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BackgroundHIV-associated cardiovascular disease (CVD) risk in combination antiretroviral therapy (cART)-treated perinatally HIV-infected patients (PHIV+) remains unknown due to the young age of this population. Lipoprotein(a) (Lp(a)) has been established as an independent causal risk factor for CVD in the general population but has not been well established in the population of PHIV+.MethodsWe cross-sectionally compared lipid profiles, including nonfasting Lp(a), together with total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglycerides between 35 cART-treated PHIV+ children aged 8-18 years and 37 controls who were matched for age, sex, ethnicity, and socioeconomic status. We explored associations between Lp(a) and disease- and treatment-related factors (inflammation, monocyte activation, and vascular), biomarkers, and neuroimaging outcomes using linear regression models.ResultsPHIV+ children had significantly higher levels of Lp(a) compared with controls (median, 43.6 [21.6-82.4] vs 21.8 [16.8-46.6] mg/dL; P = .033). Other lipid levels were comparable between groups. Additional assessment of apolipoprotein B, apolipoprotein CIII, apolipoprotein E, and APOE genotype revealed no significant differences. Higher Lp(a) levels were associated with higher plasma apoB levels and with lower monocyte chemoattractant protein-1 and TG levels in PHIV+ children. Lp(a) was not associated with HIV- or cART-related variables or with neuroimaging outcomes.ConclusionscART-treated PHIV+ children appear to have higher levels of Lp(a) compared with ethnicity-matched controls, which may implicate higher CVD risk in this population. Future research should focus on the association between Lp(a) and (sub)clinical CVD measurements in cART-treated PHIV+ patients.Dutch trial register numberNRT4074.

Background: We retrospectively evaluated the quality of deep learning (DL) reconstructions of on-scanner accelerated intraoperative MRI (iMRI) during respective brain tumor surgery., Methods: Accelerated iMRI was performed using dual surface coils positioned around the area of resection. A DL model was trained on the fastMRI neuro dataset to mimic the data from the iMRI protocol. The evaluation was performed on imaging material from 40 patients imaged from Nov 1, 2021, to June 1, 2023, who underwent iMRI during tumor resection surgery. A comparative analysis was conducted between the conventional compressed sense (CS) method and the trained DL reconstruction method. Blinded evaluation of multiple image quality metrics was performed by two neuroradiologists and one neurosurgeon using a 1-to-5 Likert scale (1, nondiagnostic; 2, poor; 3, acceptable; 4, good; and 5, excellent), and the favored reconstruction variant., Results: The DL reconstruction was strongly favored or favored over the CS reconstruction for 33/40, 39/40, and 8/40 of cases for readers 1, 2, and 3, respectively. For the evaluation metrics, the DL reconstructions had a higher score than their respective CS counterparts for 72%, 72%, and 14% of the cases for readers 1, 2, and 3, respectively. Still, the DL reconstructions exhibited shortcomings such as a striping artifact and reduced signal., Conclusion: DL shows promise in allowing for high-quality reconstructions of iMRI. The neuroradiologists noted an improvement in the perceived spatial resolution, signal-to-noise ratio, diagnostic confidence, diagnostic conspicuity, and spatial resolution compared to CS, while the neurosurgeon preferred the CS reconstructions across all metrics., Relevance Statement: DL shows promise to allow for high-quality reconstructions of iMRI, however, due to the challenging setting of iMRI, further optimization is needed., Key Points: iMRI is a surgical tool with a challenging image setting. DL allowed for high-quality reconstructions of iMRI. Additional optimization is needed due to the challenging intraoperative setting., Competing Interests: Declarations. Ethics approval and consent to participate: This retrospective study was approved by the Regional Medical Ethics Committee for Oslo University Hospital (REK 367336), and informed consent was acquired prior to surgery according to a broad research approval and biobank (REK 2016/17091). Only patients over 18 years were included. Consent for publication: Not applicable. Competing interests: MWAC is a shareholder of Nico-lab International Ltd. AB is a shareholder in NordicNeuroLab AS, Bergen, Norway. The remaining authors declare no competing interests., (© 2025. The Author(s).)

Accurately predicting individual antidepressant treatment response could expedite the lengthy trial-and-error process of finding an effective treatment for major depressive disorder (MDD). We tested and compared machine learning-based methods that predict individual-level pharmacotherapeutic treatment response using cortical morphometry from multisite longitudinal cohorts. We conducted an international analysis of pooled data from six sites of the ENIGMA-MDD consortium (n = 262 MDD patients; age = 36.5 ± 15.3 years; 154 (59%) female; mean response rate = 57%). Treatment response was defined as a ≥ 50% reduction in symptom severity score after 4-12 weeks post-initiation of antidepressant treatment. Structural MRI was acquired before, or < 14 days after, treatment initiation. The cortex was parcellated using FreeSurfer, from which cortical thickness and surface area were measured. We tested several machine learning pipeline configurations, which varied in (i) the way we presented the cortical data (i.e., average values per region of interest, as a vector containing voxel-wise cortical thickness and surface area measures, and as cortical thickness and surface area projections), (ii) whether we included clinical data, and the (iii) machine learning model (i.e., gradient boosting, support vector machine, and neural network classifiers) and (iv) cross-validation methods (i.e., k-fold and leave-one-site-out) we used. First, we tested if the overall predictive performance of the pipelines was better than chance, with a corrected 10-fold cross-validation permutation test. Second, we compared if some machine learning pipeline configurations outperformed others. In an exploratory analysis, we repeated our first analysis in three subpopulations, namely patients (i) from a single site, (ii) with comparable response rates, and (iii) showing the least (first quartile) and the most (fourth quartile) treatment response, which we call the extreme (non-)responders subpopulation. Finally, we explored the effect of including subcortical volumetric data on model performance. Overall, performance predicting antidepressant treatment response was not significantly better than chance (balanced accuracy = 50.5%; p = 0.66) and did not vary with alternative pipeline configurations. Exploratory analyses revealed that performance across models was only significantly better than chance in the extreme (non-)responders subpopulation (balanced accuracy = 63.9%, p = 0.001). Including subcortical data did not alter the observed model performance. Cortical structural MRI alone could not reliably predict individual pharmacotherapeutic treatment response in MDD. None of the used machine learning pipeline configurations outperformed the others. In exploratory analyses, we found that predicting response in the extreme (non-)responders subpopulation was feasible on both cortical data alone and combined with subcortical data, which suggests that specific MDD subpopulations may exhibit response-related patterns in structural data. Future work may use multimodal data to predict treatment response in MDD., (© 2025 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.)

