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We aimed to compare the ability of diffusion tensor imaging and multi‐compartment spherical mean technique to detect focal tissue damage and in distinguishing between different connectivity patterns associated with varying clinical outcomes in multiple sclerosis (MS). Seventy‐six people diagnosed with MS were scanned using a SIEMENS Prisma Fit 3T magnetic resonance imaging (MRI), employing both conventional (T1w and fluid‐attenuated inversion recovery) and advanced diffusion MRI sequences from which fractional anisotropy (FA) and microscopic FA (μFA) maps were generated. Using automated fiber quantification (AFQ), we assessed diffusion profiles across multiple white matter (WM) pathways to measure the sensitivity of anisotropy diffusion metrics in detecting localized tissue damage. In parallel, we analyzed structural brain connectivity in a specific patient cohort to fully grasp its relationships with cognitive and physical clinical outcomes. This evaluation comprehensively considered different patient categories, including cognitively preserved (CP), mild cognitive deficits (MCD), and cognitively impaired (CI) for cognitive assessment, as well as groups distinguished by physical impact: those with mild disability (Expanded Disability Status Scale [EDSS] <=3) and those with moderate–severe disability (EDSS >3). In our initial objective, we employed Ridge regression to forecast the presence of focal MS lesions, comparing the performance of μFA and FA. μFA exhibited a stronger association with tissue damage and a higher predictive precision for focal MS lesions across the tracts, achieving an R‐squared value of.57, significantly outperforming the R‐squared value of.24 for FA (p‐value <.001). In structural connectivity, μFA exhibited more pronounced differences than FA in response to alteration in both cognitive and physical clinical scores in terms of effect size and number of connections. Regarding cognitive groups, FA differences between CP and MCD groups were limited to 0.5% of connections, mainly around the thalamus, while μFA revealed changes in 2.5% of connections. In the CP and CI group comparison, which have noticeable cognitive differences, the disparity was 5.6% for FA values and 32.5% for μFA. Similarly, μFA outperformed FA in detecting WM changes between the MCD and CI groups, with 5% versus 0.3% of connections, respectively. When analyzing structural connectivity between physical disability groups, μFA still demonstrated superior performance over FA, disclosing a 2.1% difference in connectivity between regions closely associated with physical disability in MS. In contrast, FA spotted a few regions, comprising only 0.6% of total connections. In summary, μFA emerged as a more effective tool than FA in predicting MS lesions and identifying structural changes across patients with different degrees of cognitive and global disability, offering deeper insights into the complexities of MS‐related impairments. [ABSTRACT FROM AUTHOR]

A diagonal offset algorithm is presented to overcome the singular moment matrix problem in the polynomial point interpolation method. The value of terms in the diagonal line of moment matrix is changed with different offsets. The effects of the offsets on the Kronecker delta function and partition of unity properties are investigated and an optimum offset range is proposed. The algorithm is validated solving some patch tests and various elasticity problems in 2-D domain. Their results demonstrate that the proposed algorithm is very easy to implement and completely solves the singular moment matrix problem. The accuracy of the method with proposed algorithm is investigated for regular and irregular local domains. Copyright © 2007 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Disruptions to brain networks, measured using structural (sMRI), diffusion (dMRI), or functional (fMRI) MRI, have been shown in people with multiple sclerosis (PwMS), highlighting the relevance of regions in the core of the connectome but yielding mixed results depending on the studied connectivity domain. Using a multilayer network approach, we integrated these three modalities to portray an enriched representation of the brain's core-periphery organization and explore its alterations in PwMS. In this retrospective cross-sectional study, we selected PwMS and healthy controls with complete multimodal brain MRI acquisitions from 13 European centers within the MAGNIMS network. Physical disability and cognition were assessed with the Expanded Disability Status Scale (EDSS) and the symbol digit modalities test (SDMT), respectively. SMRI, dMRI, and resting-state fMRI data were parcellated into 100 cortical and 14 subcortical regions to obtain networks of morphological covariance, structural connectivity, and functional connectivity. Connectivity matrices were merged in a multiplex, from which regional coreness-the probability of a node being part of the multiplex core-and coreness disruption index (κ)-the global weakening of the core-periphery structure-were computed. The associations of κ with disease status (PwMS vs. healthy controls), clinical phenotype, level of physical disability (EDSS ≥ 4 vs. EDSS < 4), and cognitive impairment (SDMT z-score < -1.5) were tested within a linear model framework. Using random forest permutation feature importance, we assessed the relative contribution of κ in the multiplex and single-layer domains, in addition to conventional MRI measures (brain and lesion volumes), in predicting disease status, physical disability, and cognitive impairment. We studied 1048 PwMS (695F, mean ± SD age: 43.3 ± 11.4 years) and 436 healthy controls (250F, mean ± SD age: 38.3 ± 11.8 years). PwMS showed significant disruption of the multiplex core-periphery organization (κ = -0.14, Hedges' g = 0.49, p < 0.001), correlating with clinical phenotype (F = 3.90, p = 0.009), EDSS (Hedges' g = 0.18, p = 0.01), and SDMT (Hedges' g = 0.30, p < 0.001). Multiplex κ was the only connectomic measure adding to conventional MRI in predicting disease status and cognitive impairment, while physical disability also depended on single-layer contributions. In conclusion, we show that multilayer networks represent a biologically and clinically meaningful framework to model multimodal MRI data, with disruption of the core-periphery structure emerging as a potential connectomic biomarker for disease severity and cognitive impairment in PwMS., (© 2024 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.)

The first genome-wide significant multiple sclerosis severity locus, rs10191329, has been pathologically linked to cortical lesion load and brain atrophy. However, observational cohorts such as MSBase have not replicated associations with disability outcomes, instead finding other loci. We evaluated rs10191329 and MSBase loci in a unique cohort of 53 people followed for 30 years after a clinically isolated syndrome, with deep clinical phenotyping and MRI measures of inflammation and neurodegeneration. After 30 years, 26 had developed relapsing-remitting multiple sclerosis, 15 secondary progressive multiple sclerosis and 12 remained diagnosed with a clinically isolated syndrome. Genetic associations with disease severity (age-related multiple sclerosis severity score and Expanded Disability Status Scale), disease course and brain MRI features (white matter lesions, cortical lesions and grey matter fraction) were investigated using regression models and survival analyses. rs10191329 was not associated with multiple sclerosis severity, secondary progressive multiple sclerosis diagnosis or brain MRI features at 30 years. Similarly, MSBase loci were not associated with 30-year disease severity, although rs73091975 was significantly associated with lower 14-year age-related multiple sclerosis severity score in those developing multiple sclerosis. Given that effect sizes for both rs10191329 and rs73091975 were greatest between 14 and 20 years, these findings suggest genetic effects on multiple sclerosis severity may interact non-linearly with disease duration., Competing Interests: N.S. has been a clinical research fellow in a post supported by Merck (supervised by S.A.T. and D.C.) and subsequently by MRC (MR/W019906/1); he has received speaker honoraria from Merck. K.C. has received honoraria for participation and attendance of educational events from Novartis, Roche, Biogen and Merck; she has received honoraria for consultancy work from Novartis, Roche, Biogen, Merck and Viatris. F.P. received a Guarantors of Brain fellowship 2017–20. F.P. and B.K. are supported by the National Institute for Health Research (NIHR), Biomedical Research Centre initiative at University College London Hospitals (UCLH). A.J.T. reports personal fees paid to his institution from Eisai Ltd; is an editorial board member for The Lancet Neurology receiving a free subscription; is Editor-in-Chief for Multiple Sclerosis Journal receiving an honorarium from SAGE Publications; receives support for travel as member, from Clinical Trials Committee, from International Progressive MS Alliance and from the National MS Society (USA) as member, NMSS Research Programs Advisory Committee. S.A.T. has received honoraria from Roche, Merck, Novartis, Sanofi-Genzyme and Biogen in the last 3 years and co-supervises a clinical fellowship at the National Hospital for Neurology and Neurosurgery, London, UK, which is supported by Merck. W.B. has received speaker honoraria and/or acted as a consultant for Biogen, Janssen, Merck, Neuroxpharm, Novartis, Roche, Sandoz, Sanofi and Viatris. He is supported by the National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre. O.C. is a member of an independent DSMB for Novartis, gave a teaching talk on McDonald criteria in a Merck local symposium and contributed to an Advisory Board for Biogen; she is Deputy Editor of Neurology, for which she receives an honorarium. C.T. is currently being funded by a Junior Leader La Caixa Fellowship [The project that gave rise to these results received the support of a fellowship from ‘la Caixa’ Foundation (ID 100010434), fellowship code is LCF/BQ/PI20/117600080]. She has also received the 2021 Merck’s Award for the Investigation in Multiple Sclerosis (Spain) and a grant from Instituto de Salud Carlos III (ISCIII), Spain (grant ID: PI21/01860). In 2015, she received an ECTRIMS Post-doctoral Research Fellowship and has received funding from the UK Multiple Sclerosis Society (grant number 77). She has also received speaker honoraria from Roche and Novartis. She serves on the Editorial Board of Neurology and Multiple Sclerosis Journal. F.B. is supported by the UCLH Biomedical Research Centre. He is a steering committee or iDMC member for Biogen, Merck, Roche, EISAI and Prothena. He is a consultant for Roche, Biogen, Merck, IXICO, Jansen and Combinostics. He has research agreements with Merck, Biogen, GE Healthcare and Roche. He is co-founder and shareholder of Queen Square Analytics Ltd. D.C. is a consultant for Hoffmann-La Roche. In the last 3 years, he has been a consultant for Biogen; received research funding from Hoffmann-La Roche, the International Progressive Multiple Sclerosis Alliance, the Multiple Sclerosis Society and the National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre; and received speaker’s honorarium from Novartis. He co-supervises a clinical fellowship at the National Hospital for Neurology and Neurosurgery, London, UK, which is supported by Merck. The remaining authors have nothing to disclose., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain.)

