Probing brain effective connectivity in early MS patients with Granger causality analysis of task-fMRI

被引：0
|
作者：
Valente Duarte, J. [1 ,2 ]
Santos, J. [1 ]
Abreu, R. [1 ]
Soares, J. [1 ]
Batista, S. [2 ,3 ]
Lima, A. C. [3 ]
Sousa, L. [2 ,3 ]
Castelo-Branco, M. [1 ,2 ]
机构：
[1] Univ Coimbra, CIBIT, ICNAS, Coimbra, Portugal
[2] Univ Coimbra, Fac Med FMUC, Coimbra, Portugal
[3] Ctr Hosp & Univ Coimbra, Neurol Dept, Coimbra, Portugal
来源：
MULTIPLE SCLEROSIS JOURNAL | 2021年 / 27卷 / 2_SUPPL期
关键词：
D O I：
暂无
中图分类号：
R74 [神经病学与精神病学];
学科分类号：
摘要：
P519
引用
收藏
页码：470 / 471
页数：2
相关论文
共 50 条
  • [21] Altered Effective Connectivity of Default Mode Network in Relapsing Remitting Multiple Sclerosis at Resting State: A Fmri Granger Causality Analysis
    Zhou, Fuqing
    Zhuang, Ying
    Gong, Honghan
    MULTIPLE SCLEROSIS JOURNAL, 2015, 21 (06) : 817 - 817
  • [22] Altered effective connectivity of posterior thalamus in migraine with cutaneous allodynia: a resting-state fMRI study with granger causality analysis
    Ting Wang
    Ning Chen
    Wang Zhan
    Jia Liu
    Junpeng Zhang
    Qi Liu
    Hua Huang
    Li He
    Junran Zhang
    Qiyong Gong
    The Journal of Headache and Pain, 2016, 17
  • [23] Conditional Granger Causality Analysis of Effective Connectivity during Motor Imagery and Motor Execution in Stroke Patients
    Wang, Li
    Zhang, Jingna
    Zhang, Ye
    Yan, Rubing
    Liu, Hongliang
    Qiu, Mingguo
    BIOMED RESEARCH INTERNATIONAL, 2016, 2016
  • [24] Exploring the effective connectivity of resting state networks in Mild Cognitive Impairment: an fMRI study combining ICA and multivariate Granger causality analysis
    Liu, Zhenyu
    Bai, Lijun
    Dai, Ruwei
    Zhong, Chongguang
    Wang, Hu
    You, Youbo
    Wei, Wenjuan
    Tian, Jie
    2012 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2012, : 5454 - 5457
  • [25] Effective connectivity analysis of time series in frequency domain based on granger causality test
    Jiao, Zhuqing
    Ma, Kai
    Ma, Zhenghua
    ICIC Express Letters, 2015, 9 (10): : 2757 - 2764
  • [26] Random forest Granger causality for detection of effective brain connectivity using high-dimensional data
    Furqan, Mohammad Shaheryar
    Siyal, Mohammad Yakoob
    JOURNAL OF INTEGRATIVE NEUROSCIENCE, 2016, 15 (01) : 55 - 66
  • [27] Characteristics of Voxel Prediction Power in Full-brain Granger Causality Analysis of fMRI Data
    Garg, Rahul
    Cecchi, Guillermo A.
    Rao, A. Ravishankar
    MEDICAL IMAGING 2011: BIOMEDICAL APPLICATIONS IN MOLECULAR, STRUCTURAL, AND FUNCTIONAL IMAGING, 2011, 7965
  • [28] DEEP DAG LEARNING OF EFFECTIVE BRAIN CONNECTIVITY FOR FMRI ANALYSIS
    Yu, Yue
    Kan, Xuan
    Cui, Hejie
    Xu, Ran
    Zheng, Yujia
    Song, Xiangchen
    Zhu, Yanqiao
    Zhang, Kun
    Nabi, Razieh
    Guo, Ying
    Zhang, Chao
    Yang, Carl
    2023 IEEE 20TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING, ISBI, 2023,
  • [29] Effective connectivity of brain regions underlying third-party punishment: Functional MRI and Granger causality evidence
    Bellucci, Gabriele
    Chernyak, Sergey
    Hoffman, Morris
    Deshpande, Gopikrishna
    Dal Monte, Olga
    Knutson, Kristine M.
    Grafman, Jordan
    Krueger, Frank
    SOCIAL NEUROSCIENCE, 2017, 12 (02) : 124 - 134
  • [30] Upsampling to 400-ms Resolution for Assessing Effective Connectivity in Functional Magnetic Resonance Imaging Data with Granger Causality
    McFarlin, Daniel R.
    Kerr, Deborah L.
    Nitschke, Jack B.
    BRAIN CONNECTIVITY, 2013, 3 (01) : 61 - 71
12345