Incidence and severity of pulmonary embolism in COVID-19 infection: Ancestral, Alpha, Delta, and Omicron variants.

Little information is available regarding incidence and severity of pulmonary embolism (PE) across the periods of ancestral strain, Alpha, Delta, and Omicron variants. The aim of this study is to investigate the incidence and severity of PE over the dominant periods of ancestral strain and Alpha, Delta, and Omicron variants. We hypothesized that the incidence and the severity by proximity of PE in patients with the newer variants and vaccination would be decreased compared with those in ancestral and earlier variants. Patients with COVID-19 diagnosis between March 2020 and February 2022 and computed tomography pulmonary angiogram performed within a 6-week window around the diagnosis (-2 to +4 weeks) were studied retrospectively. The primary endpoints were the associations of the incidence and location of PE with the ancestral strain and each variant. Of the 720 coronavirus disease 2019 patients with computed tomography pulmonary angiogram (58.6 ± 17.2 years; 374 females), PE was diagnosed among 42/358 (12%) during the ancestral strain period, 5/60 (8%) during the Alpha variant period, 16/152 (11%) during the Delta variant period, and 13/150 (9%) during the Omicron variant period. The most proximal PE (ancestral strain vs variants) was located in the main/lobar arteries (31% vs 6%-40%), in the segmental arteries (52% vs 60%-75%), and in the subsegmental arteries (17% vs 0%-19%). There was no significant difference in both the incidence and location of PE across the periods, confirmed by multivariable logistic regression models. In summary, the incidence and severity of PE did not significantly differ across the periods of ancestral strain and Alpha, Delta, and Omicron variants.

New Enhancing MRI Lesions Associate with IL-17, Neutrophil Degranulation and Integrin Microparticles: Multi-Omics Combined with Frequent MRI in Multiple Sclerosis.

BACKGROUND: Blood-barrier (BBB) breakdown and active inflammation are hallmarks of relapsing multiple sclerosis (RMS), but the molecular events contributing to the development of new lesions are not well explored. Leaky endothelial junctions are associated with increased production of endothelial-derived extracellular microvesicles (EVs) and result in the entry of circulating immune cells into the brain. MRI with intravenous gadolinium (Gd) can visualize acute blood-barrier disruption as the initial event of the evolution of new lesions. METHODS: Here, weekly MRI with Gd was combined with proteomics, multiplex immunoassay, and endothelial stress-optimized EV array to identify early markers related to BBB disruption. Five patients with RMS with no disease-modifying treatment were monitored weekly using high-resolution 3T MRI scanning with intravenous gadolinium (Gd) for 8 weeks. Patients were then divided into three groups (low, medium, or high MRI activity) defined by the number of new, total, and maximally enhancing Gd-enhancing lesions and the number of new FLAIR lesions. Plasma samples taken at each MRI were analyzed for protein biomarkers of inflammation by quantitative proteomics, and cytokines using multiplex immunoassays. EVs were characterized with an optimized endothelial stress EV array based on exosome surface protein markers for the detection of soluble secreted EVs. RESULTS: Proteomics analysis of plasma yielded quantitative information on 208 proteins at each patient time point (n = 40). We observed the highest number of unique dysregulated proteins (DEPs) and the highest functional enrichment in the low vs. high MRI activity comparison. Complement activation and complement/coagulation cascade were also strongly overrepresented in the low vs. high MRI activity comparison. Activation of the alternative complement pathway, pathways of blood coagulation, extracellular matrix organization, and the regulation of TLR and IGF transport were unique for the low vs. high MRI activity comparison as well, with these pathways being overrepresented in the patient with high MRI activity. Principal component analysis indicated the individuality of plasma profiles in patients. IL-17 was upregulated at all time points during 8 weeks in patients with high vs. low MRI activity. Hierarchical clustering of soluble markers in the plasma indicated that all four MRI outcomes clustered together with IL-17, IL-12p70, and IL-1ß. MRI outcomes also showed clustering with EV markers CD62E/P, MIC A/B, ICAM-1, and CD42A. The combined cluster of these cytokines, EV markers, and MRI outcomes clustered also with IL-12p40 and IL-7. All four MRI outcomes correlated positively with levels of IL-17 (p < 0.001, respectively), and EV-ICAM-1 (p < 0.0003, respectively). IL-1ß levels positively correlated with the number of new Gd-enhancing lesions (p < 0.01), new FLAIR lesions (p < 0.001), and total number of Gd-enhancing lesions (p < 0.05). IL-6 levels positively correlated with the number of new FLAIR lesions (p < 0.05). Random Forests and linear mixed models identified IL-17, CCL17/TARC, CCL3/MIP-1α, and TNF-α as composite biomarkers predicting new lesion evolution. CONCLUSIONS: Combination of serial frequent MRI with proteome, neuroinflammation markers, and protein array data of EVs enabled assessment of temporal changes in inflammation and endothelial dysfunction in RMS related to the evolution of new and enhancing lesions. Particularly, the Th17 pathway and IL-1ß clustered and correlated with new lesions and Gd enhancement, indicating their importance in BBB disruption and initiating acute brain inflammation in MS. In addition to the Th17 pathway, abundant protein changes between MRI activity groups suggested the role of EVs and the coagulation system along with innate immune responses including acute phase proteins, complement components, and neutrophil degranulation.

