Search

Esclerosi múltiple; Cadena lleugera de neurofilaments sèrics Esclerosis múltiple; Cadena ligera de neurofilamentos séricos Multiple sclerosis; Serum neurofilament light chain Objective Evaluation of serum neurofilament light chain (sNfL), measured using high-throughput assays on widely accessible platforms in large, real-world MS populations, is a critical step for sNfL to be utilized in clinical practice. Methods Multiple Sclerosis Partners Advancing Technology and Health Solutions (MS PATHS) is a network of healthcare institutions in the United States and Europe collecting standardized clinical/imaging data and biospecimens during routine clinic visits. sNfL was measured in 6974 MS and 201 healthy control (HC) participants, using a high-throughput, scalable immunoassay. Results Elevated sNfL levels for age (sNfL-E) were found in 1238 MS participants (17.8%). Factors associated with sNfL-E included male sex, younger age, progressive disease subtype, diabetes mellitus, impaired renal function, and active smoking. Higher body mass index (BMI) was associated with lower odds of elevated sNfL. Active treatment with disease-modifying therapy was associated with lower odds of sNfL-E. MS participants with sNfL-E exhibited worse neurological function (patient-reported disability, walking speed, manual dexterity, and cognitive processing speed), lower brain parenchymal fraction, and higher T2 lesion volume. Longitudinal analyses revealed accelerated short-term rates of whole brain atrophy in sNfL-E participants and higher odds of new T2 lesion development, although both MS participants with or without sNfL-E exhibited faster rates of whole brain atrophy compared to HC. Findings were consistent in analyses examining age-normative sNfL Z-scores as a continuous variable. Interpretation Elevated sNfL is associated with clinical disability, inflammatory disease activity, and whole brain atrophy in MS, but interpretation needs to account for comorbidities including impaired renal function, diabetes, and smoking. Study funding was provided from the National Institutes of Health (K23NS117883 to E.S.S.; K01MH121582 to K.C.F.; U01NS111678 to P.A.C.), National Multiple Sclerosis Society (RG-1904-33834 to E.S.S.; RG-1904-33800 to P.A.C.), and Biogen.

MRI; Deep learning; Multiple sclerosis Resonancia magnética; Aprendizaje profundo; Esclerosis múltiple Ressonància magnètica; Aprenentatge profund; Esclerosi múltiple The assessment of disease activity using serial brain MRI scans is one of the most valuable strategies for monitoring treatment response in patients with multiple sclerosis (MS) receiving disease-modifying treatments. Recently, several deep learning approaches have been proposed to improve this analysis, obtaining a good trade-off between sensitivity and specificity, especially when using T1-w and T2-FLAIR images as inputs. However, the need to acquire two different types of images is time-consuming, costly and not always available in clinical practice. In this paper, we investigate an approach to generate synthetic T1-w images from T2-FLAIR images and subsequently analyse the impact of using original and synthetic T1-w images on the performance of a state-of-the-art approach for longitudinal MS lesion detection. We evaluate our approach on a dataset containing 136 images from MS patients, and 73 images with lesion activity (the appearance of new T2 lesions in follow-up scans). To evaluate the synthesis of the images, we analyse the structural similarity index metric and the median absolute error and obtain consistent results. To study the impact of synthetic T1-w images, we evaluate the performance of the new lesion detection approach when using (1) both T2-FLAIR and T1-w original images, (2) only T2-FLAIR images, and (3) both T2-FLAIR and synthetic T1-w images. Sensitivities of 0.75, 0.63, and 0.81, respectively, were obtained at the same false-positive rate (0.14) for all experiments. In addition, we also present the results obtained when using the data from the international MSSEG-2 challenge, showing also an improvement when including synthetic T1-w images. In conclusion, we show that the use of synthetic images can support the lack of data or even be used instead of the original image to homogenize the contrast of the different acquisitions in new T2 lesions detection algorithms. AC holds an FPI grant from the Ministerio de Ciencia, Innovación y Universidades with reference number PRE2018-083507. This work has been supported by DPI2020-114769RB-I00 from the Ministerio de Ciencia, Innovación y Universidades. The authors gratefully acknowledge the support of the NVIDIA Corporation with their donation of the TITAN X GPU used in this research. This work has been also supported by ICREA Academia Program.

