Clustering Patterns of Structural and Functional Neuroimaging Markers to Examine Individual Variability in Healthy Populations.

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There is a growing appreciation of the importance of individual variability in neuroimaging and cognitive neuroscience literature: aggregated or averaged group responses do not map well on to individual responses. This Individual variability has proven a substantive obstacle to describing a wide range of basic human brain function. Variability has been a particular challenge in psychiatric and other clinical research where the search for biological markers of disease has been hampered by poor characterization of general biological heterogeneity. The objective of our research program is to identify stable, reliable, and phenotypically useful groups of healthy individuals with similar functional and structural brain characteristics. This will enhance our understanding of variability in the general population, as well as validate tools for later translation in clinical samples. We propose to do this by applying and developing advanced data driven approaches to identify sub-groups or `clusters' of individuals with similar patterns of brain structure and function, within a healthy control population. We will use large multi-modal neuroimaging data sets (both publicly available and locally generated) and examine functional and structural connectivity in the brain. We will compare a range of statistical approaches to identify sub-groups (e.g. hierarchical clustering, k-means clustering, spectral clustering, and similarity network fusion). The stability/reliability of clustering solutions will be assessed via a permuted bootstrapping approach, in which the data will be repeated subsampled and the stability of cluster assignments assessed. We will then interrogate brain-behavior relationship across sub-groups using a multivariate partial least squares approach, which will allow for the identification of brain-behavior relationships which are common across entire samples or unique to specific clusters of individuals. The proposed work has the potential to advance our understanding heterogeneity of brain structure and function within the general population, and how this related to behavior. This will serve as a template for approaches that can later be applied to clinical populations. Once we have established reliable approaches for identifying important sub-groups of individuals, or neuroimaging metrics along which individuals vary dimensionally, these approaches can be transitioned into clinical samples and re-evaluated as tools for data-driven biotyping in clinical populations.