Publications in OpenAlex of which a co-author is affiliated to this organization
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| Title | DOI |
|---|---|
| https://doi.org/10.1016/s1474-4422(19)30232-7 | Case-mix, care pathways, and outcomes in patients with traumatic brain injury in CENTER-TBI: a European prospective, multicentre, longitudinal, cohort study |
| https://doi.org/10.1016/j.neuroimage.2016.12.064 | Longitudinal multiple sclerosis lesion segmentation: Resource and challenge |
| https://doi.org/10.1148/radiol.12110927 | Gliomas: Diffusion Kurtosis MR Imaging in Grading |
| https://doi.org/10.1016/j.ebiom.2020.102785 | Blood biomarkers on admission in acute traumatic brain injury: Relations to severity, CT findings and care path in the CENTER-TBI study |
| https://doi.org/10.1016/j.nicl.2015.05.003 | Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images |
| https://doi.org/10.1016/j.clineuro.2013.10.002 | Mindfulness based intervention in Parkinson's disease leads to structural brain changes on MRI |
| https://doi.org/10.1038/s41597-022-01875-5 | ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset |
| https://doi.org/10.1016/j.nicl.2019.101771 | Association of short-term cognitive decline and MCI-to-AD dementia conversion with CSF, MRI, amyloid- and 18F-FDG-PET imaging |
| https://doi.org/10.1038/s41597-022-01401-7 | A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms |
| https://doi.org/10.1186/s13195-017-0329-8 | Diffusion kurtosis imaging allows the early detection and longitudinal follow-up of amyloid-β-induced pathology |
| https://doi.org/10.1016/j.radonc.2019.05.010 | Benefits of deep learning for delineation of organs at risk in head and neck cancer |
| https://doi.org/10.3389/fnins.2020.00396 | Harmonization of Brain Diffusion MRI: Concepts and Methods |
| https://doi.org/10.1016/j.biopsych.2012.09.023 | Limbic and Callosal White Matter Changes in Euthymic Bipolar I Disorder: An Advanced Diffusion Magnetic Resonance Imaging Tractography Study |
| https://doi.org/10.1001/jamaneurol.2021.2120 | Pathological Computed Tomography Features Associated With Adverse Outcomes After Mild Traumatic Brain Injury |
| https://doi.org/10.1136/bmjopen-2019-030309 | Tolerogenic dendritic cell-based treatment for multiple sclerosis (MS): a harmonised study protocol for two phase I clinical trials comparing intradermal and intranodal cell administration |
| https://doi.org/10.3390/data5040089 | Emidec: A Database Usable for the Automatic Evaluation of Myocardial Infarction from Delayed-Enhancement Cardiac MRI |
| https://doi.org/10.1016/j.brainres.2013.07.034 | Regional gray matter volume differences and sex-hormone correlations as a function of menstrual cycle phase and hormonal contraceptives use |
| https://doi.org/10.1016/j.media.2019.101589 | Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning |
| https://doi.org/10.1002/alz.12970 | Pathogenesis of Alzheimer's disease: Involvement of the choroid plexus |
| https://doi.org/10.1155/2015/816404 | Mindfulness Training among Individuals with Parkinson’s Disease: Neurobehavioral Effects |
| https://doi.org/10.1089/neu.2018.6183 | Automatic Quantification of Computed Tomography Features in Acute Traumatic Brain Injury |
| https://doi.org/10.1002/brb3.518 | Reliable measurements of brain atrophy in individual patients with multiple sclerosis |
| https://doi.org/10.1021/acs.analchem.7b05215 | Spatial Systems Lipidomics Reveals Nonalcoholic Fatty Liver Disease Heterogeneity |
| https://doi.org/10.1177/1352458520941485 | COVID-19 in people with multiple sclerosis: A global data sharing initiative |
| https://doi.org/10.1371/journal.pone.0241373 | Variations in the Circle of Willis in a large population sample using 3D TOF angiography: The Tromsø Study |
| https://doi.org/10.1016/j.nicl.2020.102243 | Automated MRI volumetry as a diagnostic tool for Alzheimer's disease: Validation of icobrain dm |