Esclerosis múltiple; Brote; Resonancia magnética Esclerosi múltiple; Brot; Imatge per ressonància magnètica Multiple sclerosis; Outbreak; Magnetic resonance imaging Objective To study the clinico-radiological paradox in multiple sclerosis (MS) relapse by analyzing the number and location of gadolinium-enhanced (Gd+) lesions on brain MRI before methylprednisolone (MP) treatment. Methods We analyzed brain MRI from 90 relapsed MS patients in two Phase IV multicenter double-blind randomized clinical trials that showed the noninferiority of different routes and doses of MP administration. A 1.5- or 3-T brain MRI was performed at baseline before MP treatment and within 15 days of symptom onset. The number and location of Gd+ lesions were analyzed. Associations were studied using univariate analysis. Results Sixty-two percent of patients had at least 1 Gd+ brain lesion; the median number was 1 (interquartile range 0–4), and 41% of patients had 2 or more lesions. The most frequent location of Gd+ lesions was subcortical (41.4%). Gd+ brain lesions were found in 71.4% of patients with brainstem-cerebellum symptoms, 57.1% with spinal cord symptoms and 55.5% with optic neuritis (ON). Thirty percent of patients with brain symptoms did not have Gd+ lesions, and only 43.6% of patients had symptomatic Gd+ lesions. The univariate analysis showed a negative correlation between age and the number of Gd+ lesions (p = 0.002). Conclusion Most patients with relapse showed several Gd+ lesions on brain MRI, even when the clinical manifestation was outside of the brain. Our findings illustrate the clinico-radiological paradox in MS relapse and support the value of brain MRI in this scenario. Objetivo Estudiar la paradoja clínico-radiológica en el brote de la esclerosis múltiple (EM) mediante el análisis de lesiones captantes de gadolinio (Gd+) en la RM cerebral antes del tratamiento con metilprednisolona (MP). Métodos Analizamos la RM cerebral basal de 90 pacientes con EM en brote de 2 ensayos clínicos aleatorizados multicéntricos fase IV que demostraron la no inferioridad de diferentes vías y dosis de MP, realizadas antes del tratamiento con MP y en los 15 días siguientes a la aparición de los síntomas. Se analizaron el número y la localización de las lesiones Gd+. Se estudiaron las asociaciones mediante análisis univariado. Resultados El 62% de los pacientes tenía al menos una lesión Gd+ cerebral y el 41% de los pacientes tenía 2 o más lesiones. La localización más frecuente fue la subcortical (41,4%). Se encontraron lesiones Gd+ cerebrales en el 71,4% de los pacientes con síntomas de tronco cerebral o cerebelo, en el 57,1% con síntomas medulares y en el 55,5% con neuritis óptica. El 30% de los pacientes con síntomas cerebrales no tenían lesiones Gd+ y sólo el 4,.6% de los pacientes tenían lesiones Gd+ sintomáticas. El análisis univariante mostró una correlación negativa entre la edad y el número de lesiones Gd+ (p = 0,002). Conclusiones La mayoría de los pacientes en brote mostraron varias lesiones Gd+ en la RM cerebral, incluso cuando la manifestación clínica fue medular u óptica. Nuestros hallazgos ilustran la paradoja clínico-radiológica en el brote de la EM y apoyan el valor de la RM cerebral en este escenario. This work was supported in part by the Ministry of Health of Spain (grant numbers EC07/90278 and EC11/132) and personal grant Rio Hortega CM19/00042 to LMA.

Brain; Protocol design; Quantitative MRI (qMRI) Cerebro; Diseño de protocolo; Resonancia magnética cuantitativa (qMRI) Cervell; Disseny del protocol; Ressonància magnètica quantitativa (qMRI) Purpose: We investigate the feasibility of data-driven, model-free quantitative MRI (qMRI) protocol design on in vivo brain and prostate diffusion-relaxation imaging (DRI). Methods: We select subsets of measurements within lengthy pilot scans, without identifying tissue parameters for which to optimise for. We use the “select and retrieve via direct upsampling” (SARDU-Net) algorithm, made of a selector, identifying measurement subsets, and a predictor, estimating fully-sampled signals from the subsets. We implement both using artificial neural networks, which are trained jointly end-to-end. We deploy the algorithm on brain (32 diffusion-/T1-weightings) and prostate (16 diffusion-/T2-weightings) DRI scans acquired on three healthy volunteers on two separate 3T Philips systems each. We used SARDU-Net to identify sub-protocols of fixed size, assessing reproducibility and testing sub-protocols for their potential to inform multi-contrast analyses via the T1-weighted spherical mean diffusion tensor (T1-SMDT, brain) and hybrid multi-dimensional MRI (HM-MRI, prostate) models, for which sub-protocol selection was not optimised explicitly. Results: In both brain and prostate, SARDU-Net identifies sub-protocols that maximise information content in a reproducible manner across training instantiations using a small number of pilot scans. The sub-protocols support T1-SMDT and HM-MRI multi-contrast modelling for which they were not optimised explicitly, providing signal quality-of-fit in the top 5% against extensive sub-protocol comparisons. Conclusions: Identifying economical but informative qMRI protocols from subsets of rich pilot scans is feasible and potentially useful in acquisition-time-sensitive applications in which there is not a qMRI model of choice. SARDU-Net is demonstrated to be a robust algorithm for data-driven, model-free protocol design. This project was funded by the Engineering and Physical Sciences Research Council (EPSRC EP/R006032/1, M020533/1, G007748, I027084, N018702). This project has received funding under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 634541 and 666992, and from: Rosetrees Trust (United Kingdom, funding FG); Prostate Cancer United Kingdom Targeted Call 2014 (Translational Research St.2, project reference PG14-018-TR2); Cancer Research United Kingdom grant ref. A21099; Spinal Research (United Kingdom), Wings for Life (Austria), Craig H. Neilsen Foundation (United States) for jointly funding the INSPIRED study; Wings for Life (#169111); United Kingdom Multiple Sclerosis Society (grants 892/08 and 77/2017); the Department of Health’s National Institute for Health Research (NIHR) Biomedical Research Centres and UCLH NIHR Biomedical Research Centre; Champalimaud Centre for the Unknown, Lisbon (Portugal); European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 101003390. FG is currently supported by the investigator-initiated PREdICT study at the Vall d’Hebron Institute of Oncology (Barcelona), funded by AstraZeneca and CRIS Cancer Foundation.

