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Brun G, Testud B, Girard OM, Lehmann P, de Rochefort L, Besson P, Massire A, Ridley B, Girard N, Guye M, Ranjeva JP, Le Troter A. Automatic segmentation of Deep Grey Nuclei using a high-resolution 7T MRI Atlas - quantification of T1 values in healthy volunteers. Eur J Neurosci 2021; 55:438-460. [PMID: 34939245 DOI: 10.1111/ejn.15575] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 12/17/2021] [Accepted: 12/18/2021] [Indexed: 11/30/2022]
Abstract
We present a new consensus atlas of deep grey nuclei obtained by shape-based averaging of manual segmentation of two experienced neuroradiologists and optimized from 7T MP2RAGE images acquired at (0.6mm)3 in 60 healthy subjects. A group-wise normalization method was used to build a high-contrast and high-resolution T1 -weighted brain template (0.5mm)3 using data from 30 out of the 60 controls. Delineation of 24 deep grey nuclei per hemisphere, including the claustrum and twelve thalamic nuclei, was then performed by two expert neuroradiologists and reviewed by a third neuroradiologist according to tissue contrast and external references based on the Morel atlas. Corresponding deep grey matter structures were also extracted from the Morel and CIT168 atlases. The data-derived, Morel and CIT168 atlases were all applied at the individual level using non-linear registration to fit the subject reference and to extract absolute mean quantitative T1 values derived from the 3D-MP2RAGE volumes, after correction for residual B1 + biases. Three metrics (The Dice and the volumetric similarity coefficients, and a novel Hausdorff distance) were used to estimate the inter-rater agreement of manual MRI segmentation and inter-atlas variability, and these metrics were measured to quantify biases due to image registration and their impact on the measurements of the quantitative T1 values was highlighted. This represents a fully-automated segmentation process permitting the extraction of unbiased normative T1 values in a population of young healthy controls as a reference for characterizing subtle structural alterations of deep grey nuclei relevant to a range of neurological diseases.
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Affiliation(s)
- Gilles Brun
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,AP-HM, CHU Timone, Pôle d'Imagerie Médicale, CEMEREM, Marseille, France.,AP-HM, CHU Timone, Pôle d'Imagerie Médicale, Service de Neuroradiologie, Marseille, France
| | - Benoit Testud
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,AP-HM, CHU Timone, Pôle d'Imagerie Médicale, CEMEREM, Marseille, France.,AP-HM, CHU Timone, Pôle d'Imagerie Médicale, Service de Neuroradiologie, Marseille, France
| | - Olivier M Girard
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,AP-HM, CHU Timone, Pôle d'Imagerie Médicale, CEMEREM, Marseille, France
| | - Pierre Lehmann
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,AP-HM, CHU Timone, Pôle d'Imagerie Médicale, CEMEREM, Marseille, France.,AP-HM, CHU Timone, Pôle d'Imagerie Médicale, Service de Neuroradiologie, Marseille, France
| | - Ludovic de Rochefort
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,AP-HM, CHU Timone, Pôle d'Imagerie Médicale, CEMEREM, Marseille, France
| | - Pierre Besson
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,AP-HM, CHU Timone, Pôle d'Imagerie Médicale, CEMEREM, Marseille, France
| | - Aurélien Massire
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,AP-HM, CHU Timone, Pôle d'Imagerie Médicale, CEMEREM, Marseille, France
| | - Ben Ridley
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,AP-HM, CHU Timone, Pôle d'Imagerie Médicale, CEMEREM, Marseille, France.,IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italia
| | - Nadine Girard
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,AP-HM, CHU Timone, Pôle d'Imagerie Médicale, CEMEREM, Marseille, France.,AP-HM, CHU Timone, Pôle d'Imagerie Médicale, Service de Neuroradiologie, Marseille, France
| | - Maxime Guye
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,AP-HM, CHU Timone, Pôle d'Imagerie Médicale, CEMEREM, Marseille, France
| | - Jean-Philippe Ranjeva
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,AP-HM, CHU Timone, Pôle d'Imagerie Médicale, CEMEREM, Marseille, France
| | - Arnaud Le Troter
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,AP-HM, CHU Timone, Pôle d'Imagerie Médicale, CEMEREM, Marseille, France