| https://doi.org/10.1016/j.nicl.2021.102707 | icobrain ms 5.1: Combining unsupervised and supervised approaches for improving the detection of multiple sclerosis lesions |
| https://doi.org/10.1017/s0033291713002845 | White matter microstructural abnormalities in families multiply affected with bipolar I disorder: a diffusion tensor tractography study |
| https://doi.org/10.1016/j.media.2022.102706 | Factorizer: A scalable interpretable approach to context modeling for medical image segmentation |
| https://doi.org/10.1111/ene.15473 | Brain age as a surrogate marker for cognitive performance in multiple sclerosis |
| https://doi.org/10.3389/fnagi.2021.746982 | Inter- and Intra-Scanner Variability of Automated Brain Volumetry on Three Magnetic Resonance Imaging Systems in Alzheimer’s Disease and Controls |
| https://doi.org/10.1161/strokeaha.121.034444 | Prediction of Stroke Infarct Growth Rates by Baseline Perfusion Imaging |
| https://doi.org/10.1001/jamanetworkopen.2023.55800 | Artificial Intelligence Assistive Software Tool for Automated Detection and Quantification of Amyloid-Related Imaging Abnormalities |
| https://doi.org/10.1186/s13195-023-01373-9 | Subclinical epileptiform activity in the Alzheimer continuum: association with disease, cognition and detection method |
| https://doi.org/10.1515/bmt-2024-0396 | MedShapeNet – a large-scale dataset of 3D medical shapes for computer vision |
| https://doi.org/10.1038/s42003-023-05448-z | A data-driven network decomposition of the temporal, spatial, and spectral dynamics underpinning visual-verbal working memory processes |
| https://doi.org/10.1002/hbm.26476 | The spectral slope as a marker of excitation/inhibition ratio and cognitive functioning in multiple sclerosis |
| https://doi.org/10.1089/neu.2023.0553 | Imaging Findings in Acute Traumatic Brain Injury: a National Institute of Neurological Disorders and Stroke Common Data Element-Based Pictorial Review and Analysis of Over 4000 Admission Brain Computed Tomography Scans from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) Study |
| https://doi.org/10.1007/s10278-024-01099-6 | Robust Ensemble of Two Different Multimodal Approaches to Segment 3D Ischemic Stroke Segmentation Using Brain Tumor Representation Among Multiple Center Datasets |
| https://doi.org/10.1002/14651858.cd013712.pub2 | Trunk training following stroke |
| https://doi.org/10.1089/neu.2023.0029 | Prognostic Value of Serum Biomarkers in Patients With Moderate-Severe Traumatic Brain Injury, Differentiated by Marshall Computer Tomography Classification |
| https://doi.org/10.1016/j.eclinm.2024.102751 | Predicting recovery in patients with mild traumatic brain injury and a normal CT using serum biomarkers and diffusion tensor imaging (CENTER-TBI): an observational cohort study |
| https://doi.org/10.1016/j.msard.2022.104436 | Volumetric brain changes in MOGAD: A cross-sectional and longitudinal comparative analysis |
| https://doi.org/10.3389/fimmu.2024.1446748 | A future of AI-driven personalized care for people with multiple sclerosis |
| https://doi.org/10.1007/s00234-024-03280-8 | Towards validation in clinical routine: a comparative analysis of visual MTA ratings versus the automated ratio between inferior lateral ventricle and hippocampal volumes in Alzheimer’s disease diagnosis |
| https://doi.org/10.1007/978-1-4939-3118-7_8 | Strategies and Challenges in DTI Analysis |
| https://doi.org/10.1038/s41598-024-61798-6 | A deep learning model for brain segmentation across pediatric and adult populations |
| https://doi.org/10.1186/s13195-024-01491-y | Brain age as a biomarker for pathological versus healthy ageing – a REMEMBER study |
| https://doi.org/10.1038/s42003-024-07283-2 | Disrupted working memory event-related network dynamics in multiple sclerosis |
| https://doi.org/10.1111/ene.16526 | Treating to target in multiple sclerosis: Do we know how to measure whether we hit it? |
| https://doi.org/10.1016/j.tjpad.2025.100220 | Monitoring of amyloid related imaging abnormalities: SWI vs T2*-GRE |