Background: Attention-Deficit/Hyperactivity Disorder (ADHD) is commonly treated with methylphenidate (MPH). Although highly effective, MPH treatment still has a relatively high non-response rate of around 30%, highlighting the need for a better understanding of treatment response. Radiomics of T1-weighted images and Diffusion Tensor Imaging (DTI) combined with machine learning approaches could offer a novel method for assessing MPH treatment response., Purpose: To evaluate the accuracy of both conventional and radiomics approaches in predicting treatment response based on baseline T1 and DTI data in stimulant-naive ADHD participants., Methods: We performed a secondary analysis of a randomized clinical trial (ePOD-MPH), involving 47 stimulant-naive ADHD participants (23 boys aged 11.4 ± 0.4 years, 24 men aged 28.1 ± 4.3 years) who underwent 16 weeks of treatment with MPH. Baseline T1-weighted and DTI MRI scans were acquired. Treatment response was assessed at 8 weeks (during treatment) and one week after cessation of 16-week treatment (post-treatment) using the Clinical Global Impressions - Improvement scale as our primary outcome. We compared prediction accuracy using a conventional model and a radiomics model. The conventional approach included the volume of bilateral caudate, putamen, pallidum, accumbens, and hippocampus, and for DTI the mean fractional anisotropy (FA) of the entire brain white matter, bilateral Anterior Thalamic Radiation (ATR), and the splenium of the corpus callosum, totaling 14 regional features. For the radiomics approach, 380 features (shape-based and first-order statistics) were extracted from these 14 regions. XGBoost models with nested cross-validation were used and constructed for the total cohort (n = 47), as well as children (n = 23) and adults (n = 24) separately. Exact binomial tests were employed to compare model performance., Results: For the conventional model, balanced accuracy (bAcc) in predicting treatment response during treatment was 63 % for the total cohort, 32 % for children, and 36 % for adults (Area Under the Receiver Operating Characteristic Curve (AUC-ROC): 0.69, 0.33, 0.41 respectively). Radiomics models demonstrated bAcc's of 68 %, 64 %, and 64 % during treatment (AUC-ROCs of 0.73, 0.62, 0.69 respectively). These predictions were better than chance for both conventional and radiomics models in the total cohort (p = 0.04, p = 0.003 respectively). The radiomics models outperformed the conventional models during treatment in children only (p = 0.02). At post-treatment, performance was markedly reduced., Conclusion: While conventional and radiomics models performed equally well in predicting clinical improvement across children and adults during treatment, radiomics features offered enhanced structural information beyond conventional region-based volume and FA averages in children. Prediction of symptom improvement one week after treatment cessation was poor, potentially due to the transient effects of stimulant treatment on symptom improvement., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: M. Chen is funded by the China Scholarship Council (CSC) from the Ministry of Education of P.R. China. M.W.A. Caan is a shareholder of Nico.lab International Ltd. H.A. Marquering is a co-founder and shareholder of Nico.lab International Ltd., TrianecT, and inSteps. The other 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., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background and Objectives: Artificial intelligence (AI) is revolutionizing Magnetic Resonance Imaging (MRI) along the acquisition and processing chain. Advanced AI frameworks have been applied in various successive tasks, such as image reconstruction, quantitative parameter map estimation, and image segmentation. However, existing frameworks are often designed to perform tasks independently of each other or are focused on specific models or single datasets, limiting generalization. This work introduces the Advanced Toolbox for Multitask Medical Imaging Consistency (ATOMMIC), a novel open-source toolbox that streamlines AI applications for accelerated MRI reconstruction and analysis. ATOMMIC implements several tasks using deep learning (DL) models and enables MultiTask Learning (MTL) to perform related tasks in an integrated manner, targeting generalization in the MRI domain., Methods: We conducted a comprehensive literature review and analyzed 12,479 GitHub repositories to assess the current landscape of AI frameworks for MRI. Subsequently, we demonstrate how ATOMMIC standardizes workflows and improves data interoperability, enabling effective benchmarking of various DL models across MRI tasks and datasets. To showcase ATOMMIC's capabilities, we evaluated twenty-five DL models on eight publicly available datasets, focusing on accelerated MRI reconstruction, segmentation, quantitative parameter map estimation, and joint accelerated MRI reconstruction and segmentation using MTL., Results: ATOMMIC's high-performance training and testing capabilities, utilizing multiple GPUs and mixed precision support, enable efficient benchmarking of multiple models across various tasks. The framework's modular architecture implements each task through a collection of data loaders, models, loss functions, evaluation metrics, and pre-processing transformations, facilitating seamless integration of new tasks, datasets, and models. Our findings demonstrate that ATOMMIC supports MTL for multiple MRI tasks with harmonized complex-valued and real-valued data support while maintaining active development and documentation. Task-specific evaluations demonstrate that physics-based models outperform other approaches in reconstructing highly accelerated acquisitions. These high-quality reconstruction models also show superior accuracy in estimating quantitative parameter maps. Furthermore, when combining high-performing reconstruction models with robust segmentation networks through MTL, performance is improved in both tasks., Conclusions: ATOMMIC advances MRI reconstruction and analysis by leveraging MTL and ensuring consistency across tasks, models, and datasets. This comprehensive framework serves as a versatile platform for researchers to use existing AI methods and develop new approaches in medical imaging., 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., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Objective: To compare compressed sensing (CS) and the Cascades of Independently Recurrent Inference Machines (CIRIM) with respect to image quality and reconstruction times when 12-fold accelerated scans of patients with neurological deficits are reconstructed., Materials and Methods: Twelve-fold accelerated 3D T2-FLAIR images were obtained from a cohort of 62 patients with neurological deficits on 3 T MRI. Images were reconstructed offline via CS and the CIRIM. Image quality was assessed in a blinded and randomized manner by two experienced interventional neuroradiologists and one experienced pediatric neuroradiologist on imaging artifacts, perceived spatial resolution (sharpness), anatomic conspicuity, diagnostic confidence, and contrast. The methods were also compared in terms of self-referenced quality metrics, image resolution, patient groups and reconstruction time. In ten scans, the contrast ratio (CR) was determined between lesions and white matter. The effect of acceleration factor was assessed in a publicly available fully sampled dataset, since ground truth data are not available in prospectively accelerated clinical scans. Specifically, 451 FLAIR scans, including scans with white matter lesions, were adopted from the FastMRI database to evaluate structural similarity (SSIM) and the CR of lesions and white matter on ranging acceleration factors from four-fold up to 12-fold., Results: Interventional neuroradiologists significantly preferred the CIRIM for imaging artifacts, anatomic conspicuity, and contrast. One rater significantly preferred the CIRIM in terms of sharpness and diagnostic confidence. The pediatric neuroradiologist preferred CS for imaging artifacts and sharpness. Compared to CS, the CIRIM reconstructions significantly improved in terms of imaging artifacts and anatomic conspicuity (p < 0.01) for higher resolution scans while yielding a 28% higher SNR (p = 0.001) and a 5.8% lower CR (p = 0.04). There were no differences between patient groups. Additionally, CIRIM was five times faster than CS was. An increasing acceleration factor did not lead to changes in CR (p = 0.92), but led to lower SSIM (p = 0.002)., Discussion: Patients with neurological deficits can undergo MRI at a range of moderate to high acceleration. DL reconstruction outperforms CS in terms of image resolution, efficient denoising with a modest reduction in contrast and reduced reconstruction times., (© 2024. The Author(s).)

Background: Intravenous thrombolysis (IVT) before endovascular treatment (EVT) for acute ischemic stroke might induce intracerebral hemorrhages which could negatively affect patient outcomes. Measuring white matter lesions size using deep learning (DL-WML) might help safely guide IVT administration. We aimed to develop, validate, and evaluate a DL-WML volume on CT compared to the Fazekas scale (WML-Faz) as a risk factor and IVT effect modifier in patients receiving EVT directly after IVT., Methods: We developed a deep-learning model for WML segmentation on CT and validated with internal and external test sets. In a post hoc analysis of the MR CLEAN No-IV trial, we associated DL-WML volume and WML-Faz with symptomatic-intracerebral hemorrhage (sICH) and 90-day functional outcome according to the modified Rankin Scale (mRS). We used multiplicative interaction terms between WML measures and IVT administration to evaluate IVT treatment effect modification. Regression models were used to report unadjusted and adjusted common odds ratios (cOR/acOR)., Results: In total, 516 patients from the MR CLEAN No-IV trial (male/female, 291/225; age median, 71 [IQR, 62-79]) were analyzed. Both DL-WML volume and WML-Faz are associated with sICH (DL-WML volume acOR, 1.78 [95%CI, 1.17; 2.70]; WML-Faz acOR, 1.53 95%CI [1.02; 2.31]) and mRS (DL-WML volume acOR, 0.70 [95%CI, 0.55; 0.87], WML-Faz acOR, 0.73 [95%CI 0.60; 0.88]). Only in the unadjusted IVT effect modification analysis WML-Faz was associated with more sICH if IVT was given (p = 0.046). Neither WML measure was associated with worse mRS if IVT was given., Conclusion: DL-WML volume and WML-Faz had a similar relationship with functional outcome and sICH. Although more sICH might occur in patients with more severe WML-Faz receiving IVT, no worse functional outcome was observed., Clinical Relevance Statement: White matter lesion severity on baseline CT in acute ischemic stroke patients has a similar predictive value if measured with deep learning or the Fazekas scale. Safe administration of intravenous thrombolysis using white matter lesion severity should be further studied., Key Points: White matter damage is a predisposing risk factor for intracranial hemorrhage in patients with acute ischemic stroke but remains difficult to measure on CT. White matter lesion volume on CT measured with deep learning had a similar association with symptomatic intracerebral hemorrhages and worse functional outcome as the Fazekas scale. A patient-level meta-analysis is required to study the benefit of white matter lesion severity-based selection for intravenous thrombolysis before endovascular treatment., (© 2024. The Author(s), under exclusive licence to European Society of Radiology.)

Whereas meta-analytical data highlight abnormal frontocortical macrostructure (thickness/surface area/volume) in Major Depressive Disorder (MDD), the underlying microstructural processes remain uncharted, due to the use of conventional MRI scanners and acquisition techniques. We uniquely combined Ultra-High Field MRI at 7.0 Tesla with Quantitative Imaging to map intracortical myelin (proxied by longitudinal relaxation time T 1 ) and iron concentration (proxied by transverse relaxation time T 2 *), microstructural processes deemed particularly germane to cortical macrostructure. Informed by meta-analytical evidence, we focused specifically on orbitofrontal and rostral anterior cingulate cortices among adult MDD patients (N = 48) and matched healthy controls (HC; N = 10). Analyses probed the association of MDD diagnosis and clinical profile (severity, medication use, comorbid anxiety disorders, childhood trauma) with aforementioned microstructural properties. MDD diagnosis (p's < 0.05, Cohen's D = 0.55-0.66) and symptom severity (p's < 0.01, r = 0.271-0.267) both related to decreased intracortical myelination (higher T 1 values) within the lateral orbitofrontal cortex, a region tightly coupled to processing negative affect and feelings of sadness in MDD. No relations were found with local iron concentrations. These findings allow uniquely fine-grained insights on frontocortical microstructure in MDD, and cautiously point to intracortical demyelination as a possible driver of macroscale cortical disintegrity in MDD., (© 2024. The Author(s).)