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Fischi-Gomez E, Bonnier G, Ward N, Granziera C, Hadjikhani N. Ultrahigh field in vivo characterization of microstructural abnormalities in the orbitofrontal cortex and amygdala in autism. Eur J Neurosci 2021; 54:6229-6236. [PMID: 34390517 DOI: 10.1111/ejn.15420] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 07/27/2021] [Accepted: 08/05/2021] [Indexed: 11/30/2022]
Abstract
There are currently no biomarkers for autism spectrum disorder (ASD). This neurodevelopmental condition has previously been associated with histopathological findings, including increased neuronal packing density in the amygdala, abnormal laminar cytoarchitecture and increased average neuronal density in the prefrontal cortex. The present study examined whether new brain imaging technologies could reveal in vivo, in adults with ASD, the manifestation of previously described histopathological changes. Using quantitative mapping at ultrahigh field (7 Tesla), we show that we can observe microstructural alterations in the right lateral orbitofrontal cortex and the bilateral amygdala in adult individuals with ASD in vivo. These imaging alterations point to an abnormal laminar cytoarchitecture and to an increased neuronal density, similar to what has been previously described in post-mortem data in ASD. Our data demonstrate that it is possible to visualize, in vivo and at the individual level, alterations of cortical and subcortical microstructure in ASD. Future studies will be needed to extend these findings to a larger group of individuals and evaluate their association with symptomatology as well as their specificity among the different neurodevelopmental disorders.
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Affiliation(s)
- Elda Fischi-Gomez
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Signal Processing Laboratory 5, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland
| | - Guillaume Bonnier
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Noreen Ward
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Cristina Granziera
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Neurology Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Research Center for Clinical Neuroimmunology and Neuroscience (RC2NB) Basel, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Nouchine Hadjikhani
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Gillberg Neuropsychiatry Center, Sahlgrenska Academy, University of Gothenburg, Sweden
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Alemán-Gómez Y, Najdenovska E, Roine T, Fartaria MJ, Canales-Rodríguez EJ, Rovó Z, Hagmann P, Conus P, Do KQ, Klauser P, Steullet P, Baumann PS, Bach Cuadra M. Partial-volume modeling reveals reduced gray matter in specific thalamic nuclei early in the time course of psychosis and chronic schizophrenia. Hum Brain Mapp 2020; 41:4041-4061. [PMID: 33448519 PMCID: PMC7469814 DOI: 10.1002/hbm.25108] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 04/22/2020] [Accepted: 06/14/2020] [Indexed: 12/20/2022] Open
Abstract
The structural complexity of the thalamus, due to its mixed composition of gray and white matter, make it challenging to disjoint and quantify each tissue contribution to the thalamic anatomy. This work promotes the use of partial‐volume‐based over probabilistic‐based tissue segmentation approaches to better capture thalamic gray matter differences between patients at different stages of psychosis (early and chronic) and healthy controls. The study was performed on a cohort of 23 patients with schizophrenia, 41 with early psychosis and 69 age and sex‐matched healthy subjects. Six tissue segmentation approaches were employed to obtain the gray matter concentration/probability images. The statistical tests were applied at three different anatomical scales: whole thalamus, thalamic subregions and voxel‐wise. The results suggest that the partial volume model estimation of gray matter is more sensitive to detect atrophies within the thalamus of patients with psychosis. However all the methods detected gray matter deficit in the pulvinar, particularly in early stages of psychosis. This study demonstrates also that the gray matter decrease varies nonlinearly with age and between nuclei. While a gray matter loss was found in the pulvinar of patients in both stages of psychosis, reduced gray matter in the mediodorsal was only observed in early psychosis subjects. Finally, our analyses point to alterations in a sub‐region comprising the lateral posterior and ventral posterior nuclei. The obtained results reinforce the hypothesis that thalamic gray matter assessment is more reliable when the tissues segmentation method takes into account the partial volume effect.
