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Romascano D, Rebsamen M, Radojewski P, Blattner T, McKinley R, Wiest R, Rummel C. Cortical thickness and grey-matter volume anomaly detection in individual MRI scans: Comparison of two methods. Neuroimage Clin 2024; 43:103624. [PMID: 38823248 PMCID: PMC11168488 DOI: 10.1016/j.nicl.2024.103624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 05/21/2024] [Accepted: 05/25/2024] [Indexed: 06/03/2024]
Abstract
Over the past decades, morphometric analysis of brain MRI has contributed substantially to the understanding of healthy brain structure, development and aging as well as to improved characterisation of disease related pathologies. Certified commercial tools based on normative modeling of these metrics are meanwhile available for diagnostic purposes, but they are cost intensive and their clinical evaluation is still in its infancy. Here we have compared the performance of "ScanOMetrics", an open-source research-level tool for detection of statistical anomalies in individual MRI scans, depending on whether it is operated on the output of FreeSurfer or of the deep learning based brain morphometry tool DL + DiReCT. When applied to the public OASIS3 dataset, containing patients with Alzheimer's disease (AD) and healthy controls (HC), cortical thickness anomalies in patient scans were mainly detected in regions that are known as predilection areas of cortical atrophy in AD, regardless of the software used for extraction of the metrics. By contrast, anomaly detections in HCs were up to twenty-fold reduced and spatially unspecific using both DL + DiReCT and FreeSurfer. Progression of the atrophy pattern with clinical dementia rating (CDR) was clearly observable with both methods. DL + DiReCT provided results in less than 25 min, more than 15 times faster than FreeSurfer. This difference in computation time might be relevant when considering application of this or similar methodology as diagnostic decision support for neuroradiologists.
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Affiliation(s)
- David Romascano
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University Hospital Bern, CH-3010 Bern, Switzerland; Danish Research Center for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark
| | - Michael Rebsamen
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University Hospital Bern, CH-3010 Bern, Switzerland
| | - Piotr Radojewski
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University Hospital Bern, CH-3010 Bern, Switzerland; Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, sitem-insel, Bern, Switzerland
| | - Timo Blattner
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University Hospital Bern, CH-3010 Bern, Switzerland
| | - Richard McKinley
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University Hospital Bern, CH-3010 Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University Hospital Bern, CH-3010 Bern, Switzerland; Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, sitem-insel, Bern, Switzerland
| | - Christian Rummel
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University Hospital Bern, CH-3010 Bern, Switzerland; European Campus Rottal-Inn, Technische Hochschule Deggendorf, Max-Breiherr-Straße 32, D-84347 Pfarrkirchen, Germany.
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Rebsamen M, Capiglioni M, Hoepner R, Salmen A, Wiest R, Radojewski P, Rummel C. Growing importance of brain morphometry analysis in the clinical routine: The hidden impact of MR sequence parameters. J Neuroradiol 2024; 51:5-9. [PMID: 37116782 DOI: 10.1016/j.neurad.2023.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 04/19/2023] [Accepted: 04/19/2023] [Indexed: 04/30/2023]
Abstract
Volumetric assessment based on structural MRI is increasingly recognized as an auxiliary tool to visual reading, also in examinations acquired in the clinical routine. However, MRI acquisition parameters can significantly influence these measures, which must be considered when interpreting the results on an individual patient level. This Technical Note shall demonstrate the problem. Using data from a dedicated experiment, we show the influence of two crucial sequence parameters on the GM/WM contrast and their impact on the measured volumes. A simulated contrast derived from acquisition parameters TI/TR may serve as surrogate and is highly correlated (r=0.96) with the measured contrast.
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Affiliation(s)
- Michael Rebsamen
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland; Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland
| | - Milena Capiglioni
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland; Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland
| | - Robert Hoepner
- Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Anke Salmen
- Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland; Swiss Institute for Translational and Entrepreneurial Medicine, sitem-insel, Bern, Switzerland
| | - Piotr Radojewski
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland; Swiss Institute for Translational and Entrepreneurial Medicine, sitem-insel, Bern, Switzerland
| | - Christian Rummel
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland.
