Building biomarkers to bridge the brain, behaviour, and brain diseases

The brain is one of the most important and complex organs in humans. On the one hand, it dictates our behavioral outcomes, such as seeing, speaking, and walking, and, through these behaviors, it determines how we interact with and perceive the world. On the other hand, its pathological outcomes, such as the development of neurodegenerative diseases, affect the longevity and quality of millions of people’s lives. Today, the brain’s neural activities can be captured by neuroimaging modalities such as functional magnetic resonance imaging (fMRI), and its behavioral markers can be recorded using smartphone sensors. Through uncovering how patterns of these large-scale data are related to the brain’s behavioral and pathological outputs, one contributes to our understanding of the biological underpinnings of knowledge, consciousness, cognition, and behavior. In parallel, by developing targeted statistical frameworks, one begins to unveil the intrinsic spatial and temporal relationships between neural signatures, behavioral features, and the brain’s pathological outcomes, and to assess and monitor neurodegenerative diseases more effectively and accurately. This thesis consists of four inter-correlated topics aimed at building targeted and efficient biomarkers using large-scale data to inquire into human behavior and brain disorders. The four topics are arranged according to their logic flow (from relatively simple to reasonably complex) and will be present in the following order: assessing outcomes of the brain using (a) static, (b) dynamic, (c) intermediating, and (d) longitudinal biomarkers. 1. Building static biomarkers. Methodological approaches are developed to identify time-averaged biomarkers to assess brain disease status and severity. This is supported by an article on developing static biomarkers to assess the status and severity of Parkinson’s disease. 2. Building dynamic biomarkers. Methodological approaches are developed to identify and isolate dynamic markers or directed information flows in the whole brain. This is supported by an article on extracting dynamic neural information flows and using them to predict cognitive flexibility in humans. 3. Building intermediating biomarkers. In a complex regulative biological system, some markers are on the one hand associated with behavior (e.g., prodromal symptoms), and on the other hand give rise to brain disorders (e.g., psychosis). Methodological approaches are developed to identify, isolate, and quantify these intermediating biomarkers or neural mediators. This is supported by an article on brain-wide functional mediation analysis. 4. Building longitudinal biomarkers. The patterns of biomarkers fluctuate over time. Identifying biomarkers whose temporal patterns are coupled with neurodegenerative disorders longitudinally is useful for assessing the development and trajectory of the diseases. This is supported by an article on developing longitudinal biomarkers to assess multiple sclerosis over time. Taken together, the methodological explorations and neurobiological discoveries in this thesis suggest that biomarker-based predictive models are promising for advancing our understanding of the human brain and behavior, and for facilitating brain illness assessment and monitoring in an automated way.