Multiple sclerosis lesions that impair memory map to a connected memory circuit.

BACKGROUND: Nearly 1 million Americans are living with multiple sclerosis (MS) and 30-50% will experience memory dysfunction. It remains unclear whether this memory dysfunction is due to overall white matter lesion burden or damage to specific neuroanatomical structures. Here we test if MS memory dysfunction is associated with white matter lesions to a specific brain circuit. METHODS: We performed a cross-sectional analysis of standard structural images and verbal memory scores as assessed by immediate recall trials from 431 patients with MS (mean age 49.2 years, 71.9% female) enrolled at a large, academic referral center. White matter lesion locations from each patient were mapped using a validated algorithm. First, we tested for associations between memory dysfunction and total MS lesion volume. Second, we tested for associations between memory dysfunction and lesion intersection with an a priori memory circuit derived from stroke lesions. Third, we performed mediation analyses to determine which variable was most associated with memory dysfunction. Finally, we performed a data-driven analysis to derive de-novo brain circuits for MS memory dysfunction using both functional (n = 1000) and structural (n = 178) connectomes. RESULTS: Both total lesion volume (r = 0.31, p < 0.001) and lesion damage to our a priori memory circuit (r = 0.34, p < 0.001) were associated with memory dysfunction. However, lesion damage to the memory circuit fully mediated the association of lesion volume with memory performance. Our data-driven analysis identified multiple connections associated with memory dysfunction, including peaks in the hippocampus (T = 6.05, family-wise error p = 0.000008), parahippocampus, fornix and cingulate. Finally, the overall topography of our data-driven MS memory circuit matched our a priori stroke-derived memory circuit. CONCLUSIONS: Lesion locations associated with memory dysfunction in MS map onto a specific brain circuit centered on the hippocampus. Lesion damage to this circuit fully mediated associations between lesion volume and memory. A circuit-based approach to mapping MS symptoms based on lesions visible on standard structural imaging may prove useful for localization and prognosis of higher order deficits in MS.

Early neurofilament light and glial fibrillary acidic protein levels improve predictive models of multiple sclerosis outcomes.

BACKGROUND: Early risk-stratification in multiple sclerosis (MS) may impact treatment decisions. Current predictive models have identified that clinical and imaging characteristics of aggressive disease are associated with worse long-term outcomes. Serum biomarkers, neurofilament (sNfL) and glial fibrillary acidic protein (sGFAP), reflect subclinical disease activity through separate pathological processes and may contribute to predictive models of clinical and MRI outcomes. METHODS: We conducted a retrospective analysis of the Comprehensive Longitudinal Investigation of Multiple Sclerosis at the Brigham and Women's Hospital (CLIMB study), where patients with multiple sclerosis are seen every 6 months and undergo Expanded Disability Status Scale (EDSS) assessment, have annual brain MRI scans where volumetric analysis is conducted to calculate T2-lesion volume (T2LV) and brain parenchymal fraction (BPF), and donate a yearly blood sample for subsequent analysis. We included patients with newly diagnosed relapsing-remitting MS and serum samples obtained at baseline visit and 1-year follow-up (both within 3 years of onset), and were assessed at 10-year follow-up. We measured sNfL and sGFAP by single molecule array at baseline visit and at 1-year follow-up. A predictive clinical model was developed using age, sex, Expanded Disability Status Scale (EDSS), pyramidal signs, relapse rate, and spinal cord lesions at first visit. The main outcome was odds of developing of secondary progressive (SP)MS at year 10. Secondary outcomes included 10-year EDSS, brain T2LV and BPF. We compared the goodness-of-fit of the predictive clinical model with and without sNfL and sGFAP at baseline and 1-year follow-up, for each outcome by area under the receiver operating characteristic curve (AUC) or R-squared. RESULTS: A total 144 patients with median MS onset at age 37.4 years (interquartile range: 29.4-45.4), 64% female, were included. SPMS developed in 25 (17.4%) patients. The AUC for the predictive clinical model without biomarker data was 0.73, which improved to 0.77 when both sNfL and sGFAP were included in the model (P = 0.021). In this model, higher baseline sGFAP associated with developing SPMS (OR=3.3 [95%CI:1.1,10.6], P = 0.04). Adding 1-year follow-up biomarker levels further improved the model fit (AUC = 0.79) but this change was not statistically significant (P = 0.15). Adding baseline biomarker data also improved the R-squared of clinical models for 10-year EDSS from 0.24 to 0.28 (P = 0.032), while additional 1-year follow-up levels did not. Baseline sGFAP was associated with 10-year EDSS (ß=0.58 [95%CI:0.00,1.16], P = 0.05). For MRI outcomes, baseline biomarker levels improved R-squared for T2LV from 0.12 to 0.27 (P<0.001), and BPF from 0.15 to 0.20 (P = 0.042). Adding 1-year follow-up biomarker data further improved T2LV to 0.33 (P = 0.0065) and BPF to 0.23 (P = 0.048). Baseline sNfL was associated with T2LV (ß=0.34 [95%CI:0.21,0.48], P<0.001) and 1-year follow-up sNfL with BPF (ß=-2.53% [95%CI:-4.18,-0.89], P = 0.003). CONCLUSIONS: Early biomarker levels modestly improve predictive models containing clinical and MRI variables. Worse clinical outcomes, SPMS and EDSS, are associated with higher sGFAP levels and worse MRI outcomes, T2LV and BPF, are associated with higher sNfL levels. Prospective study implementing these predictive models into clinical practice are needed to determine if early biomarker levels meaningfully impact clinical practice.

