Summary

高分辨率<em>在体内</em>使用3T磁共振成像手动分割协议为人类海马子字段

Published: November 10, 2015
doi:

Summary

The goal of this manuscript is to study the hippocampus and hippocampal subfields using MRI. The manuscript describes a protocol for segmenting the hippocampus and five hippocampal substructures: cornu ammonis (CA) 1, CA2/CA3, CA4/dentate gyrus, strata radiatum/lacunosum/moleculare, and subiculum.

Abstract

人类海马已被广泛研究了记忆和正常脑功能及其在不同的神经精神疾病作用方面一直在大力研究。虽然许多成像研究治疗海马为单一神经解剖结构,它是,在实际上,有一个复杂的三维几何形状的几个子场组成。因此,它是已知的,这些子场执行专门的功能,并通过不同的疾病状态的过程中差异的影响。磁共振(MR)成像,可作为一种强大的工具来询问海马和其子场的形态。许多集团利用先进的成像软件和硬件(> 3T),以图像的子场;然而这种类型的技术可能不是在大多数研究和临床成像中心容易获得。为了满足这一需求,该手稿提供了一个详细的一步一步的协议分割全前后长度CORNU ammonis(CA)1,CA2 / CA3,CA4 /齿状回(DG),地层radiatum /腔隙/ moleculare(SR / SL / SM)和下脚:海马及其子区。该协议已经被应用到五个主题(3F,2M; 29-57岁,平均37)。协议的可靠性是由再分割或右侧每个受试者的左海马和计算使用骰子的卡伯度量重叠评估。平均骰子的卡帕(范围)在五个主题是:整海马,0.91(0.90-0.92); CA1,0.78(0.77-0.79); CA2 / CA3,0.64(0.56-0.73); CA4 /齿状回,0.83(0.81-0.85);地层radiatum /腔隙/ moleculare,0.71(0.68-0.73);和下托0.75(0.72-0.78)。这里提出的分割协议提供其他实验室与研究使用常用的MR工具海马和海马子场体内的可靠方法。

Introduction

海马是与情节记忆,空间导航和其他认知功能10,31相关联的广泛研究的内侧颞叶结构。其在神经变性和神经精神障碍,如阿尔茨海默氏症,精神分裂症和双相情感障碍的作用是证据充分的4,5,18,24,30。本手稿的目标是提供额外的细节,以对在3T获得高分辨率磁共振(MR)图像对人海马子场察看34出版的手册分割协议。此外,视频分量随本手稿将提供给研究者谁希望实现自己的数据集的协议进一步的帮助。

海马可以基于观察到的在组织学制备的验尸cytoarchitectonic差异分为子场标本12,22。这种事后的标本定义GROU次为识别和海马子区研究真理。然而这种性质的制剂需要专门的技术和设备进行染色,并且通过固定的组织的可用性是有限的,尤其是在患病人群在体内成像具有主体大得多池的优点,并且还提供了机会为后续后续研究和人群观察变化。虽然它已被证明在T2加权体外的MR图像,信号强度反映蜂窝密度13,它仍然是难以确定仅仅使用MR信号强度的子场之间的无可争议的边界。因此,已经开发了许多不同的方法用于识别MR图像组织学级别的细节。

