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Advanced intraoperative MR images (ioMRI) acquired during the resection of pediatric brain tumors could offer additional physiological information to preserve healthy tissue. With this work, we aimed to develop a protocol for ioMRI with increased sensitivity for arterial spin labeling (ASL) and diffusion MRI (dMRI), optimized for patient positioning regularly used in the pediatric neurosurgery setting. For ethical reasons, ASL images were acquired in healthy adult subjects that were imaged in the prone and supine position. After this, the ASL cerebral blood flow (CBF) was quantified and compared between both positions. To evaluate the impact of the RF coils setups on image quality, we compared different setups (two vs. four RF coils) by looking at T1‐weighted (T1w) signal‐to‐noise ratio (SNR) and contrast‐to‐noise ratio (CNR), as well as undertaking a qualitative evaluation of T1w, T2w, ASL, and dMR images. Mean ASL CBF did not differ between the surgical prone and supine positions in any of the investigated regions of interest or the whole brain. T1w SNR (gray matter: p = 0.016, 34% increase; white matter: p = 0.016, 32% increase) and CNR were higher (p = 0.016) in the four versus two RF coils setups (18.0 ± 1.8 vs. 13.9 ± 1.8). Qualitative evaluation of T1w, T2w, ASL, and dMR images resulted in acceptable to good image quality and did not differ statistically significantly between setups. Only the nonweighted diffusion image maps and corticospinal tract reconstructions yielded higher image quality and reduced susceptibility artifacts with four RF coils. Advanced ioMRI metrics were more precise with four RF coils as the standard deviation decreased. Taken together, we have investigated the practical use of advanced ioMRI during pediatric neurosurgery. We conclude that ASL CBF quantification in the surgical prone position is valid and that ASL and dMRI acquisition with two RF coils can be performed adequately for clinical use. With four versus two RF coils, the SNR of the images increases, and the sensitivity to artifacts reduces. [ABSTRACT FROM AUTHOR]

Background & Objective: Automatic lesion segmentation techniques on MRI scans of people with multiple sclerosis (pwMS) could support lesion detection and segmentation in trials and clinical practice. However, knowledge on their reliability across scanners is limited, hampering clinical implementation. The aim of this study was to investigate the within-scanner repeatability and between-scanner reproducibility of lesion segmentation tools in pwMS across three different scanners and examine their accuracy compared to manual segmentations with and without optimization., Methods: 30 pwMS underwent a scan and rescan on three MRI scanners. GE Discovery MR750 (3.0 T), Siemens Sola (1.5 T) and Siemens Vida (3.0 T)). 3D-FLuid Attenuated Inversion Recovery (3D-FLAIR) and 3D T1-weighted scans were acquired on each scanner. Lesion segmentation involved preprocessing and automatic segmentation using the Lesion Segmentation Toolbox (LST) and nicMSlesions (nicMS) as well as manual segmentation. Both automated segmentation techniques were used with default settings, and with settings optimized to match manual segmentations for each scanner specifically and combined for the three scanners. LST settings were optimized by adjusting the threshold to improve the Dice similarity coefficient (DSC) for each scanner separately and a combined threshold for all scanners. For nicMS the last layers were retrained, once with the multi-scanner data to represent a combined optimization and once separately for each scanner for scanner specific optimization. Volumes and counts were extracted. DSC was calculated for accuracy, and reliability was assessed using intra-class correlation coefficients (ICC). Differences in DSC between software was tested with a repeated measures ANOVA and when appropriate post-hoc paired t-tests using Bonferroni correction., Results: Scanner-specific optimization significantly improved DSC for LST compared to default and combined settings, except for the GE scanner. NicMS showed significantly higher DSC for both the scanner-specific and combined optimization than default. Within-scanner repeatability was excellent (ICC>0.9) for volume and counts. Between-scanner ICC for volume between Vida and Sola was higher (0.94-0.99) than between GE MR750 and Vida or Sola (0.18-0.93), with improved ICCs for nicMS scanner-specific (0.87-0.93) compared to others (0.18-0.79). This was not present for Sola vs. Vida where all ICCs were excellent (>0.94)., Conclusion: Scanner-specific optimization strategies proved effective in mitigating inter-scanner variability, addressing the issue of insufficient reproducibility and accuracy found with default settings., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: D.R.v.N., B.M., B.J. and J.P.A.K. have nothing to disclose. G.P. has received research grants from ECTRIMS, MAGNIMS, and ESNR. M.B.C. is supported by research grants from Merck and Atara Biotherapeutics. I.B. has received research support from Merck, Novartis, Teva and the Dutch MS Research Foundation. E.M.M.S. received speaker fees from Merck and Novartis. F.B. is a steering committee or Data Safety Monitoring Board member for Biogen, Merck, Eisai and Prothena. Advisory board member for Combinostics, Scottish Brain Sciences. Consultant for Roche, Celltrion, Rewind Therapeutics, Merck, Bracco. Research agreements with ADDI, Merck, Biogen, GE Healthcare, Roche. Co-founder and shareholder of Queen Square Analytics LTD. H.V. has received research support from Merck, Novartis, Pfizer, and Teva; consulting fees