2
|
Steve TA, Gargula J, Misaghi E, Nowacki TA, Schmitt LM, Wheatley BM, Gross DW. Hippocampal subfield measurement and ILAE hippocampal sclerosis subtype classification with in vivo 4.7 tesla MRI. Epilepsy Res 2020; 161:106279. [PMID: 32105992 DOI: 10.1016/j.eplepsyres.2020.106279] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 01/07/2020] [Accepted: 01/16/2020] [Indexed: 12/27/2022]
Abstract
OBJECTIVE Neuropathological studies indicate that hippocampal sclerosis (HS) consists of three subtypes (ILAE types 1-3 HS). However, HS subtypes currently can only be diagnosed by pathological analysis of hippocampal tissue resected during epilepsy surgery or at autopsy. In vivo diagnosis of HS subtypes holds potential to improve our understanding of these variants in the ipsilateral as well as contralateral hippocampus. In this study, we aimed to: i) evaluate the reliability of our histology-derived segmentation protocol when applied to in vivo MRI; and ii) characterize variability of HS subtypes along the hippocampal long axis in patients with epilepsy. METHODS Eleven subjects with unilateral HS were compared with ten healthy controls. We used 4.7 T MRI to acquire high resolution MR Images of the hippocampus in each subject. In vivo MRI-based diagnoses of HS subtypes were then determined in each patient by two methods: i) hippocampal subfield volumetry of the entire hippocampal body; and ii) subfield area analysis at multiple thin slices throughout the hippocampal body. RESULTS Hippocampal body subfield segmentation demonstrated excellent reliability and volumetry of the symptomatic hippocampus revealed abnormalities in all eleven patients. Six subjects demonstrated findings consistent with type 1 HS while five subjects had volumetry-defined atypical HS (two with type 2 HS & three with type 3 HS) in the symptomatic hippocampus, while five subjects were found to have type 3 HS in the contralateral hippocampus. Subfield area analyses demonstrated remarkable variability of HS subtypes along the hippocampal long axis, both ipsilateral and contralateral to the seizure focus. SIGNIFICANCE Our results provide preliminary evidence that determining HS Subtype using in vivo MRI may allow preoperative diagnosis of ILAE HS subtypes. Further studies are essential to determine the pathological correlates of these neuroimaging findings. The heterogeneity of abnormalities observed along the long axis of the hippocampus is consistent with previous autopsy studies and highlights the necessity of studying the entire hippocampus both ipsilateral and contralateral to the seizure focus in these future studies.
Collapse
Affiliation(s)
- Trevor A Steve
- Division of Neurology, Department of Medicine, University of Alberta, 11350 83 Ave NW, Edmonton, AB, T6G 2G3, Canada.
| | - Justine Gargula
- Division of Neurology, Department of Medicine, University of Alberta, 11350 83 Ave NW, Edmonton, AB, T6G 2G3, Canada
| | - Ehsan Misaghi
- Division of Neurology, Department of Medicine, University of Alberta, 11350 83 Ave NW, Edmonton, AB, T6G 2G3, Canada
| | - Tomasz A Nowacki
- Division of Neurology, Department of Medicine, University of Alberta, 11350 83 Ave NW, Edmonton, AB, T6G 2G3, Canada
| | - Laura M Schmitt
- Department of Laboratory Medicine & Pathology, University of Alberta, Canada
| | - B Matt Wheatley
- Division of Neurosurgery, Department of Surgery, University of Alberta, Canada
| | - Donald W Gross
- Division of Neurology, Department of Medicine, University of Alberta, 11350 83 Ave NW, Edmonton, AB, T6G 2G3, Canada
| |
Collapse
|
4
|
Yushkevich PA, Amaral RSC, Augustinack JC, Bender AR, Bernstein JD, Boccardi M, Bocchetta M, Burggren AC, Carr VA, Chakravarty MM, Chételat G, Daugherty AM, Davachi L, Ding SL, Ekstrom A, Geerlings MI, Hassan A, Huang Y, Iglesias JE, La Joie R, Kerchner GA, LaRocque KF, Libby LA, Malykhin N, Mueller SG, Olsen RK, Palombo DJ, Parekh MB, Pluta JB, Preston AR, Pruessner JC, Ranganath C, Raz N, Schlichting ML, Schoemaker D, Singh S, Stark CEL, Suthana N, Tompary A, Turowski MM, Van Leemput K, Wagner AD, Wang L, Winterburn JL, Wisse LEM, Yassa MA, Zeineh MM. Quantitative comparison of 21 protocols for labeling hippocampal subfields and parahippocampal subregions in in vivo MRI: towards a harmonized segmentation protocol. Neuroimage 2015; 111:526-41. [PMID: 25596463 PMCID: PMC4387011 DOI: 10.1016/j.neuroimage.2015.01.004] [Citation(s) in RCA: 234] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2014] [Revised: 11/25/2014] [Accepted: 01/01/2015] [Indexed: 11/16/2022] Open
Abstract
