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Pan Lin,Ph.D.
Professor,Key Laboratory of Cognitive Science, College of Biomedical Engineering, South-Central University for Nationalities, Wuhan, 430074,China
Email:tiger.lin9906@gmail.com
Our Research
The main goal of our study is to determine the underline relationship between brain metabolic, structure and cognitive function, and clinical applications. We use multimodal neuroimaging techniques (fMRI,DTI,T1,MRS,ASL and EEG) ,signal processing,complex network and machine learning to understand the brain functional and identify neuroimaging biomarkers for clinical application.
Dr. Pan Lin research focuses on three main areas:
- Multimodal neuroimaging approaches to better understand brain default mode network
- Novel dynamic brain network method development and applications for studying brain function.
- Brain image biomarkers to discover to improve diagnosis and treatments for mental disorder and brain tumor application.
Research Program Highlights
Main Question
Can we use multimodal brain neuroimaging approach, signal processing and complex network for understanding the default mode network function that are highly important for greater understanding of brain function and brain disorders?
Approach
We use arterial spin labeling,fMRI,DTI,MRS and EEG to investigate the default mode network.
Current Findings
- Our findings indicate a neuronal source for deactivation in default mode network. Our results also suggest that the assessment of metabolic indexes of neuronal activity during task and rest is important for quantifying and understanding deactivation patterns in the human brain and their implications for both healthy and clinical populations.
- Our findings show that there is a relationship between static, intrinsic resting-state DMN brain network activity and cognitive behavior. In addition, the dynamic PCC nodal degree is also related to task performance. Our findings suggest the important link between both static and dynamic PCC region temporal topological properties and underlying brain cognition and adaptive information processing function.
- The topology of the DMN changes over time as individuals’ transition between resting and task states. The nodal and global topological structure is reshaped in different brain states and shows different patterns. Furthermore, the topology metrics are dynamically altered as a function of cognitive experiences, and the modulated networks are assembled in the subsequent resting state. These findings suggest that understanding the dynamic topology of the DMN can provide a new way to better characterize DMN function.
Future Directions
We will determine whether multimodal neuroimaging can identify default mode network imaging biomarkers for mental disorder ,neurodegenerative diseases, brain tumor.
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