Severe COVID-19 pneumonia leads to post-COVID-19 lung abnormalities on follow-up CT scans.

Purpose: To investigate the association of the maximal severity of pneumonia on CT scans obtained within 6-week of diagnosis with the subsequent development of post-COVID-19 lung abnormalities (Co-LA). Methods: COVID-19 patients diagnosed at our hospital between March 2020 and September 2021 were studied retrospectively. The patients were included if they had (1) at least one chest CT scan available within 6-week of diagnosis; and (2) at least one follow-up chest CT scan available ≥ 6 months after diagnosis, which were evaluated by two independent radiologists. Pneumonia Severity Categories were assigned on CT at diagnosis according to the CT patterns of pneumonia and extent as: 1) no pneumonia (Estimated Extent, 0%); 2) non-extensive pneumonia (GGO and OP, <40%); and 3) extensive pneumonia (extensive OP and DAD, >40%). Co-LA on follow-up CT scans, categorized using a 3-point Co-LA Score (0, No Co-LA; 1, Indeterminate Co-LA; and 2, Co-LA). Results: Out of 132 patients, 42 patients (32%) developed Co-LA on their follow-up CT scans 6-24 months post diagnosis. The severity of COVID-19 pneumonia was associated with Co-LA: In 47 patients with extensive pneumonia, 33 patients (70%) developed Co-LA, of whom 18 (55%) developed fibrotic Co-LA. In 52 with non-extensive pneumonia, 9 (17%) developed Co-LA: In 33 with no pneumonia, none (0%) developed Co-LA. Conclusions: Higher severity of pneumonia at diagnosis was associated with the increased risk of development of Co-LA after 6-24 months of SARS-CoV-2 infection.

Fronto-striatal damage may contribute to resistance to fatigue-lowering medications in multiple sclerosis.

BACKGROUND AND PURPOSE: Commonly used fatigue-lowering medications have not been proven effective in treating multiple sclerosis (MS)-related fatigue. A neuroanatomical basis for treatment-resistant fatigue in MS has not been explored. The aim of this study was to investigate the association between brain diffusion abnormality patterns and resistance to fatigue-lowering treatment. METHODS: Retrospective patient stratification: 1. treatment-resistant (n = 22) received anti-fatigue and/or anti-depressant and/or anxiolytic medication and the latest two Modified Fatigue Impact Scale (MFIS) score≥38; 2. responder (n = 16): received anti-fatigue and/or antidepressant and/or anxiolytic medication while the latest MFIS was <38, and minimum one previous MFIS was ≥38; 3. non-treated never-fatigued (n = 26): received none of the above-mentioned medications and MFIS was always<38 (over minimum four years assessed with MFIS every 1-2 years). 3T brain MRI was used to perform a cross-sectional voxel-wise comparison of fractional anisotropy (FA) between the groups. RESULTS: Treatment-resistant versus responder patients showed more extensive brain damage (ie, lower FA) favoring the fronto-striatal pathways. Both groups showed more widespread brain damage than non-treated never-fatigued patients. A mean fronto-striatal FA value of 0.26 could perfectly predict response to modafinil/armodafinil. CONCLUSION: Fronto-striatal damage may play a role in the development of resistance to fatigue-lowering treatment. Fronto-striatal FA may serve as a biomarker to predict response to fatigue-lowering medications in MS.

