My research interests are mainly about understanding brain functional integrations and the organization of brain networks. My efforts can be summarized as three themes on how three factors modulate brain functional connectivity: 1) different task conditions, 2) other brain regions, 3) and neurophysiological factors. I use functional MRI (fMRI) as my main research tool, but also pursue combining different neuroimaging modalities including positron emission tomography (PET) and electroencephalography (EEG). My research philosophy is to utilize divergent research methodologies to approach my scientific questions. I have been using multiple methods to study brain connectivity, including psycho-(physio-)physiological interaction (PPI), dynamic causal modeling (DCM), independent component analysis (ICA), network analysis, sliding window method, machine learning, and meta-analysis. 

1. Brain functional connectivity in different task conditions

This line of research focuses on understanding of brain connectivity and network organizations in different task conditions. 
Di X, Biswal BB (2019). Toward Task Connectomics: Examining Whole-Brain Task Modulated Connectivity in Different Task Domains. Cereb Cortex 29(4):1572-1583
Di X, Biswal BB (2017). Psychophysiological Interactions in a Visual Checkerboard Task: Reproducibility, Reliability, and the Effects of Deconvolution. Front Neurosci 11:573.
Di X, Reynolds RC, Biswal BB (2017). Imperfect (de)convolution may introduce spurious psychophysiological interactions and how to avoid it. Hum Brain Mapp 38(4), 1723–1740.  
Di X, Huang J, Biswal BB (2017). Task-modulated brain connectivity of the amygdala: a meta-analysis of psychophysiological interactions. Brain Struct Funct 222(1):619-634. 
Di X, Fu Z, Chan SC, Hung YS, Biswal BB, Zhang Z (2015). Task-related Functional Connectivity Dynamics in a Block-designed Visual Experiment. Front. Hum. Neurosci 9:543.
Di X, Gohel S, Kim EH and Biswal BB (2013). Task vs. Rest - Different Network Configurations between the Coactivation and the Resting-State Brain Networks. Front. Hum. Neurosci. 7:493.

2. Brain functional connectivity modulated by other regions

This line of researches focuses on how brain connectivity between two brain regions is modulated by a third region.
Di X, Zhang H, Biswal BB (2020): Anterior cingulate cortex differently modulates fronto-parietal functional connectivity between resting-state and working memory tasks. Hum Brain Mapp 41:1797–1805.
Di X, Biswal BB (2015). Characterizations of resting-state modulatory interactions in human brain. J Neurophysiol 114(5):2785-96.
Di X, Biswal BB (2015). Dynamic Brain Functional Connectivity Modulated by Resting-State Networks. Brain Structure and Function 220(1):37-46.
Di X, Biswal BB (2014). Modulatory Interactions between the Default Mode Network and Task Positive Networks in Resting-State. PeerJ 2:e367.
Di X, Biswal BB (2013). Modulatory interactions of resting-state brain functional connectivity. PLoS ONE 8(8): e71163.

3. Brain metabolic connectivity and networks

This line of research focuses on brain metabolic connectivity as measured by FDG-PET. 
Di X, Woelfer M, Amend M, Wehrl H, Ionescu TM, Pichler BJ, Biswal BB, and Alzheimer's Disease Neuroimaging Initiative (2019). Interregional causal influences of brain metabolic activity reveal the spread of aging effects during normal aging. Human Brain Mapping 40(16):4657-4668.
Di X, Gohel S, Thielcke A, Wehrl HF, Biswal BB, and Alzheimer's Disease Neuroimaging Initiative (2017). Do all roads lead to Rome? A comparison of brain networks derived from inter-subject volumetric and metabolic covariance and moment-to-moment hemodynamic correlations in old individuals. Brain Struct Funct 222(8):3833–3845.
Di X, Biswal BB, Alzheimer's Disease Neuroimaging Initiative (2012). Metabolic Brain Covariant Networks as Revealed by FDG-PET with reference to resting-state fMRI networks. Brain Connect 2(5):275-83.

Research support

NJDOH – NJ Autism Center of Excellence (CAUT16APL019), 2016 - 2018 
Multimodal neuroimaging study of sex differences in children with autism spectrum disorder 
Role: PI