A sub plus cortical fMRI-based surface parcellation

被引:6
|
作者
Lewis, John D. [1 ]
Bezgin, Gleb [1 ,2 ]
Fonov, Vladimir S. [1 ]
Collins, D. Louis [1 ]
Evans, Alan C. [1 ]
机构
[1] McGill Univ, Montreal Neurol Inst, McConnell Brain Imaging Ctr, Montreal, PQ, Canada
[2] McGill Univ, Res Ctr Studies Aging, Translat Neuroimaging Lab, Verdun, PQ, Canada
基金
加拿大自然科学与工程研究理事会; 加拿大健康研究院;
关键词
boundary mapping; cortical parcellation; functional connectivity; model fitting; subcortical parcellation; white; gray contrast; MULTI-ATLAS SEGMENTATION; CEREBRAL-BLOOD-FLOW; AUTOMATIC SEGMENTATION; TEMPLATE LIBRARY; BRAIN; MRI; VOLUME; CORTEX; ROBUST; REGISTRATION;
D O I
10.1002/hbm.25675
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Both cortical and subcortical structures are organized into a large number of distinct areas reflecting functional and cytoarchitectonic differences. Mapping these areas is of fundamental importance to neuroscience. A central obstacle to this task is the inaccuracy associated with bringing results from individuals into a common space. The vast individual differences in morphology pose a serious problem for volumetric registration. Surface-based approaches fare substantially better, but have thus far been used only for cortical parcellation, leaving subcortical parcellation in volumetric space. We extend the surface-based approach to include also the subcortical deep gray-matter structures, thus achieving a uniform representation across both cortex and subcortex, suitable for use with surface-based metrics that span these structures, for example, white/gray contrast. Using data from the Enhanced Nathan Klein Institute-Rockland Sample, limited to individuals between 19 and 69 years of age, we generate a functional parcellation of both the cortical and subcortical surfaces. To assess this extended parcellation, we show that (a) our parcellation provides greater homogeneity of functional connectivity patterns than do arbitrary parcellations matching in the number and size of parcels; (b) our parcels align with known cortical and subcortical architecture; and (c) our extended functional parcellation provides an improved fit to the complexity of life-span (6-85 years) changes in white/gray contrast data compared to arbitrary parcellations matching in the number and size of parcels, supporting its use with surface-based measures. We provide our extended functional parcellation for the use of the neuroimaging community.
引用
收藏
页码:616 / 632
页数:17
相关论文
共 50 条
  • [1] Application of deep learning in fMRI-based human brain parcellation: a review
    Li, Yu
    Chen, Xun
    Ling, Qinrui
    He, Zhiyang
    Liu, Aiping
    [J]. MEASUREMENT SCIENCE AND TECHNOLOGY, 2024, 35 (03)
  • [2] rfDemons: Resting fMRI-Based Cortical Surface Registration Using the BrainSync Transform
    Joshi, Anand A.
    Li, Jian
    Chong, Minqi
    Akrami, Haleh
    Leahy, Richard M.
    [J]. MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, PT III, 2018, 11072 : 198 - 205
  • [3] A Whole Brain Atlas with Sub-parcellation of Cortical Gyri using Resting fMRI
    Joshi, Anand A.
    Choi, Soyoung
    Sonkar, Gaurav
    Chong, Minqi
    Gonzalez-Martinez, Jorge
    Nair, Dileep
    Shattuck, David W.
    Damasio, Hanna
    Leahy, Richard M.
    [J]. MEDICAL IMAGING 2017: IMAGE PROCESSING, 2017, 10133
  • [4] The parcellation of cortical areas using replicator dynamics in fMRI
    Neumann, Jane
    von Cramon, D. Yves
    Forstmann, Birte U.
    Zysset, Stefan
    Lohmann, Gabriele
    [J]. NEUROIMAGE, 2006, 32 (01) : 208 - 219
  • [5] Modeling and analysis of mechanisms underlying fMRI-based decoding of information conveyed in cortical columns
    Chaimow, Denis
    Yacoub, Essa
    Ugurbil, Kamil
    Shmuel, Amir
    [J]. NEUROIMAGE, 2011, 56 (02) : 627 - 642
  • [6] AUTOMATIC CORTICAL SURFACE PARCELLATION BASED ON FIBER DENSITY INFORMATION
    Zhang, Degang
    Guo, Lei
    Li, Gang
    Nie, Jingxin
    Deng, Fan
    Li, Kaiming
    Hu, Xintao
    Zhang, Tuo
    Jiang, Xi
    Zhu, Dajiang
    Zhao, Qun
    Liu, Tianming
    [J]. 2010 7TH IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO, 2010, : 1133 - 1136
  • [7] Fetal cortical surface atlas parcellation based on growth patterns
    Xia, Jing
    Wang, Fan
    Benkarim, Oualid M.
    Sanroma, Gerard
    Piella, Gemma
    Gonzalez Ballester, Miguel A.
    Hahner, Nadine
    Eixarch, Elisenda
    Zhang, Caiming
    Shen, Dinggang
    Li, Gang
    [J]. HUMAN BRAIN MAPPING, 2019, 40 (13) : 3881 - 3899
  • [8] Functional parcellation of the neonatal cortical surface
    Myers, Michael J.
    Labonte, Alyssa K.
    Gordon, Evan M.
    Laumann, Timothy O.
    Tu, Jiaxin C.
    Wheelock, Muriah D.
    Nielsen, Ashley N.
    Schwarzlose, Rebecca F.
    Camacho, M. Catalina
    Alexopoulos, Dimitrios
    Warner, Barbara B.
    Raghuraman, Nandini
    Luby, Joan L.
    Barch, Deanna M.
    Fair, Damien A.
    Petersen, Steven E.
    Rogers, Cynthia E.
    Smyser, Christopher D.
    Sylvester, Chad M.
    [J]. CEREBRAL CORTEX, 2024, 34 (02)
  • [9] Resting-State FMRI Single Subject Cortical Parcellation Based on Region Growing
    Blumensath, Thomas
    Behrens, Timothy E. J.
    Smith, Stephen M.
    [J]. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2012, PT II, 2012, 7511 : 188 - 195
  • [10] FMRI-BASED MEMORY AND LIE DECTION
    Wagner, Anthony
    [J]. JOURNAL OF COGNITIVE NEUROSCIENCE, 2013, : 29 - 30