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Bou Assi E, Schindler K, de Bézenac C, Denison T, Desai S, Keller SS, Lemoine É, Rahimi A, Shoaran M, Rummel C. From basic sciences and engineering to epileptology: A translational approach. Epilepsia 2023; 64 Suppl 3:S72-S84. [PMID: 36861368 DOI: 10.1111/epi.17566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 02/23/2023] [Accepted: 02/28/2023] [Indexed: 03/03/2023]
Abstract
Collaborative efforts between basic scientists, engineers, and clinicians are enabling translational epileptology. In this article, we summarize the recent advances presented at the International Conference for Technology and Analysis of Seizures (ICTALS 2022): (1) novel developments of structural magnetic resonance imaging; (2) latest electroencephalography signal-processing applications; (3) big data for the development of clinical tools; (4) the emerging field of hyperdimensional computing; (5) the new generation of artificial intelligence (AI)-enabled neuroprostheses; and (6) the use of collaborative platforms to facilitate epilepsy research translation. We highlight the promise of AI reported in recent investigations and the need for multicenter data-sharing initiatives.
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Affiliation(s)
- Elie Bou Assi
- Department of Neuroscience, Université de Montréal, Montréal, Canada
- Centre de Recherche du CHUM (CRCHUM), Montréal, Canada
| | - Kaspar Schindler
- Department of Neurology, Inselspital, Sleep-Wake-Epilepsy-Center, Bern University Hospital, Bern University, Bern, Switzerland
| | - Christophe de Bézenac
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
- The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Timothy Denison
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK
- Department of Engineering Science, University of Oxford, Oxford, UK
| | | | - Simon S Keller
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
- The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Émile Lemoine
- Centre de Recherche du CHUM (CRCHUM), Montréal, Canada
- Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, Canada
| | | | - Mahsa Shoaran
- Institute of Electrical and Micro Engineering, Neuro-X Institute, EPFL, Lausanne, Switzerland
| | - Christian Rummel
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
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Schetter M, Romascano D, Gaujard M, Rummel C, Denervaud S. Learning by Heart or with Heart: Brain Asymmetry Reflects Pedagogical Practices. Brain Sci 2023; 13:1270. [PMID: 37759871 PMCID: PMC10526483 DOI: 10.3390/brainsci13091270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 08/14/2023] [Accepted: 08/24/2023] [Indexed: 09/29/2023] Open
Abstract
Brain hemispheres develop rather symmetrically, except in the case of pathology or intense training. As school experience is a form of training, the current study tested the influence of pedagogy on morphological development through the cortical thickness (CTh) asymmetry index (AI). First, we compared the CTh AI of 111 students aged 4 to 18 with 77 adults aged > 20. Second, we investigated the CTh AI of the students as a function of schooling background (Montessori or traditional). At the whole-brain level, CTh AI was not different between the adult and student groups, even when controlling for age. However, pedagogical experience was found to impact CTh AI in the temporal lobe, within the parahippocampal (PHC) region. The PHC region has a functional lateralization, with the right PHC region having a stronger involvement in spatiotemporal context encoding, while the left PHC region is involved in semantic encoding. We observed CTh asymmetry toward the left PHC region for participants enrolled in Montessori schools and toward the right for participants enrolled in traditional schools. As these participants were matched on age, intelligence, home-life and socioeconomic conditions, we interpret this effect found in memory-related brain regions to reflect differences in learning strategies. Pedagogy modulates how new concepts are encoded, with possible long-term effects on knowledge transfer.
