Assessing Topographic Structural Connectivity of the Human Basal Ganglia and Thalamus

Abstract

The basal ganglia and thalamus play an important role in cognition, procedural learning, eye movements, control of voluntary motor movements, emotional control, habit development, and are structures that are severely impacted by neurological disorders such as Parkinsons disease, or Tourette syndrome. To understand the structural connectivity of cortical and subcortical circuits in the healthy human brain could thus be of pivotal importance for detecting changes in this circuitry and to start early intervention, to assess the progress of movement rehabilitation, or the effectiveness of therapeutic approaches in neuropsychiatry. While conventional clinical magnetic resonance imaging (MRI) is able to provide detailed information about connectivity at the macro level, the sensitivity and specificity of such structural imaging methods put limits on the amount of detail one can obtain when measuring in vivo connectivity of human basal ganglia and thalamus at routine clinical magnetic field strengths. In contrast, the multiband diffusion echo planar imaging method, which acquires multiple slices simultaneously, enables high resolution imaging of these abovementioned brain structures with only short acquisition times at 3-Tesla and higher magnetic field strengths. To unleash the greater potential of information embedded in data acquired with this technique, complementary data processing pipelines are required. Here, we use a protocol composed of multiband diffusion-, T1- and susceptibility weighted data acquisition sequences and introduce an associated pipeline based on combined manual and automated processing. The design of this data processing pipeline allows us to generate comprehensive in vivo participant-specific probabilistic patterns and visualizations of the structural connections that exist within basal ganglia and thalamic nuclei. Moreover, we are able to map specific parcellations of these nuclei into sub-territories based on their connectivity with primary motor-, and somatosensory cortex. This data processing strategy enables detailed subcortical structural connectivity mapping which could benefit early intervention and therapy methods for human movement rehabilitation and for treating neuropsychiatric disorders.