Background and Objectives: Disentangling brain aging from disease-related neurodegeneration in patients with multiple sclerosis (PwMS) is increasingly topical. The brain-age paradigm offers a window into this problem but may miss disease-specific effects. In this study, we investigated whether a disease-specific model might complement the brain-age gap (BAG) by capturing aspects unique to MS., Methods: In this retrospective study, we collected 3D T1-weighted brain MRI scans of PwMS to build (1) a cross-sectional multicentric cohort for age and disease duration (DD) modeling and (2) a longitudinal single-center cohort of patients with early MS as a clinical use case. We trained and evaluated a 3D DenseNet architecture to predict DD from minimally preprocessed images while age predictions were obtained with the DeepBrainNet model. The brain-predicted DD gap (the difference between predicted and actual duration) was proposed as a DD-adjusted global measure of MS-specific brain damage. Model predictions were scrutinized to assess the influence of lesions and brain volumes while the DD gap was biologically and clinically validated within a linear model framework assessing its relationship with BAG and physical disability measured with the Expanded Disability Status Scale (EDSS)., Results: We gathered MRI scans of 4,392 PwMS (69.7% female, age: 42.8 ± 10.6 years, DD: 11.4 ± 9.3 years) from 15 centers while the early MS cohort included 749 sessions from 252 patients (64.7% female, age: 34.5 ± 8.3 years, DD: 0.7 ± 1.2 years). Our model predicted DD better than chance (mean absolute error = 5.63 years, R 2 = 0.34) and was nearly orthogonal to the brain-age model (correlation between DD and BAGs: r = 0.06 [0.00-0.13], p = 0.07). Predictions were influenced by distributed variations in brain volume and, unlike brain-predicted age, were sensitive to MS lesions (difference between unfilled and filled scans: 0.55 years [0.51-0.59], p < 0.001). DD gap significantly explained EDSS changes ( B = 0.060 [0.038-0.082], p < 0.001), adding to BAG (Δ R 2 = 0.012, p < 0.001). Longitudinally, increasing DD gap was associated with greater annualized EDSS change ( r = 0.50 [0.39-0.60], p < 0.001), with an incremental contribution in explaining disability worsening compared with changes in BAG alone (Δ R 2 = 0.064, p < 0.001)., Discussion: The brain-predicted DD gap is sensitive to MS-related lesions and brain atrophy, adds to the brain-age paradigm in explaining physical disability both cross-sectionally and longitudinally, and may be used as an MS-specific biomarker of disease severity and progression.

Objective: We investigated the effects of adding regions to current dissemination in space (DIS) criteria for multiple sclerosis (MS)., Methods: Participants underwent brain, optic nerve, and spinal cord MRI. Baseline DIS was assessed by 2017 McDonald criteria and versions including optic nerve, temporal lobe, or corpus callosum as a fifth region (requiring 2/5), a version with all regions (requiring 3/7) and optic nerve variations requiring 3/5 and 4/5 regions. Performance was evaluated against MS diagnosis (2017 McDonald criteria) during follow-up., Results: Eighty-four participants were recruited (53F, 32.8 ± 7.1 years). 2017 McDonald DIS criteria were 87% sensitive (95% CI: 76-94), 73% specific (50-89), and 83% accurate (74-91) in identifying MS. Modified criteria with optic nerve improved sensitivity to 98% (91-100), with specificity 33% (13-59) and accuracy 84% (74-91). Criteria including temporal lobe showed sensitivity 94% (84-98), specificity 50% (28-72), and accuracy 82% (72-90); criteria including corpus callosum showed sensitivity 90% (80-96), specificity 68% (45-86), and accuracy 85% (75-91). Criteria adding all three regions (3/7 required) had sensitivity 95% (87-99), specificity 55% (32-76), and accuracy 85% (75-91). When requiring 3/5 regions (optic nerve as the fifth), sensitivity was 82% (70-91), specificity 77% (55-92), and accuracy 81% (71-89); with 4/5 regions, sensitivity was 56% (43-69), specificity 95% (77-100), and accuracy 67% (56-77)., Interpretation: Optic nerve inclusion increased sensitivity while lowering specificity. Increasing required regions in optic nerve criteria increased specificity and decreased sensitivity. Results suggest considering the optic nerve for DIS. An option of 3/5 or 4/5 regions preserved specificity, and criteria adding all three regions had highest accuracy., (© 2024 The Author(s). Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)

Background: Lower urinary tract (LUT) symptoms are reported in more than 80% of patients with multiple sclerosis (MS), most commonly an overactive bladder (OAB). The relationship between brain white matter (WM) changes in MS and OAB symptoms is poorly understood., Objectives: We aim to evaluate (i) microstructural WM differences across MS patients (pwMS) with OAB symptoms, patients without LUT symptoms, and healthy subjects using diffusion tensor imaging (DTI), and (ii) associations between clinical OAB symptom scores and DTI indices., Methods: Twenty-nine female pwMS [mean age (SD) 43.3 years (9.4)], including seventeen with OAB [mean age (SD) 46.1 years (8.6)] and nine without LUT symptoms [mean age (SD) 37.5 years (8.9)], and fourteen healthy controls (HCs) [mean age (SD) 48.5 years (20)] were scanned in a 3T MRI with a DTI protocol. Additionally, clinical scans were performed for WM lesion segmentation. Group differences in fractional anisotropy (FA) were evaluated using tract-based spatial statistics. The Urinary Symptom Profile questionnaire assessed OAB severity., Results: A statistically significant reduction in FA ( p = 0.004) was identified in microstructural WM in pwMS, compared with HCs. An inverse correlation was found between FA in frontal and parietal WM lobes and OAB scores ( p = 0.021) in pwMS. Areas of lower FA, although this did not reach statistical significance, were found in both frontal lobes and the rest of the non-dominant hemisphere in pwMS with OAB compared with pwMS without LUT symptoms ( p = 0.072)., Conclusions: This study identified that lesions affecting different WM tracts in MS can result in OAB symptoms and demonstrated the role of the WM in the neural control of LUT functions. By using DTI, the association between OAB symptom severity and WM changes were identified, adding knowledge to the current LUT working model. As MS is predominantly a WM disease, these findings suggest that regional WM involvement, including of the anterior corona radiata, anterior thalamic radiation, superior longitudinal fasciculus, and superior frontal-occipital fasciculus and a non-dominant prevalence in WM, can result in OAB symptoms. OAB symptoms in MS correlate with anisotropy changes in different white matter tracts as demonstrated by DTI. Structural impairment in WM tracts plays an important role in LUT symptoms in MS.