Multicenter Evaluation of AI-generated DIR and PSIR for Cortical and Juxtacortical Multiple Sclerosis Lesion Detection.

Background Cortical multiple sclerosis lesions are clinically relevant but inconspicuous at conventional clinical MRI. Double inversion recovery (DIR) and phase-sensitive inversion recovery (PSIR) are more sensitive but often unavailable. In the past 2 years, artificial intelligence (AI) was used to generate DIR and PSIR from standard clinical sequences (eg, T1-weighted, T2-weighted, and fluid-attenuated inversion-recovery sequences), but multicenter validation is crucial for further implementation. Purpose To evaluate cortical and juxtacortical multiple sclerosis lesion detection for diagnostic and disease monitoring purposes on AI-generated DIR and PSIR images compared with MRI-acquired DIR and PSIR images in a multicenter setting. Materials and Methods Generative adversarial networks were used to generate AI-based DIR (n = 50) and PSIR (n = 43) images. The number of detected lesions between AI-generated images and MRI-acquired (reference) images was compared by randomized blinded scoring by seven readers (all with >10 years of experience in lesion assessment). Reliability was expressed as the intraclass correlation coefficient (ICC). Differences in lesion subtype were determined using Wilcoxon signed-rank tests. Results MRI scans of 202 patients with multiple sclerosis (mean age, 46 years ± 11 [SD]; 127 women) were retrospectively collected from seven centers (February 2020 to January 2021). In total, 1154 lesions were detected on AI-generated DIR images versus 855 on MRI-acquired DIR images (mean difference per reader, 35.0% ± 22.8; P < .001). On AI-generated PSIR images, 803 lesions were detected versus 814 on MRI-acquired PSIR images (98.9% ± 19.4; P = .87). Reliability was good for both DIR (ICC, 0.81) and PSIR (ICC, 0.75) across centers. Regionally, more juxtacortical lesions were detected on AI-generated DIR images than on MRI-acquired DIR images (495 [42.9%] vs 338 [39.5%]; P < .001). On AI-generated PSIR images, fewer juxtacortical lesions were detected than on MRI-acquired PSIR images (232 [28.9%] vs 282 [34.6%]; P = .02). Conclusion Artificial intelligence-generated double inversion-recovery and phase-sensitive inversion-recovery images performed well compared with their MRI-acquired counterparts and can be considered reliable in a multicenter setting, with good between-reader and between-center interpretative agreement. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Zivadinov and Dwyer in this issue.

Diagnostic performance of computed tomography features in detecting oropharyngeal squamous cell carcinoma extranodal extension.

OBJECTIVES: Accurate pre-treatment imaging determination of extranodal extension (ENE) could facilitate the selection of appropriate initial therapy for HPV-positive oropharyngeal squamous cell carcinoma (HPV + OPSCC). Small studies have associated 7 CT features with ENE with varied results and agreement. This article seeks to determine the replicable diagnostic performance of these CT features for ENE. METHODS: Five expert academic head/neck neuroradiologists from 5 institutions evaluate a single academic cancer center cohort of 75 consecutive HPV + OPSCC patients. In a web-based virtual laboratory for imaging research and education, the experts performed training on 7 published CT features associated with ENE and then independently identified the "single most (if any) suspicious" lymph node and presence/absence of each of the features. Inter-rater agreement was assessed using percentage agreement, Gwet's AC1, and Fleiss' kappa. Sensitivity, specificity, and positive and negative predictive values were calculated for each CT feature based on histologic ENE. RESULTS: All 5 raters identified the same node in 52 cases (69%). In 15 cases (20%), at least one rater selected a node and at least one rater did not. In 8 cases (11%), all raters selected a node, but at least one rater selected a different node. Percentage agreement and Gwet's AC1 coefficients were > 0.80 for lesion identification, matted/conglomerated nodes, and central necrosis. Fleiss' kappa was always < 0.6. CT sensitivity for histologically confirmed ENE ranged 0.18-0.94, specificity 0.41-0.88, PPV 0.26-0.36, and NPV 0.78-0.96. CONCLUSIONS: Previously described CT features appear to have poor reproducibility among expert head/neck neuroradiologists and poor predictive value for histologic ENE. KEY POINTS: • Previously described CT imaging features appear to have poor reproducibility among expert head and neck subspecialized neuroradiologists as well as poor predictive value for histologic ENE. • Although it may still be appropriate to comment on the presence or absence of these CT features in imaging reports, the evidence indicates that caution is warranted when incorporating these features into clinical decision-making regarding the likelihood of ENE.

Serum NfL but not GFAP predicts cognitive decline in active progressive multiple sclerosis patients.

