Research
Introduction of The R-fMRI Lab
Founded in June 2015, the R-fMRI Lab is aimed to search objective biomarkers of major depressive disorder (MDD) based on previous works regarding the computational methodology of resting-state fMRI, the platform of brain imaging analysis and sharing, and spontaneous brain activity. At present, there are 8 members in the R-fMRI lab, namely Professor Chao-Gan Yan, Post-doc Dr. Xiao Chen, Ph.D. student Ning-Xuan Chen, Bin Lu, Hui-Xian Li, master student Zhi-Chen Zhu, Xue-Ying Li and Yu-Wei Wang and programmer Zhi-Kai Chang. In addition, there are several visiting students from multiple research institutes, including Peking University Sixth Hospital, Capital Medical University Xuanwu Hospital, 301 Hospital, Fudan University Huashan Hospital, University of Chinese Academy of Sciences, etc., which have built strong collaboration with the lab. The lab members are passionate to innovate and eager to learn with high productivity.
MDD is nowadays one of the most prevailing psychiatric conditions, but its diagnosis is still primarily based on the symptoms. The R-fMRI Lab has been searching for objective biomarkers for MDD through several avenues. By creating the REST-meta-MDD consortium containing neuroimaging data of 1,300 depressed patients and 1,128 normal controls from 25 research groups in China, the lab found decreased default mode network functional connectivity in depressed patients, driven by patients with recurrent depression, and associated with current medication treatment but not with disease duration (Yan, C.-G.*, Chen, X.#, Li, L.#, Castellanos, F. X., . . . Si, T.-M., Zuo, X.-N., Zhao, J.-P.*, Zang, Y.-F.* Reduced Default Mode Network Functional Connectivity in Patients with Recurrent Major Depressive Disorder. PNAS). In another study, our lab members and colleagues from New York University adopts a rat model and resting-state functional magnetic resonance imaging (R-fMRI) to explore the neural mechanism of maternal maltreatment affecting the spontaneous brain activity (Yan＃, Rincon-Cortes＃, Raineki , Sarro, Colcombe, Guifoyle, Yang, Gerum, Biswal, Miham, Sullivan & Castellanos, 2017, Translational Psychiatry). To be specific, compared to the control rats, maltreated rats showed more depressive-like behaviors, social withdraws, and abnormal brain developments. The R-fMRI results indicated that the amygdala-prefrontal cortex (PFC) functional connectivity was increased significantly from the adolescence and adulthood periods in control rats but not in maltreated rats. Meanwhile, the spontaneous activity of the medial prefrontal cortex showed early maturing and ceased to develop later. The R-fMRI Lab also conducted some researches on one common hallmark feature and risk factor of MDD: rumination. In a meta-analysis, we systematically examined the present evidences on the brain network underpinnings of rumination (Zhou, Chen, Shen, … Yan*. Rumination and the default mode network: Meta-analysis of brain imaging studies and implications for depression. Neuroimage. 2019). We have also investigated the network basis of rumination with a modified rumination-state paradigm and examined how medication can alter the network features of rumination.
On the platform of brain imaging information processing, the R-fMRI Lab developed DPABI (ESI top 0.1% high cited paper; Yan*, Wang, Zuo & Zang, 2016, Neuroinformatics), a toolbox for Data Processing & Analysis of Brain Imaging. DPABI incorporated DPARSF (Yan & Zang, 2010, Frontiers in Systems Neuroscience, cited for more than 2000 times), a pipeline data processing toolbox for R-fMRI. DPABI integrated the latest advances regarding algorithms for controlling head motion impact, noise reduction, and measurement standardization. DPABI (includes a module for macaque monkeys and a module for rats) also provides a user-friendly pipeline data processing toolbox to facilitate R-fMRI research on the animal model. Recently, the lab developed DPABISurf, which is a surface-based resting-state fMRI data analysis toolbox evolved from DPABI/DPARSF, as easy-to-use as DPABI/DPARSF. DPABISurf provides user-friendly graphical user interface (GUI) for pipeline surface-based preprocessing, statistical analyses and results viewing, while requires no programming/scripting skills from the users. Based on DPABI/DPABISurf/DPARSF, the lab has built a resting-state functional big data platform, known as the R-fMRI Maps Project (RMP, http://rfmri.org/maps) that shares resting-state indices. It offers the global users to accumulate database of the spontaneous brain activity for healthy and multiple brain disease for their related research. At present, RMP has shared nearly 5000 subjects’ resting-state data.
The R-fMRI Lab also develops methodology for R-fMRI. The lab has provided practical solutions for head motion correction, standardization, and multiple comparison. In a high impact study (ESI top 0.1% high cited paper), we systematically test present head motion strategies and proposed that 24-parameter regression and group-level control can control the head motion effect well (Yan, Cheung, Kelly, et al. A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. Neuroimage. 2013). We also provided a well-established protocol for controlling site effect of multi-sited large-scale datasets (ESI top 1% high cited paper; Yan, Craddock, Zuo, Zang, Milham. Standardizing the intrinsic brain: towards robust measurement of inter-individual variation in 1000 functional connectomes. Neuroimage. 2013). For multiple comparison correction, we recommended that permutation test with TFCE can best balance FWER and reproducibility after a systematical examination (ESI top 1% high cited paper; Chen X, Lu B, Yan CG*. Reproducibility of R-fMRI metrics on the impact of different strategies for multiple comparison correction and sample sizes. Hum Brain Mapp. 2018).
For public services, our lab provides online courses related to the basic principles, method and data processing of R-fMRI for vast introductory researcher (http://rfmri.org/course). We collect feedback and give responses to the users of DPABI/DPARSF through the R-fMRI network (http://rfmri.org). We offer support to promote research ideas, data processing and review recommendations. The weekly R-fMRI Journal Club is conducted through the online live streaming platform to discuss the research in the domain of R-fMRI (http://douyu.com/rfmri). The R-fMRI Lab also provides free brain imaging workshop trainings.
In addition, our lab goes for a joyful and productive team spirit, trying to provide the best working and study condition for each member. We have a roomy working place equipped with advanced facilities and office tools. With Professor Chao-Gan Yan’s guidance, the lab members are having lab meetings twice a week, as well as daily report communication. The R-fMRI Lab encourages postgraduate students and visiting students pursuing the research topics that they are truly interested in.