OBJECTIVE An increasing number of human in vivo magnetic resonance imaging (MRI) studies have focused on examining the structure and function of the subfields of the hippocampal formation (the dentate gyrus, CA fields 1-3, and the subiculum) and subregions of the parahippocampal gyrus (entorhinal, perirhinal, and parahippocampal cortices). The ability to interpret the results of such studies and to relate them to each other would be improved if a common standard existed for labeling hippocampal subfields and parahippocampal subregions. Currently, research groups label different subsets of structures and use different rules, landmarks, and cues to define their anatomical extents. This paper characterizes, both qualitatively and quantitatively, the variability in the existing manual segmentation protocols for labeling hippocampal and parahippocampal substructures in MRI, with the goal of guiding subsequent work on developing a harmonized substructure segmentation protocol. METHOD MRI scans of a single healthy adult human subject were acquired both at 3 T and 7 T. Representatives from 21 research groups applied their respective manual segmentation protocols to the MRI modalities of their choice. The resulting set of 21 segmentations was analyzed in a common anatomical space to quantify similarity and identify areas of agreement. RESULTS The differences between the 21 protocols include the region within which segmentation is performed, the set of anatomical labels used, and the extents of specific anatomical labels. The greatest overall disagreement among the protocols is at the CA1/subiculum boundary, and disagreement across all structures is greatest in the anterior portion of the hippocampal formation relative to the body and tail. CONCLUSIONS The combined examination of the 21 protocols in the same dataset suggests possible strategies towards developing a harmonized subfield segmentation protocol and facilitates comparison between published studies.
Collapse
Affiliation(s)
- Paul A Yushkevich
- Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, USA.
| | - Robert S C Amaral
- Cerebral Imaging Centre, Douglas Mental Health University Institute, McGill University, Canada
| | - Jean C Augustinack
- A.A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, USA
| | | | - Jeffrey D Bernstein
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, USA; Stanford Center for Memory Disorders, USA
| | - Marina Boccardi
- LENITEM (Laboratory of Epidemiology, Neuroimaging and Telemedicine), IRCCS Centro S. Giovanni di Dio Fatebenefratelli, Italy
| | - Martina Bocchetta
- LENITEM (Laboratory of Epidemiology, Neuroimaging and Telemedicine), IRCCS Centro S. Giovanni di Dio Fatebenefratelli, Italy; Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Alison C Burggren
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, USA
| | | | - M Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Mental Health University Institute, McGill University, Canada; Department of Psychiatry, Department of Biomedical Engineering, McGill University, Canada
| | - Gaël Chételat
- INSERM U1077, Universitè de Caen Basse-Normandie, UMR-S1077, Ecole Pratique des Hautes Etudes, CHU de Caen, U1077, Caen, France
| | - Ana M Daugherty
- Institute of Gerontology, Wayne State University, USA; Psychology Department, Wayne State University, USA
| | - Lila Davachi
- Department of Psychology, New York University, USA; Center for Neural Science, New York University, USA
| | | | - Arne Ekstrom
- Center for Neuroscience, University of California, Davis, USA; Department of Psychology, University of California, Davis, USA
| | - Mirjam I Geerlings
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Netherlands
| | - Abdul Hassan
- Center for Neuroscience, University of California, Davis, USA
| | - Yushan Huang
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - J Eugenio Iglesias
- A.A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, USA; Basque Center on Cognition, Brain and Language (BCBL), Donostia-San Sebastian, Spain
| | - Renaud La Joie
- INSERM U1077, Universitè de Caen Basse-Normandie, UMR-S1077, Ecole Pratique des Hautes Etudes, CHU de Caen, U1077, Caen, France
| | - Geoffrey A Kerchner
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, USA; Stanford Center for Memory Disorders, USA
| | | | - Laura A Libby
- Center for Neuroscience, University of California, Davis, USA
| | - Nikolai Malykhin
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada; Centre for Neuroscience, University of Alberta, Edmonton, Alberta, Canada
| | - Susanne G Mueller
- Department of Radiology, University of California, San Francisco, USA; Center for Imaging of Neurodegenerative Diseases, San Francisco VA Medical Center, USA
| | | | | | | | - John B Pluta
- Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, USA; Department of Biostatistics, University of Pennsylvania, USA
| | - Alison R Preston
