Elastic shape matching of parameterized surfaces using square root normal fields.

In this paper we define a new methodology for shape analysis of parameterized surfaces, where the main issues are: (1) choice of metric for shape comparisons and (2) invariance to reparameterization. We begin by defining a general elastic metric on the space of parameterized surfaces. The main advantages of this metric are twofold. First, it provides a natural interpretation of elastic shape deformations that are being quantified. Second, this metric is invariant under the action of the reparameterization group. We also introduce a novel representation of surfaces termed square root normal fields or SRNFs. This representation is convenient for shape analysis because, under this representation, a reduced version of the general elastic metric becomes the simple \ensuremathL2\ensuremathL2 metric. Thus, this transformation greatly simplifies the implementation of our framework. We validate our approach using multiple shape analysis examples for quadrilateral and spherical surfaces. We also compare the current results with those of Kurtek et al. [1]. We show that the proposed method results in more natural shape matchings, and furthermore, has some theoretical advantages over previous methods

Genetic parameters for�resistance to the Salmonella abortusovis vaccinal strain Rv6 in sheep

An experimental population (1216 lambs from 30 sires) of the Inra401 sheep was created in an Inra flock to allow QTL detection for susceptibility to Salmonella infection, wool and carcass traits. The Inra401 is a sheep composite line developed from two breeds: Berrichon du Cher and Romanov. At 113 days of age on average, the lambs were inoculated intravenously with 10(8 )Salmonella abortusovis Rv6 (vaccinal strain). They were slaughtered 10 days after the inoculation. Several traits were measured at inoculation and/or slaughtering to estimate the genetic resistance of the lambs to Salmonella infection: specific IgM and IgG1 antibody titres, body weight loss, spleen and pre-scapular node weights and counts of viable Salmonella persisting in these organs. This paper presents a quantitative analysis of the genetic variability of the traits related to salmonellosis susceptibility. The heritabilities of the traits varied between 0.10 and 0.64 (significantly different from zero). Thus, in sheep as well as in other species, the determinism of resistance to Salmonella infection is under genetic control. Moreover, the correlations between the traits are in agreement with the known immune mechanisms. The genetic variability observed should help QTL detection

Voxel-based morphometry (VBM) studies in schizophrenia—can white matter changes be reliably detected with VBM?

Voxel-Based Morphometry (VBM) is a hypothesis-free, whole-brain, voxel-by-voxel analytic method that attempts to compare imaging data between populations. Schizophrenia studies have utilized this method to localize differences in Diffusion Tensor Imaging (DTI) derived Fractional Anisotropy (FA), a measure of white matter integrity, between patients and healthy controls. The number of publications has grown, although it is unclear how reliable and reproducible this method is, given the subtle white matter abnormalities expected in schizophrenia. Here we analyze and combine results from 23 studies published to date that use VBM to study schizophrenia in order to evaluate the reproducibility of this method in DTI analysis. Coordinates of each region reported in DTI VBM studies published thus far in schizophrenia were plotted onto a Montreal Neurological Institute atlas, and their anatomical locations were recorded. Results indicated that the reductions of FA in patients with schizophrenia were scattered across the brain. Moreover, even the most consistently reported regions were reported independently in less than 35% of the papers studied. Other instances of reduced FA were replicated at an even lower rate. Our findings demonstrate striking inconsistency, with none of the regions reported in much more than a third of the published papers. Poor replication rate suggests that the application of VBM to DTI data may not be the optimal way for studying the subtle microstructural abnormalities that are being hypothesized in schizophrenia

Reduced interhemispheric connectivity in schizophrenia-tractography based segmentation of the corpus callosum

A reduction in interhemispheric connectivity is thought to contribute to the etiology of schizophrenia. Diffusion Tensor Imaging (DTI) measures the diffusion of water and can be used to describe the integrity of the corpus callosum white matter tracts, thereby providing information concerning possible interhemispheric connectivity abnormalities. Previous DTI studies in schizophrenia are inconsistent in reporting decreased Fractional Anisotropy (FA), a measure of anisotropic diffusion, within different portions of the corpus callosum. Moreover, none of these studies has investigated corpus callosum systematically, using anatomical subdivisions

Interactive Effects of Racial Identity and Repetitive Head Impacts on Cognitive Function, Structural MRI-Derived Volumetric Measures, and Cerebrospinal Fluid Tau and A beta