BACKGROUND: Cognitive decline is inadequately captured by the standard neurological examination. Serum neurofilament light chain (sNfL) and glial fibrillary acidic protein (sGFAP) are biomarkers of neuronal damage and astrocytic reactivity that may offer an accessible measure of the multiple sclerosis (MS) pathology linked to cognitive decline. OBJECTIVE: To investigate the association of sNfL and sGFAP with cognitive decline in MS patients at high risk for progressive pathology. METHODS: We included 94 MS patients with sustained Expanded Disability Status Score (EDSS) ⩾ 3, available serum samples and cognitive assessment performed by symbol digit modalities test (SDMT) over a median of 3.1 years. The visit for sGFAP/sNfL quantification was at confirmed EDSS ⩾ 3. Linear regression analysis on log-transformed sGFAP/sNfL assessed the association with current and future SDMT. Analyses were adjusted for age, sex, EDSS, treatment group, and recent relapse. RESULTS: sNfL was significantly associated with concurrent SDMT (adjusted change in mean SDMT = -4.5; 95% confidence interval (CI): -8.7, -0.2; p = 0.039) and predicted decline in SDMT (adjusted change in slope: -1.14; 95% CI: -1.83, -0.44; p = 0.001), particularly in active patients. sGFAP was not associated with concurrent or future SDMT. CONCLUSIONS: Higher levels of sNfL were associated with cognitive impairment and predicted cognitive decline in MS patients at high risk for having an underlying progressive pathology.

Serum GFAP and NfL Levels Differentiate Subsequent Progression and Disease Activity in Patients With Progressive Multiple Sclerosis.

BACKGROUND AND OBJECTIVES: Neurodegeneration and astrocytic activation are pathologic hallmarks of progressive multiple sclerosis (MS) and can be quantified by serum neurofilament light chain (sNfL) and glial fibrillary acidic protein (sGFAP). We investigated sNfL and sGFAP as tools for stratifying patients with progressive MS based on progression and disease activity status. METHODS: We leveraged our Comprehensive Longitudinal Investigation of MS at the Brigham and Women's Hospital (CLIMB) natural history study, which includes clinical, MRI data and serum samples collected over more than 20 years. We included patients with MS with a confirmed Expanded Disability Status Scale (EDSS) score ≥3 that corresponds with our classifier for patients at high risk of underlying progressive pathology. We analyzed sNfL and sGFAP within 6 months from the confirmed EDSS score ≥3 corresponding with our baseline visit. Patients who further developed 6-month confirmed disability progression (6mCDP) were classified as progressors. We further stratified our patients into active/nonactive based on new brain/spinal cord lesions or relapses in the 2 years before baseline or during follow-up. Statistical analysis on log-transformed sGFAP/sNfL assessed the baseline association with demographic, clinical, and MRI features and associations with future disability. RESULTS: We included 257 patients with MS who had an average EDSS score of 4.0 and a median follow-up after baseline of 7.6 years. sNfL was higher in patients with disease activity in the 2 years before baseline (adjusted ß = 1.21; 95% CI 1.04-1.42; p = 0.016), during the first 2 years of follow-up (adjusted ß = 1.17; 95% CI = 1.01-1.36; p = 0.042). sGFAP was not increased in the presence of disease activity. Higher sGFAP levels, but not sNfL levels, were associated with higher risk of 6mCDP (adjusted hazard ratio [HR] = 1.71; 95% CI = 1.19-2.45; p = 0.004). The association was stronger in patients with low sNfL (adjusted HR = 2.44; 95% CI 1.32-4.52; p = 0.005) and patients who were nonactive in the 2 years prior or after the sample. DISCUSSION: Higher levels of sGFAP correlated with subsequent progression, particularly in nonactive patients, whereas sNfL reflected acute disease activity in patients with MS at high risk of underlying progressive pathology. Thus, sGFAP and sNfL levels may be used to stratify patients with progressive MS for clinical research studies and clinical trials and may inform clinical care.

COVID-19 Vaccination reduced pneumonia severity.

Purpose: To investigate the effect of vaccinations and boosters on the severity of COVID-19 pneumonia on CT scans during the period of Delta and Omicron variants. Methods: Retrospectively studied were 303 patients diagnosed with COVID-19 between July 2021 and February 2022, who had obtained at least one CT scan within 6 weeks around the COVID-19 diagnosis (-2 to +4 weeks). The severity of pneumonia was evaluated with a 6-point scale Pneumonia Score. The association between demographic and clinical data and vaccination status (booster/additional vaccination, complete vaccination and un-vaccination) and the difference between Pneumonia Scores by vaccination status were investigated. Results: Of 303 patients (59.4 ± 16.3 years; 178 females), 62 (20 %) were in the booster/additional vaccination group, 117 (39 %) in the complete vaccination group, and 124 (41 %) in the unvaccinated group. Interobserver agreement of the Pneumonia Score was high (weighted kappa score = 0.875). Patients in the booster/additionally vaccinated group tended to be older (P = 0.0085) and have more underlying comorbidities (P < 0.0001), and the Pneumonia Scores were lower in the booster/additionally vaccinated [median 2 (IQR 0-4)] and completely vaccinated groups [median 3 (IQR 1-4)] than those in the unvaccinated group [median 4 (IQR 2-4)], respectively (P < 0.0001 and P < 0.0001, respectively). A multivariable linear analysis adjusted for confounding factors confirmed the difference. Conclusion: Vaccinated patients, with or without booster/additional vaccination, had milder COVID-19 pneumonia on CT scans than unvaccinated patients during the period of Delta and Omicron variants. This study supports the efficacy of the vaccine against COVID-19 from a radiological perspective.