Recurrent inference machines (RIM), a deep learning model that learns an iterative scheme for reconstructing sparsely sampled MRI, has been shown able to perform well on accelerated 2D and 3D MRI scans, learn from small datasets and generalize well to unseen types of data. Here we propose the dynamic recurrent inference machine (DRIM) for reconstructing sparsely sampled 4D MRI by exploiting correlations between respiratory states. The DRIM was applied to a 4D protocol for MR-guided radiotherapy of liver lesions based on repetitive interleaved coronal 2D multi-slice T 2 -weighted acquisitions. We demonstrate with an ablation study that the DRIM outperforms the RIM, increasing the SSIM score from about 0.89 to 0.95. The DRIM allowed for an approximately 2.7 times faster scan time than the current clinical protocol with only a slight loss in image sharpness. Correlations between slice locations can also be used, but were found to be of less importance, as were a majority of tested variations in network architecture, as long as the respiratory states are processed by the network. Through cross-validation, the DRIM is also shown to be robust in terms of training data. We further demonstrate a good performance across a large range of subsampling factors, and conclude through an evaluation by a radiation oncologist that reconstructed images of the liver contour and inner structures are of a clinically acceptable standard at acceleration factors 10x and 8x, respectively. Finally, we show that binning the data with respect to respiratory states prior to reconstruction comes at a slight cost to reconstruction quality, but at greater speed of the overall protocol., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Jan-Jakob Sonke reports a relationship with Elekta AB that includes: funding grants. Jan-Jakob Sonke reports a relationship with Dutch Cancer Society that includes: funding grants. Jan-Jakob Sonke reports a relationship with Netherlands Enterprise Agency that includes: funding grants. Matthan W.A. Caan reports a relationship with Nicolab International Ltd. that includes: equity or stocks. Our department is a member of the Elekta ML Linac consortium. Our department received royalties for imaged radiotherapy software from Elekta AB., (Copyright © 2024 Elsevier Ltd. 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.

Background and Objectives: Disability and cognitive impairment are known to be related to brain atrophy in multiple sclerosis (MS), but 3D-T1 imaging required for brain volumetrics is often unavailable in clinical protocols, unlike 3D-FLAIR. Here our aim was to investigate whether brain volumes derived from 3D-FLAIR images result in similar associations with disability and cognition in MS as do those derived from 3D-T1 images., Methods: 3T-MRI scans of 329 MS patients and 76 healthy controls were included in this cross-sectional study. Brain volumes were derived using FreeSurfer on 3D-T1 and compared with brain volumes derived with SynthSeg and SAMSEG on 3D-FLAIR. Relative agreement was evaluated by calculating the intraclass correlation coefficient (ICC) of the 3D-T1 and 3D-FLAIR volumes. Consistency of relations with disability and average cognition was assessed using linear regression, while correcting for age and sex. The findings were corroborated in an independent validation cohort of 125 MS patients., Results: The ICC between volume measured with FreeSurfer and those measured on 3D-FLAIR for brain, ventricle, cortex, total deep gray matter and thalamus was above 0.74 for SAMSEG and above 0.91 for SynthSeg. Worse disability and lower average cognition were similarly associated with brain (adj. R 2  = 0.24-0.27, p < 0.01; adj. R 2  = 0.26-0.29, p < 0.001) ventricle (adj. R 2  = 0.27-0.28, p < 0.001; adj. R 2  = 0.19-0.20, p < 0.001) and deep gray matter volumes (adj. R 2  = 0.24-0.28, p < 0.001; adj. R 2  = 0.27-0.28, p < 0.001) determined with all methods, except for cortical volumes derived from 3D-FLAIR., Discussion: In this cross-sectional study, brain volumes derived from 3D-FLAIR and 3D-T1 show similar relationships to disability and cognitive dysfunction in MS, highlighting the potential of these techniques in clinical datasets., (© 2023. The Author(s).)

Background: Thrombus radiomics (TR) describe complex shape and textural thrombus imaging features. We aimed to study the relationship of TR extracted from non-contrast CT with procedural and functional outcome in endovascular-treated patients with acute ischemic stroke., Methods: Thrombi were segmented on thin-slice non-contrast CT (≤1 mm) from 699 patients included in the MR CLEAN Registry. In a pilot study, we selected 51 TR with consistent values across two raters' segmentations (ICC >0.75). Random forest models using TR in addition or as a substitute to baseline clinical variables (CV) and manual thrombus measurements (MTM) were trained with 499 patients and evaluated on 200 patients for predicting successful reperfusion (extended Thrombolysis in Cerebral Ischemia (eTICI) ≥2B), first attempt reperfusion, reperfusion within three attempts, and functional independence (modified Rankin Scale (mRS) ≤2). Three texture and shape features were selected based on feature importance and related to eTICI ≥2B, number of attempts to eTICI ≥2B, and 90-day mRS with ordinal logistic regression., Results: Random forest models using TR, CV or MTM had comparable predictive performance. Thrombus texture (inverse difference moment normalized) was independently associated with reperfusion (adjusted common OR (acOR) 0.85, 95% CI 0.72 to 0.99). Thrombus volume and texture were also independently associated with the number of attempts to successful reperfusion (acOR 1.36, 95% CI 1.03 to 1.88 and acOR 1.24, 95% CI 1.04 to 1.49)., Conclusions: TR describing thrombus volume and texture were associated with more attempts to successful reperfusion. Compared with models using CV and MTM, TR had no added value for predicting procedural and functional outcome., Competing Interests: Competing interests: Amsterdam UMC received funds from Stryker for consultations by CBLMM and YBWEMR. Unrelated to this study, Amsterdam UMC received grants from the Netherlands Organization for Health Research and Development, Health Holland Top Sector LSH and Nicolab. Dr Dippel and AvdL report unrestricted grants from Stryker, Penumbra, Medtronic, Cerenovus, Thrombolytic Science, LLC, Dutch Heart Foundation, Brain Foundation Netherlands, The Netherlands Organization for Health Research and Development, and Health Holland Top Sector Life Sciences & Health for research, paid to institution. Maastricht University Medical Center received funds from Stryker, Cerenovus, Nicolab and Philips for consultation by WHvZ. NAT received funding from the AMC Medical Research. PRK is funded by INSIST: a European Union’s Horizon 2020 research and innovation program. YBWEMR is minor shareholder of Nicolab. HAM is co-founder and shareholder of Nicolab. CBLMM reports a grant from the TWIN Foundation. Unrelated to this study, CBLMM is a minor shareholder of Nicolab and reports grants from CVON/Dutch Heart Foundation, European Commission, Health Evaluation Netherlands, and Stryker, all paid to institution., (© Author(s) (or their employer(s)) 2023. No commercial re-use. See rights and permissions. Published by BMJ.)

Purpose: Pathological conditions associated with the optic nerve (ON) can cause structural changes in the nerve. Quantifying these changes could provide further understanding of disease mechanisms. We aim to develop a framework that automatically segments the ON separately from its surrounding cerebrospinal fluid (CSF) on magnetic resonance imaging (MRI) and quantifies the diameter and cross-sectional area along the entire length of the nerve., Approach: Multicenter data were obtained from retinoblastoma referral centers, providing a heterogeneous dataset of 40 high-resolution 3D T2-weighted MRI scans with manual ground truth delineations of both ONs. A 3D U-Net was used for ON segmentation, and performance was assessed in a tenfold cross-validation ( n = 32 ) and on a separate test-set ( n = 8 ) by measuring spatial, volumetric, and distance agreement with manual ground truths. Segmentations were used to quantify diameter and cross-sectional area along the length of the ON, using centerline extraction of tubular 3D surface models. Absolute agreement between automated and manual measurements was assessed by the intraclass correlation coefficient (ICC)., Results: The segmentation network achieved high performance, with a mean Dice similarity coefficient score of 0.84, median Hausdorff distance of 0.64 mm, and ICC of 0.95 on the test-set. The quantification method obtained acceptable correspondence to manual reference measurements with mean ICC values of 0.76 for the diameter and 0.71 for the cross-sectional area. Compared with other methods, our method precisely identifies the ON from surrounding CSF and accurately estimates its diameter along the nerve's centerline., Conclusions: Our automated framework provides an objective method for ON assessment in vivo ., (© 2023 The Authors.)