Objective Some studies have suggested alterations of structural brain asymmetry in attention-deficit/hyperactivity disorder (ADHD), but findings have been contradictory and based on small samples. Here, we performed the largest ever analysis of brain left-right asymmetry in ADHD, using 39 datasets of the ENIGMA consortium. Methods We analyzed asymmetry of subcortical and cerebral cortical structures in up to 1,933 people with ADHD and 1,829 unaffected controls. Asymmetry Indexes (AIs) were calculated per participant for each bilaterally paired measure, and linear mixed effects modeling was applied separately in children, adolescents, adults, and the total sample, to test exhaustively for potential associations of ADHD with structural brain asymmetries. Results There was no evidence for altered caudate nucleus asymmetry in ADHD, in contrast to prior literature. In children, there was less rightward asymmetry of the total hemispheric surface area compared to controls (t = 2.1, p = .04). Lower rightward asymmetry of medial orbitofrontal cortex surface area in ADHD (t = 2.7, p = .01) was similar to a recent finding for autism spectrum disorder. There were also some differences in cortical thickness asymmetry across age groups. In adults with ADHD, globus pallidus asymmetry was altered compared to those without ADHD. However, all effects were small (Cohen’s d from −0.18 to 0.18) and would not survive study-wide correction for multiple testing. Conclusion Prior studies of altered structural brain asymmetry in ADHD were likely underpowered to detect the small effects reported here. Altered structural asymmetry is unlikely to provide a useful biomarker for ADHD, but may provide neurobiological insights into the trait., Journal of Child Psychology and Psychiatry, 62 (10), ISSN:0021-9630, ISSN:1469-7610

Cervell; Imatge per ressonància magnètica; Aprenentatge transductiu Cerebro; Imagen de resonancia magnética; Aprendizaje transductivo Brain; Magnetic resonance imaging; Transductive learning Segmentation of brain images from Magnetic Resonance Images (MRI) is an indispensable step in clinical practice. Morphological changes of sub-cortical brain structures and quantification of brain lesions are considered biomarkers of neurological and neurodegenerative disorders and used for diagnosis, treatment planning, and monitoring disease progression. In recent years, deep learning methods showed an outstanding performance in medical image segmentation. However, these methods suffer from generalisability problem due to inter-centre and inter-scanner variabilities of the MRI images. The main objective of the study is to develop an automated deep learning segmentation approach that is accurate and robust to the variabilities in scanner and acquisition protocols. In this paper, we propose a transductive transfer learning approach for domain adaptation to reduce the domain-shift effect in brain MRI segmentation. The transductive scenario assumes that there are sets of images from two different domains: (1) source—images with manually annotated labels; and (2) target—images without expert annotations. Then, the network is jointly optimised integrating both source and target images into the transductive training process to segment the regions of interest and to minimise the domain-shift effect. We proposed to use a histogram loss in the feature level to carry out the latter optimisation problem. In order to demonstrate the benefit of the proposed approach, the method has been tested in two different brain MRI image segmentation problems using multi-centre and multi-scanner databases for: (1) sub-cortical brain structure segmentation; and (2) white matter hyperintensities segmentation. The experiments showed that the segmentation performance of a pre-trained model could be significantly improved by up to 10%. For the first segmentation problem it was possible to achieve a maximum improvement from 0.680 to 0.799 in average Dice Similarity Coefficient (DSC) metric and for the second problem the average DSC improved from 0.504 to 0.602. Moreover, the improvements after domain adaptation were on par or showed better performance compared to the commonly used traditional unsupervised segmentation methods (FIRST and LST), also achieving faster execution time. Taking this into account, this work presents one more step toward the practical implementation of deep learning algorithms into the clinical routine. KK holds FI-DGR2017 grant from the Catalan Government with reference number 2017FI_B00372. This work has been supported by DPI2017-86696-R from the Ministerio de Ciencia y Tecnologia.

