Image processing methods for human brain connectivity analysis from in-vivo diffusion MRI

The structural connectivity of the brain is considered to encode species-wise and subject-wise patterns that will unlock large areas of understanding of the human brain. Currently, diffusion MRI of the living brain enables to map the microstructure of tissue, allowing to track the pathways of fiber bundles connecting the cortical regions across the brain. These bundles are summarized in a network representation called connectome that is analyzed using graph theory. The extraction of the connectome from diffusion MRI requires a large processing flow including image enhancement, reconstruction, segmentation, registration, diffusion tracking, etc. Although a concerted effort has been devoted to the definition of standard pipelines for the connectome extraction, it is still crucial to define quality assessment protocols of these workflows. The definition of quality control protocols is hindered by the complexity of the pipelines under test and the absolute lack of gold-standards for diffusion MRI data. Here we characterize the impact on structural connectivity workflows of the geometrical deformation typically shown by diffusion MRI data due to the inhomogeneity of magnetic susceptibility across the imaged object. We propose an evaluation framework to compare the existing methodologies to correct for these artifacts including whole-brain realistic phantoms. Additionally, we design and implement an image segmentation and registration method to avoid performing the correction task and to enable processing in the native space of diffusion data. We release PySDCev, an evaluation framework for the quality control of connectivity pipelines, specialized in the study of susceptibility-derived distortions. In this context, we propose Diffantom, a whole-brain phantom that provides a solution to the lack of gold-standard data. The three correction methodologies under comparison performed reasonably, and it is difficult to determine which method is more advisable. We demonstrate that susceptibility-derived correction is necessary to increase the sensitivity of connectivity pipelines, at the cost of specificity. Finally, with the registration and segmentation tool called regseg we demonstrate how the problem of susceptibility-derived distortion can be overcome allowing data to be used in their original coordinates. This is crucial to increase the sensitivity of the whole pipeline without any loss in specificity.

Is Microarray Analysis Really Useful and Sufficient to Diagnose Nut Allergy in the Mediterranean Area?

Background: Component-based diagnosis on multiplex platforms is widely used in food allergy but its clinical performance has not been evaluated in nut allergy. Objective: To assess the diagnostic performance of a commercial protein microarray in the determination of specific IgE (sIgE) in peanut, hazelnut, and walnut allergy. Methods: sIgE was measured in 36 peanut-allergic, 36 hazelnut-allergic, and 44 walnut-allergic patients by ISAC 112, and subsequently, sIgE against available components was determined by ImmunoCAP in patients with negative ISAC results. ImmunoCAP was also used to measure sIgE to Ara h 9, Cor a 8, and Jug r 3 in a subgroup of lipid transfer protein (LTP)-sensitized nut-allergic patients (positive skin prick test to LTP-enriched extract). sIgE levels by ImmunoCAP were compared with ISAC ranges. Results: Most peanut-, hazelnut-, and walnut-allergic patients were sensitized to the corresponding nut LTP (Ara h 9, 66.7%; Cor a 8, 80.5%; Jug r 3, 84% respectively). However, ISAC did not detect sIgE in 33.3% of peanut-allergic patients, 13.9% of hazelnut-allergic patients, or 13.6% of walnut-allergic patients. sIgE determination by ImmunoCAP detected sensitization to Ara h 9, Cor a 8, and Jug r 3 in, respectively, 61.5% of peanut-allergic patients, 60% of hazelnut-allergic patients, and 88.3% of walnut-allergic patients with negative ISAC results. In the subgroup of peach LTP?sensitized patients, Ara h 9 sIgE was detected in more cases by ImmunoCAP than by ISAC (94.4% vs 72.2%, P<.05). Similar rates of Cor a 8 and Jug r 3 sensitization were detected by both techniques. Conclusions: The diagnostic performance of ISAC was adequate for hazelnut and walnut allergy but not for peanut allergy. sIgE sensitivity against Ara h 9 in ISAC needs to be improved.

Is the performance of ImmunoCAP ISAC 112 sufficient to diagnose peach and apple allergies?

Food allergies constitute a public health issue, with a reported overall estimated prevalence of 6% in Europe1 and Rosacea as the main allergenic fruits among adults.2 The commercial microarray ImmunoCAP ISAC 112 (Thermofisher, Uppsala, Sweden) is a semiquantitative and reproducible in vitro diagnostic tool used for the determination of specific IgE (sIgE).3 However, its panel of allergens does not have the best accuracy when it comes to determining fruit allergies in the Mediterranean area: the inclusion of the thaumatinlike protein (TLP) Pru p 2 or the apple lipid transfer protein (LTP) Mal d 3 has been proposed to improve the diagnosis of peach4 and apple5 allergies, respectively, in the Mediterranean basin. We sought to determine the usefulness of a component-resolved microarray for the diagnosis of peach and apple allergies in the Mediterranean area.