Abstract
Frontotemporal dementia (FTD) is classically considered to be a neurodegenerative disease with cortical changes. Recent structural imaging findings, however, highlight that subcortical and in particular striatal regions are also affected in the FTD syndrome. The influence of striatal pathology on cognitive and behavioural changes in FTD is virtually unexplored. In the current study we employ the Weather Prediction Task (WPT), a probabilistic learning task which taps into striatal dysfunction, in a group of FTD patients. We also regressed the patients' behavioural performance with their grey matter atrophy via voxel-based morphometry (VBM) to identify the grey matter contributions to WPT performance in FTD. Based on previous studies we expected to see striatal and frontal atrophy to be involved in impaired probabilistic learning. Our behavioural results show that patients performed on a similar level to controls overall, however, there was a large variability of patient performance in the first 30 trials of the task, which are critical in the acquisition of the probabilistic learning rules. A VBM analysis covarying the performance for the first 30 trials across participants showed that atrophy in striatal but also frontal brain regions correlated with WPT performance in these trials. Closer inspection of performance across the first 30 trials revealed a subgroup of FTD patients that performed significantly poorly than the remaining patients and controls on the WPT, despite achieving the same level of probabilistic learning as the other groups in later trials. Additional VBM analyses revealed that the subgroup of FTD patients with poor early probabilistic learning in the first 30 trials showed greater striatal atrophy compared to the remaining FTD patients and controls. These findings suggest that the integrity of fronto-striatal regions is important for probabilistic learning in FTD, with striatal integrity in particular, determining the acquisition learning rate. These findings will therefore have implications for developing an easily administered version of the probabilistic learning task which can be used by clinicians to assess striatal functioning in neurodegenerative syndromes.