Body mass index; Structural brain abnormalities Índex de massa corporal; Anormalitats estructurals del cervell Índice de masa corporal; Anomalías estructurales del cerebro We aimed to evaluate the relationship between obesity and structural brain abnormalities assessed by magnetic resonance imaging using data from 45 observational epidemiological studies, where five articles reported prospective longitudinal results. In cross-sectional studies’ analyses, the pooled weighted mean difference for total brain volume (TBV) and gray matter volume (GMV) in obese/overweight participants was -11.59 (95 % CI: -23.17 to -0.02) and -10.98 (95 % CI: -20.78 to -1.18), respectively. TBV was adversely associated with BMI and WC, GMV with BMI, and hippocampal volume with BMI, WC, and WHR. WC/WHR are associated with a risk of lacunar and white matter hyperintensity (WMH). In longitudinal studies’ analyses, BMI was not statistically associated with the overall structural brain abnormalities (for continuous BMI: RR = 1.02, 95 % CI: 0.94–1.12; for categorial BMI: RR = 1.18, 95 % CI: 0.75–1.85). Small sample size of prospective longitudinal studies limited the power of its pooled estimates. A higher BMI is associated with lower brain volume while greater WC/WHR, but not BMI, is related to a risk of lacunar infarct and WMH. Future longitudinal research is needed to further elucidate the specific causal relationships and explore preventive measures. This work was supported by the National Natural Science Foundation of China (No. 82070851, 81870556, 81930019, 81770686, 81970591), Beijing Municipal Administration of Hospital’s Youth Program (QML20170204), Excellent Talents in Dongcheng District of Beijing.

MRI; Multi-echo QSM; Quantitative susceptibility mapping Imágen por resonancia magnética; QSM de ecos múltiples; Mapeo cuantitativo de susceptibilidad Imatge per ressonància magnètica; QSM de ressò múltiple; Mapeig quantitatiu de susceptibilitat Purpose To compare different multi-echo combination methods for MRI QSM. Given the current lack of consensus, we aimed to elucidate how to optimally combine multi-echo gradient-recalled echo signal phase information, either before or after applying Laplacian-base methods (LBMs) for phase unwrapping or background field removal. Methods Multi-echo gradient-recalled echo data were simulated in a numerical head phantom, and multi-echo gradient-recalled echo images were acquired at 3 Tesla in 10 healthy volunteers. To enable image-based estimation of gradient-recalled echo signal noise, 5 volunteers were scanned twice in the same session without repositioning. Five QSM processing pipelines were designed: 1 applied nonlinear phase fitting over TEs before LBMs; 2 applied LBMs to the TE-dependent phase and then combined multiple TEs via either TE-weighted or SNR-weighted averaging; and 2 calculated TE-dependent susceptibility maps via either multi-step or single-step QSM and then combined multiple TEs via magnitude-weighted averaging. Results from different pipelines were compared using visual inspection; summary statistics of susceptibility in deep gray matter, white matter, and venous regions; phase noise maps (error propagation theory); and, in the healthy volunteers, regional fixed bias analysis (Bland–Altman) and regional differences between the means (nonparametric tests). Results Nonlinearly fitting the multi-echo phase over TEs before applying LBMs provided the highest regional accuracy of and the lowest phase noise propagation compared to averaging the LBM-processed TE-dependent phase. This result was especially pertinent in high-susceptibility venous regions. Conclusion For multi-echo QSM, we recommend combining the signal phase by nonlinear fitting before applying LBMs. Supported by the UK Engineering and Physical Sciences Research Council (EPSRC), award number: 1489882 (e.b.); by the EPSRC-funded UCL Centre for Doctoral Training in Medical Imaging, grant EP/L016478/1 (a.k.), and the Department of Health's National Institute for Health Research funded Biomedical Research Centre at University College London Hospitals (a.k.); by the UCL Leonard Wolfson Experimental Neurology Centre, grant PR/ylr/18575 (d.l.t) The Queen Square MS Centre, where part of the MRI scans for this work were performed, is supported by grants from the UK MS Society and by the National Institute for Health Research University College London Hospitals Biomedical Research Centre (UCLH/BRC). F. Grussu was supported by PREdICT, a study at the Vall d'Hebron Institute of Oncology in Barcelona funded by AstraZeneca (f.g.), and funding from the postdoctoral fellowships program Beatriu de Pinós (2020 BP 00117), funded by the Secretary of Universities and Research, Government of Catalonia (f.g.)