Background: A larger thrombus in patients with acute ischemic stroke might result in more complex endovascular treatment procedures, resulting in poorer patient outcomes. Current evidence on thrombus volume and length related to procedural and functional outcomes remains contradicting. This study aimed to assess the prognostic value of thrombus volume and thrombus length and whether this relationship differs between first-line stent retrievers and aspiration devices for endovascular treatment., Methods: In this multicenter retrospective cohort study, 670 of 3279 patients from the MR CLEAN Registry (Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands) for endovascularly treated large vessel occlusions were included. Thrombus volume (0.1 mL) and length (0.1 mm) based on manual segmentations and measurements were related to reperfusion grade (expanded Treatment in Cerebral Infarction score) after endovascular treatment, the number of retrieval attempts, symptomatic intracranial hemorrhage, and a shift for functional outcome at 90 days measured with the reverted ordinal modified Rankin Scale (odds ratio >1 implies a favorable outcome). Univariable and multivariable linear and logistic regression were used to report common odds ratios (cORs)/adjusted cOR and regression coefficients (B/aB) with 95% CIs. Furthermore, a multiplicative interaction term was used to analyze the relationship between first-line device choice, stent retrievers versus aspiration device, thrombus volume, and outcomes., Results: Thrombus volume was associated with functional outcome (adjusted cOR, 0.83 [95% CI, 0.71-0.97]) and number of retrieval attempts (aB, 0.16 [95% CI, 0.16-0.28]) but not with the other outcome measures. Thrombus length was only associated with functional independence (adjusted cOR, 0.45 [95% CI, 0.24-0.85]). Patients with more voluminous thrombi had worse functional outcomes if endovascular treatment was based on first-line stent retrievers (interaction cOR, 0.67 [95% CI, 0.50-0.89]; P =0.005; adjusted cOR, 0.74 [95% CI, 0.55-1.0]; P =0.04)., Conclusions: In this study, patients with a more voluminous thrombus required more endovascular thrombus retrieval attempts and had a worse functional outcome. Patients with a lengthier thrombus were less likely to achieve functional independence at 90 days. For more voluminous thrombi, first-line stent retrieval compared with first-line aspiration might be associated with worse functional outcome.

Background: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is a highly effective surgical treatment for patients with advanced Parkinson disease (PD). Combining 7.0-Tesla (7T) T2- and diffusion-weighted imaging (DWI) sequences allows for selective segmenting of the motor part of the STN and, thus, for possible optimization of DBS., Materials and Methods: 7T T2 and DWI sequences were obtained, and probabilistic segmentation of motor, associative, and limbic STN segments was performed. Left- and right-sided motor outcome (Movement Disorders Society Unified Parkinson's Disease Rating Scale) scores were used for evaluating the correspondence between the active electrode contacts in selectively segmented STN and the clinical DBS effect. The Bejjani line was reviewed for crossing of segments., Results: A total of 50 STNs were segmented in 25 patients and proved highly feasible. Although the highest density of motor connections was situated in the dorsolateral STN for all patients, the exact partitioning of segments differed considerably. For all the active electrode contacts situated within the predominantly motor-connected segment of the STN, the average hemi-body Unified Parkinson's Disease Rating Scale motor improvement was 80%; outside this segment, it was 52% (p < 0.01). The Bejjani line was situated in the motor segment for 32 STNs., Conclusion: The implementation of 7T T2 and DWI segmentation of the STN in DBS for PD is feasible and offers insight into the location of the motor segment. Segmentation-guided electrode placement is likely to further improve motor response in DBS for PD. However, commercially available DBS software for postprocessing imaging would greatly facilitate widespread implementation., (Copyright © 2022 International Neuromodulation Society. Published by Elsevier Inc. All rights reserved.)

Objective: To improve accelerated MRI reconstruction through a densely connected cascading deep learning reconstruction framework., Materials and Methods: A cascading deep learning reconstruction framework (reference model) was modified by applying three architectural modifications: input-level dense connections between cascade inputs and outputs, an improved deep learning sub-network, and long-range skip-connections between subsequent deep learning networks. An ablation study was performed, where five model configurations were trained on the NYU fastMRI neuro dataset with an end-to-end scheme conjunct on four- and eightfold acceleration. The trained models were evaluated by comparing their respective structural similarity index measure (SSIM), normalized mean square error (NMSE), and peak signal to noise ratio (PSNR)., Results: The proposed densely interconnected residual cascading network (DIRCN), utilizing all three suggested modifications achieved a SSIM improvement of 8% and 11%, a NMSE improvement of 14% and 23%, and a PSNR improvement of 2% and 3% for four- and eightfold acceleration, respectively. In an ablation study, the individual architectural modifications all contributed to this improvement for both acceleration factors, by improving the SSIM, NMSE, and PSNR with approximately 2-4%, 4-9%, and 0.5-1%, respectively., Conclusion: The proposed architectural modifications allow for simple adjustments on an already existing cascading framework to further improve the resulting reconstructions., (© 2022. The Author(s).)

Quantitative MRI (qMRI) acquired at the ultra-high field of 7 Tesla has been used in visualizing and analyzing subcortical structures. qMRI relies on the acquisition of multiple images with different scan settings, leading to extended scanning times. Data redundancy and prior information from the relaxometry model can be exploited by deep learning to accelerate the imaging process. We propose the quantitative Recurrent Inference Machine (qRIM), with a unified forward model for joint reconstruction and R 2 * -mapping from sparse data, embedded in a Recurrent Inference Machine (RIM), an iterative inverse problem-solving network. To study the dependency of the proposed extension of the unified forward model to network architecture, we implemented and compared a quantitative End-to-End Variational Network (qE2EVN). Experiments were performed with high-resolution multi-echo gradient echo data of the brain at 7T of a cohort study covering the entire adult life span. The error in reconstructed R 2 * from undersampled data relative to reference data significantly decreased for the unified model compared to sequential image reconstruction and parameter fitting using the RIM. With increasing acceleration factor, an increasing reduction in the reconstruction error was observed, pointing to a larger benefit for sparser data. Qualitatively, this was following an observed reduction of image blurriness in R 2 * -maps. In contrast, when using the U-Net as network architecture, a negative bias in R 2 * in selected regions of interest was observed. Compressed Sensing rendered accurate, but less precise estimates of R 2 * . The qE2EVN showed slightly inferior reconstruction quality compared to the qRIM but better quality than the U-Net and Compressed Sensing. Subcortical maturation over age measured by a linearly increasing interquartile range of R 2 * in the striatum was preserved up to an acceleration factor of 9. With the integrated prior of the unified forward model, the proposed qRIM can exploit the redundancy among repeated measurements and shared information between tasks, facilitating relaxometry in accelerated MRI., Competing Interests: Declaration of Competing Interest M.W.A. Caan is shareholder of Nico-lab International Ltd., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Radiomics in neuroimaging uses fully automatic segmentation to delineate the anatomical areas for which radiomic features are computed. However, differences among these segmentation methods affect radiomic features to an unknown extent. A scan-rescan dataset (n = 46) of T1-weighted and diffusion tensor images was used. Subjects were split into a sleep-deprivation and a control group. Scans were segmented using four segmentation methods from which radiomic features were computed. First, we measured segmentation agreement using the Dice-coefficient. Second, robustness and reproducibility of radiomic features were measured using the intraclass correlation coefficient (ICC). Last, difference in predictive power was assessed using the Friedman-test on performance in a radiomics-based sleep deprivation classification application. Segmentation agreement was generally high (interquartile range = 0.77-0.90) and median feature robustness to segmentation method variation was higher (ICC > 0.7) than scan-rescan reproducibility (ICC 0.3-0.8). However, classification performance differed significantly among segmentation methods (p < 0.001) ranging from 77 to 84%. Accuracy was higher for more recent deep learning-based segmentation methods. Despite high agreement among segmentation methods, subtle differences significantly affected radiomic features and their predictive power. Consequently, the effect of differences in segmentation methods should be taken into account when designing and evaluating radiomics-based research methods., (© 2022. The Author(s).)

Background and Purpose: Supervised deep learning is the state-of-the-art method for stroke lesion segmentation on NCCT. Supervised methods require manual lesion annotations for model development, while unsupervised deep learning methods such as generative adversarial networks do not. The aim of this study was to develop and evaluate a generative adversarial network to segment infarct and hemorrhagic stroke lesions on follow-up NCCT scans., Materials and Methods: Training data consisted of 820 patients with baseline and follow-up NCCT from 3 Dutch acute ischemic stroke trials. A generative adversarial network was optimized to transform a follow-up scan with a lesion to a generated baseline scan without a lesion by generating a difference map that was subtracted from the follow-up scan. The generated difference map was used to automatically extract lesion segmentations. Segmentation of primary hemorrhagic lesions, hemorrhagic transformation of ischemic stroke, and 24-hour and 1-week follow-up infarct lesions were evaluated relative to expert annotations with the Dice similarity coefficient, Bland-Altman analysis, and intraclass correlation coefficient., Results: The median Dice similarity coefficient was 0.31 (interquartile range, 0.08-0.59) and 0.59 (interquartile range, 0.29-0.74) for the 24-hour and 1-week infarct lesions, respectively. A much lower Dice similarity coefficient was measured for hemorrhagic transformation (median, 0.02; interquartile range, 0-0.14) and primary hemorrhage lesions (median, 0.08; interquartile range, 0.01-0.35). Predicted lesion volume and the intraclass correlation coefficient were good for the 24-hour (bias, 3 mL; limits of agreement, -64-59 mL; intraclass correlation coefficient, 0.83; 95% CI, 0.78-0.88) and excellent for the 1-week (bias, -4 m; limits of agreement,-66-58 mL; intraclass correlation coefficient, 0.90; 95% CI, 0.83-0.93) follow-up infarct lesions., Conclusions: An unsupervised generative adversarial network can be used to obtain automated infarct lesion segmentations with a moderate Dice similarity coefficient and good volumetric correspondence., (© 2022 by American Journal of Neuroradiology.)

