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Taha A, Gilmore G, Abbass M, Kai J, Kuehn T, Demarco J, Gupta G, Zajner C, Cao D, Chevalier R, Ahmed A, Hadi A, Karat BG, Stanley OW, Park PJ, Ferko KM, Hemachandra D, Vassallo R, Jach M, Thurairajah A, Wong S, Tenorio MC, Ogunsanya F, Khan AR, Lau JC. Magnetic resonance imaging datasets with anatomical fiducials for quality control and registration. Sci Data 2023; 10:449. [PMID: 37438367 DOI: 10.1038/s41597-023-02330-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 06/26/2023] [Indexed: 07/14/2023] Open
Abstract
Tools available for reproducible, quantitative assessment of brain correspondence have been limited. We previously validated the anatomical fiducial (AFID) placement protocol for point-based assessment of image registration with millimetric (mm) accuracy. In this data descriptor, we release curated AFID placements for some of the most commonly used structural magnetic resonance imaging datasets and templates. The release of our accurate placements allows for rapid quality control of image registration, teaching neuroanatomy, and clinical applications such as disease diagnosis and surgical targeting. We release placements on individual subjects from four datasets (N = 132 subjects for a total of 15,232 fiducials) and 14 brain templates (4,288 fiducials), totalling more than 300 human rater hours of annotation. We also validate human rater accuracy of released placements to be within 1 - 2 mm (using more than 45,000 Euclidean distances), consistent with prior studies. Our data is compliant with the Brain Imaging Data Structure allowing for facile incorporation into neuroimaging analysis pipelines.
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Affiliation(s)
- Alaa Taha
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Canada
- School of Biomedical Engineering, Western University, London, Canada
| | - Greydon Gilmore
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Canada
- School of Biomedical Engineering, Western University, London, Canada
- Department of Clinical Neurological Sciences, Division of Neurosurgery, Western University, London, Canada
| | - Mohamad Abbass
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Canada
- Department of Clinical Neurological Sciences, Division of Neurosurgery, Western University, London, Canada
- Graduate Program in Neuroscience, Schulich School of Medicine and Dentistry, Western University, London, Canada
| | - Jason Kai
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Canada
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Canada
| | - Tristan Kuehn
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Canada
- School of Biomedical Engineering, Western University, London, Canada
| | - John Demarco
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Canada
- Department of Anatomy and Cell Biology, Schulich School of Medicine and Dentistry, Western University, London, Canada
| | - Geetika Gupta
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Canada
- Graduate Program in Neuroscience, Schulich School of Medicine and Dentistry, Western University, London, Canada
| | - Chris Zajner
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Canada
- Department of Clinical Neurological Sciences, Division of Neurosurgery, Western University, London, Canada
| | - Daniel Cao
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Canada
- Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Canada
| | - Ryan Chevalier
- Department of Clinical Neurological Sciences, Division of Neurosurgery, Western University, London, Canada
| | - Abrar Ahmed
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Canada
- Department of Clinical Neurological Sciences, Division of Neurosurgery, Western University, London, Canada
| | - Ali Hadi
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Canada
- Department of Clinical Neurological Sciences, Division of Neurosurgery, Western University, London, Canada
| | - Bradley G Karat
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Canada
- Graduate Program in Neuroscience, Schulich School of Medicine and Dentistry, Western University, London, Canada
| | - Olivia W Stanley
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Canada
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Canada
| | - Patrick J Park
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Canada
- School of Biomedical Engineering, Western University, London, Canada
| | - Kayla M Ferko
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Canada
- Graduate Program in Neuroscience, Schulich School of Medicine and Dentistry, Western University, London, Canada
| | - Dimuthu Hemachandra
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Canada
- School of Biomedical Engineering, Western University, London, Canada
| | - Reid Vassallo
- School of Biomedical Engineering, Faculty of Applied Science and Faculty of Medicine, The University of British Columbia, Vancouver, Canada
| | - Magdalena Jach
- Department of Clinical Neurological Sciences, Division of Neurosurgery, Western University, London, Canada
| | - Arun Thurairajah
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Canada
- Graduate Program in Neuroscience, Schulich School of Medicine and Dentistry, Western University, London, Canada
| | - Sandy Wong
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Canada
- Department of Clinical Neurological Sciences, Division of Neurosurgery, Western University, London, Canada
| | - Mauricio C Tenorio
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Canada
- School of Biomedical Engineering, Western University, London, Canada
| | - Feyi Ogunsanya
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Canada
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Canada
| | - Ali R Khan
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Canada
- School of Biomedical Engineering, Western University, London, Canada
- Graduate Program in Neuroscience, Schulich School of Medicine and Dentistry, Western University, London, Canada
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Canada
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, Western University, London, Canada
| | - Jonathan C Lau
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Canada.
- School of Biomedical Engineering, Western University, London, Canada.
- Department of Clinical Neurological Sciences, Division of Neurosurgery, Western University, London, Canada.
- Graduate Program in Neuroscience, Schulich School of Medicine and Dentistry, Western University, London, Canada.
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, Western University, London, Canada.
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Frey J, Cagle J, Johnson KA, Wong JK, Hilliard JD, Butson CR, Okun MS, de Hemptinne C. Past, Present, and Future of Deep Brain Stimulation: Hardware, Software, Imaging, Physiology and Novel Approaches. Front Neurol 2022; 13:825178. [PMID: 35356461 PMCID: PMC8959612 DOI: 10.3389/fneur.2022.825178] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 02/04/2022] [Indexed: 11/13/2022] Open
Abstract
Deep brain stimulation (DBS) has advanced treatment options for a variety of neurologic and neuropsychiatric conditions. As the technology for DBS continues to progress, treatment efficacy will continue to improve and disease indications will expand. Hardware advances such as longer-lasting batteries will reduce the frequency of battery replacement and segmented leads will facilitate improvements in the effectiveness of stimulation and have the potential to minimize stimulation side effects. Targeting advances such as specialized imaging sequences and "connectomics" will facilitate improved accuracy for lead positioning and trajectory planning. Software advances such as closed-loop stimulation and remote programming will enable DBS to be a more personalized and accessible technology. The future of DBS continues to be promising and holds the potential to further improve quality of life. In this review we will address the past, present and future of DBS.
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Affiliation(s)
- Jessica Frey
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
| | - Jackson Cagle
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
| | - Kara A. Johnson
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
| | - Joshua K. Wong
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
| | - Justin D. Hilliard
- Department of Neurosurgery, University of Florida, Gainesville, FL, United States
| | - Christopher R. Butson
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
- Department of Neurosurgery, University of Florida, Gainesville, FL, United States
| | - Michael S. Okun
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
| | - Coralie de Hemptinne
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
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Abbass M, Gilmore G, Taha A, Chevalier R, Jach M, Peters TM, Khan AR, Lau JC. Application of the anatomical fiducials framework to a clinical dataset of patients with Parkinson's disease. Brain Struct Funct 2022; 227:393-405. [PMID: 34687354 PMCID: PMC8741686 DOI: 10.1007/s00429-021-02408-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 10/04/2021] [Indexed: 11/24/2022]
Abstract
Establishing spatial correspondence between subject and template images is necessary in neuroimaging research and clinical applications such as brain mapping and stereotactic neurosurgery. Our anatomical fiducial (AFID) framework has recently been validated to serve as a quantitative measure of image registration based on salient anatomical features. In this study, we sought to apply the AFIDs protocol to the clinic, focusing on structural magnetic resonance images obtained from patients with Parkinson's disease (PD). We confirmed AFIDs could be placed to millimetric accuracy in the PD dataset with results comparable to those in normal control subjects. We evaluated subject-to-template registration using this framework by aligning the clinical scans to standard template space using a robust open preprocessing workflow. We found that registration errors measured using AFIDs were higher than previously reported, suggesting the need for optimization of image processing pipelines for clinical grade datasets. Finally, we examined the utility of using point-to-point distances between AFIDs as a morphometric biomarker of PD, finding evidence of reduced distances between AFIDs that circumscribe regions known to be affected in PD including the substantia nigra. Overall, we provide evidence that AFIDs can be successfully applied in a clinical setting and utilized to provide localized and quantitative measures of registration error. AFIDs provide clinicians and researchers with a common, open framework for quality control and validation of spatial correspondence and the location of anatomical structures, facilitating aggregation of imaging datasets and comparisons between various neurological conditions.
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Affiliation(s)
- Mohamad Abbass
- Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON, Canada
- Graduate Program in Neuroscience, Western University, London, ON, Canada
| | - Greydon Gilmore
- Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON, Canada
- School of Biomedical Engineering, Western University, London, ON, Canada
| | - Alaa Taha
- Department of Physiology, Western University, London, ON, Canada
| | - Ryan Chevalier
- Department of Physiology, Western University, London, ON, Canada
| | - Magdalena Jach
- Department of Physiology, Western University, London, ON, Canada
| | - Terry M Peters
- School of Biomedical Engineering, Western University, London, ON, Canada
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, ON, Canada
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, Western University, London, ON, Canada
- Department of Medical Biophysics, Western University, London, ON, Canada
- Brain and Mind Institute, Western University, London, ON, Canada
| | - Ali R Khan
- School of Biomedical Engineering, Western University, London, ON, Canada
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, ON, Canada
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, Western University, London, ON, Canada
- Department of Medical Biophysics, Western University, London, ON, Canada
- Brain and Mind Institute, Western University, London, ON, Canada
- Graduate Program in Neuroscience, Western University, London, ON, Canada
| | - Jonathan C Lau
- Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON, Canada.
- School of Biomedical Engineering, Western University, London, ON, Canada.
- Department of Neurosurgery, Emory University, Atlanta, GA, USA.
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Tsagkas C, Geiter E, Gaetano L, Naegelin Y, Amann M, Parmar K, Papadopoulou A, Wuerfel J, Kappos L, Sprenger T, Granziera C, Mallar Chakravarty M, Magon S. Longitudinal changes of deep gray matter shape in multiple sclerosis. NEUROIMAGE: CLINICAL 2022; 35:103137. [PMID: 36002960 PMCID: PMC9421532 DOI: 10.1016/j.nicl.2022.103137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 06/28/2022] [Accepted: 07/27/2022] [Indexed: 01/18/2023] Open
Abstract
Specific shape changes over time occur at the bilateral ventrolateral pallidal and the left posterolateral striatal surface in relapse-onset multiple sclerosis. These shape changes over time were not associated with disease progression. The average shape of deep gray matter structures was associated with the patients’ average disease severity as well as white matter lesion-load.
Objective This study aimed to investigate longitudinal deep gray matter (DGM) shape changes and their relationship with measures of clinical disability and white matter lesion-load in a large multiple sclerosis (MS) cohort. Materials and Methods A total of 230 MS patients (179 relapsing-remitting, 51 secondary progressive; baseline age 44.5 ± 11.3 years; baseline disease duration 12.99 ± 9.18) underwent annual clinical and MRI examinations over a maximum of 6 years (mean 4.32 ± 2.07 years). The DGM structures were segmented on the T1-weighted images using the “Multiple Automatically Generated Templates” brain algorithm. White matter lesion-load was measured on T2-weighted MRI. Clinical examination included the expanded disability status scale, 9-hole peg test, timed 25-foot walk test, symbol digit modalities test and paced auditory serial addition test. Vertex‐wise longitudinal analysis of DGM shapes was performed using linear mixed effect models and evaluated the association between average/temporal changes of DGM shapes with average/temporal changes of clinical measurements, respectively. Results A significant shrinkage over time of the bilateral ventrolateral pallidal and the left posterolateral striatal surface was observed, whereas no significant shape changes over time were observed at the bilateral thalamic and right striatal surfaces. Higher average lesion-load was associated with an average inwards displacement of the global thalamic surface with relative sparing on the posterior side (slight left-side predominance), the antero-dorso-lateral striatal surfaces bilaterally (symmetric on both sides) and the antero-lateral pallidal surface (left-side predominance). There was also an association between shrinkage of large lateral DGM surfaces with higher clinical motor and cognitive disease severity. However, there was no correlation between any DGM shape changes over time and measurements of clinical progression or lesion-load changes over time. Conclusions This study showed specific shape change of DGM structures occurring over time in relapse-onset MS. Although these shape changes over time were not associated with disease progression, we demonstrated a link between DGM shape and the patients’ average disease severity as well as white matter lesion-load.
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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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Hippocampal subfield volumes across the healthy lifespan and the effects of MR sequence on estimates. Neuroimage 2021; 233:117931. [DOI: 10.1016/j.neuroimage.2021.117931] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 02/28/2021] [Indexed: 01/18/2023] Open
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Bussy A, Patel R, Plitman E, Tullo S, Salaciak A, Bedford SA, Farzin S, Béland ML, Valiquette V, Kazazian C, Tardif CL, Devenyi GA, Chakravarty MM. Hippocampal shape across the healthy lifespan and its relationship with cognition. Neurobiol Aging 2021; 106:153-168. [PMID: 34280848 DOI: 10.1016/j.neurobiolaging.2021.03.018] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 03/02/2021] [Accepted: 03/29/2021] [Indexed: 01/18/2023]
Abstract
The study of the hippocampus across the healthy adult lifespan has rendered inconsistent findings. While volumetric measurements have often been a popular technique for analysis, more advanced morphometric techniques have demonstrated compelling results that highlight the importance and improved specificity of shape-based measures. Here, the MAGeT Brain algorithm was applied on 134 healthy individuals aged 18-81 years old to extract hippocampal subfield volumes and hippocampal shape measurements, namely: local surface area (SA) and displacement. We used linear-, second- or third-order natural splines to examine the relationships between hippocampal measures and age. In addition, partial least squares analyses were performed to relate volume and shape measurements with cognitive and demographic information. Volumetric results indicated a relative preservation of the right cornus ammonis 1 with age and a global volume reduction linked with older age, female sex, lower levels of education and cognitive performance. Vertex-wise analysis demonstrated an SA preservation in the anterior hippocampus with a peak during the sixth decade, while the posterior hippocampal SA gradually decreased across lifespan. Overall, SA decrease was linked to older age, female sex and, to a lesser extent lower levels of education and cognitive performance. Outward displacement in the lateral hippocampus and inward displacement in the medial hippocampus were enlarged with older age, lower levels of cognition and education, indicating an accentuation of the hippocampal "C" shape with age. Taken together, our findings suggest that vertex-wise analyses have higher spatial specifity and that sex, education, and cognition are implicated in the differential impact of age on hippocampal subregions throughout its anteroposterior and medial-lateral axes. This article is part of the Virtual Special Issue titled COGNITIVE NEU- ROSCIENCE OF HEALTHY AND PATHOLOGICAL AGING. The full issue can be found on ScienceDirect at https://www.sciencedirect.com/journal/neurobiology-of-aging/special-issue/105379XPWJP.
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Affiliation(s)
- Aurélie Bussy
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada.
| | - Raihaan Patel
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada
| | - Eric Plitman
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Stephanie Tullo
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada
| | - Alyssa Salaciak
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - Saashi A Bedford
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Sarah Farzin
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - Marie-Lise Béland
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - Vanessa Valiquette
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada
| | - Christina Kazazian
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - Christine L Tardif
- Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada; McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Quebec, Canada; Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Gabriel A Devenyi
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - M Mallar Chakravarty
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada; Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada
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Lateral geniculate nucleus volume changes after optic neuritis in neuromyelitis optica: A longitudinal study. NEUROIMAGE-CLINICAL 2021; 30:102608. [PMID: 33735786 PMCID: PMC7974320 DOI: 10.1016/j.nicl.2021.102608] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 01/25/2021] [Accepted: 02/17/2021] [Indexed: 12/03/2022]
Abstract
LGN correlates with structural markers of the anterior and posterior visual pathway. LGN volume may reduce after an episode of ON, shown in four patients. LGN volume does not change over time in the absence of ON episodes. Our findings argue against occult neurodegeneration in the visual pathway in NMO.
