Tractography based on non-invasive diffusion imaging is central to the study of human brain connectivity. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain data set with ground truth tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. Here, we report the encouraging finding that most state-of-the-art algorithms produce tractograms containing 90% of the ground truth bundles (to at least some extent). However, the same tractograms contain many more invalid than valid bundles, and half of these invalid bundles occur systematically across research groups. Taken together, our results demonstrate and confirm fundamental ambiguities inherent in tract reconstruction based on orientation information alone, which need to be considered when interpreting tractography and connectivity results. Our approach provides a novel framework for estimating reliability of tractography and encourages innovation to address its current limitations.

Fiber tractography based on non-invasive diffusion imaging is at the heart of connectivity studies of the human brain. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain dataset with ground truth white matter tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. While most state-of-the-art algorithms reconstructed 90% of ground truth bundles to at least some extent, on average they produced four times more invalid than valid bundles. About half of the invalid bundles occurred systematically in the majority of submissions. Our results demonstrate fundamental ambiguities inherent to tract reconstruction methods based on diffusion orientation information, with critical consequences for the approach of diffusion tractography in particular and human connectivity studies in general.

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Background and purpose: Radiation-induced changes in brain tissue may relate to post-radiotherapy (RT) cognitive decline. Our aim is to investigate changes of the brain microstructural properties after exposure to radiation during clinical protocols of RT using diffusion MRI (dMRI). Methods and Materials: The susceptibility of tissue changes to radiation was investigated in a clinically heterogenic cohort (age, pathology, tumor location, type of surgery) consisting of 121 scans of 18 patients (10 females). The imaging dataset included 18 planning CTs and 103 dMRI scans (range 2-14, median = 6 per patient) assessing pre-operative, post-operative pre-RT and post-RT states. Diffusion tensor imaging (DTI) metrics were estimated from all scans for a region-of-interest based linear relation analysis between mean dose and change in DTI metrics, while partial volume effects were regressed out. Results: The largest regional dose dependency with mean diffusivity appear in the white matter of the frontal pole in the left hemisphere by an increase of 2.61 %/(Gy x year). Full brain-wise, pooled results for white matter show fractional anisotropy to decrease by 0.85 %/(30Gy x year); mean diffusivity increase by 9.17 %/(30Gy x year); axial diffusivity increase by 7.30%/(30Gy x year) and radial diffusivity increases by 10.63%/(30Gy x year). Conclusions: White matter is susceptible to radiation with some regional variability where diffusivity metrics demonstrate the largest relative sensitivity. This suggests that dMRI is a promising tool in assessing microstructural changes after RT, which can help in understanding treatment-induced cognitive decline.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

29Diffusion magnetic resonance imaging (dMRI) is one of the most prevalent methods to investigate 30 the micro-and macrostructure of the human brain in vivo. Prior to any group analysis, dMRI data are 31 generally processed to alleviate adverse effects of known artefacts such as signal drift, data noise and 32 outliers, subject motion, and geometric distortions. These dMRI data processing steps are often 33 combined in automated pipelines, such as the one of the Human Connectome Project (HCP). While 34 improving the performance of processing tools has clearly shown its benefits at each individual step 35 along the pipeline, it remains unclear whether -and to what degree -choices for specific user-36 defined parameter settings can affect the final outcome of group analyses. In this work, we 37 demonstrate how making such a choice for a particular processing step of the pipeline drives the final 38 outcome of a group study. More specifically, we performed a dMRI group analysis on gender using 39 HCP data sets and compared the results obtained with two diffusion tensor imaging estimation 40 methods: the widely used ordinary linear least squares (OLLS) and the more reliable iterative 41 weighted linear least squares (IWLLS). Our results show that the effect sizes for group analyses are 42 significantly smaller with IWLLS than with OLLS. While previous literature has demonstrated 43 higher estimation reliability with IWLLS than with OLLS using simulations, this work now also 44 shows how OLLS can produce a larger number of false positives than IWLLS in a typical group 45 study. We therefore highly recommend using the IWLLS method. By raising awareness of how the 46 choice of estimator can artificially inflate effect size and thus alter the final outcome, this work may 47

A multidimensional signal processing method is described for detection of bleeding stroke based on microwave measurements from an antenna array placed around the head of the patient. The method is data driven and the algorithm uses samples from a healthy control group to calculate the feature used for classification. The feature is derived using a tensor approach and the higher order singular value decomposition is a key component. A leave-one-out validation method is used to evaluate the properties of the method using clinical data.

Microwave measurements from an antenna array placed around the head can be used to detect changes in dielectric properties of the brain. In this paper an algorithm is developed to provide localization information of the site of an intra cerebral hemorrhage. The algorithm is based in the hypothesis that scattering parameters for an antenna pair close to the site of the bleeding will undergo larger changes. The change is measured using a feature derived from the scattering measurements using higher order singular value decomposition and is compared with the feature derived from measurements from a control group of healthy subjects. The proposed algorithm is evaluated on clinical data and the result is compared with computed tomography images of the patients.