Network-based Brain-Behavior Associations Studies with Functionally Meaningful Nodes Xiang-zhen
1,4 Kong ,
Yi
3 Pu ,
Xu
1,4 Wang ,
Lijie
1,4 Huang ,
Xin
1,4 Hao ,
Zonglei
1,4 Zhen ,
Jia
1,2 Liu
1State
Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China; 2School of Psychology, Beijing Normal University, Beijing 100875, China; 3ARC center of Excellence in Cognition and its Disorders, Department of Cognitive Science, Macquarie University, Australia; 4Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, 100875, China.
[email protected] (XK),
[email protected] (JL)
Introduction • One important goal of cognitive neuroscience is to investigate the links between human brain and various behaviors. As brain is incredibly complex [1], network-based brain-behavior associations (NBBA) studies would allow better understanding of variability in behaviors [2]. Unlike previous NBBA studies using a prior anatomical atlas or extra task fMRI for nodes definition, which is either functionally nonspecific or rather task dependent and time-consuming, we introduced a simple but efficient method to conduct NBBA studies with functionally meaningful nodes (FMNs). • Specifically, FMNs are defined with the Neurosynth [3], which allows for automated neuroimaging meta-analysis for a psychological term of interest (e.g., emotion and memory) [e.g., 4]. As the nodes are functionally relevant and easy to access, we expect that using FMNs for network construction would facilitate the NBBA studies. • Here we applied this approach to investigate the link between human brain and navigation from the perspective of complex networks.
Methods
Results
Participants.
The main findings are:
• 202 students (124 females; 20.3±0.83 years).
• Navigation networks showed small-world organization (Sigma > 1).
• Navigation ability was assessed with the SBSOD scale. • MRI scanning included resting-state fMRI (TR/TE=2000/30 ms; flip angle=90o; 3.1253.125 3.6 mm3) and T1-weighted MRI (TR/TE/TI=2530/3.39/1100 ms; flip angle=7o; 1 1 1.33 mm3).
• Better navigation ability was associated with increased smallworldness (r=0.19, p=0.015) and modularity (r=0.22, p=0.004).
• The dataset is part of the Brain Activity Atlas project [5].
FMNs Definition. • 24 FMNs related to navigation was obtained with Neurosynth.
• The right RSC and left MOG showed the largest betweenness, suggesting that they might be hubs in the navigation network.
Network Construction. • Correlation coefficient between the average time courses from each pair of ROIs was calculated to determine the strength of the connection.
• The betweenness of the right RSC showed positive association with navigation ability (r=0.29, p