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Affiliation(s)
- Martin Schetter
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital, University of Lausanne, 1005 Lausanne, Switzerland
| | - David Romascano
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital, University of Lausanne, 1005 Lausanne, Switzerland
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital—Bern University Hospital, University of Bern, 3010 Bern, Switzerland
| | - Mathilde Gaujard
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital, University of Lausanne, 1005 Lausanne, Switzerland
| | - Christian Rummel
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital—Bern University Hospital, University of Bern, 3010 Bern, Switzerland
| | - Solange Denervaud
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital, University of Lausanne, 1005 Lausanne, Switzerland
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Rebsamen M, Radojewski P, McKinley R, Reyes M, Wiest R, Rummel C. A Quantitative Imaging Biomarker Supporting Radiological Assessment of Hippocampal Sclerosis Derived From Deep Learning-Based Segmentation of T1w-MRI. Front Neurol 2022; 13:812432. [PMID: 35250818 PMCID: PMC8894898 DOI: 10.3389/fneur.2022.812432] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 01/06/2022] [Indexed: 11/13/2022] Open
Abstract
PurposeHippocampal volumetry is an important biomarker to quantify atrophy in patients with mesial temporal lobe epilepsy. We investigate the sensitivity of automated segmentation methods to support radiological assessments of hippocampal sclerosis (HS). Results from FreeSurfer and FSL-FIRST are contrasted to a deep learning (DL)-based segmentation method.Materials and MethodsWe used T1-weighted MRI scans from 105 patients with epilepsy and 354 healthy controls. FreeSurfer, FSL, and a DL-based method were applied for brain anatomy segmentation. We calculated effect sizes (Cohen's d) between left/right HS and healthy controls based on the asymmetry of hippocampal volumes. Additionally, we derived 14 shape features from the segmentations and determined the most discriminating feature to identify patients with hippocampal sclerosis by a support vector machine (SVM).ResultsDeep learning-based segmentation of the hippocampus was the most sensitive to detecting HS. The effect sizes of the volume asymmetries were larger with the DL-based segmentations (HS left d= −4.2, right = 4.2) than with FreeSurfer (left= −3.1, right = 3.7) and FSL (left= −2.3, right = 2.5). For the classification based on the shape features, the surface-to-volume ratio was identified as the most important feature. Its absolute asymmetry yielded a higher area under the curve (AUC) for the deep learning-based segmentation (AUC = 0.87) than for FreeSurfer (0.85) and FSL (0.78) to dichotomize HS from other epilepsy cases. The robustness estimated from repeated scans was statistically significantly higher with DL than all other methods.ConclusionOur findings suggest that deep learning-based segmentation methods yield a higher sensitivity to quantify hippocampal sclerosis than atlas-based methods and derived shape features are more robust. We propose an increased asymmetry in the surface-to-volume ratio of the hippocampus as an easy-to-interpret quantitative imaging biomarker for HS.
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Affiliation(s)
- Michael Rebsamen
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland
- *Correspondence: Michael Rebsamen
| | - Piotr Radojewski
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Swiss Institute for Translational and Entrepreneurial Medicine, sitem-insel, Bern, Switzerland
| | - Richard McKinley
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Mauricio Reyes
- ARTORG Center for Biomedical Research, University of Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Swiss Institute for Translational and Entrepreneurial Medicine, sitem-insel, Bern, Switzerland
| | - Christian Rummel
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
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David B, Kröll-Seger J, Schuch F, Wagner J, Wellmer J, Woermann F, Oehl B, Van Paesschen W, Breyer T, Becker A, Vatter H, Hattingen E, Urbach H, Weber B, Surges R, Elger CE, Huppertz HJ, Rüber T. External validation of automated focal cortical dysplasia detection using morphometric analysis. Epilepsia 2021; 62:1005-1021. [PMID: 33638457 DOI: 10.1111/epi.16853] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 02/02/2021] [Accepted: 02/02/2021] [Indexed: 12/27/2022]
Abstract
OBJECTIVE Focal cortical dysplasias (FCDs) are a common cause of drug-resistant focal epilepsy but frequently remain undetected by conventional magnetic resonance imaging (MRI) assessment. The visual detection can be facilitated by morphometric analysis of T1-weighted images, for example, using the Morphometric Analysis Program (v2018; MAP18), which was introduced in 2005, independently validated for its clinical benefits, and successfully integrated in standard presurgical workflows of numerous epilepsy centers worldwide. Here we aimed to develop an artificial neural network (ANN) classifier for robust automated detection of FCDs based on these morphometric maps and probe its generalization performance in a large, independent data set. METHODS In this retrospective study, we created a feed-forward ANN for FCD detection based on the morphometric output maps of MAP18. The ANN was trained and cross-validated on 113 patients (62 female, mean age ± SD =29.5 ± 13.6 years) with manually segmented FCDs and 362 healthy controls (161 female, mean age ± SD =30.2 ± 9.6 years) acquired on 13 different scanners. In addition, we validated the performance of the trained ANN on an independent, unseen data set of 60 FCD patients (28 female, mean age ± SD =30 ± 15.26 years) and 70 healthy controls (42 females, mean age ± SD = 40.0 ± 12.54 years). RESULTS In the cross-validation, the ANN achieved a sensitivity of 87.4% at a specificity of 85.4% on the training data set. On the independent validation data set, our method still reached a sensitivity of 81.0% at a comparably high specificity of 84.3%. SIGNIFICANCE Our method shows a robust automated detection of FCDs and performance generalizability, largely independent of scanning site or MR-sequence parameters. Taken together with the minimal input requirements of a standard T1 image, our approach constitutes a clinically viable and useful tool in the presurgical diagnostic routine for drug-resistant focal epilepsy.