| https://doi.org/10.1016/j.nicl.2016.07.002 | Automated detection of cerebral microbleeds in patients with traumatic brain injury |
| https://doi.org/10.1007/s11682-016-9665-8 | Recovery from chemotherapy-induced white matter changes in young breast cancer survivors? |
| https://doi.org/10.1002/glia.23053 | Interleukin-13 immune gene therapy prevents CNS inflammation and demyelination via alternative activation of microglia and macrophages |
| https://doi.org/10.1016/j.ridd.2012.07.030 | How does brain activation differ in children with unilateral cerebral palsy compared to typically developing children, during active and passive movements, and tactile stimulation? An fMRI study |
| https://doi.org/10.1016/j.dcn.2017.08.009 | Processing of structural neuroimaging data in young children: Bridging the gap between current practice and state-of-the-art methods |
| https://doi.org/10.1016/j.brainres.2015.07.045 | Stability of resting state networks in the female brain during hormonal changes and their relation to premenstrual symptoms |
| https://doi.org/10.3233/jad-150253 | Diffusion Kurtosis Imaging: A Possible MRI Biomarker for AD Diagnosis? |
| https://doi.org/10.1007/s00330-012-2572-5 | Does the use of hormonal contraceptives cause microstructural changes in cerebral white matter? Preliminary results of a DTI and tractography study |
| https://doi.org/10.3389/fnins.2017.00398 | Machine Learning Approach for Classifying Multiple Sclerosis Courses by Combining Clinical Data with Lesion Loads and Magnetic Resonance Metabolic Features |
| https://doi.org/10.1148/radiol.2018172468 | Measurement of Whole-Brain and Gray Matter Atrophy in Multiple Sclerosis: Assessment with MR Imaging |
| https://doi.org/10.1111/bdi.12073 | White matter differences in euthymic bipolar I disorder: a combined magnetic resonance imaging and diffusion tensor imaging voxel‐based study |
| https://doi.org/10.1016/j.brainres.2012.02.027 | Brain activation to cues predicting inescapable delay in adolescent Attention Deficit/Hyperactivity Disorder: An fMRI pilot study |
| https://doi.org/10.1016/j.ebiom.2021.103777 | Relationship of admission blood proteomic biomarkers levels to lesion type and lesion burden in traumatic brain injury: A CENTER-TBI study |
| https://doi.org/10.1088/0031-9155/57/8/2169 | A DTI-based model for TMS using the independent impedance method with frequency-dependent tissue parameters |
| https://doi.org/10.1001/jamanetworkopen.2021.0994 | Neuroanatomical Substrates and Symptoms Associated With Magnetic Resonance Imaging of Patients With Mild Traumatic Brain Injury |
| https://doi.org/10.3389/fnins.2016.00576 | Two Time Point MS Lesion Segmentation in Brain MRI: An Expectation-Maximization Framework |
| https://doi.org/10.1089/neu.2018.6061 | Central versus Local Radiological Reading of Acute Computed Tomography Characteristics in Multi-Center Traumatic Brain Injury Research |
| https://doi.org/10.3389/fnins.2017.00013 | Patch-Based Super-Resolution of MR Spectroscopic Images: Application to Multiple Sclerosis |
| https://doi.org/10.1016/j.neuroimage.2019.02.023 | High-resolution metabolic mapping of gliomas via patch-based super-resolution magnetic resonance spectroscopic imaging at 7T |
| https://doi.org/10.1016/j.brainres.2019.04.018 | Motor cortex metabolite alterations in amyotrophic lateral sclerosis assessed in vivo using edited and non-edited magnetic resonance spectroscopy |
| https://doi.org/10.1007/s00429-016-1193-1 | Reproducibility of hormone-driven regional grey matter volume changes in women using SPM8 and SPM12 |
| https://doi.org/10.1007/s00221-012-3128-2 | Frontoparietal involvement in passively guided shape and length discrimination: a comparison between subcortical stroke patients and healthy controls |
| https://doi.org/10.1177/1756286418823462 | Comparing longitudinal brain atrophy measurement techniques in a real-world multiple sclerosis clinical practice cohort: towards clinical integration? |