- Department of Psychology, The University of Texas at Austin, USA; Center for Learning and Memory, The University of Texas at Austin, USA; Department of Neuroscience, The University of Texas at Austin, USA
| | - Jens C Pruessner
- McGill Centre for Studies in Aging, Faculty of Medicine, McGill University, Canada; Department of Psychology, McGill University, Canada
| | - Charan Ranganath
- Department of Psychology, University of California, Davis, USA; Center for Neuroscience, University of California, Davis, USA
| | - Naftali Raz
- Institute of Gerontology, Wayne State University, USA; Psychology Department, Wayne State University, USA
| | - Margaret L Schlichting
- Department of Psychology, The University of Texas at Austin, USA; Center for Learning and Memory, The University of Texas at Austin, USA
| | - Dorothee Schoemaker
- McGill Centre for Studies in Aging, Faculty of Medicine, McGill University, Canada; Department of Psychology, McGill University, Canada
| | - Sachi Singh
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, USA
| | - Craig E L Stark
- Department of Neurobiology and Behavior, University of California, Irvine, USA
| | - Nanthia Suthana
- Department of Neurosurgery, University of California, Los Angeles, USA
| | | | - Marta M Turowski
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, USA
| | - Koen Van Leemput
- A.A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, USA; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark
| | - Anthony D Wagner
- Department of Psychology, Stanford University, USA; Neurosciences Program, Stanford University, USA
| | - Lei Wang
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, USA; Department of Radiology, Northwestern University Feinberg School of Medicine, USA
| | - Julie L Winterburn
- Cerebral Imaging Centre, Douglas Mental Health University Institute, McGill University, Canada
| | - Laura E M Wisse
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Netherlands
| | - Michael A Yassa
- Department of Neurobiology and Behavior, University of California, Irvine, USA
| | | |
Collapse
|
6
|
Adler DH, Pluta J, Kadivar S, Craige C, Gee JC, Avants BB, Yushkevich PA. Histology-derived volumetric annotation of the human hippocampal subfields in postmortem MRI. Neuroimage 2013; 84:505-23. [PMID: 24036353 DOI: 10.1016/j.neuroimage.2013.08.067] [Citation(s) in RCA: 105] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2013] [Revised: 08/09/2013] [Accepted: 08/29/2013] [Indexed: 10/26/2022] Open
Abstract
Recently, there has been a growing effort to analyze the morphometry of hippocampal subfields using both in vivo and postmortem magnetic resonance imaging (MRI). However, given that boundaries between subregions of the hippocampal formation (HF) are conventionally defined on the basis of microscopic features that often lack discernible signature in MRI, subfield delineation in MRI literature has largely relied on heuristic geometric rules, the validity of which with respect to the underlying anatomy is largely unknown. The development and evaluation of such rules are challenged by the limited availability of data linking MRI appearance to microscopic hippocampal anatomy, particularly in three dimensions (3D). The present paper, for the first time, demonstrates the feasibility of labeling hippocampal subfields in a high resolution volumetric MRI dataset based directly on microscopic features extracted from histology. It uses a combination of computational techniques and manual post-processing to map subfield boundaries from a stack of histology images (obtained with 200μm spacing and 5μm slice thickness; stained using the Kluver-Barrera method) onto a postmortem 9.4Tesla MRI scan of the intact, whole hippocampal formation acquired with 160μm isotropic resolution. The histology reconstruction procedure consists of sequential application of a graph-theoretic slice stacking algorithm that mitigates the effects of distorted slices, followed by iterative affine and diffeomorphic co-registration to postmortem MRI scans of approximately 1cm-thick tissue sub-blocks acquired with 200μm isotropic resolution. These 1cm blocks are subsequently co-registered to the MRI of the whole HF. Reconstruction accuracy is evaluated as the average displacement error between boundaries manually delineated in both the histology and MRI following the sequential stages of reconstruction. The methods presented and evaluated in this single-subject study can potentially be applied to multiple hippocampal tissue samples in order to construct a histologically informed MRI atlas of the hippocampal formation.
Collapse
Affiliation(s)
- Daniel H Adler
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, 3600 Market Street, Suite 370, Philadelphia, PA 19104, USA; Department of Bioengineering, University of Pennsylvania, USA.
| | | | | | | | | | | | | |
Collapse
|