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Affiliation(s)
- Bastian David
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
| | | | - Fabiane Schuch
- Department of Epileptology, University Hospital Bonn, Bonn, Germany.,Department of Neurology, St. Johannes Hospital Troisdorf, Germany
| | - Jan Wagner
- Department of Neurology, University Clinic Ulm, Ulm, Germany
| | - Jörg Wellmer
- Department of Neurology, Ruhr-Epileptology, University Hospital Knappschaftskrankenhaus, Ruhr-University, Bochum, Germany
| | - Friedrich Woermann
- Epilepsy Center Bethel, Mara Hospital & Society for Epilepsy Research, Bielefeld, Germany
| | | | - Wim Van Paesschen
- Laboratory for Epilepsy Research, Department of Neurology, University Hospitals and KU Leuven, Leuven, Belgium
| | - Tobias Breyer
- Department of Radiology and Neuroradiology, Klinikum Dortmund, Dortmund, Germany
| | - Albert Becker
- Department of Neuropathology, University Hospital Bonn, Bonn, Germany
| | - Hartmut Vatter
- Department of Neurosurgery, University Hospital Bonn, Bonn, Germany
| | - Elke Hattingen
- Department of Neuroradiology, Goethe-University Frankfurt, Frankfurt am Main, Germany
| | - Horst Urbach
- Department of Neuroradiology, University of Freiburg, Freiburg, Germany
| | - Bernd Weber
- Institute of Experimental Epileptology and Cognition Research, University Hospital Bonn, Bonn, Germany
| | - Rainer Surges
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
| | | | | | - Theodor Rüber
- Department of Epileptology, University Hospital Bonn, Bonn, Germany.,Department of Neurology, Epilepsy Center Frankfurt Rhine-Main, Goethe-University Frankfurt, Frankfurt am Main, Germany.,Center for Personalized Translational Epilepsy Research (CePTER), Goethe-University Frankfurt, Frankfurt am Main, Germany
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Dobrocky T, Rebsamen M, Rummel C, Häni L, Mordasini P, Raabe A, Ulrich CT, Gralla J, Piechowiak EI, Beck J. Monro-Kellie Hypothesis: Increase of Ventricular CSF Volume after Surgical Closure of a Spinal Dural Leak in Patients with Spontaneous Intracranial Hypotension. AJNR Am J Neuroradiol 2020; 41:2055-2061. [PMID: 33177057 DOI: 10.3174/ajnr.a6782] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 07/13/2020] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE CSF loss in spontaneous intracranial hypotension disrupts a well-regulated equilibrium. We aimed to evaluate the volume shift between intracranial compartments in patients with spontaneous intracranial hypotension before and after surgical closure of the underlying spinal dural breach. MATERIALS AND METHODS In total, 19 patients with spontaneous intracranial hypotension with a proved spinal CSF leak investigated at our institution between July 2014 and March 2017 (mean age, 41.8 years; 13 women) were included. Brain MR imaging-based volumetry at baseline and after surgery was performed with FreeSurfer. In addition, the spontaneous intracranial hypotension score, ranging from 0 to 9, with 0 indicating very low and 9 very high probability of spinal CSF loss, was calculated. RESULTS Total mean ventricular CSF volume significantly increased from baseline (15.3 mL) to posttreatment MR imaging (18.0 mL), resulting in a mean absolute and relative difference, +2.7 mL and +18.8% (95% CI, +1.2 to +3.9 mL; P < .001). The change was apparent in the early follow-up (mean, 4 days). No significant change in mean total brain volume was observed (1136.9 versus 1133.1 mL, P = .58). The mean spontaneous intracranial hypotension score decreased from 6.9 ± 1.5 at baseline to 2.9 ± 1.5 postoperatively. CONCLUSIONS Our study demonstrated a substantial increase in ventricular CSF volume in the early follow-up after surgical closure of the underlying spinal dural breach and may provide a causal link between spinal CSF loss and spontaneous intracranial hypotension. The concomitant decrease in the spontaneous intracranial hypotension score postoperatively implies the restoration of an equilibrium within the CSF compartment.