Infarct volume (FIV) on follow-up diffusion-weighted imaging (FU-DWI) is only moderately associated with functional outcome in acute ischemic stroke patients. However, FU-DWI may contain other imaging biomarkers that could aid in improving outcome prediction models for acute ischemic stroke. We included FU-DWI data from the HERMES, ISLES, and MR CLEAN-NO IV databases. Lesions were segmented using a deep learning model trained on the HERMES and ISLES datasets. We assessed the performance of three classifiers in predicting functional independence for the MR CLEAN-NO IV trial cohort based on: (1) FIV alone, (2) the most important features obtained from a trained convolutional autoencoder (CAE), and (3) radiomics. Furthermore, we investigated feature importance in the radiomic-feature-based model. For outcome prediction, we included 206 patients: 144 scans were included in the training set, 21 in the validation set, and 41 in the test set. The classifiers that included the CAE and the radiomic features showed AUC values of 0.88 and 0.81, respectively, while the model based on FIV had an AUC of 0.79. This difference was not found to be statistically significant. Feature importance results showed that lesion intensity heterogeneity received more weight than lesion volume in outcome prediction. This study suggests that predictions of functional outcome should not be based on FIV alone and that FU-DWI images capture additional prognostic information.

Objective. Machine Learning methods can learn how to reconstruct magnetic resonance images (MRI) and thereby accelerate acquisition, which is of paramount importance to the clinical workflow. Physics-informed networks incorporate the forward model of accelerated MRI reconstruction in the learning process. With increasing network complexity, robustness is not ensured when reconstructing data unseen during training. We aim to embed data consistency (DC) in deep networks while balancing the degree of network complexity. While doing so, we will assess whether either explicit or implicit enforcement of DC in varying network architectures is preferred to optimize performance. Approach. We propose a scheme called Cascades of Independently Recurrent Inference Machines (CIRIM) to assess DC through unrolled optimization. Herein we assess DC both implicitly by gradient descent and explicitly by a designed term. Extensive comparison of the CIRIM to compressed sensing as well as other Machine Learning methods is performed: the End-to-End Variational Network (E2EVN), CascadeNet, KIKINet, LPDNet, RIM, IRIM, and UNet. Models were trained and evaluated on T 1 -weighted and FLAIR contrast brain data, and T 2 -weighted knee data. Both 1D and 2D undersampling patterns were evaluated. Robustness was tested by reconstructing 7.5× prospectively undersampled 3D FLAIR MRI data of multiple sclerosis (MS) patients with white matter lesions. Main results. The CIRIM performed best when implicitly enforcing DC, while the E2EVN required an explicit DC formulation. Through its cascades, the CIRIM was able to score higher on structural similarity and PSNR compared to other methods, in particular under heterogeneous imaging conditions. In reconstructing MS patient data, prospectively acquired with a sampling pattern unseen during model training, the CIRIM maintained lesion contrast while efficiently denoising the images. Significance. The CIRIM showed highly promising generalization capabilities maintaining a very fair trade-off between reconstructed image quality and fast reconstruction times, which is crucial in the clinical workflow., (Creative Commons Attribution license.)

In order to further our understanding of brain function and the underlying networks, more advanced diffusion weighted magnetic resonance imaging (DWI MRI) data are essential. Here we present freely available high-resolution multi-shell multi-directional 3 Tesla (T) DWI MRI data as part of the 'Amsterdam Ultra-high field adult lifespan database' (AHEAD). The 3T DWI AHEAD dataset include 1.28mm isotropic whole brain DWI data of 49 healthy adult participants between 18 and 90 years old. The acquired data include DWIs at three non-zero b-values (48 directions, b-value 700 s/mm 2 ; 56 directions, b-value 1000 s/mm 2 ; 64 directions, b-value 1600 s/mm 2 ) including a total of twelve volumes with a b-value of 0 s/mm 2 (b0 volumes). In addition, eight b0 volumes with a reversed phase encoding direction were acquired to correct for distortions. To facilitate future use, the DWI data have been denoised, corrected for eddy currents, susceptibility-induced off-resonance field distortions, bias fields, and are skull stripped., Competing Interests: Of the authors, M.W.A. Caan is shareholder of Nico.lab International Ltd. The other authors declare that they have no known competing financial interests or personal relationships which have or could be perceived to have influenced the work reported in this article. The data described here was acquired with financial support from STW/NWO (#14017, BUF, MJM and AA), Health∼Holland TKI-PPP (LL, MWAC), ERC Consolidator (BUF), ERC PoC (BUF), and the NWO Vici (BUF)., (© 2022 The Author(s). Published by Elsevier Inc.)

Purpose: Follow-up infarct volume (FIV) is moderately associated with functional outcome. We hypothesized that accounting for infarct location would strengthen the association of FIV with functional outcome., Methods: We included 252 patients from the HERMES collaboration with follow-up diffusion weighted imaging. Patients received endovascular treatment combined with best medical management (n = 52%) versus best medical management alone (n = 48%). FIV was quantified in low, moderate and high modified Rankin Scale (mRS)-relevant regions. We used binary logistic regression to study the relation between the total, high, moderate or low mRS-relevant FIVs and favorable outcome (mRS < 2) after 90 days. The strength of association was evaluated using the c-statistic., Results: Small lesions only occupied high mRS-relevant brain regions. Lesions additionally occupied lower mRS-relevant brain regions if FIV expanded. Higher FIV was associated with a higher risk of unfavorable outcome, as were volumes of tissue with low, moderate and high mRS relevance. In multivariable modeling, only the volume of high mRS-relevant infarct was significantly associated with favorable outcome. The c-statistic was highest (0.76) for the models that included high mRS-relevant FIV or the combination of high, moderate and low mRS-relevant FIV but was not significantly different from the model that included only total FIV (0.75)., Conclusion: This study confirms the association of FIV and unfavorable functional outcome but showed no strengthened association if lesion location was taken into account., (© 2021. The Author(s).)

In this paper, we propose the use of Recurrent Inference Machines (RIMs) to perform T 1 and T 2 mapping. The RIM is a neural network framework that learns an iterative inference process based on the signal model, similar to conventional statistical methods for quantitative MRI (QMRI), such as the Maximum Likelihood Estimator (MLE). This framework combines the advantages of both data-driven and model-based methods, and, we hypothesize, is a promising tool for QMRI. Previously, RIMs were used to solve linear inverse reconstruction problems. Here, we show that they can also be used to optimize non-linear problems and estimate relaxometry maps with high precision and accuracy. The developed RIM framework is evaluated in terms of accuracy and precision and compared to an MLE method and an implementation of the Residual Neural Network (ResNet). The results show that the RIM improves the quality of estimates compared to the other techniques in Monte Carlo experiments with simulated data, test-retest analysis of a system phantom, and in-vivo scans. Additionally, inference with the RIM is 150 times faster than the MLE, and robustness to (slight) variations of scanning parameters is demonstrated. Hence, the RIM is a promising and flexible method for QMRI. Coupled with an open-source training data generation tool, it presents a compelling alternative to previous methods., 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., (Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.)

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.)

Introduction: Radiological thrombus characteristics are associated with patient outcomes and treatment success after acute ischemic stroke. These characteristics could be expected to undergo time-dependent changes due to factors influencing thrombus architecture like blood stasis, clot contraction, and natural thrombolysis. We investigated whether stroke onset-to-imaging time was associated with thrombus length, perviousness, and density in the MR CLEAN Registry population. Methods: We included 245 patients with M1-segment occlusions and thin-slice baseline CT imaging from the MR CLEAN Registry, a nation-wide multicenter registry of patients who underwent endovascular treatment for acute ischemic stroke within 6.5 h of onset in the Netherlands. We used multivariable linear regression to investigate the effect of stroke onset-to-imaging time (per 5 min) on thrombus length (in mm), perviousness and density (both in Hounsfield Units). In the first model, we adjusted for age, sex, intravenous thrombolysis, antiplatelet use, and history of atrial fibrillation. In a second model, we additionally adjusted for observed vs. non-observed stroke onset, CT-angiography collateral score, direct presentation at a thrombectomy-capable center vs. transfer, and stroke etiology. We performed exploratory subgroup analyses for intravenous thrombolysis administration, observed vs. non-observed stroke onset, direct presentation vs. transfer, and stroke etiology. Results: Median stroke onset-to-imaging time was 83 (interquartile range 53-141) min. Onset to imaging time was not associated with thrombus length nor perviousness (β 0.002; 95% CI -0.004 to 0.007 and β -0.002; 95% CI -0.015 to 0.011 per 5 min, respectively) and was weakly associated with thrombus density in the fully adjusted model (adjusted β 0.100; 95% CI 0.005-0.196 HU per 5 min). The subgroup analyses showed no heterogeneity of these findings in any of the subgroups, except for a significantly positive relation between onset-to-imaging time and thrombus density in patients transferred from a primary stroke center (adjusted β 0.18; 95% CI 0.022-0.35). Conclusion: In our population of acute ischemic stroke patients, we found no clear association between onset-to-imaging time and radiological thrombus characteristics. This suggests that elapsed time from stroke onset plays a limited role in the interpretation of radiological thrombus characteristics and their effect on treatment results, at least in the early time window., Competing Interests: Erasmus MC received funds from Stryker by AL. Amsterdam UMC received funds from Stryker for consultations by CM and YR. MUMC received funds from Stryker and Codman for consultations by WZ. CM reports grants from the TWIN Foundation, the CVON/Dutch Heart Foundation, the European Commission. HM is cofounder and shareholder of Nico.lab. CM, MC, and YR own stock in Nico.lab. 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., (Copyright © 2021 Tolhuisen, Kappelhof, Dutra, Jansen, Guglielmi, Dippel, van Zwam, van Oostenbrugge, van der Lugt, Roos, Majoie, Caan, Marquering and the MR CLEAN Registry Investigators.)