一些团体努力重建和数字化组织的数据集,然后用这些重建以及图像配准技术本地化海马子neuroanatOMY 在体内 MR 1,2,8,9,14,15,17,32。虽然这是一个有效的技术映射一个版本的病理基础事实直接到MR图像,这种性质的重建都难以完成。的项目,如这些是由完整的内侧颞叶标本,组织学技术,组织学处理期间的数据丢失,并固定体内大脑之间的基本形态的不一致的可用性的限制。其他团体已使用高场扫描仪(7T或9.4T),以努力获得在体内或离体的影像具有足够小(0.20-0.35毫米各向同性)的体素尺寸的可视化空间定位,用于在图像对比度的差异推断子区35,37之间的界限。即使在7T-9.4T和用这样一个小的体素尺寸,海马子场cytoarchitectonic特征是不可见的。这样,手动分段协议已经开发了一pproximate对MR图像已知的组织界限。这些协议通过解释本地图像对比度的差异并限定几何规则(如直线和角),相对于可见光的结构确定的子场边界。虽然在高场强拍摄的图像能够提供详细的洞察海马子场,高场扫描仪是尚未在临床或研究设置常见,因此7T和9.4T协议目前具有有限的适用性。类似的协议已被开发用于收集在3T和4T扫描器11,20,21,23,24,25,28,33图像。许多这些协议是基于与在冠状面子1毫米体素的体素的尺寸的图像,但具有大的切片厚度(0.8-3毫米)11,20,21,23,25,28,33或大间切片的距离20,28,这两个导致各个子场的体积的估计中一个显著测量偏差。另外,许多现有的3T的协议排除的子场中的所有海马头部或尾部20,23,25,33的全部或部分,或不提供的重要子结构,结合 DG与CA2 / CA3或不包括地层radiatum /腔隙/ moleculare详细分割在CA)11,20,21,23,24,25,28,33。因此,有必要在该领域的协议,可以可靠地在整个头部,身体和海马的尾部是基于在临床和研究设置通常可获得的一个扫描器确定相关子场的详细描述。努力目前由海马子区组(www.hippocampalsubfields.com)正在进行协调实验室之间海马子场分割过程,类似于现有的协调工作对整个海马分割6,以及比较21的现有协议的初始纸最近发表38 。从这个小组的工作将进一步阐明最佳分割PROCE的既定程序。

这份手稿提供了详细的书面和视频指令的可靠实现高分辨率3T磁共振成像被温特和他的同事34前面所述的海马子分段协议。该协议对整个海马五个图像健康对照组和五个海马子字段(CA1,CA2 / CA3,CA4 /齿状回,地层radiatum /腔隙/ moleculare和下脚)得到落实。这些分割的图像都可以在公网上(cobralab.ca/atlases/Hippocampus)来。该协议与划分图像将是谁希望学习详细的海马神经解剖学磁共振图像组非常有用。

Protocol

研究参与者 (; 29-57岁,平均37。3楼,2M)谁是自由的神经和神经精神障碍和重度​​颅脑外伤的情况下在这个手稿的协议,从健康志愿者采集的五个有代表性的高清晰度图像的开发。所有受试者入组,在该中心的成瘾和精神健康(CAMH)。这项研究是由CAMH研究伦理委员会,并符合赫尔辛基宣言进行。提供书面所有受试者知情同意进行数据采集和共享。有关用于收集这些图像采?…

Representative Results

。结果从协议可靠性测试总结于表2对于整个双侧海马,平均空间交叠由骰子的卡伯测定为0.91和0.90的范围内- 0.92。子场卡伯值范围为0.64(CA2 / CA3)至0.83(CA4 /齿状回)。平均体积为所有子场和整个海马列于表3中。卷在整个海马范围从2456.72-3325.02毫米3。该CA2 / CA3是最小的子场在208.33毫米3,而CA1是最大的857.46毫米3。 <p class="jove_conte…

Discussion

在MR图像海马子分割很好的体现在文献中。但是,现有的协议排除的海马20,23,33,35部,仅适用于定影图像37,或要求超高场扫描仪进行图像采集35,37。这个手稿提供一个分割协议,它包括海马五个主要分部(CA1,CA2 / CA3,CA4 /齿状回,SR / SL / SM,并下托),并跨越结构的整个前 – 后长度。完整的分段地图集可在公众网上(cobralab.ca/atlases/Hippocampus)来。这项工作是适用于神经?…

Declarações

The authors have nothing to disclose.

Acknowledgements

笔者想从CAMH基金会的认可支持,感谢迈克尔和宋佳柯纳的Kimel家庭,和保罗·加芬克尔新研究员催化剂奖。该项目资金由全宗德RECHERCHES桑特魁北克健康研究加拿大学院(CIHR),自然科学和加拿大,韦斯顿脑研究所,加拿大阿尔茨海默氏症协会工程研究理事会,和迈克尔J. Fox基金会帕金森病研究(MMC),以及CIHR,安大略省精神卫生基金会,NARSAD和心理健康国家研究所(R01MH099167)(ANV)。作者还要感谢Anusha Ravichandran寻求帮助获取图像。