Objectives Lateral geniculate nucleus (LGN) volume is reduced after optic neuritis (ON) in neuromyelitis optica spectrum disorders (NMOSD). We aimed at a longitudinal assessment of LGN volume in NMOSD. Methods Twenty-nine patients with aquaporin 4-IgG seropositive NMOSD (age: 47.8 ± 14.6 years (y), female: n = 27, history of ON (NMO-ON): n = 17, median time since ON: 3[1.2–12.1]y) and 18 healthy controls (HC; age: 39.3 ± 15.8y; female: n = 13) were included. Median follow-up was 4.1[1.1–4.7]y for patients and 1.7[0.9–3.2]y for HC. LGN volume was measured using a multi-atlas-based approach of automated segmentation on 3 Tesla magnetic resonance images. Retinal optical coherence tomography and probabilistic tractography of the optic radiations (OR) were also performed. Results At baseline, NMO-ON patients had lower LGN volumes (395.4 ± 48.9 mm3) than patients without ON (NMO-NON: 450.7 ± 55.6 mm3; p = 0.049) and HC (444.5 ± 61.5 mm3, p = 0.025). LGN volume was associated with retinal neuroaxonal loss and microstructural OR damage. Longitudinally, there was no change in LGN volumes in the absence of ON, neither in all patients (B = −0.6, SE = 1.4, p = 0.670), nor in NMO-ON (B = −0.8, SE = 1.6, p = 0.617) and NMO-NON (B = 1.7, SE = 3.5, p = 0.650). However, in four patients with new ON during follow-up, LGN volume was reduced at last visit (median time since ON: 2.6 [1.8–3.9] y) compared to the measurement before ON (352 ± 52.7 vs. 371.1 ± 55.9 mm3; t = −3.6, p = 0.036). Conclusion Although LGN volume is reduced after ON in NMOSD, this volume loss is not progressive over longer follow-up or independent of ON. Thus, our findings -at least in this relatively small cohort- do not support occult neurodegeneration of the afferent visual pathway in NMOSD.
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9
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Huber E, Patel R, Hupp M, Weiskopf N, Chakravarty MM, Freund P. Extrapyramidal plasticity predicts recovery after spinal cord injury. Sci Rep 2020; 10:14102. [PMID: 32839540 PMCID: PMC7445170 DOI: 10.1038/s41598-020-70805-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 08/05/2020] [Indexed: 11/09/2022] Open
Abstract
Spinal cord injury (SCI) leads to wide-spread neurodegeneration across the neuroaxis. We explored trajectories of surface morphology, demyelination and iron concentration within the basal ganglia-thalamic circuit over 2 years post-SCI. This allowed us to explore the predictive value of neuroimaging biomarkers and determine their suitability as surrogate markers for interventional trials. Changes in markers of surface morphology, myelin and iron concentration of the basal ganglia and thalamus were estimated from 182 MRI datasets acquired in 17 SCI patients and 21 healthy controls at baseline (1-month post injury for patients), after 3, 6, 12, and 24 months. Using regression models, we investigated group difference in linear and non-linear trajectories of these markers. Baseline quantitative MRI parameters were used to predict 24-month clinical outcome. Surface area contracted in the motor (i.e. lower extremity) and pulvinar thalamus, and striatum; and expanded in the motor thalamus and striatum in patients compared to controls over 2-years. In parallel, myelin-sensitive markers decreased in the thalamus, striatum, and globus pallidus, while iron-sensitive markers decreased within the left caudate. Baseline surface area expansions within the striatum (i.e. motor caudate) predicted better lower extremity motor score at 2-years. Extensive extrapyramidal neurodegenerative and reorganizational changes across the basal ganglia-thalamic circuitry occur early after SCI and progress over time; their magnitude being predictive of functional recovery. These results demonstrate a potential role of extrapyramidal plasticity during functional recovery after SCI.
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Affiliation(s)
- E Huber
- Spinal Cord Injury Center Balgrist, University Hospital Zurich, Zurich, Switzerland
| | - R Patel
- Computational Brain Anatomy Laboratory (CoBrA Lab), Douglas Research Centre, Montreal, QC, Canada.,Department of Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - M Hupp
- Spinal Cord Injury Center Balgrist, University Hospital Zurich, Zurich, Switzerland
| | - N Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Linnéstraße 5, 04103, Leipzig, Germany
| | - M M Chakravarty
- Computational Brain Anatomy Laboratory (CoBrA Lab), Douglas Research Centre, Montreal, QC, Canada.,Department of Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada.,Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - P Freund
- Spinal Cord Injury Center Balgrist, University Hospital Zurich, Zurich, Switzerland. .,Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, UK. .,Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, University College London, London, UK. .,Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
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10
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Xiao Y, Lau JC, Hemachandra D, Gilmore G, Khan AR, Peters TM. Image Guidance in Deep Brain Stimulation Surgery to Treat Parkinson's Disease: A Comprehensive Review. IEEE Trans Biomed Eng 2020; 68:1024-1033. [PMID: 32746050 DOI: 10.1109/tbme.2020.3006765] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Deep brain stimulation (DBS) is an effective therapy as an alternative to pharmaceutical treatments for Parkinson's disease (PD). Aside from factors such as instrumentation, treatment plans, and surgical protocols, the success of the procedure depends heavily on the accurate placement of the electrode within the optimal therapeutic targets while avoiding vital structures that can cause surgical complications and adverse neurologic effects. Although specific surgical techniques for DBS can vary, interventional guidance with medical imaging has greatly contributed to the development, outcomes, and safety of the procedure. With rapid development in novel imaging techniques, computational methods, and surgical navigation software, as well as growing insights into the disease and mechanism of action of DBS, modern image guidance is expected to further enhance the capacity and efficacy of the procedure in treating PD. This article surveys the state-of-the-art techniques in image-guided DBS surgery to treat PD, and discusses their benefits and drawbacks, as well as future directions on the topic.
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11
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Tullo S, Patel R, Devenyi GA, Salaciak A, Bedford SA, Farzin S, Wlodarski N, Tardif CL, Breitner JCS, Chakravarty MM. MR-based age-related effects on the striatum, globus pallidus, and thalamus in healthy individuals across the adult lifespan. Hum Brain Mapp 2019; 40:5269-5288. [PMID: 31452289 PMCID: PMC6864890 DOI: 10.1002/hbm.24771] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 07/17/2019] [Accepted: 08/05/2019] [Indexed: 01/18/2023] Open
Abstract
While numerous studies have used magnetic resonance imaging (MRI) to elucidate normative age-related trajectories in subcortical structures across the human lifespan, there exists substantial heterogeneity among different studies. Here, we investigated the normative relationships between age and morphology (i.e., volume and shape), and microstructure (using the T1-weighted/T2-weighted [T1w/T2w] signal ratio as a putative index of myelin and microstructure) of the striatum, globus pallidus, and thalamus across the adult lifespan using a dataset carefully quality controlled, yielding a final sample of 178 for the morphological analyses, and 162 for the T1w/T2w analyses from an initial dataset of 253 healthy subjects, aged 18-83. In accordance with previous cross-sectional studies of adults, we observed age-related volume decrease that followed a quadratic relationship between age and bilateral striatal and thalamic volumes, and a linear relationship in the globus pallidus. Our shape indices consistently demonstrated age-related posterior and medial areal contraction bilaterally across all three structures. Beyond morphology, we observed a quadratic inverted U-shaped relationship between T1w/T2w signal ratio and age, with a peak value occurring in middle age (at around 50 years old). After permutation testing, the Akaike information criterion determined age relationships remained significant for the bilateral globus pallidus and thalamus, for both the volumetric and T1w/T2w analyses. Our findings serve to strengthen and expand upon previous volumetric analyses by providing a normative baseline of morphology and microstructure of these structures to which future studies investigating patients with various disorders can be compared.
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Affiliation(s)
- Stephanie Tullo
- Integrated Program in NeuroscienceMcGill UniversityMontrealQuebecCanada
- Computational Brain Anatomy Laboratory, Cerebral Imaging CentreDouglas Mental Health University InstituteVerdunQuebecCanada
| | - Raihaan Patel
- Computational Brain Anatomy Laboratory, Cerebral Imaging CentreDouglas Mental Health University InstituteVerdunQuebecCanada
- Department of Biological and Biomedical EngineeringMcGill UniversityMontrealQuebecCanada
| | - Gabriel A. Devenyi
- Computational Brain Anatomy Laboratory, Cerebral Imaging CentreDouglas Mental Health University InstituteVerdunQuebecCanada
- Department of PsychiatryMcGill UniversityMontrealQuebecCanada
| | - Alyssa Salaciak
- Computational Brain Anatomy Laboratory, Cerebral Imaging CentreDouglas Mental Health University InstituteVerdunQuebecCanada
| | - Saashi A. Bedford
- Integrated Program in NeuroscienceMcGill UniversityMontrealQuebecCanada
- Computational Brain Anatomy Laboratory, Cerebral Imaging CentreDouglas Mental Health University InstituteVerdunQuebecCanada
| | - Sarah Farzin
- Computational Brain Anatomy Laboratory, Cerebral Imaging CentreDouglas Mental Health University InstituteVerdunQuebecCanada
| | - Nancy Wlodarski
- Computational Brain Anatomy Laboratory, Cerebral Imaging CentreDouglas Mental Health University InstituteVerdunQuebecCanada
| | - Christine L. Tardif
- McConnell Brain Imaging CenterMontreal Neurological Institute, McGill UniversityMontrealQuebecCanada
| | | | - John C. S. Breitner
- Centre for the Studies on the Prevention of ADDouglas Mental Health University InstituteVerdunQuebecCanada
| | - M. Mallar Chakravarty
- Integrated Program in NeuroscienceMcGill UniversityMontrealQuebecCanada
- Computational Brain Anatomy Laboratory, Cerebral Imaging CentreDouglas Mental Health University InstituteVerdunQuebecCanada
- Department of Biological and Biomedical EngineeringMcGill UniversityMontrealQuebecCanada
- Department of PsychiatryMcGill UniversityMontrealQuebecCanada
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12
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Duerden EG, Chakravarty MM, Lerch JP, Taylor MJ. Sex-Based Differences in Cortical and Subcortical Development in 436 Individuals Aged 4-54 Years. Cereb Cortex 2019; 30:2854-2866. [PMID: 31814003 DOI: 10.1093/cercor/bhz279] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2019] [Revised: 10/14/2019] [Accepted: 10/19/2019] [Indexed: 11/13/2022] Open
Abstract
Sex-based differences in brain development have long been established in ex vivo studies. Recent in vivo studies using magnetic resonance imaging (MRI) have offered considerable insight into sex-based variations in brain maturation. However, reports of sex-based differences in cortical volumes and thickness are inconsistent. We examined brain maturation in a cross-sectional, single-site cohort of 436 individuals (201 [46%] males) aged 4-54 years (median = 16 years). Cortical thickness, cortical surface area, subcortical surface area, volumes of the cerebral cortex, white matter (WM), cortical and subcortical gray matter (GM), including the thalamic subnuclei, basal ganglia, and hippocampi were calculated using automatic segmentation pipelines. Subcortical structures demonstrated distinct curvilinear trajectories from the cortex, in both volumetric maturation and surface-area expansion in relation to age. Surface-area analysis indicated that dorsal regions of the thalamus, globus pallidus and striatum, regions demonstrating structural connectivity with frontoparietal cortices, exhibited extensive expansion with age, and were inversely related to changes seen in cortical maturation, which contracted with age. Furthermore, surface-area expansion was more robust in males in comparison to females. Age- and sex-related maturational changes may reflect alterations in dendritic and synaptic architecture known to occur during development from early childhood through to mid-adulthood.
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Affiliation(s)
- Emma G Duerden
- Diagnostic Imaging, Hospital for Sick Children, Toronto, Ontario, Canada.,Faculty of Education, Western University, London, Ontario, Canada
| | - M Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada.,Departments of Psychiatry and Biomedical Engineering, McGill University, Montreal, Quebec, Canada
| | - Jason P Lerch
- Wellcome Centre for Integrative Neuroimaging, University of Oxford.,Mouse Imaging Centre, Hospital for Sick Children, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Margot J Taylor
- Diagnostic Imaging, Hospital for Sick Children, Toronto, Ontario, Canada.,Department of Psychology, University of Toronto, Toronto, Ontario, Canada.,Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
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13
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Lau JC, Parrent AG, Demarco J, Gupta G, Kai J, Stanley OW, Kuehn T, Park PJ, Ferko K, Khan AR, Peters TM. A framework for evaluating correspondence between brain images using anatomical fiducials. Hum Brain Mapp 2019; 40:4163-4179. [PMID: 31175816 DOI: 10.1002/hbm.24693] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2018] [Revised: 05/24/2019] [Accepted: 05/29/2019] [Indexed: 12/26/2022] Open
Abstract
Accurate spatial correspondence between template and subject images is a crucial step in neuroimaging studies and clinical applications like stereotactic neurosurgery. In the absence of a robust quantitative approach, we sought to propose and validate a set of point landmarks, anatomical fiducials (AFIDs), that could be quickly, accurately, and reliably placed on magnetic resonance images of the human brain. Using several publicly available brain templates and individual participant datasets, novice users could be trained to place a set of 32 AFIDs with millimetric accuracy. Furthermore, the utility of the AFIDs protocol is demonstrated for evaluating subject-to-template and template-to-template registration. Specifically, we found that commonly used voxel overlap metrics were relatively insensitive to focal misregistrations compared to AFID point-based measures. Our entire protocol and study framework leverages open resources and tools, and has been developed with full transparency in mind so that others may freely use, adopt, and modify. This protocol holds value for a broad number of applications including alignment of brain images and teaching neuroanatomy.