Background: Deep brain stimulation (DBS) is a new treatment option for patients with therapy-resistant obsessive-compulsive disorder (OCD). Approximately 60% of patients benefit from DBS, which might be improved if a biomarker could identify patients who are likely to respond. Therefore, we evaluated the use of preoperative structural magnetic resonance imaging (MRI) in predicting treatment outcome for OCD patients on the group- and individual-level., Methods: In this retrospective study, we analyzed preoperative MRI data of a large cohort of patients who received DBS for OCD (n = 57). We used voxel-based morphometry to investigate whether grey matter (GM) or white matter (WM) volume surrounding the DBS electrode (nucleus accumbens (NAc), anterior thalamic radiation), and whole-brain GM/WM volume were associated with OCD severity and response status at 12-month follow-up. In addition, we performed machine learning analyses to predict treatment outcome at an individual-level and evaluated its performance using cross-validation., Results: Larger preoperative left NAc volume was associated with lower OCD severity at 12-month follow-up (p FWE  < 0.05). None of the individual-level regression/classification analyses exceeded chance-level performance., Conclusions: These results provide evidence that patients with larger NAc volumes show a better response to DBS, indicating that DBS success is partly determined by individual differences in brain anatomy. However, the results also indicate that structural MRI data alone does not provide sufficient information to guide clinical decision making at an individual level yet., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

X-linked adrenoleukodsytrophy (ALD) is a genetic neuro-metabolic disorder, causing a slowly progressive myelopathy in adult male and female patients. New disease modifying therapies for myelopathy are under development. This calls for new (imaging) markers able to measure disease severity and progression in clinical trials. In this prospective cohort study, we measured cerebral metabolite levels with Magnetic Resonance Spectroscopy (MRS), and evaluated their potential as biomarkers for disease severity and neurodegeneration in ALD. We used a comprehensive protocol of 3T Magnetic Resonance Spectroscopic Imaging (MRSI) and 7T Single Voxel Spectroscopy (SVS) in a large cohort of adult ALD males without cerebral demyelination. One hundred seven baseline scans - 59 obtained in ALD patients (42 3T MRSI and 17 7T SVS) and 48 obtained in healthy male controls (32 3T MRSI and 16 7T SVS) - and 82 one and two-year follow-up scans (66 3T MRSI and 16 7T SVS) of ALD patients were included. Both protocols showed significantly lower concentration ratios of N-acetylaspartate/creatine (tNAA/tCr) and Glx (glutamine + glutamate)/tCr in the grey and white matter of patients, compared to controls. A novel finding is the higher level of inositol (Ins)/tCr and choline containing compounds (tCho)/tCr in ALD patients without cerebral demyelination. Furthermore, tNAA/tCr correlated strongly with clinical measures of severity of myelopathy. There was no detectable change in metabolite ratios after one-year or two-year follow-up. Our results imply that cerebral metabolite levels - and more specifically the tNAA/tCr ratio - measured with MRS, have potential value as (imaging) biomarkers in ALD., (Copyright © 2021. Published by Elsevier Inc.)

Background: Classical Galactosemia (CG) is an inherited disorder of galactose metabolism caused by a deficiency of the galactose-1-phosphate uridylyltransferase (GALT) enzyme resulting in neurocognitive complications. As in many Inborn Errors of Metabolism, the metabolic pathway of CG is well-defined, but the pathophysiology and high variability in clinical outcome are poorly understood. The aim of this study was to investigate structural changes of the brain of CG patients on MRI and their association with clinical outcome., Methods: In this prospective cohort study an MRI protocol was developed to evaluate gray matter (GM) and white matter (WM) volume of the cerebrum and cerebellum, WM hyperintensity volume, WM microstructure and myelin content with the use of conventional MRI techniques, diffusion tensor imaging (DTI) and quantitative T1 mapping. The association between several neuroimaging parameters and both neurological and intellectual outcome was investigated., Results: Twenty-one patients with CG (median age 22 years, range 8-47) and 24 controls (median age 30, range 16-52) were included. Compared to controls, the WM of CG patients was lower in volume and the microstructure of WM was impaired both in the whole brain and corticospinal tract (CST) and the lower R1 values of WM, GM and the CST were indicative of less myelin. The volume of WM lesions were comparable between patients and controls. The 9/16 patients with a poor neurological outcome (defined as the presence of a tremor and/or dystonia), demonstrated a lower WM volume, an impaired WM microstructure and lower R1 values of the WM indicative of less myelin content compared to 7/16 patients without movement disorders. In 15/21 patients with a poor intellectual outcome (defined as an IQ < 85) both GM and WM were affected with a lower cerebral and cerebellar WM and GM volume compared to 6/21 patients with an IQ ≥ 85. Both the severity of the tremor (as indicated by the Tremor Rating Scale) and IQ (as continuous measure) were associated with several neuroimaging parameters such as GM volume, WM volume, CSF volume, WM microstructure parameters and R1 values of GM and WM., Conclusion: In this explorative study performed in patients with Classical Galactosemia, not only WM but also GM pathology was found, with more severe brain abnormalities on MRI in patients with a poor neurological and intellectual outcome. The finding that structural changes of the brain were associated with the severity of long-term complications indicates that quantitative MRI techniques could be of use to explain neurological and cognitive dysfunction as part of the disease spectrum. Based on the clinical outcome of patients, the absence of widespread WM lesions and the finding that both GM and WM are affected, CG could be primarily a GM disease with secondary damage to the WM as a result of neuronal degeneration. To investigate this further the course of GM and WM should be evaluated in longitudinal research, which could also clarify if CG is a neurodegenerative disease., Competing Interests: Declaration of Competing Interest Mendy M. Welsink-Karssies, Anouk G.M. Schrantee, Mirian C.H. Janssen, Esmee Oussoren, Stefan D. Roosendaal, Marc Engelen and Maaike de Vries declare they have no conflict of interest. Matthan W.A. Caan is shareholder or Nico.lab Ltd. Carla E.M. Hollak is involved in premarketing studies with Sanofi, Protalix and Idorsia in the field of lysosomal storage disorders. She reports no conflicts of interest in relation to the current study. Annet M. Bosch was member of an advisory board of Biomarin., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Sub-millimeter imaging at 7T has opened new possibilities for qualitatively and quantitatively studying brain structure as it evolves throughout the life span. However, subject motion introduces image blurring on the order of magnitude of the spatial resolution and is thus detrimental to image quality. Such motion can be corrected for, but widespread application has not yet been achieved and quantitative evaluation is lacking. This raises a need to quantitatively measure image sharpness throughout the brain. We propose a method to quantify sharpness of brain structures at sub-voxel resolution, and use it to assess to what extent limited motion is related to image sharpness. The method was evaluated in a cohort of 24 healthy volunteers with a wide and uniform age range, aiming to arrive at results that largely generalize to larger populations. Using 3D fat-excited motion navigators, quantitative R 1 , R 2 * and Quantitative Susceptibility Maps and T 1 -weighted images were retrospectively corrected for motion. Sharpness was quantified in all modalities for selected regions of interest (ROI) by fitting the sigmoidally shaped error function to data within locally homogeneous clusters. A strong, almost linear correlation between motion and sharpness improvement was observed, and motion correction significantly improved sharpness. Overall, the Full Width at Half Maximum reduced from 0.88 mm to 0.70 mm after motion correction, equivalent to a 2.0 times smaller voxel volume. Motion and sharpness were not found to correlate with the age of study participants. We conclude that in our data, motion correction using fat navigators is overall able to restore the measured sharpness to the imaging resolution, irrespective of the amount of motion observed during scanning., Competing Interests: Declaration of Competing Interest M.W.A. Caan is shareholder of Nico-lab Ltd., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