Younger age at multiple sclerosis onset is associated with worse outcomes at age 50.

OBJECTIVE: Older age at multiple sclerosis (MS) onset has been associated with worse 10-year outcomes. However, disease duration often exceeds 10 years and age-related comorbidities may also contribute to disability. We investigated patients with>10 years disease duration to determine how age at MS onset is associated with clinical, MRI and occupational outcomes at age 50. METHODS: We included patients enrolled in the Comprehensive Longitudinal Investigation of Multiple Sclerosis at the Brigham and Women's Hospital with disease duration>10 years. Outcomes at age 50 included the Expanded Disability Status Scale (EDSS), development of secondary-progressive multiple sclerosis (SPMS), brain T2-lesion volume (T2LV) and brain parenchymal fraction (BPF), and occupational status. We assessed how onset age was independently associated with each outcome when adjusting for the date of visit closest to age 50, sex, time to first treatment, number of treatments by age 50 and exposure to high-efficacy treatments by age 50. RESULTS: We included 661 patients with median onset at 31.4 years. The outcomes at age 50 were worse the younger first symptoms developed: for every 5 years earlier, the EDSS was 0.22 points worse (95% CI: 0.04 to 0.40; p=0.015), odds of SPMS 1.33 times higher (95% CI: 1.08 to 1.64; p=0.008), T2LV 1.86 mL higher (95% CI: 1.02 to 2.70; p<0.001), BPF 0.97% worse (95% CI: 0.52 to 1.42; p<0.001) and odds of unemployment from MS 1.24 times higher (95% CI: 1.01 to 1.53; p=0.037). CONCLUSIONS: All outcomes at age 50 were worse in patients with younger age at onset. Decisions to provide high-efficacy treatments should consider younger age at onset, equating to a longer expected disease duration, as a poor prognostic factor.

Magnetic resonance elastography to study the effect of amyloid plaque accumulation in a mouse model.

BACKGROUND AND PURPOSE: Biomechanical changes in the brain have not been fully elucidated in Alzheimer's disease (AD). We aimed to investigate the effect of ß-amyloid accumulation on mouse brain viscoelasticity. METHODS: Magnetic resonance elastography was used to calculate magnitude of the viscoelastic modulus (|G*|), elasticity (Gd ), and viscosity (Gl ) in the whole brain parenchyma (WB) and bilateral hippocampi of 9 transgenic J20 (AD) mice (5 males/4 females) and 10 wild-type (WT) C57BL/6 mice (5 males/5 females) at 11 and 14 months of age. RESULTS: Cross-sectional analyses showed no significant difference between AD and WT mice at either timepoints. No sex-specific differences were observed at 11 months of age, but AD females showed significantly higher hippocampal |G*| and Gl and WB |G*|, Gd , and Gl compared to both AD and WT males at 14 months of age. Similar trending differences were found between female AD and female WT animals but did not reach significance. Longitudinal analyses showed significant increases in hippocampal |G*|, Gd , and Gl , and significant decreases in WB |G*|, Gd , and Gl between 11 and 14 months in both AD and WT mice. Each subgroup showed significant increases in all hippocampal and significant decreases in all WB measures, with the exception of AD females, which showed no significant changes in WB |G*|, Gd , or Gl . CONCLUSION: Aging had region-specific effects on cerebral viscoelasticity, namely, WB softening and hippocampal stiffening. Amyloid plaque deposition may have sex-specific effects, which require further scrutiny.

Opportunities for Understanding MS Mechanisms and Progression With MRI Using Large-Scale Data Sharing and Artificial Intelligence.