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Affiliation(s)
- T Dobrocky
- From the Department of Diagnostic and Interventional Neuroradiology (T.D., C.R., P.M., J.G., E.I.P.)
| | - M Rebsamen
- Support Center for Advanced Neuroimaging (M.R., C.R.)
| | - C Rummel
- From the Department of Diagnostic and Interventional Neuroradiology (T.D., C.R., P.M., J.G., E.I.P.).,Support Center for Advanced Neuroimaging (M.R., C.R.)
| | - L Häni
- Department of Neurosurgery (L.H., A.R., C.T.U., J.B.), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - P Mordasini
- From the Department of Diagnostic and Interventional Neuroradiology (T.D., C.R., P.M., J.G., E.I.P.)
| | - A Raabe
- Department of Neurosurgery (L.H., A.R., C.T.U., J.B.), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - C T Ulrich
- Department of Neurosurgery (L.H., A.R., C.T.U., J.B.), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - J Gralla
- From the Department of Diagnostic and Interventional Neuroradiology (T.D., C.R., P.M., J.G., E.I.P.)
| | - E I Piechowiak
- From the Department of Diagnostic and Interventional Neuroradiology (T.D., C.R., P.M., J.G., E.I.P.)
| | - J Beck
- Department of Neurosurgery (L.H., A.R., C.T.U., J.B.), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.,Department of Neurosurgery (J.B.), Medical Center-University of Freiburg, Freiburg, Germany
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Guo S, Xiao B, Wu C. Identifying subtypes of mild cognitive impairment from healthy aging based on multiple cortical features combined with volumetric measurements of the hippocampal subfields. Quant Imaging Med Surg 2020; 10:1477-1489. [PMID: 32676366 DOI: 10.21037/qims-19-872] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Background Mild cognitive impairment (MCI) is subtle cognitive decline with an estimated 10-15% yearly conversion rate toward Alzheimer's disease (AD). It remains unexplored in brain cortical association areas in different lobes and its changes with progression and conversion of MCI. Methods Brain structural magnetic resonance (MR) images were collected from 102 stable MCI (sMCI) patients. One hundred eleven were converted MCI (cMCI) patients, and 109 were normal control (NC). The cortical surface features and volumes of subcortical hippocampal subfields were calculated using the FreeSurfer software, followed by an analysis of variance (ANOVA) model, to reveal the differences between the NC-sMCI, NC-cMCI, and sMCI-cMCI groups. Afterward, the support vector machine-recursive feature elimination (SVM-RFE) method was applied to determine the differences between the groups. Results The experimental results showed that there were progressive degradations in either range or degree of the brain structure from NC to sMCI, and then to cMCI. The SVM classifier obtained accuracies with 64.62%, 78.96%, and 70.33% in the sMCI-NC, cMCI-NC, and cMCI-sMCI groups, respectively, using the volumes of hippocampal subfields independently. The combination of the volumes from the hippocampal subfields and cortical measurements could significantly increase the performance to 71.86%, 84.64%, and 76.86% for the sMCI-NC, cMCI-NC, and cMCI-sMCI classifications, respectively. Also, the brain regions corresponding to the dominant features with strong discriminative power were widely located in the temporal, frontal, parietal, olfactory cortexes, and most of the hippocampal subfields, which were associated with cognitive decline, memory impairment, spatial navigation, and attention control. Conclusions The combination of cortical features with the volumes of hippocampal subfields could supply critical information for MCI detection and its conversion.