Background: Quantitative maps obtained with diffusion weighted (DW) imaging, such as fractional anisotropy (FA) -calculated by fitting the diffusion tensor (DT) model to the data,-are very useful to study neurological diseases. To fit this map accurately, acquisition times of the order of several minutes are needed because many noncollinear DW volumes must be acquired to reduce directional biases. Deep learning (DL) can be used to reduce acquisition times by reducing the number of DW volumes. We already developed a DL network named "one-minute FA," which uses 10 DW volumes to obtain FA maps, maintaining the same characteristics and clinical sensitivity of the FA maps calculated with the standard method using more volumes. Recent publications have indicated that it is possible to train DL networks and obtain FA maps even with 4 DW input volumes, far less than the minimum number of directions for the mathematical estimation of the DT., Methods: Here we investigated the impact of reducing the number of DW input volumes to 4 or 7, and evaluated the performance and clinical sensitivity of the corresponding DL networks trained to calculate FA, while comparing results also with those using our one-minute FA. Each network training was performed on the human connectome project open-access dataset that has a high resolution and many DW volumes, used to fit a ground truth FA. To evaluate the generalizability of each network, they were tested on two external clinical datasets, not seen during training, and acquired on different scanners with different protocols, as previously done., Results: Using 4 or 7 DW volumes, it was possible to train DL networks to obtain FA maps with the same range of values as ground truth - map, only when using HCP test data; pathological sensitivity was lost when tested using the external clinical datasets: indeed in both cases, no consistent differences were found between patient groups. On the contrary, our "one-minute FA" did not suffer from the same problem., Conclusion: When developing DL networks for reduced acquisition times, the ability to generalize and to generate quantitative biomarkers that provide clinical sensitivity must be addressed., Competing Interests: CGW-K was a shareholder in Queen Square Analytics Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Gaviraghi, Ricciardi, Palesi, Brownlee, Vitali, Prados, Kanber and Gandini Wheeler-Kingshott.)

Background: Conventional magnetic resonance imaging (MRI) does not account for all disability in multiple sclerosis., Objective: The objective was to assess the ability of graph metrics from diffusion-based structural connectomes to explain motor function beyond conventional MRI in early demyelinating clinically isolated syndrome (CIS)., Methods: A total of 73 people with CIS underwent conventional MRI, diffusion-weighted imaging and clinical assessment within 3 months from onset. A total of 28 healthy controls underwent MRI. Structural connectomes were produced. Differences between patients and controls were explored; clinical associations were assessed in patients. Linear regression models were compared to establish relevance of graph metrics over conventional MRI., Results: Local efficiency ( p = 0.045), clustering ( p = 0.034) and transitivity ( p = 0.036) were reduced in patients. Higher assortativity was associated with higher Expanded Disability Status Scale (EDSS) (β = 74.9, p = 0.026) scores. Faster timed 25-foot walk (T25FW) was associated with higher assortativity (β = 5.39, p = 0.026), local efficiency (β = 27.1, p = 0.041) and clustering (β = 36.1, p = 0.032) and lower small-worldness (β = -3.27, p = 0.015). Adding graph metrics to conventional MRI improved EDSS ( p = 0.045, Δ R 2 = 4) and T25FW ( p < 0.001, Δ R 2 = 13.6) prediction., Conclusion: Graph metrics are relevant early in demyelination. They show differences between patients and controls and have relationships with clinical outcomes. Segregation (local efficiency, clustering, transitivity) was particularly relevant. Combining graph metrics with conventional MRI better explained disability., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Background: Lower limb muscle magnetic resonance imaging (MRI) obtained fat fraction (FF) can detect disease progression in patients with Charcot-Marie-Tooth disease 1A (CMT1A). However, analysis is time-consuming and requires manual segmentation of lower limb muscles. We aimed to assess the responsiveness, efficiency and accuracy of acquiring FF MRI using an artificial intelligence-enabled automated segmentation technique., Methods: We recruited 20 CMT1A patients and 7 controls for assessment at baseline and 12 months. The three-point-Dixon fat water separation technique was used to determine thigh-level and calf-level muscle FF at a single slice using regions of interest defined using Musclesense, a trained artificial neural network for lower limb muscle image segmentation. A quality control (QC) check and correction of the automated segmentations was undertaken by a trained observer., Results: The QC check took on average 30 seconds per slice to complete. Using QC checked segmentations, the mean calf-level FF increased significantly in CMT1A patients from baseline over an average follow-up of 12.5 months (1.15%±1.77%, paired t-test p=0.016). Standardised response mean (SRM) in patients was 0.65. Without QC checks, the mean FF change between baseline and follow-up, at 1.15%±1.68% (paired t-test p=0.01), was almost identical to that seen in the corrected data, with a similar overall SRM at 0.69., Conclusions: Using automated image segmentation for the first time in a longitudinal study in CMT, we have demonstrated that calf FF has similar responsiveness to previously published data, is efficient with minimal time needed for QC checks and is accurate with minimal corrections needed., Competing Interests: Competing interests: HZ has served at scientific advisory boards and/or as a consultant for Abbvie, Acumen, Alector, Alzinova, ALZPath, Annexon, Apellis, Artery Therapeutics, AZTherapies, CogRx, Denali, Eisai, Nervgen, Novo Nordisk, Optoceutics, Passage Bio, Pinteon Therapeutics, Prothena, Red Abbey Labs, reMYND, Roche, Samumed, Siemens Healthineers, Triplet Therapeutics and Wave, has given lectures in symposia sponsored by Cellectricon, Fujirebio, Alzecure, Biogen and Roche, and is a cofounder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program (outside submitted work)., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Optic neuritis (ON) is a common feature of inflammatory demyelinating diseases (IDDs) such as multiple sclerosis (MS), aquaporin 4-antibody neuromyelitis optica spectrum disorder (AQP4 + NMOSD) and myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD). However, the involvement of the optic chiasm (OC) in IDD has not been fully investigated., Aims: To examine OC differences in non-acute IDD patients with (ON+) and without ON (ON-) using magnetisation transfer ratio (MTR), to compare differences between MS, AQP4 + NMOSD and MOGAD and understand their associations with other neuro-ophthalmological markers., Methods: Twenty-eight relapsing-remitting multiple sclerosis (RRMS), 24 AQP4 + NMOSD, 28 MOGAD patients and 32 healthy controls (HCs) underwent clinical evaluation, MRI and optical coherence tomography (OCT) scan. Multivariable linear regression models were applied., Results: ON + IDD patients showed lower OC MTR than HCs (28.87 ± 4.58 vs 31.65 ± 4.93; p = 0.004). When compared with HCs, lower OC MTR was found in ON + AQP4 + NMOSD (28.55 ± 4.18 vs 31.65 ± 4.93; p = 0.020) and MOGAD (28.73 ± 4.99 vs 31.65 ± 4.93; p = 0.007) and in ON- AQP4 + NMOSD (28.37 ± 7.27 vs 31.65 ± 4.93; p = 0.035). ON+ RRMS had lower MTR than ON- RRMS (28.87 ± 4.58 vs 30.99 ± 4.76; p = 0.038). Lower OC MTR was associated with higher number of ON (regression coefficient (RC) = -1.15, 95% confidence interval (CI) = -1.819 to -0.490, p = 0.001), worse visual acuity (RC = -0.026, 95% CI = -0.041 to -0.011, p = 0.001) and lower peripapillary retinal nerve fibre layer (pRNFL) thickness (RC = 1.129, 95% CI = 0.199 to 2.059, p = 0.018) when considering the whole IDD group., Conclusion: OC microstructural damage indicates prior ON in IDD and is linked to reduced vision and thinner pRNFL., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship and/or publication of this article: AB has received a research grant from the Italian Society of Neurology; she has been awarded a MAGNIMS-ECTRIMS fellowship in 2023. RC received speaker honoraria/travel support from Roche, Merck Serono, UCB, Sanofi-Genzyme, Novartis, and Janssen, and received a research grant from the Italian Ministry of University and Research. FP received a Guarantors of Brain fellowship 2017-2020. CT is currently being funded by a Miguel Servet contract, awarded by the Instituto de Salud Carlos III (ISCIII), Ministerio de Ciencia e Innovación de España (CP23/00117). She has also received a 2020 Junior Leader La Caixa Fellowship (fellowship code: LCF/BQ/PI20/11760008), awarded by “la Caixa” Foundation (ID 100010434), a 2021 Merck’s Award for the Investigation in MS, awarded by Fundación Merck Salud (Spain), a 2021 Research Grant (PI21/01860) awarded by the ISCIII, Ministerio de Ciencia e Innovación de España, and a FORTALECE research grant (FORT23/00034) also by the ISCIII, Ministerio de Ciencia e Innovación de España. In 2015, she received an ECTRIMS Post-doctoral Research Fellowship and has received funding from the UK MS Society. She is a member of the Editorial Board of Neurology Journal and Multiple Sclerosis Journal. She has also received honoraria from Roche, Novartis, Merck, Immunic Therapeutics, and Bristol Myers Squibb. She is a steering committee member of the O’HAND trial and of the Consensus group on Follow-on DMTs. BK is supported by the NIHR BRC at UCL. FDA has received congress fees or speaker honoraria and/or acted as a consultant for Neurology Academy, Coloplast, Janssen, Merck, Novartis, Roche,Sanofi. WJB has received speaker honoraria and/or acted as a consultant for Biogen, Janssen, Merck, Neuraxpharm, Novartis, Roche, Sanofi, Sandoz and Viatris; he is supported by the NIHR UCLH Biomedical Research Centre. FB is Steering committee or Data Safety Monitoring Board member for Biogen, Merck, ATRI/ACTC and Prothena. He is consultant for Roche, Celltrion, Rewind Therapeutics, Merck, IXICO, Jansen, Combinostics. He has research agreements with Merck, Biogen, GE Healthcare, Roche. Co-founder and shareholder of Queen Square Analytics LTD. OC is a member of independent DSMB for Novartis, gave a teaching talk on McDonald criteria in a Merck local symposium, and contributed to an Advisory Board for Biogen; she is Deputy Editor of Neurology, for which she receives an honorarium. Merck. AD, VC, DW, JS, MCY, and RN have no disclosure. ATT has speaker honoraria from Merck, Biomedia, Sereno Symposia International Foundation, Bayer and At the Limits and meeting expenses from Merck, Biogen Idec and Novartis. Supported by recent grants from the MRC (MR/S026088/1), NIHR BRC (541/CAP/OC/818837) and RoseTrees Trust (A1332 and PGL21/10079). Associate editor for Frontiers in Neurology – Neuro-ophthalmology section and on the editorial board for Neurology and Multiple Sclerosis Journal.