Patients with multiple sclerosis (MS) have heterogeneous clinical presentations, symptoms, and progression over time, making MS difficult to assess and comprehend in vivo. The combination of large-scale data sharing and artificial intelligence creates new opportunities for monitoring and understanding MS using MRI. First, development of validated MS-specific image analysis methods can be boosted by verified reference, test, and benchmark imaging data. Using detailed expert annotations, artificial intelligence algorithms can be trained on such MS-specific data. Second, understanding disease processes could be greatly advanced through shared data of large MS cohorts with clinical, demographic, and treatment information. Relevant patterns in such data that may be imperceptible to a human observer could be detected through artificial intelligence techniques. This applies from image analysis (lesions, atrophy, or functional network changes) to large multidomain datasets (imaging, cognition, clinical disability, genetics). After reviewing data sharing and artificial intelligence, we highlight 3 areas that offer strong opportunities for making advances in the next few years: crowdsourcing, personal data protection, and organized analysis challenges. Difficulties as well as specific recommendations to overcome them are discussed, in order to best leverage data sharing and artificial intelligence to improve image analysis, imaging, and the understanding of MS.

Targeted Blood Brain Barrier Opening With Focused Ultrasound Induces Focal Macrophage/Microglial Activation in Experimental Autoimmune Encephalomyelitis.

Experimental autoimmune encephalomyelitis (EAE) is a model of multiple sclerosis (MS). EAE reflects important histopathological hallmarks, dissemination, and diversity of the disease, but has only moderate reproducibility of clinical and histopathological features. Focal lesions are less frequently observed in EAE than in MS, and can neither be constrained to specific locations nor timed to occur at a pre-specified moment. This renders difficult any experimental assessment of the pathogenesis of lesion evolution, including its inflammatory, degenerative (demyelination and axonal degeneration), and reparatory (remyelination, axonal sprouting, gliosis) component processes. We sought to develop a controlled model of inflammatory, focal brain lesions in EAE using focused ultrasound (FUS). We hypothesized that FUS induced focal blood brain barrier disruption (BBBD) will increase the likelihood of transmigration of effector cells and subsequent lesion occurrence at the sonicated location. Lesion development was monitored with conventional magnetic resonance imaging (MRI) as well as with magnetic resonance elastography (MRE) and further analyzed by histopathological means. EAE was induced in 12 6-8 weeks old female C57BL/6 mice using myelin oligodendrocyte glycoprotein (MOG) peptide. FUS-induced BBBD was performed 6, 7, and 9 days after immunization in subgroups of four animals and in an additional control group. MRI and MRE were performed on a 7T horizontal bore small animal MRI scanner. Imaging was conducted longitudinally 2 and 3 weeks after disease induction and 1 week after sonication in control animals, respectively. The scan protocol comprised contrast-enhanced T1-weighted and T2-weighted sequences as well as MRE with a vibration frequency of 1 kHz. Animals were sacrificed for histopathology after the last imaging time point. The overall clinical course of EAE was mild. A total of seven EAE animals presented with focal T2w hyperintense signal alterations in the sonicated hemisphere. These were most frequent in the group of animals sonicated 9 days after immunization. Histopathology revealed foci of activated microglia/macrophages in the sonicated right hemisphere of seven EAE animals. Larger cellular infiltrates or apparent demyelination were not seen. Control animals showed no abnormalities on MRI and did not have clusters of activated microglia/macrophages at the sites targeted with FUS. None of the animals had hemorrhages or gross tissue damage as potential side effects of FUS. EAE-animals tended to have lower values of viscoelasticity and elasticity in the sonicated compared to the contralateral parenchyma. This trend was significant when comparing the right sonicated to the left normal hemisphere and specifically the right sonicated compared to the left normal cortex in animals that underwent FUS-BBBD 9 days after immunization (right vs. left hemisphere: mean viscoelasticity 6.1 vs. 7.2 kPa; p = 0.003 and mean elasticity 4.9 vs. 5.7 kPa, p = 0.024; right vs. left cortex: mean viscoelasticity 5.8 vs. 7.5 kPa; p = 0.004 and mean elasticity 5 vs. 6.5 kPa; p = 0.008). A direct comparison of the biomechanical properties of focal T2w hyperintensities with normal appearing brain tissue did not yield significant results. Control animals showed no differences in viscoelasticity between sonicated and contralateral brain parenchyma. We here provide first evidence for a controlled lesion induction model in EAE using FUS-induced BBBD. The observed lesions in EAE are consistent with foci of activated microglia that may be interpreted as targeted initial inflammatory activity and which have been described as pre-active lesions in MS. Such foci can be identified and monitored with MRI. Moreover, the increased inflammatory activity in the sonicated brain parenchyma seems to have an effect on overall tissue matrix structure as reflected by changes of biomechanical parameters.

Usability of a Mobile App for Real-Time Assessment of Fatigue and Related Symptoms in Patients With Multiple Sclerosis: Observational Study.