Materials

Discovery MR750 3T GE NA Or equivalent 3T scanner
Minc Tool Kit McConnell Brain Imaging Center, Montreal Neurological Institute NA Open source: http://www.bic.mni.mcgill.ca/ServicesSoftware/ServicesSoftwareMincToolKit

Referências

  1. Adler, D. H., et al. Reconstruction of the human hippocampus in 3D from histology and high-resolution ex-vivo MRI. IEEE Intl. Symp. on Biomed. Img. , 294-297 (2012).
  2. Adler, D. H., et al. Histology-derived volumetric annotation of the human hippocampal subfields in postmortem MRI. NeuroImage. 84 (1), 505-523 (2014).
  3. Amaral, D. G. A golgi study of cell types in the hilar region of the hippocampus in the rat. J. Comp. Neurol. 182 (4 Pt 2), 851-914 (1978).
  4. Blumberg, H. P., et al. Amygdala and Hippocampal Volumes in Adolescents and Adults With Bipolar Disorder. Arch Gen Psychiatry. 60 (12), 1201-1208 (2003).
  5. Braak, H., Braak, E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol . 82 (4), 239-259 (1991).
  6. Boccardi, M., et al. Survey of protocols for the manual segmentation of the hippocampus: preparatory steps towards a joint EADC-ADNI harmonized protocol. J. Alzheimer’s Dis. 26 (3), 61-75 (2011).
  7. Chakravarty, M. M., et al. Performing label-fusion-based segmentation using multiple automatically generated templates. Hum. Brain Mapp. 34 (10), 2635-2654 (2013).
  8. Chakravarty, M. M., Bertrand, G., Hodge, C. P., Sadikot, A. F., Collins, D. L. The creation of a brain atlas for image guided neurosurgery using serial histological data. NeuroImage. 30 (2), 359-376 (2006).
  9. Collins, D. L., Neelin, P., Peters, T. M., Evans, A. C. Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. J. Comput. Assist. Tomogr. 18 (2), 192-205 (1994).
  10. Heijer, F. V., et al. Structural and diffusion MRI measures of the hippocampus and memory performance. NeuroImage. 63 (4), 1782-1789 (2012).
  11. Duncan, K., Tompary, A., Davachi, L. Associative encoding and retrieval are predicted by functional connectivity in distinct hippocampal area ca1 pathways. The Journal of Neuroscience. 34 (34), 11188-11198 (2014).
  12. Duvernoy, H. M. . The Human Hippocampus: Functional Anatomy Vascularization, and Serial Sections with MRI. , (2005).
  13. Fatterpekar, G. M., et al. Cytoarchitecture of the human cerebral cortex: MR microscopy of excised specimens at 9.4 Tesla. Am. J. Neuroradiol. 23 (8), 1313-1321 (2002).
  14. Frey, S., Pandya, D. N., Chakravarty, M. M., Bailey, L., Petrides, M., Collins, D. L. An MRI based average macaque monkey stereotaxic atlas and space (MNI monkey space). NeuroImage. 55 (4), 1435-1442 (2011).
  15. Goubran, M., Crukley, C., de Ribaupierre, S., Peters, T. M., Khan, A. R. Image registration of ex-vivo. MRI to sparsely sectioned histology of hippocampal and neocortical temporal lobe specimens. NeuroImage. 83, 770-781 (2013).
  16. Heckemann, R. A., Hajnal, J. V., Aljabar, P., Rueckert, D., Hammers, A. Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. NeuroImage. 33 (1), 115-126 (2006).
  17. Holmes, C. J., Hoge, R., Collins, L., Woods, R., Toga, A. W., Evans, A. C. Enhancement of MR images using registration for signal averaging. J. Comput. Assist. Tomogr. 22 (2), 324-333 (1998).
  18. Karnik-Henry, M. S., Wang, L., Barch, D. M., Harms, M. P., Campanella, C., Csernansky, J. G. Medial temporal lobe structure and cognition in individuals with schizophrenia and in their non-psychotic siblings. Schizophrenia Research. 138 (2-3), 128-135 (2012).
  19. Kim, J. S., et al. Automated 3-D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification. NeuroImage. 27 (1), 210-221 (2005).
  20. La Joie, R., et al. Differential effect of age on hippocampal subfields assessed using a new high-resolution 3T MR sequence. NeuroImage. 53 (2), 506-514 (2010).