Background: We assessed the ability of a brain-and-cord-matched quantitative magnetic resonance imaging (qMRI) protocol to differentiate patients with progressive multiple sclerosis (PMS) from controls, in terms of normal-appearing (NA) tissue abnormalities, and explain disability., Methods: A total of 27 patients and 16 controls were assessed on the Expanded Disability Status Scale (EDSS), 25-foot timed walk (TWT), 9-hole peg (9HPT) and symbol digit modalities (SDMT) tests. All underwent 3T brain and (C2-C3) cord structural imaging and qMRI (relaxometry, quantitative magnetisation transfer, multi-shell diffusion-weighted imaging), using a fast brain-and-cord-matched protocol with brain-and-cord-unified imaging readouts. Lesion and NA-tissue volumes and qMRI metrics reflecting demyelination and axonal loss were obtained. Random forest analyses identified the most relevant volumetric/qMRI measures to clinical outcomes. Confounder-adjusted linear regression estimated the actual MRI-clinical associations., Results: Several qMRI/volumetric differences between patients and controls were observed ( p < 0.01). Higher NA-deep grey matter quantitative-T1 (EDSS: beta = 7.96, p = 0.006; 9HPT: beta = -0.09, p = 0.004), higher NA-white matter orientation dispersion index (TWT: beta = -3.21, p = 0.005; SDMT: beta = -847.10, p < 0.001), lower whole-cord bound pool fraction (9HPT: beta = 0.79, p = 0.001) and higher NA-cortical grey matter quantitative-T1 (SDMT = -94.31, p < 0.001) emerged as particularly relevant predictors of greater disability., Conclusion: Fast brain-and-cord-matched qMRI protocols are feasible and identify demyelination - combined with other mechanisms - as key for disability accumulation in PMS., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Background: Multiple sclerosis cortical lesions are areas of demyelination and neuroaxonal loss. Retinal layer thickness, measured with optical coherence tomography (OCT), is an emerging biomarker of neuroaxonal loss. Studies have reported correlations between cortical lesions and retinal layer thinning in established multiple sclerosis, suggesting a shared pathophysiological process. Here, we assessed the correlation between cortical lesions and OCT metrics at the onset of multiple sclerosis, examining, for the first time, associations with physical or cognitive disability., Objective: To examine the relationship between cortical lesions, optic nerve and retinal layer thicknesses, and physical and cognitive disability at the first demyelinating event., Methods: Thirty-nine patients and 22 controls underwent 3T-MRI, optical coherence tomography, and clinical tests. We identified cortical lesions on phase-sensitive inversion recovery sequences, including occipital cortex lesions. We measured the estimated total intracranial volume and the white matter lesion volume. OCT metrics included peripapillary retinal nerve fibre layer (pRNFL), ganglion cell and inner plexiform layer (GCIPL) and inner nuclear layer (INL) thicknesses., Results: Higher total cortical and leukocortical lesion volumes correlated with thinner pRNFL (B = -0.0005, 95 % CI -0.0008 to -0.0001, p = 0.01; B = -0.0005, 95 % CI -0.0008 to -0.0001, p = 0.01, respectively). Leukocortical lesion number correlated with colour vision deficits (B = 0.58, 95 %CI 0.039 to 1,11, p = 0.036). Thinner GCIPL correlated with a higher Expanded Disability Status Scale (B = -0.06, 95 % CI -1.1 to -0.008, p = 0.026). MS diagnosis (n = 18) correlated with higher cortical and leukocortical lesion numbers (p = 0.004 and p = 0.003), thinner GCIPL (p = 0.029) and INL (p = 0.041)., Conclusion: The association between cortical lesions and axonal damage in the optic nerve reinforces the role of neurodegenerative processes in MS pathogenesis at onset., Competing Interests: Declaration of competing interest MF is supported by a grant from the MRC (MR/S026088/1). SC is funded by Rosetrees and Bermuda Trust (PGL21/10079). FP received a Guarantors of Brain fellowship 2017-2020 and is supported by National Institute for Health Research (NIHR), Biomedical Research Centre initiative at University College London Hospitals (UCLH). ATT is supported by recent awards from the MRC (MR/S026088/1), NIHR BRC (541/CAP/OC/818837) and Roserrees Trust (A1332 and PGL21/10079) and MSIF; ATT has received speaker honoraria from Biomedia and Merck and meeting expenses from Biogen Idec and Merck. He was the UK PI for two clinical trials sponsored by MEDDAY pharmaceutical company (MD1003 in optic neuropathy [MS-ON - NCT02220244] and progressive MS [MS-SPI2 - NCT02220244]. The remaining authors report no disclosures., (Copyright © 2023. Published by Elsevier B.V.)