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Affiliation(s)
- Shengwen Guo
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
| | - Benheng Xiao
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
| | - Congling Wu
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
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Rebsamen M, Suter Y, Wiest R, Reyes M, Rummel C. Brain Morphometry Estimation: From Hours to Seconds Using Deep Learning. Front Neurol 2020; 11:244. [PMID: 32322235 PMCID: PMC7156625 DOI: 10.3389/fneur.2020.00244] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Accepted: 03/13/2020] [Indexed: 12/13/2022] Open
Abstract
Motivation: Brain morphometry from magnetic resonance imaging (MRI) is a promising neuroimaging biomarker for the non-invasive diagnosis and monitoring of neurodegenerative and neurological disorders. Current tools for brain morphometry often come with a high computational burden, making them hard to use in clinical routine, where time is often an issue. We propose a deep learning-based approach to predict the volumes of anatomically delineated subcortical regions of interest (ROI), and mean thicknesses and curvatures of cortical parcellations directly from T1-weighted MRI. Advantages are the timely availability of results while maintaining a clinically relevant accuracy. Materials and Methods: An anonymized dataset of 574 subjects (443 healthy controls and 131 patients with epilepsy) was used for the supervised training of a convolutional neural network (CNN). A silver-standard ground truth was generated with FreeSurfer 6.0. Results: The CNN predicts a total of 165 morphometric measures directly from raw MR images. Analysis of the results using intraclass correlation coefficients showed, in general, good correlation with FreeSurfer generated ground truth data, with some of the regions nearly reaching human inter-rater performance (ICC > 0.75). Cortical thicknesses predicted by the CNN showed cross-sectional annual age-related gray matter atrophy rates both globally (thickness change of -0.004 mm/year) and regionally in agreement with the literature. A statistical test to dichotomize patients with epilepsy from healthy controls revealed similar effect sizes for structures affecting all subtypes as reported in a large-scale epilepsy study. Conclusions: We demonstrate the general feasibility of using deep learning to estimate human brain morphometry directly from T1-weighted MRI within seconds. A comparison of the results to other publications shows accuracies of comparable magnitudes for the subcortical volumes and cortical thicknesses.
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Affiliation(s)
- Michael Rebsamen
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland
- Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland
| | - Yannick Suter
- Insel Data Science Center, Inselspital, Bern University Hospital, Bern, Switzerland
- ARTORG Center for Biomedical Research, University of Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Mauricio Reyes
- Insel Data Science Center, Inselspital, Bern University Hospital, Bern, Switzerland
- ARTORG Center for Biomedical Research, University of Bern, Bern, Switzerland
| | - Christian Rummel
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland
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Vinke EJ, Huizinga W, Bergtholdt M, Adams HH, Steketee RM, Papma JM, de Jong FJ, Niessen WJ, Ikram MA, Wenzel F, Vernooij MW. Normative brain volumetry derived from different reference populations: impact on single-subject diagnostic assessment in dementia. Neurobiol Aging 2019; 84:9-16. [DOI: 10.1016/j.neurobiolaging.2019.07.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Revised: 07/11/2019] [Accepted: 07/16/2019] [Indexed: 02/05/2023]
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11
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Linker RA, Chan A. Navigating choice in multiple sclerosis management. Neurol Res Pract 2019; 1:5. [PMID: 33324871 PMCID: PMC7650058 DOI: 10.1186/s42466-019-0005-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Accepted: 11/21/2018] [Indexed: 01/10/2023] Open
Abstract
Background With the advent of modern immunotherapies for relapsing-remitting multiple sclerosis (RRMS) and the increasing amount of treatment options on the market, MS has evolved as a treatable disease. Yet, at the same time, new challenges for the treating neurologists arise. Main body This review article covers some of these challenges, including when and how to start treatment, treatment monitoring, and finally considerations on what the increasing choice in treatment options brings to disease management and longer-term planning. Among others, these important issues comprise pregnancy, treatment sequencing, switching or even stopping treatment. Conclusion The ultimate goal for navigating choices in RRMS management is to choose the right drug for the right patient at the right time Throughout the article, there is a strong focus on practical aspects and individual decision making in MS to meet the concept of personalized medicine.