  21. Libby, L. A., Ekstrom, A. D., Ragland, J. D., Ranganath, C. Differential connectivity of perirhinal and parahippocampal cortices within human hippocampal subregions revealed by high-resolution functional imaging. The Journal of Neuroscience. 32 (19), 6550-6560 (2012).
  22. Mai, J. K., Paxinos, G., Voss, T. . Atlas of the Human Brain. , (2008).
  23. Mueller, S. G., et al. Measurement of hippocampal subfields and age-related changes with high resolution MRI at 4T. Neurobiol Aging. 28 (5), 719-726 (2006).
  24. Narr, K. L., et al. Regional specificity of hippocampal volume reductions in first-episode schizophrenia. NeuroImage. 21 (4), 1563-1575 (2004).
  25. Olsen, R. K., Palombo, D. J., Rabin, J. S., Levine, B., Ryan, J. D., Rosenbaum, R. S. Volumetric Analysis of Medial Temporal Lobe Subregions in Development Amnesia using High-Resolution Magnetic Resonance Imaging. Hippocampus. 23 (10), 855-860 (2013).
  26. Park, M. T. M., et al. Derivation of high-resolution MRI atlases of the human cerebellum at 3T and segmentation using multiple automatically generated templates. NeuroImage. 95, 217-231 (2014).
  27. Pipitone, J., et al. Multi-atlas Segmentation of the Whole Hippocampus and Subfields Using Multiple Automatically Generated Templates. NeuroImage. 101, 494-512 (2014).
  28. Pluta, J., Yushkevich, P., Das, S., Wolk, D. In vivo analysis of hippocampal subfield atrophy in mild cognitive impairment via semi-automatic segmentation of T2-weighted MRI.Journal of Alzheimer’s Disease. 31 (1), 85-99 (2012).
  29. Pruessner, J. C., et al. Volumetry of hippocampus and amygdala with high-resolution MRI and three- dimensional analysis software: minimizing the discrepancies between laboratories. Cereb Cortex. 10 (4), 433-442 (2000).
  30. Sabuncu, M. R., et al. The dynamics of cortical and hippocampal atrophy in Alzheimer disease. Archives of Neurology. 68 (8), 1040-1048 (2011).
  31. Scoville, W. B., Milner, B. Loss of recent memory after bilateral hippocampal lesions. J. Neuropsych. and Clin. Neurosci. 12 (1), 103-113 (1957).
  32. Toga, A. W., Thompson, P. M., Mori, S., Amunts, K., Zilles, K. Towards multimodal atlases of the human brain. Nat. Rev. Neurosci. 7 (12), 952-966 (2006).
  33. van Leemput, K., et al. Automated segmentation of hippocampal subfields from ultra-high resolution in vivo. MRI. Hippocampus. 19 (6), 549-557 (2009).
  34. Winterburn, J. L., et al. A novel in vivo atlas of human hippocampal subfields using high-resolution 3 T magnetic resonance imaging. NeuroImage. 74, 254-265 (2013).
  35. Wisse, L. E. M., Gerritsen, L., Zwanenburg, J. J. M., Kuijf, H. J. Subfields of the hippocampal formation at 7 T MRI: in vivo. volumetric assessment. NeuroImage. 61 (4), 1043-1049 (2012).
  36. Yelnik, J., et al. A three-dimensional, histological and deformable atlas of the human basal ganglia. I. Atlas construction based on immunohistochemical and MRI data. NeuroImage. 34 (2), 618-638 (2007).
  37. Yushkevich, P. A., et al. A high-resolution computational atlas of the human hippocampus from postmortem magnetic resonance imaging at 9.4 T. NeuroImage. 44 (2), 385-398 (2009).
  38. Yushkevich, P. A., et al. Quantitative Comparison of 21 Protocols for Labeling Hippocampal Subfields and Parahippocampal Subregions in In Vivo MRI: Towards a Harmonized Segmentation Protocol. NeuroImage. , (2015).
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Winterburn, J., Pruessner, J. C., Sofia, C., Schira, M. M., Lobaugh, N. J., Voineskos, A. N., Chakravarty, M. M. High-resolution In Vivo Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T Magnetic Resonance Imaging. J. Vis. Exp. (105), e51861, doi:10.3791/51861 (2015).

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