Background: Visual snow syndrome (VSS) is associated with functional connectivity (FC) dysregulation of visual networks (VNs). We hypothesized that mindfulness-based cognitive therapy, customized for visual symptoms (MBCT-vision), can treat VSS and modulate dysfunctional VNs., Methods: An open-label feasibility study for an 8-week MBCT-vision treatment program was conducted. Primary (symptom severity; impact on daily life) and secondary (WHO-5; CORE-10) outcomes at Week 9 and Week 20 were compared with baseline. Secondary MRI outcomes in a subcohort compared resting-state functional and diffusion MRI between baseline and Week 20., Results: Twenty-one participants (14 male participants, median 30 years, range 22-56 years) recruited from January 2020 to October 2021. Two (9.5%) dropped out. Self-rated symptom severity (0-10) improved: baseline (median [interquartile range (IQR)] 7 [6-8]) vs Week 9 (5.5 [3-7], P = 0.015) and Week 20 (4 [3-6], P < 0.001), respectively. Self-rated impact of symptoms on daily life (0-10) improved: baseline (6 [5-8]) vs Week 9 (4 [2-5], P = 0.003) and Week 20 (2 [1-3], P < 0.001), respectively. WHO-5 Wellbeing (0-100) improved: baseline (median [IQR] 52 [36-56]) vs Week 9 (median 64 [47-80], P = 0.001) and Week 20 (68 [48-76], P < 0.001), respectively. CORE-10 Distress (0-40) improved: baseline (15 [12-20]) vs Week 9 (12.5 [11-16.5], P = 0.003) and Week 20 (11 [10-14], P = 0.003), respectively. Within-subject fMRI analysis found reductions between baseline and Week 20, within VN-related FC in the i) left lateral occipital cortex (size = 82 mL, familywise error [FWE]-corrected P value = 0.006) and ii) left cerebellar lobules VIIb/VIII (size = 65 mL, FWE-corrected P value = 0.02), and increases within VN-related FC in the precuneus/posterior cingulate cortex (size = 69 mL, cluster-level FWE-corrected P value = 0.02)., Conclusions: MBCT-vision was a feasible treatment for VSS, improved symptoms and modulated FC of VNs. This study also showed proof-of-concept for intensive mindfulness interventions in the treatment of neurological conditions., Competing Interests: The authors report no conflicts of interest., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the North American Neuro-Opthalmology Society.)

Background and Purpose: Newly appearing lesions in multiple sclerosis (MS) may evolve into chronically active, slowly expanding lesions (SELs), leading to sustained disability progression. The aim of this study was to evaluate the incidence of newly appearing lesions developing into SELs, and their correlation to clinical evolution and treatment., Methods: A retrospective analysis of a fingolimod trial in primary progressive MS (PPMS; INFORMS, NCT00731692) was undertaken. Data were available from 324 patients with magnetic resonance imaging scans up to 3 years after screening. New lesions at year 1 were identified with convolutional neural networks, and SELs obtained through a deformation-based method. Clinical disability was assessed annually by Expanded Disability Status Scale (EDSS), Nine-Hole Peg Test, Timed 25-Foot Walk, and Paced Auditory Serial Addition Test. Linear, logistic, and mixed-effect models were used to assess the relationship between the Jacobian expansion in new lesions and SELs, disability scores, and treatment status., Results: One hundred seventy patients had ≥1 new lesions at year 1 and had a higher lesion count at screening compared to patients with no new lesions (median = 27 vs. 22, p = 0.007). Among the new lesions (median = 2 per patient), 37% evolved into definite or possible SELs. Higher SEL volume and count were associated with EDSS worsening and confirmed disability progression. Treated patients had lower volume and count of definite SELs (β = -0.04, 95% confidence interval [CI] = -0.07 to -0.01, p = 0.015; β = -0.36, 95% CI = -0.67 to -0.06, p = 0.019, respectively)., Conclusions: Incident chronic active lesions are common in PPMS, and fingolimod treatment can reduce their number., (© 2023 The Authors. European Journal of Neurology published by John Wiley & Sons Ltd on behalf of European Academy of Neurology.)

Missed fractures are a costly healthcare issue, not only negatively impacting patient lives, leading to potential long-term disability and time off work, but also responsible for high medicolegal disbursements that could otherwise be used to improve other healthcare services. When fractures are overlooked in children, they are particularly concerning as opportunities for safeguarding may be missed. Assistance from artificial intelligence (AI) in interpreting medical images may offer a possible solution for improving patient care, and several commercial AI tools are now available for radiology workflow implementation. However, information regarding their development, evidence for performance and validation as well as the intended target population is not always clear, but vital when evaluating a potential AI solution for implementation. In this article, we review the range of available products utilizing AI for fracture detection (in both adults and children) and summarize the evidence, or lack thereof, behind their performance. This will allow others to make better informed decisions when deciding which product to procure for their specific clinical requirements., Competing Interests: None declared., (© The Author(s) 2023. Published by Oxford University Press on behalf of the British Institute of Radiology.)

Artificial intelligence (AI)-based tools are widely employed, but their use for diagnosis and prognosis of neurological disorders is still evolving. Here we analyse a cross-sectional multicentre structural MRI dataset of 696 people with epilepsy and 118 control subjects. We use an innovative machine-learning algorithm, Subtype and Stage Inference, to develop a novel data-driven disease taxonomy, whereby epilepsy subtypes correspond to distinct patterns of spatiotemporal progression of brain atrophy.In a discovery cohort of 814 individuals, we identify two subtypes common to focal and idiopathic generalized epilepsies, characterized by progression of grey matter atrophy driven by the cortex or the basal ganglia. A third subtype, only detected in focal epilepsies, was characterized by hippocampal atrophy. We corroborate external validity via an independent cohort of 254 people and confirm that the basal ganglia subtype is associated with the most severe epilepsy.Our findings suggest fundamental processes underlying the progression of epilepsy-related brain atrophy. We deliver a novel MRI- and AI-guided epilepsy taxonomy, which could be used for individualized prognostics and targeted therapeutics., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Guarantors of Brain.)

Purpose: This study aimed to assess the image quality of apparent diffusion coefficient (ADC) maps derived from conventional diffusion-weighted MRI and fractional intracellular volume maps (FIC) from VERDICT MRI (Vascular, Extracellular, Restricted Diffusion for Cytometry in Tumours) in patients from the INNOVATE trial. The inter-reader agreement was also assessed., Methods: Two readers analysed both ADC and FIC maps from 57 patients enrolled in the INNOVATE prospective trial. Image quality was assessed using the Prostate Imaging Quality (PI-QUAL) score and a subjective image quality Likert score (Likert-IQ). The image quality of FIC and ADC were compared using a Wilcoxon Signed Ranks test. The inter-reader agreement was assessed with Cohen's kappa., Results: There was no statistically significant difference between the PI-QUAL score for FIC datasets compared to ADC datasets for either reader (p = 0.240 and p = 0.614). Using the Likert-IQ score, FIC image quality was higher compared to ADC (p = 0.021) as assessed by reader-1 but not for reader-2 (p = 0.663). The inter-reader agreement was 'fair' for PI-QUAL scoring of datasets with FIC maps at 0.27 (95% confidence interval; 0.08-0.46) and ADC datasets at 0.39 (95% confidence interval 0.22-0.57). For Likert scoring, the inter-reader agreement was also 'fair' for FIC maps at 0.38 (95% confidence interval; 0.10-0.65) and substantial for ADC maps at 0.62 (95% confidence interval; 0.39-0.86)., Conclusion: Image quality was comparable for FIC and ADC. The inter-reader agreement was similar when using PIQUAL for both FIC and ADC datasets but higher for ADC maps compared to FIC maps using the image quality Likert score., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Crown Copyright © 2023. Published by Elsevier B.V. All rights reserved.)