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Affiliation(s)
- Jonathan C Lau
- Department of Clinical Neurological Sciences, Division of Neurosurgery, Western University, London, Ontario, Canada.,Imaging Research Laboratories, Robarts Research Institute, Western University, London, Ontario, Canada.,Centre for Functional and Metabolic Mapping, Robarts Research Institute, Western University, London, Ontario, Canada.,School of Biomedical Engineering, Western University, London, Ontario, Canada
| | - Andrew G Parrent
- Department of Clinical Neurological Sciences, Division of Neurosurgery, Western University, London, Ontario, Canada
| | - John Demarco
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Ontario, Canada.,Centre for Functional and Metabolic Mapping, Robarts Research Institute, Western University, London, Ontario, Canada
| | - Geetika Gupta
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Ontario, Canada.,Centre for Functional and Metabolic Mapping, Robarts Research Institute, Western University, London, Ontario, Canada
| | - Jason Kai
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Ontario, Canada.,Centre for Functional and Metabolic Mapping, Robarts Research Institute, Western University, London, Ontario, Canada.,Department of Medical Biophysics, Western University, London, Ontario, Canada
| | - Olivia W Stanley
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Ontario, Canada.,Centre for Functional and Metabolic Mapping, Robarts Research Institute, Western University, London, Ontario, Canada.,Department of Medical Biophysics, Western University, London, Ontario, Canada
| | - Tristan Kuehn
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Ontario, Canada.,Centre for Functional and Metabolic Mapping, Robarts Research Institute, Western University, London, Ontario, Canada.,School of Biomedical Engineering, Western University, London, Ontario, Canada
| | - Patrick J Park
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Ontario, Canada.,Centre for Functional and Metabolic Mapping, Robarts Research Institute, Western University, London, Ontario, Canada
| | - Kayla Ferko
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Ontario, Canada.,Centre for Functional and Metabolic Mapping, Robarts Research Institute, Western University, London, Ontario, Canada.,Brain and Mind Institute, Western University, London, Ontario, Canada.,Graduate Program in Neuroscience, Western University, London, Ontario, Canada
| | - Ali R Khan
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Ontario, Canada.,Centre for Functional and Metabolic Mapping, Robarts Research Institute, Western University, London, Ontario, Canada.,School of Biomedical Engineering, Western University, London, Ontario, Canada.,Department of Medical Biophysics, Western University, London, Ontario, Canada.,Brain and Mind Institute, Western University, London, Ontario, Canada.,Graduate Program in Neuroscience, Western University, London, Ontario, Canada
| | - Terry M Peters
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Ontario, Canada.,Centre for Functional and Metabolic Mapping, Robarts Research Institute, Western University, London, Ontario, Canada.,School of Biomedical Engineering, Western University, London, Ontario, Canada.,Department of Medical Biophysics, Western University, London, Ontario, Canada.,Brain and Mind Institute, Western University, London, Ontario, Canada
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14
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Papadopoulou A, Oertel FC, Gaetano L, Kuchling J, Zimmermann H, Chien C, Siebert N, Asseyer S, Bellmann-Strobl J, Ruprecht K, Chakravarty MM, Scheel M, Magon S, Wuerfel J, Paul F, Brandt AU. Attack-related damage of thalamic nuclei in neuromyelitis optica spectrum disorders. J Neurol Neurosurg Psychiatry 2019; 90:1156-1164. [PMID: 31127016 DOI: 10.1136/jnnp-2018-320249] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 03/18/2019] [Accepted: 04/01/2019] [Indexed: 11/04/2022]
Abstract
OBJECTIVES In neuromyelitis optica spectrum disorders (NMOSD) thalamic damage is controversial, but thalamic nuclei were never studied separately. We aimed at assessing volume loss of thalamic nuclei in NMOSD. We hypothesised that only specific nuclei are damaged, by attacks affecting structures from which they receive afferences: the lateral geniculate nucleus (LGN), due to optic neuritis (ON) and the ventral posterior nucleus (VPN), due to myelitis. METHODS Thirty-nine patients with aquaporin 4-IgG seropositive NMOSD (age: 50.1±14.1 years, 36 women, 25 with prior ON, 36 with prior myelitis) and 37 healthy controls (age: 47.8 ± 12.5 years, 32 women) were included in this cross-sectional study. Thalamic nuclei were assessed in magnetic resonance images, using a multi-atlas-based approach of automated segmentation. Retinal optical coherence tomography was also performed. RESULTS Patients with ON showed smaller LGN volumes (181.6±44.2 mm3) compared with controls (198.3±49.4 mm3; B=-16.97, p=0.004) and to patients without ON (206.1±50 mm3 ; B=-23.74, p=0.001). LGN volume was associated with number of ON episodes (Rho=-0.536, p<0.001), peripapillary retinal nerve fibre layer thickness (B=0.70, p<0.001) and visual function (B=-0.01, p=0.002). Although VPN was not smaller in patients with myelitis (674.3±67.5 mm3) than controls (679.7±68.33; B=-7.36, p=0.594), we found reduced volumes in five patients with combined myelitis and brainstem attacks (B=-76.18, p=0.017). Volumes of entire thalamus and other nuclei were not smaller in patients than controls. CONCLUSION These findings suggest attack-related anterograde degeneration rather than diffuse thalamic damage in NMOSD. They also support a potential role of LGN volume as an imaging marker of structural brain damage in these patients.
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Affiliation(s)
- Athina Papadopoulou
- Neurocure Clinical Research Center, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of health, Berlin, Germany.,Experimental and Clinical Research Center, Max Delbrueck Center for Molecular Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.,Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedicine, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Frederike Cosima Oertel
- Neurocure Clinical Research Center, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of health, Berlin, Germany.,Experimental and Clinical Research Center, Max Delbrueck Center for Molecular Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Laura Gaetano
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedicine, University Hospital Basel and University of Basel, Basel, Switzerland.,Medical Image Analysis Center, Basel, Switzerland.,F. Hoffmann-La Roche, Basel, Switzerland
| | - Joseph Kuchling
- Neurocure Clinical Research Center, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of health, Berlin, Germany.,Experimental and Clinical Research Center, Max Delbrueck Center for Molecular Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Hanna Zimmermann
- Neurocure Clinical Research Center, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of health, Berlin, Germany.,Experimental and Clinical Research Center, Max Delbrueck Center for Molecular Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Claudia Chien
- Neurocure Clinical Research Center, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of health, Berlin, Germany.,Experimental and Clinical Research Center, Max Delbrueck Center for Molecular Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Nadja Siebert
- Neurocure Clinical Research Center, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of health, Berlin, Germany.,Experimental and Clinical Research Center, Max Delbrueck Center for Molecular Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Susanna Asseyer
- Neurocure Clinical Research Center, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of health, Berlin, Germany.,Experimental and Clinical Research Center, Max Delbrueck Center for Molecular Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Judith Bellmann-Strobl
- Neurocure Clinical Research Center, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of health, Berlin, Germany.,Experimental and Clinical Research Center, Max Delbrueck Center for Molecular Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Klemens Ruprecht
- Neurocure Clinical Research Center, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of health, Berlin, Germany.,Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - M Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Québec, Canada.,Department of Psychiatry and Biomedical engineering, McGill University, Montreal, Québec, Canada
| | - Michael Scheel
- Neurocure Clinical Research Center, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of health, Berlin, Germany.,Department of Neuroradiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Stefano Magon
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedicine, University Hospital Basel and University of Basel, Basel, Switzerland.,Medical Image Analysis Center, Basel, Switzerland
| | - Jens Wuerfel
- Medical Image Analysis Center, Basel, Switzerland
| | - Friedemann Paul
- Neurocure Clinical Research Center, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of health, Berlin, Germany.,Experimental and Clinical Research Center, Max Delbrueck Center for Molecular Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.,Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Alexander Ulrich Brandt
- Neurocure Clinical Research Center, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of health, Berlin, Germany .,Experimental and Clinical Research Center, Max Delbrueck Center for Molecular Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.,Department of Neurology, University of California Irvine, Irvine, California, USA
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15
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Wheeler AL, Felsky D, Viviano JD, Stojanovski S, Ameis SH, Szatmari P, Lerch JP, Chakravarty MM, Voineskos AN. BDNF-Dependent Effects on Amygdala-Cortical Circuitry and Depression Risk in Children and Youth. Cereb Cortex 2019; 28:1760-1770. [PMID: 28387866 DOI: 10.1093/cercor/bhx086] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2016] [Accepted: 03/24/2017] [Indexed: 01/03/2023] Open
Abstract
The brain-derived neurotrophic factor (BDNF) is critical for brain development, and the functional BDNF Val66Met polymorphism is implicated in risk for mood disorders. The objective of this study was to determine how the Val66Met polymorphism influences amygdala-cortical connectivity during neurodevelopment and assess the relevance for mood disorders. Age- and sex-specific effects of the BDNF Val66Met polymorphism on amygdala-cortical connectivity were assessed by examining covariance of amygdala volumes with thickness throughout the cortex in a sample of Caucasian youths ages 8-22 that were part of the Philadelphia Neurodevelopmental Cohort (n = 339). Follow-up analyses assessed corresponding BDNF genotype effects on resting-state functional connectivity (n = 186) and the association between BDNF genotype and major depressive disorder (MDD) (n = 2749). In adolescents, amygdala-cortical covariance was significantly stronger in Met allele carriers compared with Val/Val homozygotes in amygdala-cortical networks implicated in depression; these differences were driven by females. In follow-up analyses, the Met allele was also associated with stronger resting-state functional connectivity in adolescents and increased likelihood of MDD in adolescent females. The BDNF Val66Met polymorphism may confer risk for mood disorders in females through effects on amygdala-cortical connectivity during adolescence, coinciding with a period in the lifespan when onset of depression often occurs, more commonly in females.
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Affiliation(s)
- Anne L Wheeler
- Research Imaging Centre, Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada M5T 1R8.,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada M5T 1R8.,Program in Neuroscience and Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada M5G 0A4
| | - Daniel Felsky
- Research Imaging Centre, Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada M5T 1R8.,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada M5T 1R8
| | - Joseph D Viviano
- Research Imaging Centre, Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada M5T 1R8
| | - Sonja Stojanovski
- Program in Neuroscience and Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada M5G 0A4
| | - Stephanie H Ameis
- Research Imaging Centre, Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada M5T 1R8.,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada M5T 1R8.,Program in Neuroscience and Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada M5G 0A4.,Child Youth and Emerging Adult Program, Centre for Addiction and Mental Health, Toronto, Ontario, Canada M5T 1R8
| | - Peter Szatmari
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada M5T 1R8.,Program in Neuroscience and Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada M5G 0A4.,Child Youth and Emerging Adult Program, Centre for Addiction and Mental Health, Toronto, Ontario, Canada M5T 1R8
| | - Jason P Lerch
- Program in Neuroscience and Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada M5G 0A4.,Medical Biophysics, University of Toronto, Toronto, Ontario, Canada M5G 1L7
| | - M Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Institute, Montreal, Quebec, Canada H4H 1R3.,Department of Biomedical Engineering, McGill University, 3775 rue University Montreal, Quebec, Canada H3A 2B4
| | - Aristotle N Voineskos
- Research Imaging Centre, Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada M5T 1R8.,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada M5T 1R8.,Child Youth and Emerging Adult Program, Centre for Addiction and Mental Health, Toronto, Ontario, Canada M5T 1R8
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16
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Pagnozzi AM, Fripp J, Rose SE. Quantifying deep grey matter atrophy using automated segmentation approaches: A systematic review of structural MRI studies. Neuroimage 2019; 201:116018. [PMID: 31319182 DOI: 10.1016/j.neuroimage.2019.116018] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 07/01/2019] [Accepted: 07/12/2019] [Indexed: 12/13/2022] Open
Abstract
The deep grey matter (DGM) nuclei of the brain play a crucial role in learning, behaviour, cognition, movement and memory. Although automated segmentation strategies can provide insight into the impact of multiple neurological conditions affecting these structures, such as Multiple Sclerosis (MS), Huntington's disease (HD), Alzheimer's disease (AD), Parkinson's disease (PD) and Cerebral Palsy (CP), there are a number of technical challenges limiting an accurate automated segmentation of the DGM. Namely, the insufficient contrast of T1 sequences to completely identify the boundaries of these structures, as well as the presence of iso-intense white matter lesions or extensive tissue loss caused by brain injury. Therefore in this systematic review, 269 eligible studies were analysed and compared to determine the optimal approaches for addressing these technical challenges. The automated approaches used among the reviewed studies fall into three broad categories, atlas-based approaches focusing on the accurate alignment of atlas priors, algorithmic approaches which utilise intensity information to a greater extent, and learning-based approaches that require an annotated training set. Studies that utilise freely available software packages such as FIRST, FreeSurfer and LesionTOADS were also eligible, and their performance compared. Overall, deep learning approaches achieved the best overall performance, however these strategies are currently hampered by the lack of large-scale annotated data. Improving model generalisability to new datasets could be achieved in future studies with data augmentation and transfer learning. Multi-atlas approaches provided the second-best performance overall, and may be utilised to construct a "silver standard" annotated training set for deep learning. To address the technical challenges, providing robustness to injury can be improved by using multiple channels, highly elastic diffeomorphic transformations such as LDDMM, and by following atlas-based approaches with an intensity driven refinement of the segmentation, which has been done with the Expectation Maximisation (EM) and level sets methods. Accounting for potential lesions should be achieved with a separate lesion segmentation approach, as in LesionTOADS. Finally, to address the issue of limited contrast, R2*, T2* and QSM sequences could be used to better highlight the DGM due to its higher iron content. Future studies could look to additionally acquire these sequences by retaining the phase information from standard structural scans, or alternatively acquiring these sequences for only a training set, allowing models to learn the "improved" segmentation from T1-sequences alone.
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Affiliation(s)
- Alex M Pagnozzi
- CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia.
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia
| | - Stephen E Rose
- CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia
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Papadopoulou A, Gaetano L, Pfister A, Altermatt A, Tsagkas C, Morency F, Brandt AU, Hardmeier M, Chakravarty MM, Descoteaux M, Kappos L, Sprenger T, Magon S. Damage of the lateral geniculate nucleus in MS: Assessing the missing node of the visual pathway. Neurology 2019; 92:e2240-e2249. [PMID: 30971483 DOI: 10.1212/wnl.0000000000007450] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2018] [Accepted: 01/10/2019] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE To study if the thalamic lateral geniculate nucleus (LGN) is affected in multiple sclerosis (MS) due to anterograde degeneration from optic neuritis (ON) or retrograde degeneration from optic radiation (OR) pathology, and if this is relevant for visual function. METHODS In this cross-sectional study, LGN volume of 34 patients with relapsing-remitting MS and 33 matched healthy controls (HC) was assessed on MRI using atlas-based automated segmentation (MAGeT). ON history, thickness of the ganglion cell-inner plexiform layer (GC-IPL), OR lesion volume, and fractional anisotropy (FA) of normal-appearing OR (NAOR-FA) were assessed as measures of afferent visual pathway damage. Visual function was tested, including low-contrast letter acuity (LCLA) and Hardy-Rand-Rittler (HRR) plates for color vision. RESULTS LGN volume was reduced in patients vs HC (165.5 ± 45.5 vs 191.4 ± 47.7 mm3, B = -25.89, SE = 5.83, p < 0.001). It was associated with GC-IPL thickness (B = 0.95, SE = 0.33, p = 0.006) and correlated with OR lesion volume (Spearman ρ = -0.53, p = 0.001), and these relationships remained after adjustment for normalized brain volume. There was no association between NAOR-FA and LGN volume (B = -133.28, SE = 88.47, p = 0.137). LGN volume was not associated with LCLA (B = 5.5 × 10-5, SE = 0.03, p = 0.998), but it correlated with HRR color vision (ρ = 0.39, p = 0.032). CONCLUSIONS LGN volume loss in MS indicates structural damage with potential functional relevance. Our results suggest both anterograde degeneration from the retina and retrograde degeneration from the OR lesions as underlying causes. LGN volume is a promising marker reflecting damage of the visual pathway in MS, with the advantage of individual measurement per patient on conventional MRI.
