Deep gray matter nuclei are the synaptic relays, responsible to route signals between specific brain areas. Dentate nuclei (DNs) represent the main output channel of the cerebellum and yet are often unexplored especially in humans. We developed a multimodal MRI approach to identify DNs topography on the basis of their connectivity as well as their microstructural features. Based on results, we defined DN parcellations deputed to motor and to higher‐order functions in humans in vivo. Whole‐brain probabilistic tractography was performed on 25 healthy subjects from the Human Connectome Project to infer DN parcellations based on their connectivity with either the cerebral or the cerebellar cortex, in turn. A third DN atlas was created inputting microstructural diffusion‐derived metrics in an unsupervised fuzzy c‐means classification algorithm. All analyses were performed in native space, with probability atlas maps generated in standard space. Cerebellar lobule‐specific connectivity identified one motor parcellation, accounting for about 30% of the DN volume, and two non‐motor parcellations, one cognitive and one sensory, which occupied the remaining volume. The other two approaches provided overlapping results in terms of geometrical distribution with those identified with cerebellar lobule‐specific connectivity, although with some differences in volumes. A gender effect was observed with respect to motor areas and higher‐order function representations. This is the first study that indicates that more than half of the DN volumes is involved in non‐motor functions and that connectivity‐based and microstructure‐based atlases provide complementary information. These results represent a step‐ahead for the interpretation of pathological conditions involving cerebro‐cerebellar circuits.

Dentate nuclei (DNs) segmentation is helpful for assessing their potential involvement in neurological diseases. Once DNs have been segmented, it becomes possible to investigate whether DNs they are microstructurally affected, through analysis of quantitative MRI parameters, such as the ones derived from diffusion weighted imaging (DWI). This study, therefore, aimed to develop a fully automated segmentation method using the non-DWI (b0) images from a DWI dataset to obtain DN masks inherently registered with parameter maps.Three different automatic methods were applied to healthy subjects in order to segment the DNs: registration to SUIT (a spatially unbiased atlas template of the cerebellum and brainstem), OPAL (Optimized Patch Match for Label fusion) and CNN (Convolutional Neural Network). DNs manual segmentation was considered the gold standard. Results show that the segmentation obtained with SUIT has an average Dice Similarity Coefficient (DSC) of 0.4907±0.0793 between the automatic SUIT masks and the gold standard. A comparison with manual masks was also performed for OPAL (DSC = 0.7624 ± 0.1786) and CNN (DSC = 0.8658 ± 0.0255), showing a better performance when using CNN.OPAL and CNN were optimised on heathy subjects’ data with high spatial resolution from the Human Connectome Project. The three methods were further used to segment the DNs of a subset of subjects affected by Temporal Lobe Epilepsy (TLE). This subset was derived from a 3T MRI research study which included DWI data acquired with a coarser resolution. In TLE dataset, SUIT performed similarly to using the HCP dataset, with a DSC = 0.4145 ± 0.1023. Using TLE data, OPAL performed worse than using HCP data: after changing the probability threshold the DSC was 0.4522 ± 0.1178.CNN was able to extract the DNs using the TLE data without need for retraining and with a good DSC = 0.7368 ± 0.0799. Statistical comparison of quantitative parameters derived from DWI analysis, as well as volumes of each DN, revealed altered and lateralised changes in TLE patients compared to healthy controls.The proposed CNN is therefore a viable option for accurate extraction of DNs from b0 images of DWI data with different resolutions and acquired at different sites.

Dentate nuclei (DNs) segmentation is helpful for assessing their potential involvement in neurological diseases. Once DNs have been segmented, it becomes possible to investigate whether DNs are microstructurally affected, through analysis of quantitative MRI parameters, such as those derived from diffusion weighted imaging (DWI). This study developed a fully automated segmentation method using the non-DWI (b0) images from a DWI dataset to obtain DN masks inherently registered with parameter maps. Three different automatic methods were applied to healthy subjects: registration to SUIT (a spatially unbiased atlas template of the cerebellum and brainstem), OPAL (

Fractional anisotropy (FA) is a quantitative map sensitive to microstructural properties of tissues in vivo and it is extensively used to study the healthy and pathological brain. This map is classically calculated by model fitting (standard method) and requires many diffusion weighted (DW) images for data quality and unbiased readings, hence needing the acquisition time of several minutes. Here, we adapted the U-net architecture to be generalized and to obtain good quality FA from DW volumes acquired in 1 minute. Our network requires 10 input DW volumes (hence fast acquisition), is robust to the direction of application of the diffusion gradients (hence generalized), and preserves/improves map quality (hence good quality maps). We trained the network on the human connectome project (HCP) data using the standard model-fitting method on the entire set of DW directions to extract FA (ground truth). We addressed the generalization problem, i.e., we trained the network to be applicable, without retraining, to clinical datasets acquired on different scanners with different DW imaging protocols. The network was applied to two different clinical datasets to assess FA quality and sensitivity to pathology in temporal lobe epilepsy and multiple sclerosis, respectively. For HCP data, when compared to the ground truth FA, the FA obtained from 10 DW volumes using the network was significantly better (p <10−4) than the FA obtained using the standard pipeline. For the clinical datasets, the network FA retained the same microstructural characteristics as the FA calculated with all DW volumes using the standard method. At the subject level, the comparison between white matter (WM) ground truth FA values and network FA showed the same distribution; at the group level, statistical differences of WM values detected in the clinical datasets with the ground truth FA were reproduced when using values from the network FA, i.e., the network retained sensitivity to pathology. In conclusion, the proposed network provides a clinically available method to obtain FA from a generic set of 10 DW volumes acquirable in 1 minute, augmenting data quality compared to direct model fitting, reducing the possibility of bias from sub-sampled data, and retaining FA pathological sensitivity, which is very attractive for clinical applications.

Quantitative maps obtained from Diffusion Tensor are very useful for investigating microstructural changes that occur in brain diseases. However, the long acquisition times required for a fully sampled diffusion-weighted space makes their clinical use unfeasible. Here we have adapted a U-net that obtains reliable Fractional Anisotropy (FA) maps from a reduced set of 10 Diffusion Weighted volumes (that can be acquired in less than 1 min). Our network was applied to two independent, clinical datasets, without retraining, and produced FA that retained clinical sensitivity and characteristic FA value distributions in the brain white matter.