Mapping the network of white-matter connections in living human brains.
Magnetic resonance diffusion imaging and computational tractography are the only technologies that enable neuroscientists to measure white matter in the living human brain. Diffusion MRI provides a way to measure the changes in brain structures and tissue properties evolving over long time frames – weeks, months and years. In the decade since their development, these technologies revolutionized our understanding of the importance of white-matter for health and disease. Prior to these measurements, the white matter was thought of as a passive cabling system. But modern measurements show that white matter axons and glia respond to experience and that the tissue properties of the white matter are transformed during development and following training. The white matter pathways comprise a set of active wires and the responses and properties of these wires predict human cognitive and emotional abilities in health and disease. We can now predict confidently that to crack the neural code in mapping the human brain, neuroscientists will have to develop an account of the connections and tissue properties of these active wires. Diffusion imaging and computational tractography enable investigators to map the network of white matter tracts. Whereas there are many impressive findings using this technology, it is widely agreed that there is an urgent need to keep developing and improving tractography methods.
Validation and statistical inference in living connectomes.
At Stanford University, I developed a technology, called LiFE – Linear Fascicle Evaluation, to perform both tractography validation and statistical testing on the network of brain connections. Previous validation methods are extrinsic. Investigators have demonstrated that tractography algorithms generate reasonable estimates by checking in phantoms and ex-vivo tissue. These methods do not take into account the quality of a specific tractography solution (the connectome) obtained from a specific group of subjects and a specific instrument. Extrinsic validations ask us to believe a tractography solution obtained from a child with a 3T GE scanner based on a validation carried out on a ex-vivo macaque brain from a 7T Siemens scanner. LiFE can be applied to the data at hand rather than appealing to validations based on very different datasets – I call this method native validation. I utilize Big Data and machine-learning methods to treat connectomes as models of the measured white-matter signal. The model generates a prediction of the measured diffusion signal. The model prediction-error is used to identify the network of brain connections best supporting the measured diffusion signal by rejecting false connections. The technology a principled way to test hypotheses about the geometric structure of white-matter fascicles and to compare the accuracy of different connectomes – I call the method virtual lesion. These method can be applied to any type of diffusion data in healthy and diseased populations. These new methods improve current techniques in fundamental ways. A full open-source software implementation of the method is available here.