Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.

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The description of the superficial white matter (SWM) functional and structural organization is still an unachieved task. In particular, their shape has not been assessed in detail using diffusion Magnetic Resonance Imaging (dMRI) tractography. This work aims to characterize the different shapes of the short-range association connections present in an SWM multi-subject bundle atlas derived from probabilistic dMRI tractography datasets. First, we calculated a representative centroid shape for each atlas bundle. Next, we computed a distance matrix that encodes the similarity between every pair of centroids. For the distance matrix computation, centroids were first aligned using a streamline-based registration, reducing the 3D spatial separation effect and allowing us to focus only on shape differences. Then, we applied a hierarchical clustering algorithm over the affinity graph derived from the distance matrix. As a result, we obtained ten classes with distinctive shapes, ranging from a straight line form to U and C arrangements. The most predominant shapes were: (i) short open U, (ii) short closed U, and (iii) short C. Moreover, we used the shape information to filter out noisy streamlines in the atlas bundles and applied an automatic segmentation algorithm to 25 subjects of the HCP database. Our results show that the filtering steps help to segment more dense bundles with fewer outliers, improving the identification of the brain's short fibers.

This paper presents a new cortical parcellation method based on group-wise connectivity and hierarchical clustering. A preliminary sub-parcellation is performed using intra-subject and inter-subject fiber clustering to obtain representative bundles among subjects with similar shapes and trajectories. The sub-parcellation is obtained by intersecting fiber clusters with cortical meshes. Next, mean connectivity and mean overlap matrices are computed over the sub-parcels to obtain spatial and connectivity information. To hierarchize the information, we propose to weight both matrices, to obtain an affinity graph, and then a dendrogram to merge or divide parcels by their hierarchy. Finally, to obtain homogeneous parcels, the method computes morphological operations. By selecting a different number of clusters over the dendrogram, the method obtains a different number of parcels and a variation in the resulting parcel sizes, depending on the parameters used. We computed the coefficient of variation (CV ) of the parcel size to evaluate the homogeneity of the parcels. Preliminary results suggest that the use of representative clusters and the integration of sub-parcel overlap and connectivity strength provide useful information to generate cortical parcellations at different levels of granularity. Even results are preliminary, this novel method allows researchers to add group-wise connectivity strength and spatial information for the construction of diffusion-based parcellations. Future work will include a detailed analysis of parameters, such as the matrix weights and the number of sub-parcel clusters, and the generation of hierarchical parcellations to improve the insight into the cortex subdivision and hierarchy among parcels.

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