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Affiliation(s)
- Yasser Alemán-Gómez
- Center for Psychiatric Neuroscience, Department of Psychiatry, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Medical Image Analysis Laboratory (MIAL), Centre d'Imagerie BioMédicale (CIBM), Switzerland
| | - Elena Najdenovska
- Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Medical Image Analysis Laboratory (MIAL), Centre d'Imagerie BioMédicale (CIBM), Switzerland.,Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Timo Roine
- Center for Psychiatric Neuroscience, Department of Psychiatry, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Medical Image Analysis Laboratory (MIAL), Centre d'Imagerie BioMédicale (CIBM), Switzerland
| | - Mário João Fartaria
- Medical Image Analysis Laboratory (MIAL), Centre d'Imagerie BioMédicale (CIBM), Switzerland.,Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
| | - Erick J Canales-Rodríguez
- Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,FIDMAG Germanes Hospitalàries Research Foundation, Sant Boi de Llobregat, Barcelona, Spain
| | - Zita Rovó
- Center for Psychiatric Neuroscience, Department of Psychiatry, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Patric Hagmann
- Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Philippe Conus
- Service of General Psychiatry, Department of Psychiatry, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Kim Q Do
- Center for Psychiatric Neuroscience, Department of Psychiatry, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Paul Klauser
- Center for Psychiatric Neuroscience, Department of Psychiatry, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Pascal Steullet
- Center for Psychiatric Neuroscience, Department of Psychiatry, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Philipp S Baumann
- Center for Psychiatric Neuroscience, Department of Psychiatry, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Service of General Psychiatry, Department of Psychiatry, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Meritxell Bach Cuadra
- Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Medical Image Analysis Laboratory (MIAL), Centre d'Imagerie BioMédicale (CIBM), Switzerland.,Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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Boto J, Askin NC, Regnaud A, Kober T, Gkinis G, Lazeyras F, Lövblad KO, Vargas MI. Cerebral Gray and White Matter Involvement in Anorexia Nervosa Evaluated by T1, T2, and T2* Mapping. J Neuroimaging 2019; 29:598-604. [PMID: 31259451 DOI: 10.1111/jon.12647] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Revised: 05/29/2019] [Accepted: 06/14/2019] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND AND PURPOSE Changes in the brain composition of anorexics could potentially be expected, opening the door to new imaging approaches where quantitative and qualitative MRI have a role. Our purpose was to investigate anorexia-related brain dehydration and myelin depletion by analyzing T1, T2, and T2* relaxation times of different brain structures in anorexics and controls. METHODS Thirty-eight anorexic female patients (mean age, 26.2 years; age range, 16.2-48.7 years; mean BMI, 14.5 kg/m2 ; BMI range, 10.0-18.4 kg/m2 ) underwent brain MRI between August 2014 and August 2018. Controls were 16 healthy females (mean age, 28.0 years; age range, 22.3-34.7 years; mean BMI, 20.9 kg/m2 ; BMI range, 18.4-26.6 kg/m2 ). T1, T2, and T2* relaxation times were obtained for different brain structures in anorexics and controls as part of this retrospective case-control study. RESULTS The T1 relaxation times of gray and white matter were significantly lower in anorexics (P = .009), whereas the T2 relaxation times of gray matter were higher (P < .001). There were no statistically significant differences in gray matter T2* relaxation times or in white matter T2 and T2* relaxation times between anorexics and controls. Occipital lobe gray matter showed the shortest T1, T2, and T2* relaxation times of all brain regions (P < .05). CONCLUSIONS T1 shortening in anorexics suggests both dehydration and myelin loss, whereas T2 prolongation points toward myelin loss (myelin water has lower T2), which seems to be less discernible in white matter. Shorter overall relaxation times in the most posterior regions of the brain suggest higher iron content.