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Affiliation(s)
- Athina Papadopoulou
- From the Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research, and Biomedical Engineering (A. Papadopoulou, L.G., A. Pfister, C.T., M.H., L.K., T.S., S.M.), and Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering (A. Papadopoulou, L.G., A.A., C.T., S.M.), University Hospital Basel and University of Basel, Switzerland; NeuroCure Clinical Research Center (NCRC) (A. Papadopoulou, A.U.B.), and Experimental and Clinical Research Center (A. Papadopoulou, A.U.B.), Max Delbrück Center for Molecular Medicine, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Germany; Medical Image Analysis Center (MIAC) (L.G., A.A., C.T., S.M.), Basel, Switzerland; Imeka Solutions (F.M.), Sherbrooke, Canada; Department of Neurology (A.U.B.), University of California Irvine; Cerebral Imaging Centre (M.M.C.), Douglas Mental Health University Institute; Departments of Psychiatry and Biomedical Engineering (M.M.C.), McGill University, Montreal; University of Sherbrooke (M.D.), Canada; and Department of Neurology (T.S.), DKD Helios Klinik Wiesbaden, Germany. The present address for L.G. is F. Hoffmann-La Roche, Basel, Switzerland.
| | - Laura Gaetano
- From the Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research, and Biomedical Engineering (A. Papadopoulou, L.G., A. Pfister, C.T., M.H., L.K., T.S., S.M.), and Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering (A. Papadopoulou, L.G., A.A., C.T., S.M.), University Hospital Basel and University of Basel, Switzerland; NeuroCure Clinical Research Center (NCRC) (A. Papadopoulou, A.U.B.), and Experimental and Clinical Research Center (A. Papadopoulou, A.U.B.), Max Delbrück Center for Molecular Medicine, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Germany; Medical Image Analysis Center (MIAC) (L.G., A.A., C.T., S.M.), Basel, Switzerland; Imeka Solutions (F.M.), Sherbrooke, Canada; Department of Neurology (A.U.B.), University of California Irvine; Cerebral Imaging Centre (M.M.C.), Douglas Mental Health University Institute; Departments of Psychiatry and Biomedical Engineering (M.M.C.), McGill University, Montreal; University of Sherbrooke (M.D.), Canada; and Department of Neurology (T.S.), DKD Helios Klinik Wiesbaden, Germany. The present address for L.G. is F. Hoffmann-La Roche, Basel, Switzerland
| | - Armanda Pfister
- From the Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research, and Biomedical Engineering (A. Papadopoulou, L.G., A. Pfister, C.T., M.H., L.K., T.S., S.M.), and Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering (A. Papadopoulou, L.G., A.A., C.T., S.M.), University Hospital Basel and University of Basel, Switzerland; NeuroCure Clinical Research Center (NCRC) (A. Papadopoulou, A.U.B.), and Experimental and Clinical Research Center (A. Papadopoulou, A.U.B.), Max Delbrück Center for Molecular Medicine, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Germany; Medical Image Analysis Center (MIAC) (L.G., A.A., C.T., S.M.), Basel, Switzerland; Imeka Solutions (F.M.), Sherbrooke, Canada; Department of Neurology (A.U.B.), University of California Irvine; Cerebral Imaging Centre (M.M.C.), Douglas Mental Health University Institute; Departments of Psychiatry and Biomedical Engineering (M.M.C.), McGill University, Montreal; University of Sherbrooke (M.D.), Canada; and Department of Neurology (T.S.), DKD Helios Klinik Wiesbaden, Germany. The present address for L.G. is F. Hoffmann-La Roche, Basel, Switzerland
| | - Anna Altermatt
- From the Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research, and Biomedical Engineering (A. Papadopoulou, L.G., A. Pfister, C.T., M.H., L.K., T.S., S.M.), and Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering (A. Papadopoulou, L.G., A.A., C.T., S.M.), University Hospital Basel and University of Basel, Switzerland; NeuroCure Clinical Research Center (NCRC) (A. Papadopoulou, A.U.B.), and Experimental and Clinical Research Center (A. Papadopoulou, A.U.B.), Max Delbrück Center for Molecular Medicine, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Germany; Medical Image Analysis Center (MIAC) (L.G., A.A., C.T., S.M.), Basel, Switzerland; Imeka Solutions (F.M.), Sherbrooke, Canada; Department of Neurology (A.U.B.), University of California Irvine; Cerebral Imaging Centre (M.M.C.), Douglas Mental Health University Institute; Departments of Psychiatry and Biomedical Engineering (M.M.C.), McGill University, Montreal; University of Sherbrooke (M.D.), Canada; and Department of Neurology (T.S.), DKD Helios Klinik Wiesbaden, Germany. The present address for L.G. is F. Hoffmann-La Roche, Basel, Switzerland
| | - Charidimos Tsagkas
- From the Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research, and Biomedical Engineering (A. Papadopoulou, L.G., A. Pfister, C.T., M.H., L.K., T.S., S.M.), and Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering (A. Papadopoulou, L.G., A.A., C.T., S.M.), University Hospital Basel and University of Basel, Switzerland; NeuroCure Clinical Research Center (NCRC) (A. Papadopoulou, A.U.B.), and Experimental and Clinical Research Center (A. Papadopoulou, A.U.B.), Max Delbrück Center for Molecular Medicine, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Germany; Medical Image Analysis Center (MIAC) (L.G., A.A., C.T., S.M.), Basel, Switzerland; Imeka Solutions (F.M.), Sherbrooke, Canada; Department of Neurology (A.U.B.), University of California Irvine; Cerebral Imaging Centre (M.M.C.), Douglas Mental Health University Institute; Departments of Psychiatry and Biomedical Engineering (M.M.C.), McGill University, Montreal; University of Sherbrooke (M.D.), Canada; and Department of Neurology (T.S.), DKD Helios Klinik Wiesbaden, Germany. The present address for L.G. is F. Hoffmann-La Roche, Basel, Switzerland
| | - Felix Morency
- From the Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research, and Biomedical Engineering (A. Papadopoulou, L.G., A. Pfister, C.T., M.H., L.K., T.S., S.M.), and Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering (A. Papadopoulou, L.G., A.A., C.T., S.M.), University Hospital Basel and University of Basel, Switzerland; NeuroCure Clinical Research Center (NCRC) (A. Papadopoulou, A.U.B.), and Experimental and Clinical Research Center (A. Papadopoulou, A.U.B.), Max Delbrück Center for Molecular Medicine, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Germany; Medical Image Analysis Center (MIAC) (L.G., A.A., C.T., S.M.), Basel, Switzerland; Imeka Solutions (F.M.), Sherbrooke, Canada; Department of Neurology (A.U.B.), University of California Irvine; Cerebral Imaging Centre (M.M.C.), Douglas Mental Health University Institute; Departments of Psychiatry and Biomedical Engineering (M.M.C.), McGill University, Montreal; University of Sherbrooke (M.D.), Canada; and Department of Neurology (T.S.), DKD Helios Klinik Wiesbaden, Germany. The present address for L.G. is F. Hoffmann-La Roche, Basel, Switzerland
| | - Alexander U Brandt
- From the Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research, and Biomedical Engineering (A. Papadopoulou, L.G., A. Pfister, C.T., M.H., L.K., T.S., S.M.), and Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering (A. Papadopoulou, L.G., A.A., C.T., S.M.), University Hospital Basel and University of Basel, Switzerland; NeuroCure Clinical Research Center (NCRC) (A. Papadopoulou, A.U.B.), and Experimental and Clinical Research Center (A. Papadopoulou, A.U.B.), Max Delbrück Center for Molecular Medicine, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Germany; Medical Image Analysis Center (MIAC) (L.G., A.A., C.T., S.M.), Basel, Switzerland; Imeka Solutions (F.M.), Sherbrooke, Canada; Department of Neurology (A.U.B.), University of California Irvine; Cerebral Imaging Centre (M.M.C.), Douglas Mental Health University Institute; Departments of Psychiatry and Biomedical Engineering (M.M.C.), McGill University, Montreal; University of Sherbrooke (M.D.), Canada; and Department of Neurology (T.S.), DKD Helios Klinik Wiesbaden, Germany. The present address for L.G. is F. Hoffmann-La Roche, Basel, Switzerland
| | - Martin Hardmeier
- From the Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research, and Biomedical Engineering (A. Papadopoulou, L.G., A. Pfister, C.T., M.H., L.K., T.S., S.M.), and Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering (A. Papadopoulou, L.G., A.A., C.T., S.M.), University Hospital Basel and University of Basel, Switzerland; NeuroCure Clinical Research Center (NCRC) (A. Papadopoulou, A.U.B.), and Experimental and Clinical Research Center (A. Papadopoulou, A.U.B.), Max Delbrück Center for Molecular Medicine, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Germany; Medical Image Analysis Center (MIAC) (L.G., A.A., C.T., S.M.), Basel, Switzerland; Imeka Solutions (F.M.), Sherbrooke, Canada; Department of Neurology (A.U.B.), University of California Irvine; Cerebral Imaging Centre (M.M.C.), Douglas Mental Health University Institute; Departments of Psychiatry and Biomedical Engineering (M.M.C.), McGill University, Montreal; University of Sherbrooke (M.D.), Canada; and Department of Neurology (T.S.), DKD Helios Klinik Wiesbaden, Germany. The present address for L.G. is F. Hoffmann-La Roche, Basel, Switzerland
| | - Mallar M Chakravarty
- From the Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research, and Biomedical Engineering (A. Papadopoulou, L.G., A. Pfister, C.T., M.H., L.K., T.S., S.M.), and Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering (A. Papadopoulou, L.G., A.A., C.T., S.M.), University Hospital Basel and University of Basel, Switzerland; NeuroCure Clinical Research Center (NCRC) (A. Papadopoulou, A.U.B.), and Experimental and Clinical Research Center (A. Papadopoulou, A.U.B.), Max Delbrück Center for Molecular Medicine, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Germany; Medical Image Analysis Center (MIAC) (L.G., A.A., C.T., S.M.), Basel, Switzerland; Imeka Solutions (F.M.), Sherbrooke, Canada; Department of Neurology (A.U.B.), University of California Irvine; Cerebral Imaging Centre (M.M.C.), Douglas Mental Health University Institute; Departments of Psychiatry and Biomedical Engineering (M.M.C.), McGill University, Montreal; University of Sherbrooke (M.D.), Canada; and Department of Neurology (T.S.), DKD Helios Klinik Wiesbaden, Germany. The present address for L.G. is F. Hoffmann-La Roche, Basel, Switzerland
| | - Maxime Descoteaux
- From the Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research, and Biomedical Engineering (A. Papadopoulou, L.G., A. Pfister, C.T., M.H., L.K., T.S., S.M.), and Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering (A. Papadopoulou, L.G., A.A., C.T., S.M.), University Hospital Basel and University of Basel, Switzerland; NeuroCure Clinical Research Center (NCRC) (A. Papadopoulou, A.U.B.), and Experimental and Clinical Research Center (A. Papadopoulou, A.U.B.), Max Delbrück Center for Molecular Medicine, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Germany; Medical Image Analysis Center (MIAC) (L.G., A.A., C.T., S.M.), Basel, Switzerland; Imeka Solutions (F.M.), Sherbrooke, Canada; Department of Neurology (A.U.B.), University of California Irvine; Cerebral Imaging Centre (M.M.C.), Douglas Mental Health University Institute; Departments of Psychiatry and Biomedical Engineering (M.M.C.), McGill University, Montreal; University of Sherbrooke (M.D.), Canada; and Department of Neurology (T.S.), DKD Helios Klinik Wiesbaden, Germany. The present address for L.G. is F. Hoffmann-La Roche, Basel, Switzerland
| | - Ludwig Kappos
- From the Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research, and Biomedical Engineering (A. Papadopoulou, L.G., A. Pfister, C.T., M.H., L.K., T.S., S.M.), and Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering (A. Papadopoulou, L.G., A.A., C.T., S.M.), University Hospital Basel and University of Basel, Switzerland; NeuroCure Clinical Research Center (NCRC) (A. Papadopoulou, A.U.B.), and Experimental and Clinical Research Center (A. Papadopoulou, A.U.B.), Max Delbrück Center for Molecular Medicine, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Germany; Medical Image Analysis Center (MIAC) (L.G., A.A., C.T., S.M.), Basel, Switzerland; Imeka Solutions (F.M.), Sherbrooke, Canada; Department of Neurology (A.U.B.), University of California Irvine; Cerebral Imaging Centre (M.M.C.), Douglas Mental Health University Institute; Departments of Psychiatry and Biomedical Engineering (M.M.C.), McGill University, Montreal; University of Sherbrooke (M.D.), Canada; and Department of Neurology (T.S.), DKD Helios Klinik Wiesbaden, Germany. The present address for L.G. is F. Hoffmann-La Roche, Basel, Switzerland
| | - Till Sprenger
- From the Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research, and Biomedical Engineering (A. Papadopoulou, L.G., A. Pfister, C.T., M.H., L.K., T.S., S.M.), and Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering (A. Papadopoulou, L.G., A.A., C.T., S.M.), University Hospital Basel and University of Basel, Switzerland; NeuroCure Clinical Research Center (NCRC) (A. Papadopoulou, A.U.B.), and Experimental and Clinical Research Center (A. Papadopoulou, A.U.B.), Max Delbrück Center for Molecular Medicine, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Germany; Medical Image Analysis Center (MIAC) (L.G., A.A., C.T., S.M.), Basel, Switzerland; Imeka Solutions (F.M.), Sherbrooke, Canada; Department of Neurology (A.U.B.), University of California Irvine; Cerebral Imaging Centre (M.M.C.), Douglas Mental Health University Institute; Departments of Psychiatry and Biomedical Engineering (M.M.C.), McGill University, Montreal; University of Sherbrooke (M.D.), Canada; and Department of Neurology (T.S.), DKD Helios Klinik Wiesbaden, Germany. The present address for L.G. is F. Hoffmann-La Roche, Basel, Switzerland
| | - Stefano Magon
- From the Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research, and Biomedical Engineering (A. Papadopoulou, L.G., A. Pfister, C.T., M.H., L.K., T.S., S.M.), and Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering (A. Papadopoulou, L.G., A.A., C.T., S.M.), University Hospital Basel and University of Basel, Switzerland; NeuroCure Clinical Research Center (NCRC) (A. Papadopoulou, A.U.B.), and Experimental and Clinical Research Center (A. Papadopoulou, A.U.B.), Max Delbrück Center for Molecular Medicine, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Germany; Medical Image Analysis Center (MIAC) (L.G., A.A., C.T., S.M.), Basel, Switzerland; Imeka Solutions (F.M.), Sherbrooke, Canada; Department of Neurology (A.U.B.), University of California Irvine; Cerebral Imaging Centre (M.M.C.), Douglas Mental Health University Institute; Departments of Psychiatry and Biomedical Engineering (M.M.C.), McGill University, Montreal; University of Sherbrooke (M.D.), Canada; and Department of Neurology (T.S.), DKD Helios Klinik Wiesbaden, Germany. The present address for L.G. is F. Hoffmann-La Roche, Basel, Switzerland
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Bot M, van den Munckhof P, Schmand BA, de Bie RMA, Schuurman PR. Electrode Penetration of the Caudate Nucleus in Deep Brain Stimulation Surgery for Parkinson's Disease. Stereotact Funct Neurosurg 2018; 96:223-230. [PMID: 30176664 DOI: 10.1159/000489944] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2017] [Accepted: 05/10/2018] [Indexed: 11/19/2022]
Abstract
OBJECTIVE To evaluate the possible influence of electrode trajectories penetrating the caudate nucleus (CN) on cognitive outcomes in deep brain stimulation (DBS) surgery for Parkinson's disease (PD). BACKGROUND It is currently unclear how mandatory CN avoidance during trajectory planning is. DESIGN/METHODS Electrode trajectories were determined to be inside, outside, or in border region of the CN. Pre- and postoperative neuropsychological tests of each trajectory group were compared in order to evaluate possible differences in cognitive outcomes 12 months after bilateral STN DBS. RESULTS One hundred six electrode tracks in 53 patients were evaluated. Bilateral penetration of the CN occurred in 15 (28%) patients, while unilateral penetration occurred in 28 (53%). In 19 (36%) patients tracks were located in the border region of the CN. There was no electrode penetration of the CN in 10 (19%) patients. No difference in cognitive outcomes was found between the different groups. CONCLUSION Cognitive outcome was not influenced by DBS electrode tracks penetrating the CN. It is both feasible and sensible to avoid electrode tracks through the CN when possible, considering its function and anatomical position. However, penetration of the CN can be considered without major concerns regarding cognitive decline when this facilitates optimal trajectory planning due to specific individual anatomical variations.