from Merck, and speaker honoraria from Novartis; all funds were paid to his institution. J. K. received research grants for multicentre investigator initiated trials DOT-MS trial, ClinicalTrials. gov Identifier: NCT04260711 (ZonMW) and BLOOMS trial (ZonMW and Treatmeds), ClinicalTrials. gov Identifier: NCT05296161); received consulting fees for F. Hoffmann-La Roche, Biogen, Teva, Merck, Novartis and Sanofi/Genzyme (all payments to institution); reports speaker relationships with F. Hoffmann-La Roche, Biogen, Immunic, Teva, Merck, Novartis and Sanofi/Genzyme (all payments to institution); adjudication committee of MS clinical trial of Immunic (payments to institution only). M.M.S. serves on the editorial board of Neurology and Frontiers in Neurology; receives research support from the Dutch MS Research Foundation, Eurostars-EUREKA, ARSEP, Amsterdam Neuroscience, MAGNIMS, and ZonMW (Vidi grant, project number 09150172010056); and has served as a consultant for or received research support from Atara Biotherapeutics, Biogen, Celgene/Bristol Meyers Squibb, EIP, Sanofi, MedDay, and Merck. H.J.M.M.M. is supported by the Dutch Heart Foundation (03–004-2020-T049), by the Eurostars-2 joint programme with co-funding from the European Union Horizon 2020 research and innovation programme (ASPIRE E!113701), provided by the Netherlands Enterprise Agency (RvO), and by the EU Joint Program for Neurodegenerative Disease Research, provided by the Netherlands Organisation for health Research and Development and Alzheimer Nederland (DEBBIE JPND2020-568–106)., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Objective: Response to antidepressant treatment in major depressive disorder varies substantially between individuals, which lengthens the process of finding effective treatment. The authors sought to determine whether a multimodal machine learning approach could predict early sertraline response in patients with major depressive disorder. They assessed the predictive contribution of MR neuroimaging and clinical assessments at baseline and after 1 week of treatment., Methods: This was a preregistered secondary analysis of data from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, a multisite double-blind, placebo-controlled randomized clinical trial that included 296 adult outpatients with unmedicated recurrent or chronic major depressive disorder. MR neuroimaging and clinical data were collected before and after 1 week of treatment. Performance in predicting response and remission, collected after 8 weeks, was quantified using balanced accuracy (bAcc) and area under the receiver operating characteristic curve (AUROC) scores., Results: A total of 229 patients were included in the analyses (mean age, 38 years [SD=13]; 66% female). Internal cross-validation performance in predicting response to sertraline (bAcc=68% [SD=10], AUROC=0.73 [SD=0.03]) was significantly better than chance. External cross-validation on data from placebo nonresponders (bAcc=62%, AUROC=0.66) and placebo nonresponders who were switched to sertraline (bAcc=65%, AUROC=0.68) resulted in differences that suggest specificity for sertraline treatment compared with placebo treatment. Finally, multimodal models outperformed unimodal models., Conclusions: The study results confirm that early sertraline treatment response can be predicted; that the models are sertraline specific compared with placebo; that prediction benefits from integrating multimodal MRI data with clinical data; and that perfusion imaging contributes most to these predictions. Using this approach, a lean and effective protocol could individualize sertraline treatment planning to improve psychiatric care.

Objective: Cross-sectional studies, including one from our NOVICE cohort [Neurological Visual and Cognitive performance in children with treated perinatally acquired HIV (PHIV) compared with matched HIV-negative controls], have revealed that the brains of children with PHIV have lower white matter and grey matter volumes, more white matter hyperintensities (WMH) and poorer white matter integrity. This longitudinal study investigates whether these differences change over time., Methods: We approached all NOVICE participants to repeat MRI after 4.6 ± 0.3 years, measuring total white matter and grey matter volume, WMH volume and white matter integrity, obtained by T1-weighted, fluid-attenuated inversion recovery (FLAIR) and diffusion tensor imaging (DTI), respectively. We compared rates of change between groups using multivariable linear mixed effects models, adjusted for sex and age at enrolment. We investigated determinants of developmental deviation, and explored associations with cognitive development., Results: Twenty out of 31 (65%) PHIV-positive, and 20 out of 37 (54%) HIV-negative participants underwent follow-up MRI. Groups did not significantly differ in terms of age and sex. Over time, we found no statistically different changes between groups for white matter and WMH volumes, and for white matter integrity (P > 0.1). Total grey matter volume decreased significantly less in PHIV [group∗time 10 ml, 95% confidence interval -1 to 20, P = 0.078], but this difference in rate of change lost statistical significance after additional adjustment for height (group∗time 9 ml, 95% confidence interval -2 to 20, P = 0.112). We found no HIV-associated determinants for potential reduced grey matter pruning, nor associations with cognitive development., Conclusion: While using long-term antiretroviral treatment, structural brain development of adolescents growing up with perinatally acquired HIV appears largely normal., (Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc.)