Cerebral white matter hyperintensities (WMH) persist in children and adults living with HIV, despite effective combination antiretroviral therapy (cART). As age and principal routes of transmission differ between children (perinatally) and adults (behaviorally), comparing the characteristics and determinants of WMH between these populations may increase our understanding of the pathophysiology of WMH. From separate cohorts of 31 children (NOVICE) and 74 adults (AGEhIV), we cross-sectionally assessed total WMH volume and number of WMH per location (periventricular vs. deep) using fluid-attenuated inversion recovery (FLAIR) MRI images. WMH were either periventricular when within 10mm of the lateral ventricles, or deep otherwise. We assessed patient- or HIV-related determinants of total WMH volume (adjusted for intracranial volume) and location of WMH using logistic regression, while stratifying on children and adults. At enrollment, median age of participants was 13.8 years (IQR 11.4-15.9) for children and 53.4 years (IQR 48.3-60.8) for adults and 27/31 children (87%) and 74/74 adults (100%) had an HIV RNA viral load <200 copies/mL. WMH were present in 16/27 (52%) children and 74/74 adults (100%). The prevalence of deep WMH was not different between groups, (16/16 [100%] in children vs. 71/74 [96%] in adults, p = 0,999), yet periventricular WMH were more prevalent in adults (74/74 [100%]) compared to children (9/16; 56%) (p<0.001). Median WMH volume was higher in adults compared to children (1182 mm3 [425-2617] vs. 109 mm3 [61.7-625], p<0.001). In children, boys were more likely to have deep WMH compared to girls. In adults, older age was associated with higher total WMH volume, and age, hypertension and lower CD4+ T-lymphocyte nadir with a higher number of periventricular WMH. Our findings suggest that the location of WMH differs between children and adults living with HIV, hinting at a different underlying pathogenesis., Competing Interests: M.W.A. Caan is a shareholder of Nico-lab Inc, which is a potential conflict of interest as this organization develops AI supporting radiological stroke workup. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Arterial spin labeling (ASL) has undergone significant development since its inception, with a focus on improving standardization and reproducibility of its acquisition and quantification. In a community-wide effort towards robust and reproducible clinical ASL image processing, we developed the software package ExploreASL, allowing standardized analyses across centers and scanners. The procedures used in ExploreASL capitalize on published image processing advancements and address the challenges of multi-center datasets with scanner-specific processing and artifact reduction to limit patient exclusion. ExploreASL is self-contained, written in MATLAB and based on Statistical Parameter Mapping (SPM) and runs on multiple operating systems. To facilitate collaboration and data-exchange, the toolbox follows several standards and recommendations for data structure, provenance, and best analysis practice. ExploreASL was iteratively refined and tested in the analysis of >10,000 ASL scans using different pulse-sequences in a variety of clinical populations, resulting in four processing modules: Import, Structural, ASL, and Population that perform tasks, respectively, for data curation, structural and ASL image processing and quality control, and finally preparing the results for statistical analyses on both single-subject and group level. We illustrate ExploreASL processing results from three cohorts: perinatally HIV-infected children, healthy adults, and elderly at risk for neurodegenerative disease. We show the reproducibility for each cohort when processed at different centers with different operating systems and MATLAB versions, and its effects on the quantification of gray matter cerebral blood flow. ExploreASL facilitates the standardization of image processing and quality control, allowing the pooling of cohorts which may increase statistical power and discover between-group perfusion differences. Ultimately, this workflow may advance ASL for wider adoption in clinical studies, trials, and practice., 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., (Copyright © 2020. Published by Elsevier Inc.)

Background: Several studies have reported improved cognitive outcomes after kidney transplantation, but most studies either did not include controls or lacked extensive neuroimaging. In addition, there is uncertainty whether kidney donation is a safe procedure in terms of cognitive outcomes., Methods: We prospectively studied neurocognitive function in kidney transplant recipients. The primary outcome was change in neurocognitive function after 1 year compared with baseline, which was evaluated using the Amsterdam Neuropsychological Task battery and verbal fluency tests. Secondary outcomes included changes in depression and anxiety (measured by the Hospital Anxiety and Depression scale) and changes in fatigue (measured by the Checklist for Individual Strength). We included kidney donors to control for learning effects, socioeconomic status, and surgery. In addition, kidney transplant recipients were evaluated with MRI scans at baseline and at year 1. The MRI protocol included conventional MRI, automated volumetric measurement, diffusion tensor imaging, magnetic resonance spectroscopy, arterial spin labeling, and a resting state functional MRI., Results: Twenty-seven recipients and 24 donors were included. For both recipients and donors, neuropsychologic testing scores improved 1 year after transplantation (donation). Recipient improvement significantly exceeded donor improvement on tasks measuring attention and working memory. These improvements were associated with increases in white matter volume and N -acetylaspartate/creatine (a marker for neuronal integrity)., Conclusions: Attention and working memory improve significantly 1 year after kidney transplantation. Learning effects do not account for these improvements because recipient improvement in these areas exceeds donor improvement and correlates with an improvement in white matter integrity after transplantation. Kidney donation appears to be a safe procedure in terms of cognitive outcomes., (Copyright © 2020 The Author(s). Transplantation Direct. Published by Wolters Kluwer Health, Inc.)

Objective: To assess and compare the diagnostic performance of qualitative and (semi-)quantitative MRI and ultrasound for distinguishing chronic inflammatory demyelinating polyneuropathy (CIDP) and multifocal motor neuropathy (MMN) from segmental spinal muscular atrophy (sSMA)., Methods: Patients with CIDP (n = 13), MMN (n = 10), or sSMA (n = 12) and healthy volunteers (n = 30) were included. MRI of the brachial plexus, using short tau inversion recovery (STIR), nerve-specific T2-weighted (magnetic resonance neurography [MRN]), and diffusion tensor imaging (DTI) sequences, was evaluated. Furthermore, with ultrasound, cross-sectional areas of the nerves were evaluated. Three radiologists blinded for diagnosis qualitatively scored hypertrophy and increased signal intensity (STIR and MRN), and intraobserver and interobserver agreement was assessed. For the (semi-)quantitative modalities, group differences and receiver operator characteristics were calculated., Results: Hypertrophy and increased signal intensity were found in all groups including healthy controls. Intraobserver and interobserver agreements varied considerably (intraclass correlation coefficients 0.00-0.811 and 0.101-0.491, respectively). DTI showed significant differences ( p < 0.05) among CIDP, MMN, sSMA, and controls for fractional anisotropy, axial diffusivity, and radial diffusivity in the brachial plexus. Ultrasound showed significant differences in cross-sectional area ( p < 0.05) among CIDP, MMN, and sSMA in upper arm and brachial plexus. For distinguishing immune-mediated neuropathies (CIDP and MMN) from sSMA, ultrasound yielded the highest area under the curve (0.870)., Conclusion: Qualitative assessment of hypertrophy and signal hyperintensity on STIR or MRN is of limited value. DTI measures may discriminate among CIDP, MMN, and sSMA. Currently, ultrasound may be the most appropriate diagnostic imaging aid in the clinical setting., (© 2019 American Academy of Neurology.)