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Affiliation(s)
- Maarten Bot
- Department of Neurosurgery, Academic Medical Center, Amsterdam, the Netherlands
| | | | - Ben A Schmand
- Department of Psychology, Academic Medical Center, Amsterdam, the Netherlands
| | - Rob M A de Bie
- Department of Neurology, Academic Medical Center, Amsterdam, the Netherlands
| | - P Richard Schuurman
- Department of Neurosurgery, Academic Medical Center, Amsterdam, the Netherlands
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Tullo S, Devenyi GA, Patel R, Park MTM, Collins DL, Chakravarty MM. Warping an atlas derived from serial histology to 5 high-resolution MRIs. Sci Data 2018; 5:180107. [PMID: 29917012 PMCID: PMC6007088 DOI: 10.1038/sdata.2018.107] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2017] [Accepted: 04/06/2018] [Indexed: 11/09/2022] Open
Abstract
Previous work from our group demonstrated the use of multiple input atlases to a modified multi-atlas framework (MAGeT-Brain) to improve subject-based segmentation accuracy. Currently, segmentation of the striatum, globus pallidus and thalamus are generated from a single high-resolution and -contrast MRI atlas derived from annotated serial histological sections. Here, we warp this atlas to five high-resolution MRI templates to create five de novo atlases. The overall goal of this work is to use these newly warped atlases as input to MAGeT-Brain in an effort to consolidate and improve the workflow presented in previous manuscripts from our group, allowing for simultaneous multi-structure segmentation. The work presented details the methodology used for the creation of the atlases using a technique previously proposed, where atlas labels are modified to mimic the intensity and contrast profile of MRI to facilitate atlas-to-template nonlinear transformation estimation. Dice's Kappa metric was used to demonstrate high quality registration and segmentation accuracy of the atlases. The final atlases are available at https://github.com/CobraLab/atlases/tree/master/5-atlas-subcortical.
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Affiliation(s)
- Stephanie Tullo
- Integrated Program in Neuroscience, McGill University, Montreal, Canada.,Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Canada
| | - Gabriel A Devenyi
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Canada.,Department of Psychiatry, McGill University, Montreal, Canada
| | - Raihaan Patel
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Canada.,Department of Biological and Biomedical Engineering, McGill University, Montreal, Canada
| | - Min Tae M Park
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Canada.,Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - D Louis Collins
- Department of Biological and Biomedical Engineering, McGill University, Montreal, Canada.,McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada
| | - M Mallar Chakravarty
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Canada.,Department of Psychiatry, McGill University, Montreal, Canada.,Department of Biological and Biomedical Engineering, McGill University, Montreal, Canada
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Evaluating accuracy of striatal, pallidal, and thalamic segmentation methods: Comparing automated approaches to manual delineation. Neuroimage 2018; 170:182-198. [DOI: 10.1016/j.neuroimage.2017.02.069] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2016] [Revised: 02/03/2017] [Accepted: 02/24/2017] [Indexed: 12/16/2022] Open
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Zhang L, Wang Q, Gao Y, Wu G, Shen D. Concatenated Spatially-localized Random Forests for Hippocampus Labeling in Adult and Infant MR Brain Images. Neurocomputing 2017; 229:3-12. [PMID: 28133417 PMCID: PMC5268165 DOI: 10.1016/j.neucom.2016.05.082] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Automatic labeling of the hippocampus in brain MR images is highly demanded, as it has played an important role in imaging-based brain studies. However, accurate labeling of the hippocampus is still challenging, partially due to the ambiguous intensity boundary between the hippocampus and surrounding anatomies. In this paper, we propose a concatenated set of spatially-localized random forests for multi-atlas-based hippocampus labeling of adult/infant brain MR images. The contribution in our work is two-fold. First, each forest classifier is trained to label just a specific sub-region of the hippocampus, thus enhancing the labeling accuracy. Second, a novel forest selection strategy is proposed, such that each voxel in the test image can automatically select a set of optimal forests, and then dynamically fuses their respective outputs for determining the final label. Furthermore, we enhance the spatially-localized random forests with the aid of the auto-context strategy. In this way, our proposed learning framework can gradually refine the tentative labeling result for better performance. Experiments show that, regarding the large datasets of both adult and infant brain MR images, our method owns satisfactory scalability by segmenting the hippocampus accurately and efficiently.
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Affiliation(s)
- Lichi Zhang
- Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University
| | - Qian Wang
- Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University
| | - Yaozong Gao
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill; Department of Computer Science, University of North Carolina at Chapel Hill
| | - Guorong Wu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill; Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
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22
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Leh SE, Kälin AM, Schroeder C, Park MTM, Chakravarty MM, Freund P, Gietl AF, Riese F, Kollias S, Hock C, Michels L. Volumetric and shape analysis of the thalamus and striatum in amnestic mild cognitive impairment. J Alzheimers Dis 2016; 49:237-49. [PMID: 26444755 DOI: 10.3233/jad-150080] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Alterations in brain structures, including progressive neurodegeneration, are a hallmark in patients with Alzheimer's disease (AD). However, pathological mechanisms, such as the accumulation of amyloid and the proliferation of tau, are thought to begin years, even decades, before the initial clinical manifestations of AD. In this study, we compare the brain anatomy of amnestic mild cognitive impairment patients (aMCI, n = 16) to healthy subjects (CS, n = 22) using cortical thickness, subcortical volume, and shape analysis, which we believe to be complimentary to volumetric measures. We were able to replicate "classical" cortical thickness alterations in aMCI in the hippocampus, amygdala, putamen, insula, and inferior temporal regions. Additionally, aMCI showed significant thalamic and striatal shape differences. We observed higher global amyloid deposition in aMCI, a significant correlation between striatal displacement and global amyloid, and an inverse correlation between executive function and right-hemispheric thalamic displacement. In contrast, no volumetric differences were detected in thalamic, striatal, and hippocampal regions. Our results provide new evidence for early subcortical neuroanatomical changes in patients with aMCI, which are linked to cognitive abilities and amyloid deposition. Hence, shape analysis may aid in the identification of structural biomarkers for identifying individuals at highest risk of conversion to AD.
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Affiliation(s)
- Sandra E Leh
- Division of Psychiatry Research and Psychogeriatric Medicine, University of Zurich, Switzerland
| | - Andrea M Kälin
- Division of Psychiatry Research and Psychogeriatric Medicine, University of Zurich, Switzerland
| | - Clemens Schroeder
- Division of Psychiatry Research and Psychogeriatric Medicine, University of Zurich, Switzerland
| | - Min Tae M Park
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Canada.,Schulich School of Medicine and Dentistry, The University of Western Ontario, London, Canada
| | - M Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Canada.,Departments of Psychiatry and Biomedical Engineering, McGill University, Montreal, Canada
| | - Patrick Freund
- Spinal Cord Injury Center, University Hospital Balgrist, Switzerland.,Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, University College London, London, UK.,Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, UK.,Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Anton F Gietl
- Division of Psychiatry Research and Psychogeriatric Medicine, University of Zurich, Switzerland
| | - Florian Riese
- Division of Psychiatry Research and Psychogeriatric Medicine, University of Zurich, Switzerland
| | - Spyros Kollias
- Institute of Neuroradiology, University of Zurich, Switzerland
| | - Christoph Hock
- Division of Psychiatry Research and Psychogeriatric Medicine, University of Zurich, Switzerland
| | - Lars Michels
- Institute of Neuroradiology, University of Zurich, Switzerland
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23
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Bhagwat N, Pipitone J, Winterburn JL, Guo T, Duerden EG, Voineskos AN, Lepage M, Miller SP, Pruessner JC, Chakravarty MM. Manual-Protocol Inspired Technique for Improving Automated MR Image Segmentation during Label Fusion. Front Neurosci 2016; 10:325. [PMID: 27486386 PMCID: PMC4949270 DOI: 10.3389/fnins.2016.00325] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2015] [Accepted: 06/28/2016] [Indexed: 01/08/2023] Open
Abstract
Recent advances in multi-atlas based algorithms address many of the previous limitations in model-based and probabilistic segmentation methods. However, at the label fusion stage, a majority of algorithms focus primarily on optimizing weight-maps associated with the atlas library based on a theoretical objective function that approximates the segmentation error. In contrast, we propose a novel method—Autocorrecting Walks over Localized Markov Random Fields (AWoL-MRF)—that aims at mimicking the sequential process of manual segmentation, which is the gold-standard for virtually all the segmentation methods. AWoL-MRF begins with a set of candidate labels generated by a multi-atlas segmentation pipeline as an initial label distribution and refines low confidence regions based on a localized Markov random field (L-MRF) model using a novel sequential inference process (walks). We show that AWoL-MRF produces state-of-the-art results with superior accuracy and robustness with a small atlas library compared to existing methods. We validate the proposed approach by performing hippocampal segmentations on three independent datasets: (1) Alzheimer's Disease Neuroimaging Database (ADNI); (2) First Episode Psychosis patient cohort; and (3) A cohort of preterm neonates scanned early in life and at term-equivalent age. We assess the improvement in the performance qualitatively as well as quantitatively by comparing AWoL-MRF with majority vote, STAPLE, and Joint Label Fusion methods. AWoL-MRF reaches a maximum accuracy of 0.881 (dataset 1), 0.897 (dataset 2), and 0.807 (dataset 3) based on Dice similarity coefficient metric, offering significant performance improvements with a smaller atlas library (< 10) over compared methods. We also evaluate the diagnostic utility of AWoL-MRF by analyzing the volume differences per disease category in the ADNI1: Complete Screening dataset. We have made the source code for AWoL-MRF public at: https://github.com/CobraLab/AWoL-MRF.
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Affiliation(s)
- Nikhil Bhagwat
- Institute of Biomaterials and Biomedical Engineering, University of TorontoToronto, ON, Canada; Cerebral Imaging Centre, Douglas Mental Health University InstituteVerdun, QC, Canada; Kimel Family Translational Imaging-Genetics Research Lab, Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental HealthToronto, ON, Canada
| | - Jon Pipitone
- Kimel Family Translational Imaging-Genetics Research Lab, Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health Toronto, ON, Canada
| | - Julie L Winterburn
- Institute of Biomaterials and Biomedical Engineering, University of TorontoToronto, ON, Canada; Cerebral Imaging Centre, Douglas Mental Health University InstituteVerdun, QC, Canada; Kimel Family Translational Imaging-Genetics Research Lab, Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental HealthToronto, ON, Canada
| | - Ting Guo
- Neurosciences and Mental Health, The Hospital for Sick Children Research InstituteToronto, ON, Canada; Department of Paediatrics, The Hospital for Sick Children and the University of TorontoToronto, ON, Canada
| | - Emma G Duerden
- Neurosciences and Mental Health, The Hospital for Sick Children Research InstituteToronto, ON, Canada; Department of Paediatrics, The Hospital for Sick Children and the University of TorontoToronto, ON, Canada
| | - Aristotle N Voineskos
- Kimel Family Translational Imaging-Genetics Research Lab, Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental HealthToronto, ON, Canada; Department of Psychiatry, University of TorontoToronto, ON, Canada
| | - Martin Lepage
- Cerebral Imaging Centre, Douglas Mental Health University InstituteVerdun, QC, Canada; Department of Psychiatry, McGill UniversityMontreal, QC, Canada
| | - Steven P Miller
- Neurosciences and Mental Health, The Hospital for Sick Children Research InstituteToronto, ON, Canada; Department of Paediatrics, The Hospital for Sick Children and the University of TorontoToronto, ON, Canada
| | - Jens C Pruessner
- Cerebral Imaging Centre, Douglas Mental Health University InstituteVerdun, QC, Canada; McGill Centre for Studies in AgingMontreal, QC, Canada
| | - M Mallar Chakravarty
- Institute of Biomaterials and Biomedical Engineering, University of TorontoToronto, ON, Canada; Cerebral Imaging Centre, Douglas Mental Health University InstituteVerdun, QC, Canada; Department of Psychiatry, McGill UniversityMontreal, QC, Canada; Biological and Biomedical Engineering, McGill UniversityMontreal, QC, Canada
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24
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Xiao Y, Zitella LM, Duchin Y, Teplitzky BA, Kastl D, Adriany G, Yacoub E, Harel N, Johnson MD. Multimodal 7T Imaging of Thalamic Nuclei for Preclinical Deep Brain Stimulation Applications. Front Neurosci 2016; 10:264. [PMID: 27375422 PMCID: PMC4901062 DOI: 10.3389/fnins.2016.00264] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2015] [Accepted: 05/25/2016] [Indexed: 01/14/2023] Open
Abstract
Precise neurosurgical targeting of electrode arrays within the brain is essential to the successful treatment of a range of brain disorders with deep brain stimulation (DBS) therapy. Here, we describe a set of computational tools to generate in vivo, subject-specific atlases of individual thalamic nuclei thus improving the ability to visualize thalamic targets for preclinical DBS applications on a subject-specific basis. A sequential nonlinear atlas warping technique and a Bayesian estimation technique for probabilistic crossing fiber tractography were applied to high field (7T) susceptibility-weighted and diffusion-weighted imaging, respectively, in seven rhesus macaques. Image contrast, including contrast within thalamus from the susceptibility-weighted images, informed the atlas warping process and guided the seed point placement for fiber tractography. The susceptibility-weighted imaging resulted in relative hyperintensity of the intralaminar nuclei and relative hypointensity in the medial dorsal nucleus, pulvinar, and the medial/ventral border of the ventral posterior nuclei, providing context to demarcate borders of the ventral nuclei of thalamus, which are often targeted for DBS applications. Additionally, ascending fiber tractography of the medial lemniscus, superior cerebellar peduncle, and pallidofugal pathways into thalamus provided structural demarcation of the ventral nuclei of thalamus. The thalamic substructure boundaries were validated through in vivo electrophysiological recordings and post-mortem blockface tissue sectioning. Together, these imaging tools for visualizing and segmenting thalamus have the potential to improve the neurosurgical targeting of DBS implants and enhance the selection of stimulation settings through more accurate computational models of DBS.