Multiple sclerosis risk has a well-established polygenic component, yet the genetic contribution to disease course and severity remains unclear and difficult to examine. Accurately measuring disease progression requires long-term study of clinical and radiological outcomes with sufficient follow-up duration to confidently confirm disability accrual and multiple sclerosis phenotypes. In this retrospective study, we explore genetic influences on long-term disease course and severity; in a unique cohort of clinically isolated syndrome patients with homogenous 30-year disease duration, deep clinical phenotyping and advanced MRI metrics. Sixty-one clinically isolated syndrome patients [41 female (67%): 20 male (33%)] underwent clinical and MRI assessment at baseline, 1-, 5-, 10-, 14-, 20- and 30-year follow-up (mean age ± standard deviation: 60.9 ± 6.5 years). After 30 years, 29 patients developed relapsing-remitting multiple sclerosis, 15 developed secondary progressive multiple sclerosis and 17 still had a clinically isolated syndrome. Twenty-seven genes were investigated for associations with clinical outcomes [including disease course and Expanded Disability Status Scale (EDSS)] and brain MRI (including white matter lesions, cortical lesions, and brain tissue volumes) at the 30-year follow-up. Genetic associations with changes in EDSS, relapses, white matter lesions and brain atrophy (third ventricular and medullary measurements) over 30 years were assessed using mixed-effects models. HLA-DRB1*1501 -positive ( n = 26) patients showed faster white matter lesion accrual [+1.96 lesions/year (0.64-3.29), P = 3.8 × 10 -3 ], greater 30-year white matter lesion volumes [+11.60 ml, (5.49-18.29), P = 1.27 × 10 -3 ] and higher annualized relapse rates [+0.06 relapses/year (0.005-0.11), P = 0.031] compared with HLA-DRB1*1501 -negative patients ( n = 35). PVRL2 -positive patients ( n = 41) had more cortical lesions (+0.83 [0.08-1.66], P = 0.042), faster EDSS worsening [+0.06 points/year (0.02-0.11), P = 0.010], greater 30-year EDSS [+1.72 (0.49-2.93), P = 0.013; multiple sclerosis cases: +2.60 (1.30-3.87), P = 2.02 × 10 -3 ], and greater risk of secondary progressive multiple sclerosis [odds ratio (OR) = 12.25 (1.15-23.10), P = 0.031] than PVRL2 -negative patients ( n = 18). In contrast, IRX1 -positive ( n = 30) patients had preserved 30-year grey matter fraction [+0.76% (0.28-1.29), P = 8.4 × 10 -3 ], lower risk of cortical lesions [OR = 0.22 (0.05-0.99), P = 0.049] and lower 30-year EDSS [-1.35 (-0.87,-3.44), P = 0.026; multiple sclerosis cases: -2.12 (-0.87, -3.44), P = 5.02 × 10 -3 ] than IRX1 -negative patients ( n = 30). In multiple sclerosis cases, IRX1 -positive patients also had slower EDSS worsening [-0.07 points/year (-0.01,-0.13), P = 0.015] and lower risk of secondary progressive multiple sclerosis [OR = 0.19 (0.04-0.92), P = 0.042]. These exploratory findings support diverse genetic influences on pathological mechanisms associated with multiple sclerosis disease course. HLA-DRB1*1501 influenced white matter inflammation and relapses, while IRX1 (protective) and PVRL2 (adverse) were associated with grey matter pathology (cortical lesions and atrophy), long-term disability worsening and the risk of developing secondary progressive multiple sclerosis., Competing Interests: N.S. has been a clinical research fellow in a post supported by Merck (supervised by S.A.T. and D.C.) and subsequently by MRC (MR/W019906/1). K.C has received honoraria for participation and attendance of educational events from Novartis, Roche, Biogen and Merck; she has received honoraria for consultancy work from Novartis, Roche, Biogen, Merck and Viatris. F.P.C. received a Guarantors of Brain fellowship 2017–2020. F.P.C. and B.K. are supported by the National Institute for Health Research (NIHR), Biomedical Research Centre initiative at University College London Hospitals (UCLH). A.J.T. reports personal fees paid to his institution from Eisai Ltd; is an editorial board member for The Lancet Neurology receiving a free subscription; is Editor-in-Chief for MS Journal receiving an honorarium from SAGE Publications; receives support for travel as member, Clinical Trials Committee, International PPMS Alliance, and from the National MS Society (NMSS) (USA) as member, NMSS Research Programs Advisory Committee. S.A.T. receives support from the UCLH Biomedical Research Centre; has received honoraria from Roche, Merck, Novartis, Sanofi-Genzyme and Biogen in the last 3 years and co-supervises a clinical fellowship at the National Hospital for Neurology and Neurosurgery, London, which is supported by Merck. W.B. has received speaker honoraria for educational activities and/or acted as a consultant for Biogen, Janssen, Merck, Novartis, Roche, Sanofi-Genzyme and Viatris. O.C. is a member of independent data and safety monitoring board for Novartis, gave a teaching talk on McDonald criteria in a Merck local symposium, and contributed to an Advisory Board for Biogen; she is Deputy Editor of Neurology, for which she receives an honorarium. C.T. is currently being funded by a Junior Leader La Caixa Fellowship (The project that gave rise to these results received the support of a fellowship from ‘la Caixa’ Foundation (ID 100010434); fellowship code is LCF/BQ/PI20/117600080; She has also received the 2021 Merck’s Award for the Investigation in MS (Spain) and a grant from Instituto de Salud Carlos III (ISCIII), Spain (grant ID: PI21/01860); In 2015, she received an ECTRIMS Post-doctoral Research Fellowship and has received funding from the UK Multiple Sclerosis Society (grant number 77); She has also received speaker honoraria from Roche and Novartis; She serves on the Editorial Board of Neurology and Multiple Sclerosis Journal. F.B. is supported by the UCLH biomedical research centre; He is a steering committee or iDMC member for Biogen, Merck, Roche, EISAI and Prothena; He is a consultant for Roche, Biogen, Merck, IXICO, Jansen, Combinostics; He has research agreements with Merck, Biogen, GE Healthcare, Roche; He is co-founder and shareholder of Queen Square Analytics LTD. D.C. is a consultant for Hoffmann-La Roche; In the last three years he has been a consultant for Biogen, received research funding from Hoffmann-La Roche, the International Progressive Multiple Sclerosis Alliance, the Multiple Sclerosis Society, and the National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre, and speaker’s honorarium from Novartis; He co-supervises a clinical fellowship at the National Hospital for Neurology and Neurosurgery, London, which is supported by Merck. The remaining authors, L.H., R.S., C.A.M.G.W.K. and H.H.: nothing to disclose., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Guarantors of Brain.)

