Peer into Your Brain via Contiguous-scale Data

Jianfeng Feng
Shanghai National Centre for Mathematic Sciences, Fudan University

2018-04-20 ~ 2018-04-20


Room 1114, Sciences Building No. 1


With the available data of huge samples of contiguous-scales both for healthy controls and patients including depression, autism and schizophrenia etc, we are in the position to quantify human brain activities such as creativity, happiness, IQ and EQ etc and search the roots of various mental disorders. With novel mathematical and machine learning approaches, we first introduced functional entropy and entropy rate of resting state to characterize the dynamic behaivour of our brain. It is further found that the functional entropy is an increasing function of age, but a decreasing function of creativity and IQ. Its biological mechanisms are explored. With the brain wide associate study approach, for the first time in the literature we are able to identify the roots of a few mental disorders. For example, for depression, we found that the most altered regions are located in the lateral and medial orbitofrontal cortex for punishment and reward. Follow-up rTMS at the lateral orbitofrontal cortex demonstrated significant outcomes of the treatments. Finally we discuss some of our recent results on brain-inspired AI and their applications.


Jianfeng Feng is a thousand-talent program (second round) professor, the chair professor of Shanghai National Centre for Mathematic Sciences, and the Dean of Brain-inspired AI Institute in Fudan University.  He has been developing new mathematical, statistical and computational theories and methods to meet the challenges raised in neuroscience and mental health researches. Recently, his research interests are mainly in big data analysis and mining for neuroscience and brain diseases. He was awarded the Royal Society Wolfson Research Merit Award in 2011, as a scientist ‘being of great achievements or potentials’. He has made considerable contributions on modelling single neurons and neuronal networks, machine learning, and causality analysis with publications on Molecular Psychiatry, Brain, PNAS, PRL, J Neuroscience etc. He has proposed and developed BWAS method (Brain-wide association study), and successfully applied it to search the roots in depression, schizophrenia and autism; developed functional entropy method and applied it to the study of ageing, IQ and creativity etc.