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Affiliation(s)
- YiZi Xiao
- Department of Biomedical Engineering, University of Minnesota Minneapolis, MN, USA
| | - Laura M Zitella
- Department of Biomedical Engineering, University of Minnesota Minneapolis, MN, USA
| | - Yuval Duchin
- Center for Magnetic Resonance Research, University of Minnesota Minneapolis, MN, USA
| | - Benjamin A Teplitzky
- Department of Biomedical Engineering, University of Minnesota Minneapolis, MN, USA
| | - Daniel Kastl
- Department of Biomedical Engineering, University of Minnesota Minneapolis, MN, USA
| | - Gregor Adriany
- Center for Magnetic Resonance Research, University of Minnesota Minneapolis, MN, USA
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota Minneapolis, MN, USA
| | - Noam Harel
- Center for Magnetic Resonance Research, University of Minnesota Minneapolis, MN, USA
| | - Matthew D Johnson
- Department of Biomedical Engineering, University of MinnesotaMinneapolis, MN, USA; Institute for Translational Neuroscience, University of MinnesotaMinneapolis, MN, USA
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25
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Morphological Abnormalities of Thalamic Subnuclei in Migraine: A Multicenter MRI Study at 3 Tesla. J Neurosci 2016; 35:13800-6. [PMID: 26446230 DOI: 10.1523/jneurosci.2154-15.2015] [Citation(s) in RCA: 53] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
UNLABELLED The thalamus contains third-order relay neurons of the trigeminal system, and animal models as well as preliminary imaging studies in small cohorts of migraine patients have suggested a role of the thalamus in headache pathophysiology. However, larger studies using advanced imaging techniques in substantial patient populations are lacking. In the present study, we investigated changes of thalamic volume and shape in a large multicenter cohort of patients with migraine. High-resolution T1-weighted MRI data acquired at 3 tesla in 131 patients with migraine (38 with aura; 30.8 ± 9 years old; 109 women; monthly attack frequency: 3.2 ± 2.5; disease duration: 14 ± 8.4 years) and 115 matched healthy subjects (29 ± 7 years old; 81 women) from four international tertiary headache centers were analyzed. The thalamus and thalamic subnuclei, striatum, and globus pallidus were segmented using a fully automated multiatlas approach. Deformation-based shape analysis was performed to localize surface abnormalities. Differences between patients with migraine and healthy subjects were assessed using an ANCOVA model. After correction for multiple comparisons, performed using the false discovery rate approach (p < 0.05 corrected), significant volume reductions of the following thalamic nuclei were observed in migraineurs: central nuclear complex (F(1,233) = 6.79), anterior nucleus (F(1,237) = 7.38), and lateral dorsal nucleus (F(1,238) = 6.79). Moreover, reduced striatal volume (F(1,238) = 6.9) was observed in patients. This large-scale study indicates structural thalamic abnormalities in patients with migraine. The thalamic nuclei with abnormal volumes are densely connected to the limbic system. The data hence lend support to the view that higher-order integration systems are altered in migraine. SIGNIFICANCE STATEMENT This multicenter imaging study shows morphological thalamic abnormalities in a large cohort of patients with episodic migraine compared with healthy subjects using state-of-the-art MRI and advanced, fully automated multiatlas segmentation techniques. The results stress that migraine is a disorder of the CNS in which not only is brain function abnormal, but also brain structure is undergoing significant remodeling.
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26
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Danet L, Barbeau EJ, Eustache P, Planton M, Raposo N, Sibon I, Albucher JF, Bonneville F, Peran P, Pariente J. Thalamic amnesia after infarct: The role of the mammillothalamic tract and mediodorsal nucleus. Neurology 2015; 85:2107-15. [PMID: 26567269 PMCID: PMC4691690 DOI: 10.1212/wnl.0000000000002226] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2015] [Accepted: 08/19/2015] [Indexed: 11/15/2022] Open
Abstract
Objective: To improve current understanding of the mechanisms behind thalamic amnesia, as it is unclear whether it is directly related to damage to specific nuclei, in particular to the anterior or mediodorsal nuclei, or indirectly related to lesions of the mammillothalamic tract (MTT). Methods: We recruited 12 patients with a left thalamic infarction and 25 healthy matched controls. All underwent a comprehensive neuropsychological assessment of verbal and visual memory, executive functions, language, and affect, and a high-resolution structural volumetric MRI scan. Thalamic lesions were manually segmented and automatically localized with a computerized thalamic atlas. As well as comparing patients with controls, we divided patients into subgroups with intact or damaged MTT. Results: Only one patient had a small lesion of the anterior nucleus. Most of the lesions included the mediodorsal (n = 11) and intralaminar nuclei (n = 12). Patients performed worse than controls on the verbal memory tasks, but the 5 patients with intact MTT who showed isolated lesions of the mediodorsal nucleus (MD) only displayed moderate memory impairment. The 7 patients with a damaged MTT performed worse on the verbal memory tasks than those whose MTT was intact. Conclusions: Lesions in the MTT and in the MD result in memory impairment, severely in the case of MTT and to a lesser extent in the case of MD, thus highlighting the roles played by these 2 structures in memory circuits.
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Affiliation(s)
- Lola Danet
- From Inserm (L.D., P.E., M.P., F.B., P.P., J.P.) and Université de Toulouse III, UPS (L.D., P.E., M.P., F.B., P.P., J.P.), Imagerie Cérébrale et Handicaps Neurologiques, UMR 825, and Service de Neurologie, Pôle Neurosciences, CHU Purpan (L.D., M.P., N.R., J.-F.A., F.B., J.P.), Centre Hospitalier Universitaire de Toulouse; Centre de Recherche Cerveau et Cognition (CNRS, CerCO, UMR 5549) (L.D., E.J.B.), Université de Toulouse; and CHU de Bordeaux, Unité Neurovasculaire (I.S.), University of Bordeaux, France.
| | - Emmanuel J Barbeau
- From Inserm (L.D., P.E., M.P., F.B., P.P., J.P.) and Université de Toulouse III, UPS (L.D., P.E., M.P., F.B., P.P., J.P.), Imagerie Cérébrale et Handicaps Neurologiques, UMR 825, and Service de Neurologie, Pôle Neurosciences, CHU Purpan (L.D., M.P., N.R., J.-F.A., F.B., J.P.), Centre Hospitalier Universitaire de Toulouse; Centre de Recherche Cerveau et Cognition (CNRS, CerCO, UMR 5549) (L.D., E.J.B.), Université de Toulouse; and CHU de Bordeaux, Unité Neurovasculaire (I.S.), University of Bordeaux, France
| | - Pierre Eustache
- From Inserm (L.D., P.E., M.P., F.B., P.P., J.P.) and Université de Toulouse III, UPS (L.D., P.E., M.P., F.B., P.P., J.P.), Imagerie Cérébrale et Handicaps Neurologiques, UMR 825, and Service de Neurologie, Pôle Neurosciences, CHU Purpan (L.D., M.P., N.R., J.-F.A., F.B., J.P.), Centre Hospitalier Universitaire de Toulouse; Centre de Recherche Cerveau et Cognition (CNRS, CerCO, UMR 5549) (L.D., E.J.B.), Université de Toulouse; and CHU de Bordeaux, Unité Neurovasculaire (I.S.), University of Bordeaux, France
| | - Mélanie Planton
- From Inserm (L.D., P.E., M.P., F.B., P.P., J.P.) and Université de Toulouse III, UPS (L.D., P.E., M.P., F.B., P.P., J.P.), Imagerie Cérébrale et Handicaps Neurologiques, UMR 825, and Service de Neurologie, Pôle Neurosciences, CHU Purpan (L.D., M.P., N.R., J.-F.A., F.B., J.P.), Centre Hospitalier Universitaire de Toulouse; Centre de Recherche Cerveau et Cognition (CNRS, CerCO, UMR 5549) (L.D., E.J.B.), Université de Toulouse; and CHU de Bordeaux, Unité Neurovasculaire (I.S.), University of Bordeaux, France
| | - Nicolas Raposo
- From Inserm (L.D., P.E., M.P., F.B., P.P., J.P.) and Université de Toulouse III, UPS (L.D., P.E., M.P., F.B., P.P., J.P.), Imagerie Cérébrale et Handicaps Neurologiques, UMR 825, and Service de Neurologie, Pôle Neurosciences, CHU Purpan (L.D., M.P., N.R., J.-F.A., F.B., J.P.), Centre Hospitalier Universitaire de Toulouse; Centre de Recherche Cerveau et Cognition (CNRS, CerCO, UMR 5549) (L.D., E.J.B.), Université de Toulouse; and CHU de Bordeaux, Unité Neurovasculaire (I.S.), University of Bordeaux, France
| | - Igor Sibon
- From Inserm (L.D., P.E., M.P., F.B., P.P., J.P.) and Université de Toulouse III, UPS (L.D., P.E., M.P., F.B., P.P., J.P.), Imagerie Cérébrale et Handicaps Neurologiques, UMR 825, and Service de Neurologie, Pôle Neurosciences, CHU Purpan (L.D., M.P., N.R., J.-F.A., F.B., J.P.), Centre Hospitalier Universitaire de Toulouse; Centre de Recherche Cerveau et Cognition (CNRS, CerCO, UMR 5549) (L.D., E.J.B.), Université de Toulouse; and CHU de Bordeaux, Unité Neurovasculaire (I.S.), University of Bordeaux, France
| | - Jean-François Albucher
- From Inserm (L.D., P.E., M.P., F.B., P.P., J.P.) and Université de Toulouse III, UPS (L.D., P.E., M.P., F.B., P.P., J.P.), Imagerie Cérébrale et Handicaps Neurologiques, UMR 825, and Service de Neurologie, Pôle Neurosciences, CHU Purpan (L.D., M.P., N.R., J.-F.A., F.B., J.P.), Centre Hospitalier Universitaire de Toulouse; Centre de Recherche Cerveau et Cognition (CNRS, CerCO, UMR 5549) (L.D., E.J.B.), Université de Toulouse; and CHU de Bordeaux, Unité Neurovasculaire (I.S.), University of Bordeaux, France
| | - Fabrice Bonneville
- From Inserm (L.D., P.E., M.P., F.B., P.P., J.P.) and Université de Toulouse III, UPS (L.D., P.E., M.P., F.B., P.P., J.P.), Imagerie Cérébrale et Handicaps Neurologiques, UMR 825, and Service de Neurologie, Pôle Neurosciences, CHU Purpan (L.D., M.P., N.R., J.-F.A., F.B., J.P.), Centre Hospitalier Universitaire de Toulouse; Centre de Recherche Cerveau et Cognition (CNRS, CerCO, UMR 5549) (L.D., E.J.B.), Université de Toulouse; and CHU de Bordeaux, Unité Neurovasculaire (I.S.), University of Bordeaux, France
| | - Patrice Peran
- From Inserm (L.D., P.E., M.P., F.B., P.P., J.P.) and Université de Toulouse III, UPS (L.D., P.E., M.P., F.B., P.P., J.P.), Imagerie Cérébrale et Handicaps Neurologiques, UMR 825, and Service de Neurologie, Pôle Neurosciences, CHU Purpan (L.D., M.P., N.R., J.-F.A., F.B., J.P.), Centre Hospitalier Universitaire de Toulouse; Centre de Recherche Cerveau et Cognition (CNRS, CerCO, UMR 5549) (L.D., E.J.B.), Université de Toulouse; and CHU de Bordeaux, Unité Neurovasculaire (I.S.), University of Bordeaux, France
| | - Jérémie Pariente
- From Inserm (L.D., P.E., M.P., F.B., P.P., J.P.) and Université de Toulouse III, UPS (L.D., P.E., M.P., F.B., P.P., J.P.), Imagerie Cérébrale et Handicaps Neurologiques, UMR 825, and Service de Neurologie, Pôle Neurosciences, CHU Purpan (L.D., M.P., N.R., J.-F.A., F.B., J.P.), Centre Hospitalier Universitaire de Toulouse; Centre de Recherche Cerveau et Cognition (CNRS, CerCO, UMR 5549) (L.D., E.J.B.), Université de Toulouse; and CHU de Bordeaux, Unité Neurovasculaire (I.S.), University of Bordeaux, France
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27
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Pallavaram S, DʼHaese PF, Lake W, Konrad PE, Dawant BM, Neimat JS. Fully automated targeting using nonrigid image registration matches accuracy and exceeds precision of best manual approaches to subthalamic deep brain stimulation targeting in Parkinson disease. Neurosurgery 2015; 76:756-65. [PMID: 25988929 DOI: 10.1227/neu.0000000000000714] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Finding the optimal location for the implantation of the electrode in deep brain stimulation (DBS) surgery is crucial for maximizing the therapeutic benefit to the patient. Such targeting is challenging for several reasons, including anatomic variability between patients as well as the lack of consensus about the location of the optimal target. OBJECTIVE To compare the performance of popular manual targeting methods against a fully automatic nonrigid image registration-based approach. METHODS In 71 Parkinson disease subthalamic nucleus (STN)-DBS implantations, an experienced functional neurosurgeon selected the target manually using 3 different approaches: indirect targeting using standard stereotactic coordinates, direct targeting based on the patient magnetic resonance imaging, and indirect targeting relative to the red nucleus. Targets were also automatically predicted by using a leave-one-out approach to populate the CranialVault atlas with the use of nonrigid image registration. The different targeting methods were compared against the location of the final active contact, determined through iterative clinical programming in each individual patient. RESULTS Targeting by using standard stereotactic coordinates corresponding to the center of the motor territory of the STN had the largest targeting error (3.69 mm), followed by direct targeting (3.44 mm), average stereotactic coordinates of active contacts from this study (3.02 mm), red nucleus-based targeting (2.75 mm), and nonrigid image registration-based automatic predictions using the CranialVault atlas (2.70 mm). The CranialVault atlas method had statistically smaller variance than all manual approaches. CONCLUSION Fully automatic targeting based on nonrigid image registration with the use of the CranialVault atlas is as accurate and more precise than popular manual methods for STN-DBS.
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Affiliation(s)
- Srivatsan Pallavaram
- *Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee; ‡Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, Tennessee
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28
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Yang C, Wang Q, Wu W, Xue Y, Lu W, Wu S. Thalamic segmentation based on improved fuzzy connectedness in structural MRI. Comput Biol Med 2015; 66:222-34. [PMID: 26433197 DOI: 10.1016/j.compbiomed.2015.09.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2015] [Revised: 08/26/2015] [Accepted: 09/02/2015] [Indexed: 10/23/2022]
Abstract
Thalamic segmentation serves an important function in localizing targets for deep brain stimulation (DBS). However, thalamic nuclei are still difficult to identify clearly from structural MRI. In this study, an improved algorithm based on the fuzzy connectedness framework was developed. Three-dimensional T1-weighted images in axial orientation were acquired through a 3D SPGR sequence by using a 1.5 T GE magnetic resonance scanner. Twenty-five normal images were analyzed using the proposed method, which involved adaptive fuzzy connectedness combined with confidence connectedness (AFCCC). After non-brain tissue removal and contrast enhancement, the seed point was selected manually, and confidence connectedness was used to perform an ROI update automatically. Both image intensity and local gradient were taken as image features in calculating the fuzzy affinity. Moreover, the weight of the features could be automatically adjusted. Thalamus, ventrointermedius (Vim), and subthalamic nucleus were successfully segmented. The results were evaluated with rules, such as similarity degree (SD), union overlap, and false positive. SD of thalamus segmentation reached values higher than 85%. The segmentation results were also compared with those achieved by the region growing and level set methods, respectively. Higher SD of the proposed method, especially in Vim, was achieved. The time cost using AFCCC was low, although it could achieve high accuracy. The proposed method is superior to the traditional fuzzy connectedness framework and involves reduced manual intervention in time saving.
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Affiliation(s)
- Chunlan Yang
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100022, China.
| | - Qian Wang
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100022, China
| | - Weiwei Wu
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100022, China
| | - Yanqing Xue
- Department of Radiotherapy, Beijing Geriatric Hospital, Beijing 100095, China
| | - Wangsheng Lu
- Center of Neurosurgery, PLA NAVY General Hospital, Beijing 100037, China
| | - Shuicai Wu
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100022, China
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Striatal morphology is associated with tobacco cigarette craving. Neuropsychopharmacology 2015; 40:406-11. [PMID: 25056595 PMCID: PMC4443954 DOI: 10.1038/npp.2014.185] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2014] [Revised: 06/27/2014] [Accepted: 07/15/2014] [Indexed: 01/28/2023]
Abstract
The striatum has a clear role in addictive disorders and is involved in drug-related craving. Recently, enhanced striatal volume was associated with greater lifetime nicotine exposure, suggesting a bridge between striatal function and structural phenotypes. To assess this link between striatal structure and function, we evaluated the relationship between striatal morphology and this brain region's well-established role in craving. In tobacco smokers, we assessed striatal volume, surface area, and shape using a new segmentation methodology coupled with local shape indices. Striatal morphology was then related with two measures of craving: state-based craving, assessed by the brief questionnaire of smoking urges (QSU), and craving induced by smoking-related images. A positive association was found between left striatal volume and surface area with both measures of craving. A more specific relationship was found between both craving measures and the dorsal, but not in ventral striatum. Evaluating dorsal striatal subregions showed a single relationship between the caudate and QSU. Although cue-induced craving and the QSU were both associated with enlarged striatal volume and surface area, these measures were differentially associated with global or more local striatal volumes. We also report a connection between greater right striatal shape deformations and cue-induced craving. Shape deformations associated with cue-induced craving were specific to striatal subregions involved in habitual responding to rewarding stimuli, which is relevant given the habitual nature of cue-induced craving. The current findings confirm a relationship between striatal function and morphology and suggest that variation in striatal morphology may be a biomarker for craving severity.
