Selective vulnerability of different types of commissural neurons for amyloid β-protein-induced neurodegeneration in APP23 mice correlates with dendritic tree morphology

The amyloid β-protein (Aβ) is the main component of Alzheimer's disease-related senile plaques. Although Aβ is associated with the development of Alzheimer's disease, it has not been shown which forms of Aβ induce neurodegeneration in vivo and which types of neurons are vulnerable. To address these questions, we implanted DiI crystals into the left frontocentral cortex of APP23 transgenic mice overexpressing mutant human APP (amyloid precursor protein gene) and of littermate controls. Traced commissural neurons in layer III of the right frontocentral cortex were quantified in 3-, 5-, 11- and 15-month-old mice. Three different types of commissural neurons were traced. At 3 months of age no differences in the number of labelled commissural neurons were seen in APP23 mice compared with wild-type mice. A selective reduction of the heavily ramified type of neurons was observed in APP23 mice compared with wild-type animals at 5, 11 and 15 months of age, starting when the first Aβ-deposits occurred in the frontocentral cortex at 5 months. The other two types of commissural neurons did not show alterations at 5 and 11 months. At 15 months, the number of traced sparsely ramified pyramidal neurons was reduced in addition to that of the heavily ramified neurons in APP23 mice compared with wild-type mice. At this time Aβ-deposits were seen in the neo- and allocortex as well as in the basal ganglia and the thalamus. In summary, our results show that Aβ induces progressive degeneration of distinct types of commissural neurons. Degeneration of the most vulnerable neurons starts in parallel with the occurrence of the first fibrillar Aβ-deposits in the neocortex, that is, with the detection of aggregated Aβ. The involvement of additional neuronal subpopulations is associated with the expansion of Aβ-deposition into further brain regions. The vulnerability of different types of neurons to Aβ, thereby, is presumably related to the complexity of their dendritic morpholog

Foundations of Data Science : a comprehensive overview formed at the 1st International Symposium on the Science of Data Science

We present a summary of the 1st International Symposium on the Science of Data Science, organized in Summer 2021 as a satellite event of the 8th Swiss Conference on Data Science held in Lucerne, Switzerland. We discuss what establishes the scientific core of the discipline of data science by introducing the corresponding research question, providing a concise overview of relevant related prior work, followed by a summary of the individual workshop contributions. Finally, we expand on the common views which were formed during the extensive workshop discussions

Deep learning-based simultaneous multi-phase deformable image registration of sparse 4D-CBCT

Purpose: Respiratory gated 4D-CBCT suffers from sparseness artefacts caused by the limited number of projections available for each respiratory phase/amplitude. These artefacts severely impact deformable image registration methods used to extract motion information. We use deep learning-based methods to predict displacement vector-fields (DVF) from sparse 4D-CBCT images to alleviate the impacts of sparseness artefacts. Methods: We trained U-Net-type convolutional neural network models to predict multiple (10) DVFs in a single forward pass given multiple sparse, gated CBCT and an optional artefact-free reference image as inputs. The predicted DVFs are used to warp the reference image to the different motion states, resulting in an artefact-free image for each state. The supervised training uses data generated by a motion simulation framework. The training dataset consists of 560 simulated 4D-CBCT images of 56 different patients; the generated data include fully sampled ground-truth images that are used to train the network. We compare the results of our method to pairwise image registration (reference image to single sparse image) using a) the deeds algorithm and b) VoxelMorph with image pair inputs. Results: We show that our method clearly outperforms pairwise registration using the deeds algorithm alone. PSNR improved from 25.8 to 46.4, SSIM from 0.9296 to 0.9999. In addition, the runtime of our learning-based method is orders of magnitude shorter (2 seconds instead of 10 minutes). Our results also indicate slightly improved performance compared to pairwise registration (delta-PSNR=1.2). We also trained a model that does not require the artefact-free reference image (which is usually not available) during inference demonstrating only marginally compromised results (delta-PSNR=-0.8). Conclusion: To the best of our knowledge, this is the first time CNNs are used to predict multi-phase DVFs in a single forward pass. This enables novel applications such as 4D-auto-segmentation, motion compensated image reconstruction, motion analyses, and patient motion modeling

Mitigation of motion-induced artifacts in cone beam computed tomography using deep convolutional neural networks

Background: Cone beam computed tomography (CBCT) is often employed on radiation therapy treatment devices (linear accelerators) used in image-guided radiation therapy (IGRT). For each treatment session, it is necessary to obtain the image of the day in order to accurately position the patient, and to enable adaptive treatment capabilities including auto-segmentation and dose calculation. Reconstructed CBCT images often suffer from artifacts, in particular those induced by patient motion. Deep-learning based approaches promise ways to mitigate such artifacts. Purpose: We propose a novel deep-learning based approach with the goal to reduce motion induced artifacts in CBCT images and improve image quality. It is based on supervised learning and includes neural network architectures employed as pre- and/or post-processing steps during CBCT reconstruction. Methods: Our approach is based on deep convolutional neural networks which complement the standard CBCT reconstruction, which is performed either with the analytical Feldkamp-Davis-Kress (FDK) method, or with an iterative algebraic reconstruction technique (SART-TV). The neural networks, which are based on refined U-net architectures, are trained end-to-end in a supervised learning setup. Labeled training data are obtained by means of a motion simulation, which uses the two extreme phases of 4D CT scans, their deformation vector fields, as well as time-dependent amplitude signals as input. The trained networks are validated against ground truth using quantitative metrics, as well as by using real patient CBCT scans for a qualitative evaluation by clinical experts. Results: The presented novel approach is able to generalize to unseen data and yields significant reductions in motion induced artifacts as well as improvements in image quality compared with existing state-of-the-art CBCT reconstruction algorithms (up to +6.3 dB and +0.19 improvements in peak signal-to-noise ratio, PSNR, and structural similarity index measure, SSIM, respectively), as evidenced by validation with an unseen test dataset, and confirmed by a clincal evaluation on real patient scans (up to 74% preference for motion artifact reduction over standard reconstruction). Conclusions: For the first time, it is demonstrated, also by means of clinical evaluation, that inserting deep neural networks as pre- and post-processing plugins in the existing 3D CBCT reconstruction and trained end-to-end yield significant improvements in image quality and reduction of motion artifacts