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Affiliation(s)
- José Boto
- Division of Neuroradiology, Geneva University Hospital and Faculty of Medicine of Geneva, Geneva, Switzerland
| | - Nurten Ceren Askin
- Division of Radiology, Geneva University Hospital and Faculty of Medicine of Geneva, Geneva, Switzerland
| | - Alice Regnaud
- Division of Neuroradiology, Geneva University Hospital and Faculty of Medicine of Geneva, Geneva, Switzerland
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthcare HC CEMEA SUI DI BM PI, Siemens ACIT, Lausanne, Switzerland.,Department of Radiology, University Hospital (CHUV), Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | | | - François Lazeyras
- Division of Radiology, Geneva University Hospital and Faculty of Medicine of Geneva, Geneva, Switzerland
| | - Karl-Olof Lövblad
- Division of Neuroradiology, Geneva University Hospital and Faculty of Medicine of Geneva, Geneva, Switzerland
| | - Maria Isabel Vargas
- Division of Neuroradiology, Geneva University Hospital and Faculty of Medicine of Geneva, Geneva, Switzerland
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Wang S, Yang M, Du S, Yang J, Liu B, Gorriz JM, Ramírez J, Yuan TF, Zhang Y. Wavelet Entropy and Directed Acyclic Graph Support Vector Machine for Detection of Patients with Unilateral Hearing Loss in MRI Scanning. Front Comput Neurosci 2016; 10:106. [PMID: 27807415 PMCID: PMC5069288 DOI: 10.3389/fncom.2016.00106] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2016] [Accepted: 09/28/2016] [Indexed: 12/17/2022] Open
Abstract
HighlightsWe develop computer-aided diagnosis system for unilateral hearing loss detection in structural magnetic resonance imaging. Wavelet entropy is introduced to extract image global features from brain images. Directed acyclic graph is employed to endow support vector machine an ability to handle multi-class problems. The developed computer-aided diagnosis system achieves an overall accuracy of 95.1% for this three-class problem of differentiating left-sided and right-sided hearing loss from healthy controls.
Aim: Sensorineural hearing loss (SNHL) is correlated to many neurodegenerative disease. Now more and more computer vision based methods are using to detect it in an automatic way. Materials: We have in total 49 subjects, scanned by 3.0T MRI (Siemens Medical Solutions, Erlangen, Germany). The subjects contain 14 patients with right-sided hearing loss (RHL), 15 patients with left-sided hearing loss (LHL), and 20 healthy controls (HC). Method: We treat this as a three-class classification problem: RHL, LHL, and HC. Wavelet entropy (WE) was selected from the magnetic resonance images of each subjects, and then submitted to a directed acyclic graph support vector machine (DAG-SVM). Results: The 10 repetition results of 10-fold cross validation shows 3-level decomposition will yield an overall accuracy of 95.10% for this three-class classification problem, higher than feedforward neural network, decision tree, and naive Bayesian classifier. Conclusions: This computer-aided diagnosis system is promising. We hope this study can attract more computer vision method for detecting hearing loss.
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Affiliation(s)
- Shuihua Wang
- School of Electronic Science and Engineering, Nanjing UniversityNanjing, China; School of Computer Science and Technology, Nanjing Normal UniversityNanjing, China; Hunan Provincial Key Laboratory of Network Investigational Technology, Hunan Police AcademyChangsha, China
| | - Ming Yang
- Department of Radiology, Nanjing Children's Hospital, Nanjing Medical UniversityNanjing, China; Key Laboratory of Intelligent Computing and Information Processing in Fujian Provincial University, Quanzhou Normal UniversityQuanzhou, China
| | - Sidan Du
- School of Electronic Science and Engineering, Nanjing University Nanjing, China
| | - Jiquan Yang
- Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing, China
| | - Bin Liu
- Department of Radiology, Zhong-Da Hospital of Southeast University Nanjing, China
| | - Juan M Gorriz
- Department of Signal Theory, Networking and Communications, University of Granada Granada, Spain
| | - Javier Ramírez
- Department of Signal Theory, Networking and Communications, University of Granada Granada, Spain
| | - Ti-Fei Yuan
- School of Computer Science and Technology, Nanjing Normal UniversityNanjing, China; State Key Lab of CAD & CG, Zhejiang UniversityHangzhou, China
| | - Yudong Zhang
- School of Computer Science and Technology, Nanjing Normal UniversityNanjing, China; Key Laboratory of Statistical Information Technology and Data Mining, State Statistics BureauChengdu, China
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