Localising activity in the human midbrain with conventional functional MRI
(fMRI) is challenging because the midbrain nuclei are small and located in an
area that is prone to physiological artefacts. In the first section of this thesis I
present a replicable and automated method to improve the detection and
localisation of midbrain fMRI signals. I designed a visual fMRI task that was
predicted would activate the superior colliculi bilaterally. A novel anatomical
registration pathway was used to optimise the localisation of the small
midbrain nuclei in stereotactic space. This pathway was compared to
conventional registration pathways, and was shown to significantly improve
midbrain registration. To reduce the physiological artefacts in the functional
data, I estimated and removed structured noise using a modified version of a
previously described physiological noise model (PNM). Whereas a
conventional analysis revealed only unilateral colliculi activity, the PNM
analysis revealed the predicted bilateral activity.
I then used these methods to investigate the role of the midbrain
dopaminergic system in reward. Midbrain dopamine neurons play central
roles in positive reward prediction errors (PEs). Non-human studies now
indicate that dopamine neurons also code for aversive, punishing events, and
may code negative PEs. However, our understanding of how such events are
coded in the human dopaminergic midbrain is limited, and has been hindered
by the technical challenge of using fMRI in this region. Here I show that during
a financial reward and punishment task the ventral tegmental area (VTA)
codes for both positive and negative PEs. The fMRI data from the midbrain
contained a significant amount of physiological noise, but once I applied the
PNM responses were also observed in the substantia nigra pars compacta
(SNc), with negative PE signals extending more laterally. These findings
indicate that both the human VTA and SNc code for both positive and
negative PEs.