Techniques such as myelin water imaging, magnetization transfer ratio, neurite orientation dispersion and density imaging, and quantitative susceptibility mapping are converting the cortex from a radiological blind spot into a measurable substrate of disease. Fluid biomarkers, including cerebrospinal fluid profiles of chemokines and osteopontin, complement these imaging advances by capturing the intrathecal inflammatory milieu that drives grey matter injury.

Artificial intelligence stands poised to multiply these gains. Deep learning algorithms trained on ultrahigh-field and synthetic double inversion recovery images are already improving cortical lesion detection across centers, while machine learning models that fuse imaging with high-dimensional blood signatures promise individualized prediction of disease trajectory and treatment response. If these tools mature, the review concludes, clinicians could move beyond population-level risk scores toward precision medicine — predicting each patient’s demyelination and repair capacity and tailoring therapies to the specific cortical endotype driving their disease. What was invisible for most of multiple sclerosis history may soon become its most informative biomarker.

Subject of Research: Cortical lesion pathology, imaging and clinical relevance in multiple sclerosis

Article Title: Cortical lesions in multiple sclerosis: 25 years of progress, remaining challenges and future directions

Article References: Calabrese, M., Magliozzi, R., Tamanti, A., Ziccardi, S., Eccher, C., Biasi, P., Pizzini, F. B., & Marastoni, D. (2026). Cortical lesions in multiple sclerosis: 25 years of progress, remaining challenges and future directions. Nature Reviews Neurology. https://doi.org/10.1038/s41582-026-01263-2

Image Credits: AI Generated

DOI: 10.1038/s41582-026-01263-2

Keywords: multiple sclerosis, cortical lesions, meningeal inflammation, MRI, 7 Tesla imaging, grey matter pathology, progression independent of relapse activity, cognitive impairment, disease-modifying therapy, artificial intelligence, fluid biomarkers, quantitative MRI