BACKGROUND: Although fatigue is one of the most debilitating symptoms in patients with multiple sclerosis (MS), its pathogenesis is not well understood. Neurogenic, inflammatory, endocrine, and metabolic mechanisms have been proposed. Taking into account the temporal dynamics and comorbid mood symptoms of fatigue may help differentiate fatigue phenotypes. These phenotypes may reflect different pathogeneses and may respond to different mechanism-specific treatments. Although several tools have been developed to assess various symptoms (including fatigue), monitor clinical status, or improve the perceived level of fatigue in patients with MS, options for a detailed, real-time assessment of MS-related fatigue and relevant comorbidities are still limited. OBJECTIVE: This study aims to present a novel mobile app specifically designed to differentiate fatigue phenotypes using circadian symptom monitoring and state-of-the-art characterization of MS-related fatigue and its related symptoms. We also aim to report the first findings regarding patient compliance and the relationship between compliance and patient characteristics, including MS disease severity. METHODS: After developing the app, we used it in a prospective study designed to investigate the brain magnetic resonance imaging correlates of MS-related fatigue. In total, 64 patients with MS were recruited into this study and asked to use the app over a 2-week period. The app features the following modules: Visual Analogue Scales (VASs) to assess circadian changes in fatigue, depression, anxiety, and pain; daily sleep diaries (SLDs) to assess sleep habits and quality; and 10 one-time questionnaires to assess fatigue, depression, anxiety, sleepiness, physical activity, and motivation, as well as several other one-time questionnaires that were created to assess those relevant aspects of fatigue that were not captured by existing fatigue questionnaires. The app prompts subjects to assess their symptoms multiple times a day and enables real-time symptom monitoring through a web-accessible portal. RESULTS: Of 64 patients, 56 (88%) used the app, of which 51 (91%) completed all one-time questionnaires and 47 (84%) completed all one-time questionnaires, VASs, and SLDs. Patients reported no issues with the usage of the app, and there were no technical issues with our web-based data collection system. The relapsing-remitting MS to secondary-progressive MS ratio was significantly higher in patients who completed all one-time questionnaires, VASs, and SLDs than in those who completed all one-time questionnaires but not all VASs and SLDs (P=.01). No other significant differences in demographics, fatigue, or disease severity were observed between the degrees of compliance. CONCLUSIONS: The app can be used with reasonable compliance across patients with relapsing-remitting and secondary-progressive MS irrespective of demographics, fatigue, or disease severity.

Development and evaluation of a manual segmentation protocol for deep grey matter in multiple sclerosis: Towards accelerated semi-automated references.

BACKGROUND: Deep grey matter (dGM) structures, particularly the thalamus, are clinically relevant in multiple sclerosis (MS). However, segmentation of dGM in MS is challenging; labeled MS-specific reference sets are needed for objective evaluation and training of new methods. OBJECTIVES: This study aimed to (i) create a standardized protocol for manual delineations of dGM; (ii) evaluate the reliability of the protocol with multiple raters; and (iii) evaluate the accuracy of a fast-semi-automated segmentation approach (FASTSURF). METHODS: A standardized manual segmentation protocol for caudate nucleus, putamen, and thalamus was created, and applied by three raters on multi-center 3D T1-weighted MRI scans of 23 MS patients and 12 controls. Intra- and inter-rater agreement was assessed through intra-class correlation coefficient (ICC); spatial overlap through Jaccard Index (JI) and generalized conformity index (CIgen). From sparse delineations, FASTSURF reconstructed full segmentations; accuracy was assessed both volumetrically and spatially. RESULTS: All structures showed excellent agreement on expert manual outlines: intra-rater JI > 0.83; inter-rater ICC ≥ 0.76 and CIgen ≥ 0.74. FASTSURF reproduced manual references excellently, with ICC ≥ 0.97 and JI ≥ 0.92. CONCLUSIONS: The manual dGM segmentation protocol showed excellent reproducibility within and between raters. Moreover, combined with FASTSURF a reliable reference set of dGM segmentations can be produced with lower workload.

Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data.

Multiple sclerosis (MS) can be divided into four phenotypes based on clinical evolution. The pathophysiological boundaries of these phenotypes are unclear, limiting treatment stratification. Machine learning can identify groups with similar features using multidimensional data. Here, to classify MS subtypes based on pathological features, we apply unsupervised machine learning to brain MRI scans acquired in previously published studies. We use a training dataset from 6322 MS patients to define MRI-based subtypes and an independent cohort of 3068 patients for validation. Based on the earliest abnormalities, we define MS subtypes as cortex-led, normal-appearing white matter-led, and lesion-led. People with the lesion-led subtype have the highest risk of confirmed disability progression (CDP) and the highest relapse rate. People with the lesion-led MS subtype show positive treatment response in selected clinical trials. Our findings suggest that MRI-based subtypes predict MS disability progression and response to treatment and may be used to define groups of patients in interventional trials.