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Chakravarty MM, Rapoport JL, Giedd JN, Raznahan A, Shaw P, Collins DL, Lerch JP, Gogtay N. Striatal shape abnormalities as novel neurodevelopmental endophenotypes in schizophrenia: a longitudinal study. Hum Brain Mapp 2014; 36:1458-69. [PMID: 25504933 DOI: 10.1002/hbm.22715] [Citation(s) in RCA: 58] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2014] [Revised: 11/15/2014] [Accepted: 11/30/2014] [Indexed: 01/04/2023] Open
Abstract
There are varying, often conflicting, reports with respect to altered striatal volume and morphometry in the major psychoses due to the influences of antipsychotic medications on striatal volume. Thus, disassociating disease effects from those of medication become exceedingly difficult. For the first time, using a longitudinally studied sample of structural magnetic resonance images from patients with childhood onset schizophrenia (COS; neurobiologically contiguous with the adult onset form of schizophrenia), their nonpsychotic siblings (COSSIBs), and novel shape mapping algorithms that are volume independent, we report the familial contribution of striatal morphology in schizophrenia. The results of our volumetric analyses demonstrate age-related increases in overall striatal volumes specific only to COS. However, both COS and COSSIBs showed overlapping shape differences in the striatal head, which normalized in COSSIBs by late adolescence. These results mirror previous studies from our group, demonstrating cortical thickness deficits in COS and COSSIBs as these deficits normalize in COSSIBs in the same age range as our striatal findings. Finally, there is a single region of nonoverlapping outward displacement in the dorsal aspect of the caudate body, potentially indicative of a response to medication. Striatal shape may be considered complimentary to volume as an endophenotype, and, in some cases may provide information that is not detectable using standard volumetric techniques. Our striatal shape findings demonstrate the striking localization of abnormalities in striatal the head. The neuroanatomical localization of these findings suggest the presence of abnormalities in the striatal-prefrontal circuits in schizophrenia and resilience mechanisms in COSSIBs with age dependent normalization.
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Affiliation(s)
- M Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Canada; Department of Psychiatry, McGill University, Montreal, Canada; Department of Biomedical Engineering, McGill University, Montreal, Canada
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31
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Pipitone J, Park MTM, Winterburn J, Lett TA, Lerch JP, Pruessner JC, Lepage M, Voineskos AN, Chakravarty MM. Multi-atlas segmentation of the whole hippocampus and subfields using multiple automatically generated templates. Neuroimage 2014; 101:494-512. [DOI: 10.1016/j.neuroimage.2014.04.054] [Citation(s) in RCA: 268] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2013] [Revised: 04/15/2014] [Accepted: 04/19/2014] [Indexed: 11/16/2022] Open
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Patch-based label fusion segmentation of brainstem structures with dual-contrast MRI for Parkinson's disease. Int J Comput Assist Radiol Surg 2014; 10:1029-41. [PMID: 25249471 DOI: 10.1007/s11548-014-1119-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2014] [Accepted: 09/10/2014] [Indexed: 12/11/2022]
Abstract
PURPOSE Parkinson's disease (PD) is a neurodegenerative disorder that impairs the motor functions. Both surgical treatment and study of PD require delineation of basal ganglia nuclei morphology. While many automatic volumetric segmentation methods have been proposed for the lentiform nucleus, few have attempted to identify the key brainstem substructures including the subthalamic nucleus (STN), substantia nigra (SN), and red nucleus (RN) due to their small size and poor contrast in conventional T1W MRI. METHODS A dual-contrast patch-based label fusion method was developed to segment the SN, STN, and RN using multivariate cross-correlation. Two different MRI contrasts (T2*w and phase) are produced from a multi-contrast multi-echo FLASH MRI sequence, enabling visualization of these nuclei. T1-T2* fusion MRI was used to resolve the issue of poor nuclei (i.e., the STN, SN, and RN) contrast on T1w MRI, and to mitigate susceptibility artifacts that may hinder accurate nonlinear registration on T2*w MRI. Unbiased group-wise registration was used for anatomical normalization between the atlas library and the target subject. The performance of the proposed method was compared with a state-of-the-art single-contrast label fusion technique. RESULTS The proposed method outperformed a state-of-the-art single-contrast patch-based method in segmenting the STN, RN and SN, and the results were better than those reported in previous literature. CONCLUSION Our dual-contrast patch-based label fusion method was superior to a single-contrast method for segmenting brainstem nuclei using a multi-contrast multi-echo FLASH MRI sequence. The method is promising for the treatment and research of Parkinson's disease. This method can be extended for multiple alternative image contrasts and other fields of applications.
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Gawryluk JR, Mazerolle EL, Beyea SD, D'Arcy RCN. Functional MRI activation in white matter during the Symbol Digit Modalities Test. Front Hum Neurosci 2014; 8:589. [PMID: 25136311 PMCID: PMC4120763 DOI: 10.3389/fnhum.2014.00589] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2014] [Accepted: 07/15/2014] [Indexed: 01/11/2023] Open
Abstract
Background: Recent evidence shows that functional magnetic resonance imaging (fMRI) can detect activation in white matter (WM). Such advances have important implications for understanding WM dysfunction. A key step in linking neuroimaging advances to the evaluation of clinical disorders is to examine whether WM activation can be detected at the individual level during clinical tests associated with WM function. We used an adapted Symbol Digit Modalities Test (SDMT) in a 4T fMRI study of healthy adults. Results: Results from 17 healthy individuals revealed WM activation in 88% of participants (15/17). The activation was in either the corpus callosum (anterior and/or posterior) or internal capsule (left and/or right). Conclusions: The findings link advances in fMRI to an established clinical test of WM function. Future work should focus on evaluating patients with WM dysfunction.
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Affiliation(s)
- Jodie R Gawryluk
- Department of Psychology/Neuroscience, University of Victoria Victoria, BC, Canada
| | - Erin L Mazerolle
- Faculty of Medicine, Department of Radiology, University of Calgary Calgary, AB, Canada
| | - Steven D Beyea
- Biomedical Translational Imaging Centre, IWK Health Centre Halifax, NS, Canada
| | - Ryan C N D'Arcy
- Applied Sciences, Simon Fraser University Burnaby, BC, Canada ; Fraser Health Authority, Surrey Memorial Hospital Surrey, BC, Canada
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Derivation of high-resolution MRI atlases of the human cerebellum at 3T and segmentation using multiple automatically generated templates. Neuroimage 2014; 95:217-31. [DOI: 10.1016/j.neuroimage.2014.03.037] [Citation(s) in RCA: 95] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2014] [Revised: 02/25/2014] [Accepted: 03/12/2014] [Indexed: 01/18/2023] Open
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35
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Xiao Y, Fonov V, Bériault S, Subaie FA, Chakravarty MM, Sadikot AF, Pike GB, Collins DL. Multi-contrast unbiased MRI atlas of a Parkinson’s disease population. Int J Comput Assist Radiol Surg 2014; 10:329-41. [DOI: 10.1007/s11548-014-1068-y] [Citation(s) in RCA: 56] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2013] [Accepted: 04/29/2014] [Indexed: 11/24/2022]
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Magon S, Chakravarty MM, Amann M, Weier K, Naegelin Y, Andelova M, Radue EW, Stippich C, Lerch JP, Kappos L, Sprenger T. Label-fusion-segmentation and deformation-based shape analysis of deep gray matter in multiple sclerosis: the impact of thalamic subnuclei on disability. Hum Brain Mapp 2014; 35:4193-203. [PMID: 24510715 DOI: 10.1002/hbm.22470] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2013] [Revised: 12/03/2013] [Accepted: 01/06/2014] [Indexed: 11/11/2022] Open
Abstract
Deep gray matter (DGM) atrophy has been reported in patients with multiple sclerosis (MS) already at early stages of the disease and progresses throughout the disease course. We studied DGM volume and shape and their relation to disability in a large cohort of clinically well-described MS patients using new subcortical segmentation methods and shape analysis. Structural 3D magnetic resonance images were acquired at 1.5 T in 118 patients with relapsing remitting MS. Subcortical structures were segmented using a multiatlas technique that relies on the generation of an automatically generated template library. To localize focal morphological changes, shape analysis was performed by estimating the vertex-wise displacements each subject must undergo to deform to a template. Multiple linear regression analysis showed that the volume of specific thalamic nuclei (the ventral nuclear complex) together with normalized gray matter volume explains a relatively large proportion of expanded disability status scale (EDSS) variability. The deformation-based displacement analysis confirmed the relation between thalamic shape and EDSS scores. Furthermore, white matter lesion volume was found to relate to the shape of all subcortical structures. This novel method for the analysis of subcortical volume and shape allows depicting specific contributions of DGM abnormalities to neurological deficits in MS patients. The results stress the importance of ventral thalamic nuclei in this respect.
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Affiliation(s)
- Stefano Magon
- Department of Neurology, University Hospital Basel, Switzerland; Medical Image Analysis Center, University Hospital Basel, Switzerland
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Longitudinal four-dimensional mapping of subcortical anatomy in human development. Proc Natl Acad Sci U S A 2014; 111:1592-7. [PMID: 24474784 DOI: 10.1073/pnas.1316911111] [Citation(s) in RCA: 223] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
Growing access to large-scale longitudinal structural neuroimaging data has fundamentally altered our understanding of cortical development en route to human adulthood, with consequences for basic science, medicine, and public policy. In striking contrast, basic anatomical development of subcortical structures such as the striatum, pallidum, and thalamus has remained poorly described--despite these evolutionarily ancient structures being both intimate working partners of the cortical sheet and critical to diverse developmentally emergent skills and disorders. Here, to begin addressing this disparity, we apply methods for the measurement of subcortical volume and shape to 1,171 longitudinally acquired structural magnetic resonance imaging brain scans from 618 typically developing males and females aged 5-25 y. We show that the striatum, pallidum, and thalamus each follow curvilinear trajectories of volume change, which, for the striatum and thalamus, peak after cortical volume has already begun to decline and show a relative delay in males. Four-dimensional mapping of subcortical shape reveals that (i) striatal, pallidal, and thalamic domains linked to specific fronto-parietal association cortices contract with age whereas other subcortical territories expand, and (ii) each structure harbors hotspots of sexually dimorphic change over adolescence--with relevance for sex-biased mental disorders emerging in youth. By establishing the developmental dynamism, spatial heterochonicity, and sexual dimorphism of human subcortical maturation, these data bring our spatiotemporal understanding of subcortical development closer to that of the cortex--allowing evolutionary, basic, and clinical neuroscience to be conducted within a more comprehensive developmental framework.
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Pace DF, Aylward SR, Niethammer M. A locally adaptive regularization based on anisotropic diffusion for deformable image registration of sliding organs. IEEE TRANSACTIONS ON MEDICAL IMAGING 2013; 32:2114-26. [PMID: 23899632 PMCID: PMC4112204 DOI: 10.1109/tmi.2013.2274777] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
We propose a deformable image registration algorithm that uses anisotropic smoothing for regularization to find correspondences between images of sliding organs. In particular, we apply the method for respiratory motion estimation in longitudinal thoracic and abdominal computed tomography scans. The algorithm uses locally adaptive diffusion tensors to determine the direction and magnitude with which to smooth the components of the displacement field that are normal and tangential to an expected sliding boundary. Validation was performed using synthetic, phantom, and 14 clinical datasets, including the publicly available DIR-Lab dataset. We show that motion discontinuities caused by sliding can be effectively recovered, unlike conventional regularizations that enforce globally smooth motion. In the clinical datasets, target registration error showed improved accuracy for lung landmarks compared to the diffusive regularization. We also present a generalization of our algorithm to other sliding geometries, including sliding tubes (e.g., needles sliding through tissue, or contrast agent flowing through a vessel). Potential clinical applications of this method include longitudinal change detection and radiotherapy for lung or abdominal tumours, especially those near the chest or abdominal wall.
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Oreshkin BN, Arbel T. Uncertainty driven probabilistic voxel selection for image registration. IEEE TRANSACTIONS ON MEDICAL IMAGING 2013; 32:1777-1790. [PMID: 23708789 DOI: 10.1109/tmi.2013.2264467] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
This paper presents a novel probabilistic voxel selection strategy for medical image registration in time-sensitive contexts, where the goal is aggressive voxel sampling (e.g., using less than 1% of the total number) while maintaining registration accuracy and low failure rate. We develop a Bayesian framework whereby, first, a voxel sampling probability field (VSPF) is built based on the uncertainty on the transformation parameters. We then describe a practical, multi-scale registration algorithm, where, at each optimization iteration, different voxel subsets are sampled based on the VSPF. The approach maximizes accuracy without committing to a particular fixed subset of voxels. The probabilistic sampling scheme developed is shown to manage the tradeoff between the robustness of traditional random voxel selection (by permitting more exploration) and the accuracy of fixed voxel selection (by permitting a greater proportion of informative voxels).
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40
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Tourdias T, Saranathan M, Levesque IR, Su J, Rutt BK. Visualization of intra-thalamic nuclei with optimized white-matter-nulled MPRAGE at 7T. Neuroimage 2013; 84:534-45. [PMID: 24018302 DOI: 10.1016/j.neuroimage.2013.08.069] [Citation(s) in RCA: 92] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2013] [Revised: 07/27/2013] [Accepted: 08/29/2013] [Indexed: 12/01/2022] Open
Abstract
Novel MR image acquisition strategies have been investigated to elicit contrast within the thalamus, but direct visualization of individual thalamic nuclei remains a challenge because of their small size and the low intrinsic contrast between adjacent nuclei. We present a step-by-step specific optimization of the 3D MPRAGE pulse sequence at 7T to visualize the intra-thalamic nuclei. We first measured T1 values within different sub-regions of the thalamus at 7T in 5 individuals. We used these to perform simulations and sequential experimental measurements (n=17) to tune the parameters of the MPRAGE sequence. The optimal set of parameters was used to collect high-quality data in 6 additional volunteers. Delineation of thalamic nuclei was performed twice by one rater and MR-defined nuclei were compared to the classic Morel histological atlas. T1 values within the thalamus ranged from 1400ms to 1800ms for adjacent nuclei. Using these values for theoretical evaluations combined with in vivo measurements, we showed that a short inversion time (TI) close to the white matter null regime (TI=670ms) enhanced the contrast between the thalamus and the surrounding tissues, and best revealed intra-thalamic contrast. At this particular nulling regime, lengthening the time between successive inversion pulses (TS=6000ms) increased the thalamic signal and contrast and lengthening the α pulse train time (N*TR) further increased the thalamic signal. Finally, a low flip angle during the gradient echo acquisition (α=4°) was observed to mitigate the blur induced by the evolution of the magnetization along the α pulse train. This optimized set of parameters enabled the 3D delineation of 15 substructures in all 6 individuals; these substructures corresponded well with the known anatomical structures of the thalamus based on the classic Morel atlas. The mean Euclidean distance between the centers of mass of MR- and Morel atlas-defined nuclei was 2.67mm (±1.02mm). The reproducibility of the MR-defined nuclei was excellent with intraclass correlation coefficient measured at 0.997 and a mean Euclidean distance between corresponding centers of mass found at first versus second readings of 0.69mm (±0.38mm). This 7T strategy paves the way to better identification of thalamic nuclei for neurosurgical planning and investigation of regional changes in neurological disorders.