Background: Optic neuropathy is a near ubiquitous feature of Friedreich's ataxia (FRDA). Previous studies have examined varying aspects of the anterior and posterior visual pathways but none so far have comprehensively evaluated the heterogeneity of degeneration across different areas of the retina, changes to the macula layers and combined these with volumetric MRI studies of the visual cortex and frataxin level., Methods: We investigated 62 genetically confirmed FRDA patients using an integrated approach as part of an observational cohort study. We included measurement of frataxin protein levels, clinical evaluation of visual and neurological function, optical coherence tomography to determine retinal nerve fibre layer thickness and macular layer volume and volumetric brain MRI., Results: We demonstrate that frataxin level correlates with peripapillary retinal nerve fibre layer thickness and that retinal sectors differ in their degree of degeneration. We also shown that retinal nerve fibre layer is thinner in FRDA patients than controls and that this thinning is influenced by the AAO and GAA1. Furthermore we show that the ganglion cell and inner plexiform layers are affected in FRDA. Our MRI data indicate that there are borderline correlations between retinal layers and areas of the cortex involved in visual processing., Conclusion: Our study demonstrates the uneven distribution of the axonopathy in the retinal nerve fibre layer and highlight the relative sparing of the papillomacular bundle and temporal sectors. We show that thinning of the retinal nerve fibre layer is associated with frataxin levels, supporting the use the two biomarkers in future clinical trials design. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society., (© 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.)

Background: Olfactory impairments and anosmia from COVID-19 infection typically resolve within 2-4 weeks, although in some cases, symptoms persist longer. COVID-19-related anosmia is associated with olfactory bulb atrophy, however, the impact on cortical structures is relatively unknown, particularly in those with long-term symptoms., Methods: In this exploratory, observational study, we studied individuals who experienced COVID-19-related anosmia, with or without recovered sense of smell, and compared against individuals with no prior COVID-19 infection (confirmed by antibody testing, all vaccine naïve). MRI Imaging was carried out between the 15th July and 17th November 2020 at the Queen Square House Clinical Scanning Facility, UCL, United Kingdom. Using functional magnetic resonance imaging (fMRI) and structural imaging, we assessed differences in functional connectivity (FC) between olfactory regions, whole brain grey matter (GM) cerebral blood flow (CBF) and GM density., Findings: Individuals with anosmia showed increased FC between the left orbitofrontal cortex (OFC), visual association cortex and cerebellum and FC reductions between the right OFC and dorsal anterior cingulate cortex compared to those with no prior COVID-19 infection ( p  < 0.05, from whole brain statistical parametric map analysis). Individuals with anosmia also showed greater CBF in the left insula, hippocampus and ventral posterior cingulate when compared to those with resolved anosmia ( p  < 0.05, from whole brain statistical parametric map analysis)., Interpretation: This work describes, for the first time to our knowledge, functional differences within olfactory areas and regions involved in sensory processing and cognitive functioning. This work identifies key areas for further research and potential target sites for therapeutic strategies., Funding: This study was funded by the National Institute for Health and Care Research and supported by the Queen Square Scanner business case., Competing Interests: XG is a founder, shareholder and CEO of Gold Standard Phantoms, a company providing calibration and test objects for MRI. ED’A received funding from the H2020 Research and Innovation Action Grants Human Brain Project 785907 and 945539 (SGA2 and SGA3), and from the MNL Project “Local Neuronal Microcircuits” of the Centro Fermi (Rome, Italy). CGWK received funding from the H2020 Research and Innovation Action Grants Human Brain Project 945539 (SGA3), from the MS Society (#77), from Wings for Life (#169111), BRC (#BRC704/CAP/CGW), MRC (#MR/S026088/1), Ataxia UK. CGWK is a shareholder in Queen Square Analytics Ltd. RLB reports receiving consulting fees from Pfizer, Eli-Lilly, Gila Therapeutics Inc., and ViiV Healthcare and consulting fees, lecture fees from Novo Nordisk and participating in clinical trials for Novo Nordisk. RLB receives National Institute for Health and Care Research Professorship funding RP-2015-06-005 and JW is funded under this funding too. JM receives UCL/UCLH National Institute for Health and Care Research BRC funding., (© 2023 The Authors.)

Introduction: Conventional MRI is routinely used for the characterization of pathological changes in multiple sclerosis (MS), but due to its lack of specificity is unable to provide accurate prognoses, explain disease heterogeneity and reconcile the gap between observed clinical symptoms and radiological evidence. Quantitative MRI provides measures of physiological abnormalities, otherwise invisible to conventional MRI, that correlate with MS severity. Analyzing quantitative MRI measures through machine learning techniques has been shown to improve the understanding of the underlying disease by better delineating its alteration patterns., Methods: In this retrospective study, a cohort of healthy controls (HC) and MS patients with different subtypes, followed up 15 years from clinically isolated syndrome (CIS), was analyzed to produce a multi-modal set of quantitative MRI features encompassing relaxometry, microstructure, sodium ion concentration, and tissue volumetry. Random forest classifiers were used to train a model able to discriminate between HC, CIS, relapsing remitting (RR) and secondary progressive (SP) MS patients based on these features and, for each classification task, to identify the relative contribution of each MRI-derived tissue property to the classification task itself., Results and Discussion: Average classification accuracy scores of 99 and 95% were obtained when discriminating HC and CIS vs. SP, respectively; 82 and 83% for HC and CIS vs. RR; 76% for RR vs. SP, and 79% for HC vs. CIS. Different patterns of alterations were observed for each classification task, offering key insights in the understanding of MS phenotypes pathophysiology: atrophy and relaxometry emerged particularly in the classification of HC and CIS vs. MS, relaxometry within lesions in RR vs. SP, sodium ion concentration in HC vs. CIS, and microstructural alterations were involved across all tasks., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Ricciardi, Grussu, Kanber, Prados, Yiannakas, Solanky, Riemer, Golay, Brownlee, Ciccarelli, Alexander and Gandini Wheeler-Kingshott.)

Background and Objectives: Relapsing-remitting multiple sclerosis (RRMS), aquaporin-4 antibody-positive neuromyelitis optica spectrum disorder (AQP4-NMOSD), and myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) may have overlapping clinical features. There is an unmet need for imaging markers that differentiate between them when serologic testing is unavailable or ambiguous. We assessed whether imaging characteristics typical of MS discriminate RRMS from AQP4-NMOSD and MOGAD, alone and in combination., Methods: Adult, nonacute patients with RRMS, APQ4-NMOSD, and MOGAD and healthy controls were prospectively recruited at the National Hospital for Neurology and Neurosurgery (London, United Kingdom) and the Walton Centre (Liverpool, United Kingdom) between 2014 and 2019. They underwent conventional and advanced brain, cord, and optic nerve MRI and optical coherence tomography (OCT)., Results: A total of 91 consecutive patients (31 RRMS, 30 APQ4-NMOSD, and 30 MOGAD) and 34 healthy controls were recruited. The most accurate measures differentiating RRMS from AQP4-NMOSD were the proportion of lesions with the central vein sign (CVS) (84% vs 33%, accuracy/specificity/sensitivity: 91/88/93%, p < 0.001), followed by cortical lesions (median: 2 [range: 1-14] vs 1 [0-1], accuracy/specificity/sensitivity: 84/90/77%, p = 0.002) and white matter lesions (mean: 39.07 [±25.8] vs 9.5 [±14], accuracy/specificity/sensitivity: 78/84/73%, p = 0.001). The combination of higher proportion of CVS, cortical lesions, and optic nerve magnetization transfer ratio reached the highest accuracy in distinguishing RRMS from AQP4-NMOSD (accuracy/specificity/sensitivity: 95/92/97%, p < 0.001). The most accurate measures favoring RRMS over MOGAD were white matter lesions (39.07 [±25.8] vs 1 [±2.3], accuracy/specificity/sensitivity: 94/94/93%, p = 0.006), followed by cortical lesions (2 [1-14] vs 1 [0-1], accuracy/specificity/sensitivity: 84/97/71%, p = 0.004), and retinal nerve fiber layer thickness (RNFL) (mean: 87.54 [±13.83] vs 75.54 [±20.33], accuracy/specificity/sensitivity: 80/79/81%, p = 0.009). Higher cortical lesion number combined with higher RNFL thickness best differentiated RRMS from MOGAD (accuracy/specificity/sensitivity: 84/92/77%, p < 0.001)., Discussion: Cortical lesions, CVS, and optic nerve markers achieve a high accuracy in distinguishing RRMS from APQ4-NMOSD and MOGAD. This information may be useful in clinical practice, especially outside the acute phase and when serologic testing is ambiguous or not promptly available., Classification of Evidence: This study provides Class II evidence that selected conventional and advanced brain, cord, and optic nerve MRI and OCT markers distinguish adult patients with RRMS from AQP4-NMOSD and MOGAD., (Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.)