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Affiliation(s)
- Ralf A Linker
- Department of Neurology, University of Regensburg, Universitätsstr. 84, 93053 Regensburg, Germany
| | - Andrew Chan
- Ambulantes Neurozentrum, Inselspital, Bern University Hospital, Freiburgstr. 4, 3010 Bern, Switzerland
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12
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Johnen A, Bürkner PC, Landmeyer NC, Ambrosius B, Calabrese P, Motte J, Hessler N, Antony G, König IR, Klotz L, Hoshi MM, Aly L, Groppa S, Luessi F, Paul F, Tackenberg B, Bergh FT, Kümpfel T, Tumani H, Stangel M, Weber F, Bayas A, Wildemann B, Heesen C, Zettl UK, Zipp F, Hemmer B, Meuth SG, Gold R, Wiendl H, Salmen A. Can we predict cognitive decline after initial diagnosis of multiple sclerosis? Results from the German National early MS cohort (KKNMS). J Neurol 2018; 266:386-397. [PMID: 30515631 PMCID: PMC6373354 DOI: 10.1007/s00415-018-9142-y] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Revised: 11/14/2018] [Accepted: 11/26/2018] [Indexed: 02/05/2023]
Abstract
BACKGROUND Cognitive impairment (CI) affects approximately one-third of the patients with early multiple sclerosis (MS) and clinically isolated syndrome (CIS). Little is known about factors predicting CI and progression after initial diagnosis. METHODS Neuropsychological screening data from baseline and 1-year follow-up of a prospective multicenter cohort study (NationMS) involving 1123 patients with newly diagnosed MS or CIS were analyzed. Employing linear multilevel models, we investigated whether demographic, clinical and conventional MRI markers at baseline were predictive for CI and longitudinal cognitive changes. RESULTS At baseline, 22% of patients had CI (impairment in ≥2 cognitive domains) with highest frequencies and severity in processing speed and executive functions. Demographics (fewer years of academic education, higher age, male sex), clinical (EDSS, depressive symptoms) but no conventional MRI characteristics were linked to baseline CI. At follow-up, only 14% of patients showed CI suggesting effects of retesting. Neither baseline characteristics nor initiation of treatment between baseline and follow-up was able to predict cognitive changes within the follow-up period of 1 year. CONCLUSIONS Identification of risk factors for short-term cognitive change in newly diagnosed MS or CIS is insufficient using only demographic, clinical and conventional MRI data. Change-sensitive, re-test reliable cognitive tests and more sophisticated predictors need to be employed in future clinical trials and cohort studies of early-stage MS to improve prediction.
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Affiliation(s)
- Andreas Johnen
- Department of Neurology, University Hospital Münster, Westfälische-Wilhelms-University Münster, Münster, Germany.
| | - Paul-Christian Bürkner
- Department of Statistics, Faculty of Psychology, Westfälische-Wilhelms-University, Münster, Germany
| | - Nils C Landmeyer
- Department of Neurology, University Hospital Münster, Westfälische-Wilhelms-University Münster, Münster, Germany
| | - Björn Ambrosius
- Department of Neurology, St. Josef-Hospital, Ruhr-University Bochum, Bochum, Germany
| | - Pasquale Calabrese
- Department of Neuropsychology and Behavioral Neurology, University of Basel, Basel, Switzerland
| | - Jeremias Motte
- Department of Neurology, St. Josef-Hospital, Ruhr-University Bochum, Bochum, Germany
| | - Nicole Hessler
- Institute of Medical Biometry and Statistics, University of Lübeck, University Hospital Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
| | - Gisela Antony
- Central Information Office (CIO), Philipps-University Marburg, Marburg, Germany
| | - Inke R König
- Institute of Medical Biometry and Statistics, University of Lübeck, University Hospital Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
| | - Luisa Klotz
- Department of Neurology, University Hospital Münster, Westfälische-Wilhelms-University Münster, Münster, Germany
| | - Muna-Miriam Hoshi
- Department of Neurology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Lilian Aly
- Department of Neurology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Sergiu Groppa
- Department of Neurology and Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Felix Luessi
- Department of Neurology and Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Friedemann Paul
- NeuroCure Clinical Research Center and Experimental and Clinical Research Center, Charité, University Medicine Berlin and Max Delbrueck Center for Molecular Medicine, Berlin, Germany
| | - Björn Tackenberg
- Department of Neurology, Philipps-University Marburg, Marburg, Germany
| | | | - Tania Kümpfel
- Institute of Clinical Neuroimmunology, Ludwig Maximilian University of Munich, Munich, Germany
| | - Hayrettin Tumani
- Department of Neurology, University of Ulm, Ulm, Germany
- Clinic of Neurology Dietenbronn, Schwendi, Germany
| | - Martin Stangel
- Department of Neurology, Hannover Medical School, Hannover, Germany
