NEURAL CONNECTIVITY OF THE AMYGDALA IN THE HUMAN BRAIN: A DIFFUSION TENSOR IMAGING STUDY

被引:2
|
作者
Jang, Sung Ho [1 ]
Kwon, Hyeok Gyu [1 ]
机构
[1] Yeungnam Univ, Coll Med, Dept Phys Med & Rehabil, Taegu 705717, South Korea
基金
新加坡国家研究基金会;
关键词
Amygdala; neural connectivity; diffusion tensor imaging; emotion; memory; ANATOMICAL CONNECTIVITY; CORTICAL PROJECTIONS; PREFRONTAL CORTEX; TRACTOGRAPHY; NUCLEUS; MOTOR; DYSFUNCTION; EMOTION; PATHWAY; BODY;
D O I
10.14311/NNW.2014.24.033
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Several diffusion tensor imaging (DTI) studies have reported on the anatomical neural tracts between the amygdala and specific brain regions. However, no study on the neural connectivity of the amygdala has been reported. In the current study, using probabilistic DTI tractography, we attempted to investigate the neural connectivity of the amygdala in normal subjects. Forty eight healthy subjects were recruited for this study. A seed region of interest was drawn at the amygdala using the FMRIB Software Library based on probabilistic DTI tractography. Connectivity was defined as the incidence of connection between the amygdala and each brain region at the threshold of 1 and 5 streamlines. The amygdala showed 100% connectivity to the hippocampus, thalamus, hypothalamus, and medial temporal cortex regardless of the thresholds. In contrast, regarding the thresholds of 1 and 5 streamlines, the amygdala showed high conncetivity (over 60%) to the globus pallidus (100% and 92.7%), brainstem (83.3% and 78.1%), put amen (72.9% and 63.5%), occipito-temporal cortex (72.9% and 67.7%), orbitofrontal cortex (70.8 and 43.8%), caudate nucleus (63.5% and 45.8%), and ventromedial prefrontal cortex (63.5% and 31.3%), respectively. The amygdala showed high connectivity to the hippocampus, thalamus, hypothalamus, medial temporal cortex, basal ganglia, brainstem, occipito-temporal cortex, orbitofrontal cortex, and ventromedial prefrontal cortex. We believe that the methods and results of this study provide useful information to clinicians and researchers studying the amygdala.
引用
下载
收藏
页码:591 / 599
页数:9
相关论文
共 50 条
  • [21] Quantifying human brain connectivity from diffusion tensor MRI
    Sebastiani, Giovanni
    De Pasquale, Francesco
    Barone, Piero
    JOURNAL OF MATHEMATICAL IMAGING AND VISION, 2006, 25 (02) : 227 - 244
  • [22] Aging of the mesolimbic tract in the human brain: A diffusion tensor imaging study
    Seo, Jeong-Pyo
    Ryu, Heun Jae
    MEDICINE, 2022, 101 (41) : E30924
  • [23] Quantifying Human Brain Connectivity from Diffusion Tensor MRI
    Giovanni Sebastiani
    Francesco de Pasquale
    Piero Barone
    Journal of Mathematical Imaging and Vision, 2006, 25 : 227 - 244
  • [24] Acute Effects of Alcohol on the Human Brain: Diffusion Tensor Imaging Study
    Kong, L. M.
    Zheng, W. B.
    Lian, G. P.
    Zhang, H. D.
    AMERICAN JOURNAL OF NEURORADIOLOGY, 2012, 33 (05) : 928 - 934
  • [25] Abnormalities of brain neural circuits related to obesity: A Diffusion Tensor Imaging study
    Papageorgiou, Ioannis
    Astrakas, Loukas G.
    Xydis, Vassileios
    Alexiou, George A.
    Bargiotas, Panagiotis
    Tzarouchi, Loukia
    Zikou, Anastasia K.
    Kiortsis, Dimitrios N.
    Argyropoulou, Maria I.
    MAGNETIC RESONANCE IMAGING, 2017, 37 : 116 - 121
  • [26] Aging of the Nigrostriatal Tract in the Human Brain: A Diffusion Tensor Imaging Study
    Seo, Jeong-Pyo
    Koo, Dong-Kyun
    MEDICINA-LITHUANIA, 2021, 57 (09):
  • [27] Aging of the cingulum in the human brain: Preliminary study of a diffusion tensor imaging study
    Jang, Sung Ho
    Kwon, Yong Hyun
    Lee, Mi Young
    Kim, Jae-Ryong
    Seo, Jeong Pyo
    NEUROSCIENCE LETTERS, 2016, 610 : 213 - 217
  • [28] Computing the probability of local brain connectivity using diffusion tensor imaging
    Shimony, Joshua S.
    Epstein, Adrian A.
    Bretthorst, G. Larry
    BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING, 2007, 954 : 346 - +
  • [29] Brain Connectivity Network Analysis and Classifications from Diffusion Tensor Imaging
    Saad, Murtaza
    Islam, Sheikh Md. Rabiul
    2019 1ST INTERNATIONAL CONFERENCE ON ROBOTICS, ELECTRICAL AND SIGNAL PROCESSING TECHNIQUES (ICREST), 2019, : 422 - 427
  • [30] Hyper-brain connectivity in binge drinking college students: a diffusion tensor imaging study
    Kashfi, Karl
    Al-Khalil, Kareem
    Hou, Jiancheng
    Fang, Dan
    Anderson, Ron
    Rajmohan, Ravi
    Syapin, Peter
    O'Boyle, Michael W.
    NEUROCASE, 2017, 23 (3-4) : 179 - 186