Background In men suspected of having prostate cancer (PCa), up to 50% of men with positive multiparametric MRI (mpMRI) findings (Prostate Imaging Reporting and Data System [PI-RADS] or Likert score of 3 or higher) have no clinically significant (Gleason score ≤3+3, benign) biopsy findings. Vascular, Extracellular, and Restricted Diffusion for Cytometry in Tumor (VERDICT) MRI analysis could improve the stratification of positive mpMRI findings. Purpose To evaluate VERDICT MRI, mpMRI-derived apparent diffusion coefficient (ADC), and prostate-specific antigen density (PSAD) as determinants of clinically significant PCa (csPCa). Materials and Methods Between April 2016 and December 2019, men suspected of having PCa were prospectively recruited from two centers and underwent VERDICT MRI and mpMRI at one center before undergoing targeted biopsy. Biopsied lesion ADC, lesion-derived fractional intracellular volume (FIC), and PSAD were compared between men with csPCa and those without csPCa, using nonparametric tests subdivided by Likert scores. Area under the receiver operating characteristic curve (AUC) was calculated to test diagnostic performance. Results Among 303 biopsy-naive men, 165 study participants (mean age, 65 years ± 7 [SD]) underwent targeted biopsy; of these, 73 had csPCa. Median lesion FIC was higher in men with csPCa (FIC, 0.53) than in those without csPCa (FIC, 0.18) for Likert 3 ( P = .002) and Likert 4 (0.60 vs 0.28, P < .001) lesions. Median lesion ADC was lower for Likert 4 lesions with csPCa (0.86 × 10 -3 mm 2 /sec) compared with lesions without csPCa (1.12 × 10 -3 mm 2 /sec, P = .03), but there was no evidence of a difference for Likert 3 lesions (0.97 × 10 -3 mm 2 /sec vs 1.20 × 10 -3 mm 2 /sec, P = .09). PSAD also showed no difference for Likert 3 (0.17 ng/mL 2 vs 0.12 ng/mL 2 , P = .07) or Likert 4 (0.14 ng/mL 2 vs 0.12 ng/mL 2 , P = .47) lesions. The diagnostic performance of FIC (AUC, 0.96; 95% CI: 0.93, 1.00) was higher ( P = .02) than that of ADC (AUC, 0.85; 95% CI: 0.79, 0.91) and PSAD (AUC, 0.74; 95% CI: 0.66, 0.82) for the presence of csPCa in biopsied lesions. Conclusion Lesion fractional intracellular volume enabled better classification of clinically significant prostate cancer than did apparent diffusion coefficient and prostate-specific antigen density. Clinical trial registration no. NCT02689271 © RSNA, 2022 Online supplemental material is available for this article.

Multiple sclerosis (MS) is a chronic autoimmune disease characterized by demyelinating lesions that are often visible on magnetic resonance imaging (MRI). Segmentation of these lesions can provide imaging biomarkers of disease burden that can help monitor disease progression and the imaging response to treatment. Manual delineation of MRI lesions is tedious and prone to subjective bias, while automated lesion segmentation methods offer objectivity and speed, the latter being particularly important when analysing large datasets. Lesion segmentation can be broadly categorised into two groups: cross-sectional methods, which use imaging data acquired at a single time-point to characterise MRI lesions; and longitudinal methods, which use imaging data from the same subject acquired at two or more different time-points to characterise lesions over time. The main objective of longitudinal segmentation approaches is to more accurately detect the presence of new MS lesions and the growth or remission of existing lesions, which may be effective biomarkers of disease progression and treatment response. This paper reviews articles on longitudinal MS lesion segmentation methods published over the past 10 years. These are divided into traditional machine learning methods and deep learning techniques. PubMed articles using longitudinal information and comparing fully automatic two time point segmentations in any step of the process were selected. Nineteen articles were reviewed. There is an increasing number of deep learning techniques for longitudinal MS lesion segmentation that are promising to help better understand disease progression., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Objective: In multiple sclerosis chronic demyelination is associated with axonal loss, and ultimately contributes to irreversible progressive disability. Enhancing remyelination may slow, or even reverse, disability. We recently trialled bexarotene versus placebo in 49 people with multiple sclerosis. While the primary MRI outcome was negative, there was converging neurophysiological and MRI evidence of efficacy. Multiple factors influence lesion remyelination. In this study we undertook a systematic exploratory analysis to determine whether treatment response - measured by change in magnetisation transfer ratio - is influenced by location (tissue type and proximity to CSF) or the degree of abnormality (using baseline magnetisation transfer ratio and T1 values)., Methods: We examined treatment effects at the whole lesion level, the lesion component level (core, rim and perilesional tissues) and at the individual lesion voxel level., Results: At the whole lesion level, significant treatment effects were seen in GM but not WM lesions. Voxel-level analyses detected significant treatment effects in WM lesion voxels with the lowest baseline MTR, and uncovered gradients of treatment effect in both WM and CGM lesional voxels, suggesting that treatment effects were lower near CSF spaces. Finally, larger treatment effects were seen in the outer and surrounding components of GM lesions compared to inner cores., Interpretation: Remyelination varies markedly within and between lesions. The greater remyelinating effect in GM lesions is congruent with neuropathological observations. For future remyelination trials, whole GM lesion measures require less complex post-processing compared to WM lesions (which require voxel level analyses) and markedly reduce sample sizes., (© 2022 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)

Fractional anisotropy (FA) is a quantitative map sensitive to microstructural properties of tissues in vivo and it is extensively used to study the healthy and pathological brain. This map is classically calculated by model fitting (standard method) and requires many diffusion weighted (DW) images for data quality and unbiased readings, hence needing the acquisition time of several minutes. Here, we adapted the U-net architecture to be generalized and to obtain good quality FA from DW volumes acquired in 1 minute. Our network requires 10 input DW volumes (hence fast acquisition), is robust to the direction of application of the diffusion gradients (hence generalized), and preserves/improves map quality (hence good quality maps). We trained the network on the human connectome project (HCP) data using the standard model-fitting method on the entire set of DW directions to extract FA (ground truth). We addressed the generalization problem, i.e., we trained the network to be applicable, without retraining, to clinical datasets acquired on different scanners with different DW imaging protocols. The network was applied to two different clinical datasets to assess FA quality and sensitivity to pathology in temporal lobe epilepsy and multiple sclerosis, respectively. For HCP data, when compared to the ground truth FA, the FA obtained from 10 DW volumes using the network was significantly better ( p <10 -4 ) than the FA obtained using the standard pipeline. For the clinical datasets, the network FA retained the same microstructural characteristics as the FA calculated with all DW volumes using the standard method. At the subject level, the comparison between white matter (WM) ground truth FA values and network FA showed the same distribution; at the group level, statistical differences of WM values detected in the clinical datasets with the ground truth FA were reproduced when using values from the network FA, i.e., the network retained sensitivity to pathology. In conclusion, the proposed network provides a clinically available method to obtain FA from a generic set of 10 DW volumes acquirable in 1 minute, augmenting data quality compared to direct model fitting, reducing the possibility of bias from sub-sampled data, and retaining FA pathological sensitivity, which is very attractive for clinical applications., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Gaviraghi, Ricciardi, Palesi, Brownlee, Vitali, Prados, Kanber and Gandini Wheeler-Kingshott.)