Background: Deep brain stimulation (DBS) is an innovative treatment for treatment-refractory depression. DBS is usually targeted at specific anatomical landmarks, with patients responding to DBS in approximately 50% of cases. Attention has recently shifted to white matter tracts to explain DBS response, with initial open-label trials targeting white matter tracts yielding much higher response rates (>70%)., Objective/hypothesis: Our aim was to associate distance to individual white matter tracts around the stimulation target in the ventral anterior limb of the internal capsule to treatment response., Methods: We performed diffusion magnetic resonance tractography of the superolateral branch of the medial forebrain bundle and the anterior thalamic radiation in fourteen patients that participated in our randomized clinical trial. We combined the tract reconstructions with the postoperative images to identify the DBS leads and estimated the distance between tracts and leads, which we subsequently associated with treatment response., Results: Stimulation closer to both tracts was significantly correlated to a larger symptom decrease (r = 0.61, p = 0.02), suggesting that stimulation more proximal to the tracts was beneficial. Biophysical modelling indicated that 37.5% of tracts were even outside the volume of activated tissue. There was no difference in lead placement with respect to anatomical landmarks, which could mean that differences in treatment response were driven by individual differences in white matter anatomy., Conclusions: Our results suggest that deep brain stimulation of the ventral anterior limb of the internal capsule could benefit from targeting white matter bundles. We recommend acquiring diffusion magnetic resonance data for each individual patient., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Given the restricted total scanning time for clinical neuroimaging, it is unclear whether clinical diffusion MRI protocols would benefit more from higher spatial resolution or higher angular resolution. In this work, we investigated the relative benefit of improving spatial or angular resolution in diffusion MRI to separate two parallel running white matter tracts that are targets for deep brain stimulation: the anterior thalamic radiation and the supero-lateral branch of the medial forebrain bundle. Both these tracts are situated in the ventral anterior limb of the internal capsule, and recent studies suggest that targeting a specific tract could improve treatment efficacy. Therefore, we scanned 19 healthy volunteers at 3T and 7T according to three diffusion MRI protocols with respectively standard clinical settings, increased spatial resolution of 1.4 mm, and increased angular resolution (64 additional gradient directions at b = 2200s/mm 2 ). We performed probabilistic tractography for all protocols and quantified the separability of both tracts. The higher spatial resolution protocol improved separability by 41% with respect to the clinical standard, presumably due to decreased partial voluming. The higher angular resolution protocol resulted in increased apparent tract volumes and overlap, which is disadvantageous for application in precise treatment planning. We thus recommend to increase the spatial resolution for deep brain stimulation planning to 1.4 mm while maintaining angular resolution. This recommendation complements the general advice to aim for high angular resolution to resolve crossing fibers, confirming that the specific application and anatomical considerations are leading in clinical diffusion MRI protocol optimization., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Psychotic disorders are characterized by prominent deficits in associative learning and memory for which there are currently no effective treatments. Functional magnetic resonance imaging (fMRI) studies in psychotic disorders have identified deficits in fronto-temporal activation during associative learning and memory. The underlying pathology of these findings remains unclear. Postmortem data have suggested these deficits may be related to loss of muscarinic M 1 receptor mediated signaling. This is supported by an in-vivo study showing improvements in these symptoms after treatment with the experimental M 1/4 receptor agonist xanomeline. The current study tests whether reported deficits in fronto-temporal activation could be mediated by loss of M 1 receptor signaling in psychotic disorders., Methods: Twenty-six medication-free subjects diagnosed with a psychotic disorder and 29 age-, gender-, and IQ-matched healthy controls underwent two functional magnetic resonance imaging (fMRI) sessions, one under placebo and one under selective M 1 antagonist biperiden, while performing the paired associated learning task. M 1 binding potentials (BP ND ) were measured in the dorsolateral prefrontal cortex (DLPFC) and hippocampus using 123 I-IDEX single photon emission computed tomography., Results: In the subjects with psychotic disorders DLPFC hypoactivation was only found in the memory phase of the task. In both learning and memory phases of the task, M 1 antagonism by biperiden elicited significantly greater hyperactivation of the parahippocampal gyrus and superior temporal gyrus in subjects with a psychotic disorders compared to controls. Greater hyperactivation of these areas after biperiden was associated with greater hippocampal M 1 receptor binding during learning, with no association found with M 1 receptor binding in the DLPFC. M 1 receptor binding in the DLPFC was related to greater functional sensitivity to biperiden of the cingulate gyrus during the memory phase., Conclusion: The current study is the first to show differences in M 1 receptor mediated functional sensitivity between subjects with a psychotic disorder and controls during a paired associate learning and memory task. Results point to subjects with psychotic disorders having a loss of M 1 receptor reserve in temporal-limbic areas., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Objective: To prospectively determine the potential of diffusion MRI (dMRI) of the cervical spinal cord and the corticospinal tracts in brain as surrogate outcome measure for progression of myelopathy in men with adrenoleukodystrophy, as better outcome measures to quantify progression of myelopathy would enable clinical trials with fewer patients and shorter follow-up., Methods: Clinical assessment of myelopathy included Expanded Disability Status Scale (EDSS), Severity Scoring System for Progressive Myelopathy (SSPROM), Timed Up-and-Go, and 6-Minute Walk Test. Applied dMRI metrics included fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity., Results: Data were available for 33 controls and 52 patients. First, cross-sectionally, differences between groups (controls vs patients; controls vs asymptomatic patients vs symptomatic patients) were statistically significant for fractional anisotropy, mean diffusivity, and radial diffusivity in spinal cord and brain corticospinal tracts (effect size 0.31-0.68). Correlations between dMRI metrics and clinical measures were moderate to strong (correlation coefficient 0.35-0.60). Second, longitudinally (n = 36), change on clinical measures was significant after 2-year follow-up for EDSS, SSPROM, and Timed Up-and-Go ( p ≤ 0.021, effect size ≤0.14). Change on brain fractional anisotropy and radial diffusivity was slightly larger ( p ≤ 0.002, effect sizes 0.16-0.28). In addition, a statistically significant change was detectable in asymptomatic patients using brain dMRI and not using the clinical measures. Change on clinical measures did not correlate to change on dMRI metrics., Conclusion: Although effect sizes were small, our prospective data illustrate the potential of dMRI as surrogate outcome measure for progression of myelopathy in men with adrenoleukodystrophy., (© 2019 American Academy of Neurology.)

Early nutritional deprivation may cause irreversible damage to the brain and seems to affect cognitive function in older age. We investigated whether prenatal undernutrition was associated with brain perfusion differences in older age. We acquired Arterial spin labeling scans in 118 Dutch famine birth cohort members. Using linear regression analyses, cerebral blood flow was compared between exposed and unexposed groups in gray matter (GM) and white matter (WM), perfusion territories, the neurodegeneration-related regions anterior and posterior cingulate cortex and precuneus. Furthermore, we compared the GM/WM ratio and the spatial coefficient of variation as a proxy of overall cerebrovascular health. The WM arterial spin labeling signal and the GM/WM ratio were significantly lower and higher, respectively, among exposed participants (-2.5 mL/100 g/min [95% CI: -4.3 to -0.8; p = 0.01] and 0.48 [0.19 to 0.76; p = 0.002], respectively). Exposed men had lower cerebral blood flow in anterior and posterior cingulate cortices (-8.0 mL/100 g/min [-15.1 to -0.9; p = 0.03]; -11.4 mL/100 g/min [-19.6 to -3.2; p = 0.02]) and higher spatial coefficient of variation (0.05 [0.00 to 0.09; p = 0.05]). The latter seemed largely mediated by higher 2h-glucose levels at age 50. Our findings suggest that prenatal undernutrition affects brain perfusion parameters providing further evidence for life-long effects of undernutrition during early brain development., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Objective: We aim to give an overview of the available evidence on brain structure and function in PHIV-infected patients (PHIV+) using long-term combination antiretroviral therapy (cART) and how differences change over time., Methods: We conducted an electronic search using MEDLINE, Embase, and PsycINFO. We used the following selection criteria: cohort and cross-sectional studies that reported on brain imaging differences between PHIV+ of all ages who used cART for at least six months before neuroimaging and HIV-negative controls. Two reviewers independently selected studies, performed data extraction, and assessed quality of studies., Results: After screening 1500 abstracts and 343 full-text articles, we identified 19 eligible articles. All included studies had a cross-sectional design and used MRI with different modalities: structural MRI (n = 7), diffusion tensor imaging (DTI) (n = 6), magnetic resonance spectroscopy (n = 5), arterial spin labeling (n = 1), and resting-state functional neuroimaging (n = 1). Studies showed considerable methodological limitations and heterogeneity, preventing us to perform meta-analyses. DTI data on white matter microstructure suggested poorer directional diffusion in cART-treated PHIV+ compared with controls. Other modalities were inconclusive., Conclusion: Evidence may suggest brain structure and function differences in the population of PHIV+ on long-term cART compared with the HIV-negative population. Because of a small study population, and considerable heterogeneity and methodological limitations, the extent of brain structure and function differences on neuroimaging between groups remains unknown., (© 2019 American Academy of Neurology.)

BackgroundMethylphenidate (MPH) is highly effective in treating attention-deficit/hyperactivity disorder (ADHD). However, not much is known about its effect on the development of human brain white matter (WM).PurposeTo determine whether MPH modulates WM microstructure in an age-dependent fashion in a randomized double-blind placebo-controlled trial (Effects of Psychotropic Medication on Brain Development-Methylphenidate, or ePOD-MPH) among ADHD referral centers between October 13, 2011, and June 15, 2015, by using diffusion-tensor imaging (DTI).Materials and MethodsIn this prospective study (NTR3103 and NL34509.000.10), 50 stimulant treatment-naive boys and 49 young adult men diagnosed with ADHD (all types) according to Diagnostic and Statistical Manual of Mental Disorders, 4th Edition criteria were randomized to undergo treatment with MPH or placebo for 16 weeks. Before and 1 week after treatment cessation, study participants underwent MRI, including DTI. The outcome measure was change in fractional anisotropy (FA), which was assessed in three regions of interest (ROIs), as well as in a voxel-based analysis in brain WM. Data were analyzed by using intention-to-treat linear mixed models for ROI analysis and a permutation-based method for voxel-based analysis with family-wise error correction.ResultsFifty boys ( n = 25 MPH group, n = 25 placebo group; age range, 10-12 years) and 48 men ( n = 24 MPH group, n = 24 placebo group; age range, 23-40 years) were included. ROI analysis of FA yielded no main effect of time in any of the conditions. However, voxel-based analysis revealed significant ( P < .05) time-by-medication-by-age interaction effects in several association tracts of the left hemisphere, as well as in the lateral aspect of the truncus of the corpus callosum, due to greater increase in FA (standardized effect size, 5.25) in MPH-treated boys. Similar changes were not present in boys receiving a placebo, nor in adult men.ConclusionFour months of treatment with methylphenidate affects specific tracts in brain white matter in boys with attention-deficit/hyperactivity disorder. These effects seem to be age dependent, because they were not observed in adults treated with methylphenidate.© RSNA, 2019 Online supplemental material is available for this article.