| Title | Atomic Dynamic Functional Interaction Patterns for Characterization of ADHD |
| Authors | Ou, Jinli Lian, Zhichao Xie, Li Li, Xiang Wang, Peng Hao, Yun Zhu, Dajiang Jiang, Rongxin Wang, Yufeng Chen, Yaowu Zhang, Jing Liu, Tianming |
| Affiliation | Zhejiang Univ, Sch Biomed Engn & Instrument Sci, Hangzhou 310003, Zhejiang, Peoples R China. Yale Univ, Dept Stat, New Haven, CT 06520 USA. Univ Georgia, Dept Comp Sci, Cort Architecture Imaging & Discovery Lab, Athens, GA 30602 USA. Univ Georgia, Bioimaging Res Ctr, Athens, GA 30602 USA. Peking Univ, Inst Mental Hlth, Beijing 100871, Peoples R China. Fudan Univ, Sch Life Sci, Shanghai 200433, Peoples R China. |
| Keywords | brain networks functional interaction temporal dynamics nonnegative matrix factorization ADHD NONNEGATIVE MATRIX FACTORIZATION BRAIN NETWORKS CONNECTIVITY MODEL SCHIZOPHRENIA FLUCTUATIONS LOCALIZATION PROFILES CLUSTERS NUMBER |
| Issue Date | 2014 |
| Publisher | human brain mapping |
| Citation | HUMAN BRAIN MAPPING.2014,35,(10),5262-5278. |
| Abstract | Modeling abnormal temporal dynamics of functional interactions in psychiatric disorders has been of great interest in the neuroimaging field, and thus a variety of methods have been proposed so far. However, the temporal dynamics and disease-related abnormalities of functional interactions within specific data-driven discovered subnetworks have been rarely explored yet. In this work, we propose a novel computational framework composed of an effective Bayesian connectivity change point model for modeling functional brain interactions and their dynamics simultaneously and an effective variant of nonnegative matrix factorization for assessing the functional interaction abnormalities within subnetworks. This framework has been applied on the resting state fmagnetic resonance imaging (fMRI) datasets of 23 children with attention-deficit/hyperactivity disorder (ADHD) and 45 normal control (NC) children, and has revealed two atomic functional interaction patterns (AFIPs) discovered for ADHD and another two AFIPs derived for NC. Together, these four AFIPs could be grouped into two pairs, one common pair representing the common AFIPs in ADHD and NC, and the other abnormal pair representing the abnormal AFIPs in ADHD. Interestingly, by comparing the abnormal AFIP pair, two data-driven abnormal functional subnetworks are derived. Strikingly, by evaluating the approximation based on the four AFIPs, all of the ADHD children were successfully differentiated from NCs without any false positive. Hum Brain Mapp 35:5262-5278, 2014. (c) 2014 Wiley Periodicals, Inc. |
| URI | http://hdl.handle.net/20.500.11897/208558 |
| ISSN | 1065-9471 |
| DOI | 10.1002/hbm.22548 |
| Indexed | SCI(E) |
| Appears in Collections: | 第六医院 |
