3D functional models of monkey brain through elastic registration of histological sections

In this paper we describe a method for the reconstruction and visualization of functional models of monkey brains. Models are built through the registration of high resolution images obtained from the scanning of histological sections with reference photos taken during the brain slicing. From the histological sections it is also possible to acquire specifically activated neuron coordinates introducing functional information in the model. Due to the specific nature of the images (texture information is useless and the sections could be deformed when they were cut and placed on glass) we solved the registration problem by extracting corresponding cerebral cortex borders (extracted with a snake algorithm), and computing from their deformation an image transform modeled as an affine deformation plus a non-linear field evaluated as an elastically constrained deformation minimizing contour distances. Registered images and contours are used then to build 3D models of specific brains by a software tool allowing the interactive visualization of cortical volumes together with the spatially referenced neurons classified and differently colored according to their functionalities

Imaging gray matter with concomitant null point imaging from the phase sensitive inversion recovery sequence

Purpose To present an improved three-dimensional (3D) interleaved phase sensitive inversion recovery (PSIR) sequence including a concomitantly acquired new contrast, null point imaging (NPI), to help detect and classify abnormalities in cortical gray matter. Methods The 3D gradient echo PSIR images were acquired at 0.6 mm isotropic resolution on 11 multiple sclerosis (MS) patients and 9 controls subjects using a 7 Tesla (T) MRI scanner, and 2 MS patients at 3T. Cortical abnormalities were delineated on the NPI/PSIR data and later classified according to position in the cortex. Results The NPI helped detect cortical lesions within the cortical ribbon with increased, positive contrast compared with the PSIR. It also provided improved intrinsic delineation of the ribbon, increasing confidence in classifying the lesions' locations. Conclusion The proposed PSIR facilitates the classification of cortical lesions by providing two T1-weighted 3D datasets with isotropic resolution, including the NPI showing cortical lesions with clear delineation of the gray/white matter boundary and minimal partial volume effects. Magn Reson Med 76:1512–1516, 2016. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited

PanCancer analysis of somatic mutations in repetitive regions reveals recurrent mutations in snRNA U2

Current somatic mutation callers are biased against repetitive regions, preventing the identification of potential driver alterations in these loci. We developed a mutation caller for repetitive regions, and applied it to study repetitive non protein-coding genes in more than 2200 whole-genome cases. We identified a recurrent mutation at position c.28 in the gene encoding the snRNA U2. This mutation is present in B-cell derived tumors, as well as in prostate and pancreatic cancer, suggesting U2 c.28 constitutes a driver candidate associated with worse prognosis. We showed that the GRCh37 reference genome is incomplete, lacking the U2 cluster in chromosome 17, preventing the identification of mutations in this gene. Furthermore, the 5'-flanking region of WDR74, previously described as frequently mutated in cancer, constitutes a functional copy of U2. These data reinforce the relevance of non-coding mutations in cancer, and highlight current challenges of cancer genomic research in characterizing mutations affecting repetitive genes.© 2022. The Author(s)

Cortical differences in diverticular disease and correlation with symptom reports

Background Recent studies have shown that the brain of patients with gastrointestinal disease differ both structurally and functionally from that of controls. Highly somatizing diverticular disease (HSDD) patients were also shown to differ from low somatizing (LSDD) patients functional-ly. This study aimed to investigate how they differed structurally. Methods Four diseases subgroups were studied in a cross-sectional design: 20 patients with asympto-matic diverticular disease (ADD), 18 LSDD, 16 HSDD, and 18 with irritable bowel syn-drome. We divided DD patients into LSDD and HSDD using a cutoff of 6 on the Patient Health Questionnaire 12 Somatic Symptom (PHQ12-SS) scale. All patients underwent a 1-mm isotropic structural brain MRI scan and were assessed for somatization, hospital anxiety, depression, and pain catastrophizing. Whole brain volumetry, cortical thickness analysis and voxel-based morphometry were carried out using Freesurfer and SPM. Key Results We observed decreases in grey matter density in the left and right dorso-lateral prefrontal cortex (dlPFC), and in the mid-cingulate and motor cortex, and increases in the left (19, 20) and right (19, 38) Brodmann Areas. The average cortical thickness differed overall across groups (P=0.002) and regionally: HSDD>ADD in the posterior cingulate cortex (P=0.03), HSDD>LSDD in the dlPFC (P=0.03) and in the ventro-lateral PFC (P<0.001). The thickness of the anterior cingulate cortex and of the mid-prefrontal cortex were also found to correlate with Pain Catastrophizing (Spearman's ρ=0.24, P=0.043 uncorrected and Spearman's ρ=0.25, P=0.03 uncorrected). Conclusion & Inferences This is the first study of structural grey matter abnormalities in diverticular disease patients. The data shows brain differences in the pain network

Registration of 3D fetal neurosonography and MRI.

We propose a method for registration of 3D fetal brain ultrasound with a reconstructed magnetic resonance fetal brain volume. This method, for the first time, allows the alignment of models of the fetal brain built from magnetic resonance images with 3D fetal brain ultrasound, opening possibilities to develop new, prior information based image analysis methods for 3D fetal neurosonography. The reconstructed magnetic resonance volume is first segmented using a probabilistic atlas and a pseudo ultrasound image volume is simulated from the segmentation. This pseudo ultrasound image is then affinely aligned with clinical ultrasound fetal brain volumes using a robust block-matching approach that can deal with intensity artefacts and missing features in the ultrasound images. A qualitative and quantitative evaluation demonstrates good performance of the method for our application, in comparison with other tested approaches. The intensity average of 27 ultrasound images co-aligned with the pseudo ultrasound template shows good correlation with anatomy of the fetal brain as seen in the reconstructed magnetic resonance image

On the use of Process Mining and Machine Learning to support decision making in systems design

Research on process mining and machine learning techniques has recently received a significant amount of attention by product development and management communities. Indeed, these techniques allow both an automatic process and activity discovery and thus are high added value services that help reusing knowledge to support decision-making. This paper proposes a double layer framework aiming to identify the most significant process patterns to be executed depending on the design context. Simultaneously, it proposes the most significant parameters for each activity of the considered process pattern. The framework is applied on a specific design example and is partially implemented.FUI GONTRAN