| https://doi.org/10.1016/j.neuroimage.2019.116050 | Sensorimotor cortex neurometabolite levels as correlate of motor performance in normal aging: evidence from a 1H-MRS study |
| https://doi.org/10.1007/978-1-4939-3118-7_2 | Introduction to Diffusion Tensor Imaging |
| https://doi.org/10.1093/jnci/djy009 | Brain Connectivity and Cognitive Flexibility in Nonirradiated Adult Survivors of Childhood Leukemia |
| https://doi.org/10.1002/hbm.25560 | Structural brain dynamics across reading development: A longitudinal |
| https://doi.org/10.3390/brainsci11091171 | A Novel Digital Care Management Platform to Monitor Clinical and Subclinical Disease Activity in Multiple Sclerosis |
| https://doi.org/10.1002/brb3.422 | Quantifying brain volumes for Multiple Sclerosis patients follow‐up in clinical practice – comparison of 1.5 and 3 Tesla magnetic resonance imaging |
| https://doi.org/10.1016/j.heliyon.2022.e08901 | MRI biomarkers for Alzheimer's disease: the impact of functional connectivity in the default mode network and structural connectivity between lobes on diagnostic accuracy |
| https://doi.org/10.1016/j.ridd.2013.02.017 | Does somatosensory discrimination activate different brain areas in children with unilateral cerebral palsy compared to typically developing children? An fMRI study |
| https://doi.org/10.1002/hbm.24082 | Advanced MR diffusion imaging and chemotherapy‐related changes in cerebral white matter microstructure of survivors of childhood bone and soft tissue sarcoma? |
| https://doi.org/10.3389/fnhum.2020.00143 | Investigating the Added Value of FreeSurfer’s Manual Editing Procedure for the Study of the Reading Network in a Pediatric Population |
| https://doi.org/10.1088/1741-2552/ac4084 | A multi-step blind source separation approach for the attenuation of artifacts in mobile high-density electroencephalography data |
| https://doi.org/10.3233/jad-210450 | Diagnostic Performance of Automated MRI Volumetry by icobrain dm for Alzheimer’s Disease in a Clinical Setting: A REMEMBER Study |
| https://doi.org/10.3389/fnins.2021.708196 | A Contrast Augmentation Approach to Improve Multi-Scanner Generalization in MRI |
| https://doi.org/10.3390/jpm11121349 | Towards Multimodal Machine Learning Prediction of Individual Cognitive Evolution in Multiple Sclerosis |
| https://doi.org/10.1186/1745-6215-15-37 | Fluoxetine in Progressive Multiple Sclerosis (FLUOX-PMS): study protocol for a randomized controlled trial |
| https://doi.org/10.1016/j.nicl.2020.102327 | Non-invasive characterization of amyotrophic lateral sclerosis in a hTDP-43A315T mouse model: A PET-MR study |
| https://doi.org/10.1016/j.nicl.2018.05.030 | Evaluation of methods for volumetric analysis of pediatric brain data: The childmetrix pipeline versus adult-based approaches |
| https://doi.org/10.1016/j.msard.2020.102543 | Long-term effectiveness of natalizumab on MRI outcomes and no evidence of disease activity in relapsing-remitting multiple sclerosis patients treated in a Czech Republic real-world setting: A longitudinal, retrospective study |
| https://doi.org/10.1016/b978-0-12-824447-0.00016-9 | Coupled tensor decompositions for data fusion |
| https://doi.org/10.1016/j.bandc.2020.105614 | The association between MRI brain volumes and computerized cognitive scores of people with multiple sclerosis |
| https://doi.org/10.1016/j.media.2020.101833 | Theoretical analysis and experimental validation of volume bias of soft Dice optimized segmentation maps in the context of inherent uncertainty |
| https://doi.org/10.1007/978-1-4939-3118-7_10 | DTI Analysis Methods: Voxel-Based Analysis |
| https://doi.org/10.1007/978-3-030-72084-1_13 | Unsupervised 3D Brain Anomaly Detection |
| https://doi.org/10.1016/j.nicl.2018.05.018 | Callosal circularity as an early marker for Alzheimer's disease |
| https://doi.org/10.3390/a14080249 | Myocardial Infarction Quantification from Late Gadolinium Enhancement MRI Using Top-Hat Transforms and Neural Networks |
| https://doi.org/10.1007/978-3-030-46640-4_9 | Optimization with Soft Dice Can Lead to a Volumetric Bias |