| | - Frank Weber
- Neurology, Max-Planck-Institute of Psychiatry, Munich, Germany
- Neurological Clinic, Sana Kliniken des Landkreises Cham, Cham, Germany
| | - Antonios Bayas
- Department of Neurology, Klinikum Augsburg, Augsburg, Germany
| | | | - Christoph Heesen
- Institut für Neuroimmunologie und Multiple Sklerose, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| | - Uwe K Zettl
- Department of Neurology, Neuroimmunological Section, University of Rostock, Rostock, Germany
| | - Frauke Zipp
- Department of Neurology and Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Bernhard Hemmer
- Department of Neurology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Sven G Meuth
- Department of Neurology, University Hospital Münster, Westfälische-Wilhelms-University Münster, Münster, Germany
| | - Ralf Gold
- Department of Neurology, St. Josef-Hospital, Ruhr-University Bochum, Bochum, Germany
| | - Heinz Wiendl
- Department of Neurology, University Hospital Münster, Westfälische-Wilhelms-University Münster, Münster, Germany
| | - Anke Salmen
- Department of Neurology, St. Josef-Hospital, Ruhr-University Bochum, Bochum, Germany
- Department of Neurology, Inselspital Bern, Bern University Hospital and University of Bern, Bern, Switzerland
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13
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Rummel C, Slavova N, Seiler A, Abela E, Hauf M, Burren Y, Weisstanner C, Vulliemoz S, Seeck M, Schindler K, Wiest R. Personalized structural image analysis in patients with temporal lobe epilepsy. Sci Rep 2017; 7:10883. [PMID: 28883420 PMCID: PMC5589799 DOI: 10.1038/s41598-017-10707-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2016] [Accepted: 08/15/2017] [Indexed: 12/29/2022] Open
Abstract
Volumetric and morphometric studies have demonstrated structural abnormalities related to chronic epilepsies on a cohort- and population-based level. On a single-patient level, specific patterns of atrophy or cortical reorganization may be widespread and heterogeneous but represent potential targets for further personalized image analysis and surgical therapy. The goal of this study was to compare morphometric data analysis in 37 patients with temporal lobe epilepsies with expert-based image analysis, pre-informed by seizure semiology and ictal scalp EEG. Automated image analysis identified abnormalities exceeding expert-determined structural epileptogenic lesions in 86% of datasets. If EEG lateralization and expert MRI readings were congruent, automated analysis detected abnormalities consistent on a lobar and hemispheric level in 82% of datasets. However, in 25% of patients EEG lateralization and expert readings were inconsistent. Automated analysis localized to the site of resection in 60% of datasets in patients who underwent successful epilepsy surgery. Morphometric abnormalities beyond the mesiotemporal structures contributed to subtype characterisation. We conclude that subject-specific morphometric information is in agreement with expert image analysis and scalp EEG in the majority of cases. However, automated image analysis may provide non-invasive additional information in cases with equivocal radiological and neurophysiological findings.
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Affiliation(s)
- Christian Rummel
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital Bern, University of Bern, Bern, Switzerland.
| | - Nedelina Slavova
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital Bern, University of Bern, Bern, Switzerland
| | - Andrea Seiler
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital Bern, University of Bern, Bern, Switzerland.,Sleep-Wake- Epilepsy-Center, Department of Neurology, Inselspital Bern, University of Bern, Bern, Switzerland
| | - Eugenio Abela
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital Bern, University of Bern, Bern, Switzerland.,Sleep-Wake- Epilepsy-Center, Department of Neurology, Inselspital Bern, University of Bern, Bern, Switzerland
| | - Martinus Hauf
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital Bern, University of Bern, Bern, Switzerland.,Epilepsy Clinic, Tschugg, Switzerland
| | - Yuliya Burren
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital Bern, University of Bern, Bern, Switzerland.,University Hospital of Psychiatry, University of Bern, Bern, Switzerland
| | - Christian Weisstanner
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital Bern, University of Bern, Bern, Switzerland
| | - Serge Vulliemoz
- Presurgical Epilepsy Evaluation Unit, Neurology Department, University Hospital of Geneva, Geneva, Switzerland
| | - Margitta Seeck
- Presurgical Epilepsy Evaluation Unit, Neurology Department, University Hospital of Geneva, Geneva, Switzerland
| | - Kaspar Schindler
- Sleep-Wake- Epilepsy-Center, Department of Neurology, Inselspital Bern, University of Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital Bern, University of Bern, Bern, Switzerland
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