Covariance-based subdivision of the human striatum using T1-weighted MRI

The striatum plays a key role in many cognitive and emotional processes, and displays an intricate pattern of connectivity with cortical and subcortical structures. Invasive tracing work in rats and non-human primates demonstrates that the striatum can be segregated into subregions based on similar clustering of input and output fibers. In contrast, the human striatum is typically segregated according to local anatomical landmarks without considering natural boundaries formed by functional/anatomical networks. Here, we used non-invasive magnetic resonance (MR) imaging in young, healthy adults to define subregions of the human striatum based on volume correlations with other subcortical and cortical structures. We present three methods to delineate anatomical volumetric correlations based on gray matter content estimated from T1-weighted MR images. We observed both consistencies with and divergences from invasive tracing work in animals, suggesting that magnetic resonance imaging (MRI)-based covariance likely does not correspond to direct anatomical connections, although it might index other forms of connectivity or tissue type similarity. These novel approaches may be useful in understanding connectivity of other regions, and changes in connectivity in patient or ageing populations.