PURPOSE: Most previous MRI reports on training-induced white matter (WM) dynamics in vivo relied on diffusion tensor imaging (DTI), showing fractional anisotropy (FA) changes in tracts such as the fornix. FA is widely regarded as a non-specific white matter marker. With the aim of obtaining a more specific and sensitive characterization of WM microstructural dynamics, we combined multi-parametric MR with the ‘Tractometry’ approach on rats undergoing training.
MATERIALS AND METHODS: 13 Wistar rats, 2.5 months of age, were scanned before and after 5 days of training in a water maze. MRI data were acquired on a 7T/30 Bruker MRI scanner (Bruker, Germany) equipped with 400 mT/m gradients. The scanning protocol comprised of a series of MR contrasts, including: (i) CHARMED (Composite hindered and restricted model of diffusion) (ii) qMT (quantitative magnetization transfer) (iii) QSM (quantitative susceptibility mapping). The quantitative metrics that were derived from each of the methods were projected onto a fornix reconstruction. Then, both the whole tract values and the along tract values were compared between the pre- and post-training groups.
RESULTS: The medians of the whole tract values showed the expected trend between the pre- and post- groups for most of the metrics. However, only the metrics ‘magnetization transfer ratio’ (MTR) and ‘susceptibility’ showed a statistical difference between the groups. The significant changes between the pre- and post-training groups held also when observing the along tract values, where the medians of MTR and susceptibility showed significance in a number of segments along the tract. Interestingly, the DTI derived metrics MD and λ1 also showed significance in one of the segments, while no change was observed when analyzing for the whole tract values.
CONCLUSIONS: This study supports previous reports of training-induced microstructural changes in the fornix, mainly using FA or MD. Here, we provide the first evidence that other, non-DT-MRI, metrics may be more sensitive to WM dynamics and may reveal new insights into the time course and nature of microstructural plasticity. We suggest that enhanced sensitivity to training-induced plasticity may be obtained by supplementing DT-MRI metrics with complementary markers that rely on different biophysical mechanisms to diffusion.