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Affiliation(s)
- Thomas Tourdias
- Richard M. Lucas Center for Imaging, Radiology Department, Stanford University, 1201 Welch Road, Stanford, CA 94305-5488, USA.
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41
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Taylor MJ. Structure and function: how to connect? Neuroradiology 2013; 55 Suppl 2:55-64. [PMID: 23929311 DOI: 10.1007/s00234-013-1246-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2013] [Accepted: 07/11/2013] [Indexed: 10/26/2022]
Abstract
INTRODUCTION The majority, but not all, of very preterm-born infants have difficulties with a variety of cognitive functions as children. It is critical to be able to predict as early as possible those who will have difficulties, to be able to direct appropriate interventions. METHODS We are conducting multimodal structural and functional MRI studies in very preterm-born infants and following them with behavioural and neuroimaging assessments until 4 years of age. We are also completing structural and more complex functional imaging in school-aged very preterm-born children. RESULTS A number of MRI measures between preterm and term age correlate with outcome at 2 years of age. Functional and structural differences are also seen at school age; examples from these various studies are presented. CONCLUSION Structural and functional studies in preterm-born versus term-born infants and children, particularly if completed longitudinally, provide important information on the evolution of brain-behaviour correlates and can help predict outcome in this high-risk population.
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Affiliation(s)
- Margot J Taylor
- Diagnostic Imaging, Neurosciences and Mental Health Programme, Hospital for Sick Children, 555 University Ave., Toronto, Ontario, Canada.
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42
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Winterburn JL, Pruessner JC, Chavez S, Schira MM, Lobaugh NJ, Voineskos AN, Chakravarty MM. A novel in vivo atlas of human hippocampal subfields using high-resolution 3T magnetic resonance imaging. Neuroimage 2013; 74:254-65. [DOI: 10.1016/j.neuroimage.2013.02.003] [Citation(s) in RCA: 157] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2012] [Revised: 01/28/2013] [Accepted: 02/03/2013] [Indexed: 10/27/2022] Open
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Self-injurious behaviours are associated with alterations in the somatosensory system in children with autism spectrum disorder. Brain Struct Funct 2013; 219:1251-61. [PMID: 23644587 DOI: 10.1007/s00429-013-0562-2] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2012] [Accepted: 04/19/2013] [Indexed: 10/26/2022]
Abstract
Children with autism spectrum disorder (ASD) frequently engage in self-injurious behaviours, often in the absence of reporting pain. Previous research suggests that altered pain sensitivity and repeated exposure to noxious stimuli are associated with morphological changes in somatosensory and limbic cortices. Further evidence from postmortem studies with self-injurious adults has indicated alterations in the structure and organization of the temporal lobes; however, the effect of self-injurious behaviour on cortical development in children with ASD has not yet been determined. Thirty children and adolescents (mean age = 10.6 ± 2.5 years; range 7-15 years; 29 males) with a clinical diagnosis of ASD and 30 typically developing children (N = 30, mean age = 10.7 ± 2.5 years; range 7-15 years, 26 males) underwent T1-weighted magnetic resonance and diffusion tensor imaging. No between-group differences were seen in cerebral volume, surface area or cortical thickness. Within the ASD group, self-injury scores negatively correlated with thickness in the right superior parietal lobule t = 6.3, p < 0.0001, bilateral primary somatosensory cortices (SI) (right: t = 4.4, p = 0.02; left: t = 4.48, p = 0.004) and the volume of the left ventroposterior (VP) nucleus of the thalamus (r = -0.52, p = 0.008). Based on these findings, we performed an atlas-based region-of-interest diffusion tensor imaging analysis between SI and the VP nucleus and found that children who engaged in self-injury had significantly lower fractional anisotropy (r = -0.4, p = 0.04) and higher mean diffusivity (r = 0.5, p = 0.03) values in the territory of the left posterior limb of the internal capsule. Additionally, greater incidence of self-injury was associated with increased radial diffusivity values in bilateral posterior limbs of the internal capsule (left: r = 0.5, p = 0.02; right: r = 0.5, p = 0.009) and corona radiata (left: r = 0.6, p = 0.005; right: r = 0.5, p = 0.009). Results indicate that self-injury is related to alterations in somatosensory cortical and subcortical regions and their supporting white-matter pathways. Findings could reflect use-dependent plasticity in the somatosensory system or disrupted brain development that could serve as a risk marker for self-injury.
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Gerretsen P, Chakravarty MM, Mamo D, Menon M, Pollock BG, Rajji TK, Graff‐Guerrero A. Frontotemporoparietal asymmetry and lack of illness awareness in schizophrenia. Hum Brain Mapp 2013; 34:1035-43. [PMID: 22213454 PMCID: PMC6870294 DOI: 10.1002/hbm.21490] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2011] [Revised: 09/19/2011] [Accepted: 09/20/2011] [Indexed: 01/09/2023] Open
Abstract
INTRODUCTION Lack of illness awareness or anosognosia occurs in both schizophrenia and right hemisphere lesions due to stroke, dementia, and traumatic brain injury. In the latter conditions, anosognosia is thought to arise from unilateral hemispheric dysfunction or interhemispheric disequilibrium, which provides an anatomical model for exploring illness unawareness in other neuropsychiatric disorders, such as schizophrenia. METHODS Both voxel-based morphometry using Diffeomorphic Anatomical Registration through Exponentiated Lie Algebra (DARTEL) and a deformation-based morphology analysis of hemispheric asymmetry were performed on 52 treated schizophrenia subjects, exploring the relationship between illness awareness and gray matter volume. Analyses included age, gender, and total intracranial volume as covariates. RESULTS Hemispheric asymmetry analyses revealed illness unawareness was significantly associated with right < left hemisphere volumes in the anteroinferior temporal lobe (t = 4.83, P = 0.051) using DARTEL, and the dorsolateral prefrontal cortex (t = 5.80, P = 0.003) and parietal lobe (t = 4.3, P = 0.050) using the deformation-based approach. Trend level associations were identified in the right medial prefrontal cortex (t = 4.49, P = 0.127) using DARTEL. Lack of illness awareness was also strongly associated with reduced total white matter volume (r = 0.401, P < 0.01) and illness severity (r = 0.559, P < 0.01). CONCLUSION These results suggest a relationship between anosognosia and hemispheric asymmetry in schizophrenia, supporting previous volume-based MRI studies in schizophrenia that found a relationship between illness unawareness and reduced right hemisphere gray matter volume. Functional imaging studies are required to examine the neural mechanisms contributing to these structural observations.
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Affiliation(s)
- Philip Gerretsen
- Multimodal Imaging Group, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Geriatric Mental Health Program, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - M. Mallar Chakravarty
- Rotman Research Institute, Baycrest, Toronto, Ontario, Canada
- Mouse Imaging Centre (MICe), The Hospital for Sick Children, Toronto, Ontario, Canada
- Kimel Family Translational Imaging‐Genetics Research Laboratory, Research Imaging Centre, Centre for Addiction and Mental Health
| | - David Mamo
- Multimodal Imaging Group, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Geriatric Mental Health Program, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Mahesh Menon
- Multimodal Imaging Group, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Schizophrenia Program, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Bruce G. Pollock
- Geriatric Mental Health Program, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Tarek K. Rajji
- Geriatric Mental Health Program, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Ariel Graff‐Guerrero
- Multimodal Imaging Group, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Schizophrenia Program, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
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Mazerolle EL, Gawryluk JR, Dillen KNH, Patterson SA, Feindel KW, Beyea SD, Stevens MTR, Newman AJ, Schmidt MH, D’Arcy RC. Sensitivity to white matter FMRI activation increases with field strength. PLoS One 2013; 8:e58130. [PMID: 23483983 PMCID: PMC3587428 DOI: 10.1371/journal.pone.0058130] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2012] [Accepted: 02/03/2013] [Indexed: 12/12/2022] Open
Abstract
Functional magnetic resonance imaging (fMRI) activation in white matter is controversial. Given that many of the studies that report fMRI activation in white matter used high field MRI systems, we investigated the field strength dependence of sensitivity to white matter fMRI activation. In addition, we evaluated the temporal signal to noise ratio (tSNR) of the different tissue types as a function of field strength. Data were acquired during a motor task (finger tapping) at 1.5 T and 4 T. Group and individual level activation results were considered in both the sensorimotor cortex and the posterior limb of the internal capsule. We found that sensitivity increases associated with field strength were greater for white matter than gray matter. The analysis of tSNR suggested that white matter might be less susceptible to increases in physiological noise related to increased field strength. We therefore conclude that high field MRI may be particularly advantageous for fMRI studies aimed at investigating activation in both gray and white matter.
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Affiliation(s)
- Erin L. Mazerolle
- Institute for Biodiagnostics (Atlantic), National Research Council, Halifax, Nova Scotia, Canada
- Department of Psychology & Neuroscience, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Jodie R. Gawryluk
- Institute for Biodiagnostics (Atlantic), National Research Council, Halifax, Nova Scotia, Canada
- Department of Psychology & Neuroscience, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Kim N. H. Dillen
- Institute for Biodiagnostics (Atlantic), National Research Council, Halifax, Nova Scotia, Canada
- Cognitive Neuroscience, Institute of Neuroscience and Medicine, Research Centre Juelich, Juelich, Germany
| | - Steven A. Patterson
- Institute for Biodiagnostics (Atlantic), National Research Council, Halifax, Nova Scotia, Canada
- Department of Physics, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Kirk W. Feindel
- Institute for Biodiagnostics (Atlantic), National Research Council, Halifax, Nova Scotia, Canada
- School of Biomedical Engineering, Dalhousie University, Halifax, Nova Scotia, Canada
- Department of Pediatric Neurology, IWK Health Centre, Halifax, Nova Scotia, Canada
| | - Steven D. Beyea
- Institute for Biodiagnostics (Atlantic), National Research Council, Halifax, Nova Scotia, Canada
- Department of Physics, Dalhousie University, Halifax, Nova Scotia, Canada
- School of Biomedical Engineering, Dalhousie University, Halifax, Nova Scotia, Canada
- Department of Radiology, Dalhousie University, Halifax, Nova Scotia, Canada
| | - M. Tynan R Stevens
- Institute for Biodiagnostics (Atlantic), National Research Council, Halifax, Nova Scotia, Canada
- Department of Physics, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Aaron J. Newman
- Department of Psychology & Neuroscience, Dalhousie University, Halifax, Nova Scotia, Canada
| | | | - Ryan C.N. D’Arcy
- Institute for Biodiagnostics (Atlantic), National Research Council, Halifax, Nova Scotia, Canada
- Department of Psychology & Neuroscience, Dalhousie University, Halifax, Nova Scotia, Canada
- Department of Radiology, Dalhousie University, Halifax, Nova Scotia, Canada
- Department of Anatomy and Neurobiology, Dalhousie University, Halifax, Nova Scotia, Canada
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Multimodal imaging and image analysis techniques for neuromodulation. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2012. [PMID: 23206685 DOI: 10.1016/b978-0-12-404706-8.00012-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register]
Abstract
Functional neurosurgical procedures used to treat the debilitating motor symptoms of Parkinson's disease and that target small subcortical structures have typically relied on semi-qualitative manual approaches that rely upon the establishing qualitative between volumetric imaging data and print atlases. This chapter reviews many new high -precision and -accuracy techniques that can be used for the full automated localization of these targets. These techniques rely on the a priori development of neuroanatomical atlases derived from magnetic resonance imaging data, high-resolution identification of subcortical structures from histology, and spatially localized data bases of intra-operative recordings and successful surgical outcomes. Other novel structural and functional MRI techniques that allow for the direct visualization of thalamic sub nuclei are also reviewed.
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Yousefi S, Kehtarnavaz N, Gholipour A. Improved Labeling of Subcortical Brain Structures in Atlas-Based Segmentation of Magnetic Resonance Images. IEEE Trans Biomed Eng 2012; 59:1808-17. [DOI: 10.1109/tbme.2011.2122306] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Mak-Fan KM, Taylor MJ, Roberts W, Lerch JP. Measures of cortical grey matter structure and development in children with autism spectrum disorder. J Autism Dev Disord 2012; 42:419-27. [PMID: 21556969 DOI: 10.1007/s10803-011-1261-6] [Citation(s) in RCA: 54] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
The current study examined group differences in cortical volume, surface area, and thickness with age, in a group of typically developing children and a group of children with ASD aged 6-15 years. Results showed evidence of age by group interactions, suggesting atypicalities in the relation between these measures and age in the ASD group. Additional vertex-based analyses of cortical thickness revealed that specific regions in the left inferior frontal gyrus (BA 44) and left precuneus showed thicker cortex for the ASD group at younger ages only. These data support the hypothesis of an abnormal developmental trajectory of the cortex in ASD, which could have profound effects on other aspects of neural development in children with ASD.
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Sprenger T, Seifert CL, Valet M, Andreou AP, Foerschler A, Zimmer C, Collins DL, Goadsby PJ, Tölle TR, Chakravarty MM. Assessing the risk of central post-stroke pain of thalamic origin by lesion mapping. Brain 2012; 135:2536-45. [DOI: 10.1093/brain/aws153] [Citation(s) in RCA: 83] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
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Jakab A, Blanc R, Berényi EL, Székely G. Generation of individualized thalamus target maps by using statistical shape models and thalamocortical tractography. AJNR Am J Neuroradiol 2012; 33:2110-6. [PMID: 22700756 DOI: 10.3174/ajnr.a3140] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Neurosurgical interventions of the thalamus rely on transferring stereotactic coordinates from an atlas onto the patient's MR brain images. We propose a prototype application for performing thalamus target map individualization by fusing patient-specific thalamus geometric information and diffusion tensor tractography. MATERIALS AND METHODS Previously, our workgroup developed a thalamus atlas by fusing anatomic information from 7 histologically processed thalami. Thalamocortical connectivity maps were generated from DTI scans of 40 subjects by using a previously described procedure and were mapped to a standard neuroimaging space. These data were merged into a statistical shape model describing the morphologic variability of the thalamic outline, nuclei, and connectivity landmarks. This model was used to deform the atlas to individual images. Postmortem MR imaging scans were used to quantify the accuracy of nuclei predictions. RESULTS Reliable tractography-based markers were located in the ventral lateral thalamus, with the somatosensory connections coinciding with the VPLa and VPLp nuclei; and motor/premotor connections, with the VLpv and VLa nuclei. Prediction accuracy of thalamus outlines was higher with the SSM approach than the ACPC alignment of data (0.56 mm versus 1.24; Dice overlap: 0.87 versus 0.7); for individual nuclei: 0.65 mm, Dice: 0.63 (SSM); 1.24 mm, Dice: 0.4 (ACPC). CONCLUSIONS Previous studies have already applied DTI to the thalamus. As a further step in this direction, we demonstrate a hybrid approach by using statistical shape models, which have the potential to cope with intersubject variations in individual thalamus geometry.
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Affiliation(s)
- A Jakab
- Computer Vision Laboratory, Swiss Federal Institute of Technology, Zürich, Switzerland.
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