Developmental Population Neuroscience

It is the greatest challenge to open up the black-box of human brain from a perspective of population neuroscience. My long-term goal is to understand the developmental complexity of human brain as a connectome (i.e., the comprehensive high-dimensional description of the brain network, the entirety of brain connection). I use integrative neuroimaging and computational modeling methods with cognitive experiments to explore developmental connectomics, seeking knowledge that will translate into a better life style and education settings as well as diagnosis/treatment for patients. I expect that this can be achieved by establishing normative models of the brain changes across the human lifespan, and further transferring into a new stage of developmental cognitive neuroscience, namely, developmental population neuroscience.

Research summary - Discovery human neuroscience

The human brain is highly dynamic across the lifespan. The emerging network neuroscience framework together with brain imaging techniques provide unprecedented opportunities to delineate the dynamics of brain function across the lifespan. In the past decade (2010-2020), my research has mainly focused on the lifespan connectomics of the human brain by using a safe in-vivo neuroimaging method (i.e., resting-state functional magnetic resonance imaging, rfMRI) to 1) systematically evaluated the test-retest reliability for common measurements in functional connectomics derived with the spontaneous brain activity, 2) model lifespan age-related changes of human brain networks, and 3) establish open resources for reproducible brain mapping research. These works are paving the way for developing neuroimaging biomarkers of both typical and atypical brain development as well as achieving better understanding of neurobiological basis of individual differences in cognition and behavior across the lifespan.

Measurement theory of individual differences

Measurement reliability is essential for studying individual differences in human brain function, especially clinical and neurodevelopmental as well as studies of aging. In theory, reliability serves as an upper limit of validity and is measurable in practice while validity is more difficult to measure directly (e.g., specific trait and disease) thus often approximated by predictive validity. Therefore, high reliability is a required standard for both research and clinical use (e.g., clinical measurement scales need an almost prefect reliability, ICC>0.8 or >0.75). This reflects clinical call of measurements with high inter-individual differences (easily differentiating individuals) and low intra-individual differences (high individual stability across measuring occasions). It is even more crucial that lack of reliability can be an important cause of less statistical power, low reproducibility, puzzlingly high correlations, and unnecessarily large samples. I focus my research on the following questions: Are the functional connectomic measurements reliable? How the reliability of these measurements can be improved? I lead a deep resource of the Brain Consortium for Reproducibility, Replicability and Reliability (3R-BRAIN) for the research agenda of measurement theory abovementioned.

Lifespan of human brain connectome

The human connectomics across the life span aims to decipherring the lifespan development of human cognition and its brain-wide associations. Connectomics has enhanced our understanding of neurocognitive development and decline by the integration of network sciences into studies across different stages of the human life span. Highly reliable brain networks measurements motivated me to employ them for detecting the lifespan trajectories of human connectomes. We propose a generative framework for computationally modeling the connectome over the human life span. This framework regressing on the real brain imaging data highlights that across the life span, the human connectome gradually shifts from an ‘anatomically driven’ organization to one that is more ‘topological’ (see a connectivity gradient shift). It inspires the future research roadmap of the translational human connectomics leveraging a similar paradigm on the lifespan charts of human brain morphology.

Open resources for human brain mapping

A fundamental need for human brain mapping is to develop an efficient, robust, reliable and easy-to-use pipeline to mine big neuroimaging data. I developed a computational pipeline for discovery science of human brain connectomes at the macroscale with MRI technologies, the Connectome Computation System (CCS). CCS is designed with a three-level hierarchical structure that includes data cleaning and preprocessing, individual connectome mapping and connectome mining, and knowledge discovery. Several functional modules are embedded into this hierarchy to implement quality control procedures, reliability analysis and connectome visualization. I demonstrated the utility of CCS for delineating the normative trajectories of large-scale neural networks across the life span (6-85 years of age) by leveraging GAMLSS. Beyond the computational platforms, I also lead or contribute to several large-scale neuroscience efforts in China including 1) the Consortium for Reliability and Reproducibility (CoRR), 2) the Chinese Color Nest Project (CCNP), 3) the Brain Consortium for Reproducibility, Replicability and Reliability (3R-BRAIN), 4) the Chinese Imaging Genetics study (CHIMGEN), 5) the Chinese Human Connectome Project (CHCP) and 6) the Depression Imaging Research Consortium (DIRECT).