| Title: | Statistical Analysis of Human Brain Connectomes |
| Lead Institution: | University of Rochester |
| Principal investigator: | Dr Zhengwu Zhang |
Application 51659
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About
There have been remarkable advances in imaging technology, used routinely and pervasively in many human studies, that non-invasively measures human brain structure and function. Diffusion magnetic resonance imaging (dMRI) and structural MRI (sMRI) are used to infer locations of millions of white matter fiber tracts that act as highways for neural activity and communication across the brain. The collection of interconnected fiber tracts is referred to as the brain connectome. There is increasing evidence that an individual's brain connectome plays a fundamental role in cognitive functioning, behavior, and the risk of developing mental health conditions and neuropsychiatric disorders. Improved mechanistic understanding of relationships between brain connectome structure and phenotypes and exposures has the potential to revolutionize prevention and treatment of mental health disorders. However, progress in this area has been limited by large gaps between the state of the art in image acquisition and in connectome construction and data analysis. This project develops a transformative toolbox of data processing and analysis methods for better construction, representation, and analysis of human brain connectomes. These tools will be applied to the UK Biobank dataset, to enhance our understanding of how the brain connectome varies according to individual traits and exposures and with neuropsychiatric conditions. The toolbox will be rigorously validated, including assessments of reproducibility and discriminative ability based on scan-rescan data, out-of-sample predictive performance, power and type I error rates in simulation studies, and mechanistic interpretability of the results. The work has four Specific Aims: (1) Geometric reconstruction of connectomes to reduce measurement errors and enhance robustness, reproducibility and discriminative power; (2) Geometric representation of connectomes characterizing connectomes in novel ways to encode much more information than is available in typical adjacency matrix representations that rely on a single measure of connection strength between pre-specified regions of interest; (3) Relating connectomes to human traits through new multiscale models and algorithms that improve power and mechanistic insight in statistical analyses relating brain connectomes to phenotypes (cognitive functioning, behavior, mental health conditions), exposures (substance use), and covariates (age, gender). In the short term, this project will lead to new understanding of relationships between human brain connectomes, mental health, and exposures. In the long term, there is the potential both to revolutionize mechanistic understanding of how brain structure relates to disease and to transform clinical practice.2 Publications
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 12216 | DORIS: A diffusion MRI-based 10 tissue class deep learning segmentation algorithm tailored to improve anatomically-constrained tractography | Guillaume Theaud (+8) | 2022 | Frontiers in Neuroimaging |
| 7098 | Outlier detection for multi-network data | Pritam Dey (+2) | 2022 | Bioinformatics |
Enabling scientific discoveries that improve human health