Recent progress on MRI-based brain connectivity study such as the Human Connectome Project (HCP) and BRAIN Initiative in the United States has generated a surge of interests for understanding basic mechanisms of the working brain and potential applications in the diseased brain. The long-term scientific pursuit of our lab is to establish nonhuman primate disease models with using a molecular genetic method, and to investigate the primate brain networks in combination with MRI, electrophysiology and neuroanatomy techniques.
Diffusion MRI (MRI)
Diffusion-weighted magnetic resonance imaging is an emerging magnetic resonance imaging (MRI) method ever since the mid-1980s, which allows the detecting of the diffusion process of water molecules in biological tissues, in vivo and non-invasively. By calculating the biophysical trajectory of water diffusion to infer the architecture of the white matter, Diffusion weighted magnetic resonance imaging and its derivative methods have become one of the most valuable MRI techniques of pursuing the working mechanism of brain architecture. Furthermore, assessment of the microstructural integrity of the axonal fibers using a variety of diffusion indices has absorbed an increasing attention in the study of neurological diseases or psychiatric disorders.
In essence, diffusion MRI measures the dephasing of spins of protons in the presence of a spatially-varying magnetic field (‘gradient’), which changes their Larmor frequency. The intuitive mechanism here is the phase change resulting from components of incoherent displacement of spins along the axis of the applied field gradient. We are interested in the sampling scheme in diffusion-encoding space (namely q-space) and algorithms that enable fiber tractography in the whole brain scale, with the use of our specialized gradient-insert system (AC88, 80mT/m gradient strength; 880mT/m/s, slew rate). Our aim is to probe the structural foundation of the brain reward circuitry through network-level comparison of monkey model and human patients. Diffusion image-based prognostic indicators of disease course and response to therapy would be extremely valuable to assess the responsiveness of patients to specific therapeutic interventions.
Neural and physiological basis of brain structural and functional networks
There are multiple parallel functional areas (neural networks) existing in the mammalian brain that are coordinated to process diversified sensory inputs. Although the existence of interaction (communication) across many areas has been implicated by the correlation of fMRI signals, the neural and physiological correlates are not entirely clear and controversial. During the past few years, the concept of resting-state brain networks has stimulated a flurry of fMRI publications because it appears to generate a revolutionizing view of brain working mode and functional imaging per se. It has also stirred hot debates on the interpretation and significance of the functional connectivity inferred by fMRI. We are planning to combine fMRI with multi-channel extracellular recording to investigate neural and physiological interaction in the monkey brain. With using the network-level analysis methods, we will place an emphasis on unveiling the modulatory effects of external interventions including pharmaceutical and surgical treatments on the brain.
Neuropsychiatric mechanisms of mood disorders in primates
Mood disorders including major depressive disorder (MDD), generalized anxiety disorder (GAD), obsessive-compulsive disorder (OCD), eating disorders (ED), post-traumatic stress disorder (PTSD) and substance use disorder are likely accompanied by distributed system-level disturbances in brain reward circuitry. We plan to apply molecular neurobiology and gene therapy to various monkey disease models (depression-like, drug-addiction etc.), and investigate the structural and functional networks with MRI and neurophysiological recordings. In parallel, we conduct various kinds of MR studies (MRI-based morphometry, diffusion MRI, functional MRI, and MR spectroscopy etc.) on human patients. Such multi-dimensional methodogical integration could provide more insights into the relationship between neural circuit activity and genetic manipulation, and how abnormalities in neural network may contribute to the pathogenesis of psychiatric illnesses.
Large-scale network computation for diagnosis and prognosis in brain disorders
One research objective of structural and functional neuroimaging in primates is to undertake outreach work aimed at potential aid in diagnosis and prognosis in a variety of brain disorders. High-resolution mapping of the whole brain in nonhuman and human primates allows us to apply and develop a wide range of simulation and prediction algorithms to statistically identify the core features of certain brain disease, which can further help to evaluate therapeutic outcomes of clinical interventional treatments. We are dedicated to collect a large sample of comparable human-monkey imaging datasets, develop specialized machine learning algorithms that are applicable to brain research for improved understanding of the diseases and guidance to new treatment means.