Background: Factors of increased prevalence among individuals with Black racial identity (e.g., cardiovascular disease, CVD) may influence the association between exposure to repetitive head impacts (RHI) from American football and later-life neurological outcomes. Here, we tested the interaction between racial identity and RHI on neurobehavioral outcomes, brain volumetric measures, and cerebrospinal fluid (CSF) total tau (t-tau), phosphorylated tau (p-tau181), and Aβ1–42 in symptomatic former National Football League (NFL) players. Methods: 68 symptomatic male former NFL players (ages 40–69; n = 27 Black, n = 41 White) underwent neuropsychological testing, structural MRI, and lumbar puncture. FreeSurfer derived estimated intracranial volume (eICV), gray matter volume (GMV), white matter volume (WMV), subcortical GMV, hippocampal volume, and white matter (WM) hypointensities. Multivariate generalized linear models examined the main effects of racial identity and its interaction with a cumulative head impact index (CHII) on all outcomes. Age, years of education, Wide Range Achievement Test, Fourth Edition (WRAT-4) scores, CVD risk factors, and APOEε4 were included as covariates; eICV was included for MRI models. P-values were false discovery rate adjusted. Results: Compared to White former NFL players, Black participants were 4 years younger (p = 0.04), had lower WRAT-4 scores (mean difference = 8.03, p = 0.002), and a higher BMI (mean difference = 3.09, p = 0.01) and systolic blood pressure (mean difference = 8.15, p = 0.03). With regards to group differences on the basis of racial identity, compared to White former NFL players, Black participants had lower GMV (mean adjusted difference = 45649.00, p = 0.001), lower right hippocampal volume (mean adjusted difference = 271.96, p = 0.02), and higher p-tau181/t-tau ratio (mean adjusted difference = −0.25, p = 0.01). There was not a statistically significant association between the CHII with GMV, right hippocampal volume, or p-tau181/t-tau ratio. However, there was a statistically significant Race x CHII interaction for GMV (b = 2206.29, p = 0.001), right hippocampal volume (b = 12.07, p = 0.04), and p-tau181/t-tau ratio concentrations (b = −0.01, p = 0.004). Conclusion: Continued research on racial neurological disparities could provide insight into risk factors for long-term neurological disorders associated with American football play

Cell type-specific manifestations of cortical thickness heterogeneity in schizophrenia

Brain morphology differs markedly between individuals with schizophrenia, but the cellular and genetic basis of this heterogeneity is poorly understood. Here, we sought to determine whether cortical thickness (CTh) heterogeneity in schizophrenia relates to interregional variation in distinct neural cell types, as inferred from established gene expression data and person-specific genomic variation. This study comprised 1849 participants in total, including a discovery (140 cases and 1267 controls) and a validation cohort (335 cases and 185 controls). To characterize CTh heterogeneity, normative ranges were established for 34 cortical regions and the extent of deviation from these ranges was measured for each individual with schizophrenia. CTh deviations were explained by interregional gene expression levels of five out of seven neural cell types examined: (1) astrocytes; (2) endothelial cells; (3) oligodendrocyte progenitor cells (OPCs); (4) excitatory neurons; and (5) inhibitory neurons. Regional alignment between CTh alterations with cell type transcriptional maps distinguished broad patient subtypes, which were validated against genomic data drawn from the same individuals. In a predominantly neuronal/endothelial subtype (22% of patients), CTh deviations covaried with polygenic risk for schizophrenia (sczPRS) calculated specifically from genes marking neuronal and endothelial cells (r = −0.40, p = 0.010). Whereas, in a predominantly glia/OPC subtype (43% of patients), CTh deviations covaried with sczPRS calculated from glia and OPC-linked genes (r = −0.30, p = 0.028). This multi-scale analysis of genomic, transcriptomic, and brain phenotypic data may indicate that CTh heterogeneity in schizophrenia relates to inter-individual variation in cell-type specific functions. Decomposing heterogeneity in relation to cortical cell types enables prioritization of schizophrenia subsets for future disease modeling efforts

Electrophysiological and diffusion tensor imaging evidence of delayed corollary discharges in patients with schizophrenia

Patients with schizophrenia (SZ) characteristically exhibit supranormal levels of cortical activity to self-induced sensory stimuli, ostensibly because of abnormalities in the neural signals (corollary discharges, CDs) normatively involved in suppressing the sensory consequences of self-generated actions. The nature of these abnormalities is unknown. This study investigated whether SZ patients experience CDs that are abnormally delayed in their arrival at the sensory cortex. Twentyone patients with SZ and 25 matched control participants underwent electroencephalography (EEG). Participants’ level of cortical suppression was calculated as the amplitude of the N1 component evoked by a button press-elicited auditory stimulus, subtracted from the N1 amplitude evoked by the same stimulus presented passively. In the three experimental conditions, the auditory stimulus was delivered 0, 50 or 100 ms subsequent to the button-press. Fifteen SZ patients and 17 healthy controls (HCs) also underwent diffusion tensor imaging (DTI), and the fractional anisotropy (FA) of participants’ arcuate fasciculus was used to predict their level of cortical suppression in the three conditions. While the SZ patients exhibited subnormal N1 suppression to undelayed, self-generated auditory stimuli, these deficits were eliminated by imposing a 50-ms, but not a 100-ms, delay between the button-press and the evoked stimulus. Furthermore, the extent to which the 50-ms delay normalized a patient’s level of N1 suppression was linearly related to the FA of their arcuate fasciculus. These data suggest that SZ patients experience temporally delayed CDs to self-generated auditory stimuli, putatively because of structural damage to the white-matter (WM) fasciculus connecting the sites of discharge initiation and destination

Improving the predictive potential of diffusion MRI in schizophrenia using normative models-Towards subject-level classification.

Diffusion MRI studies consistently report group differences in white matter between individuals diagnosed with schizophrenia and healthy controls. Nevertheless, the abnormalities found at the group-level are often not observed at the individual level. Among the different approaches aiming to study white matter abnormalities at the subject level, normative modeling analysis takes a step towards subject-level predictions by identifying affected brain locations in individual subjects based on extreme deviations from a normative range. Here, we leveraged a large harmonized diffusion MRI dataset from 512 healthy controls and 601 individuals diagnosed with schizophrenia, to study whether normative modeling can improve subject-level predictions from a binary classifier. To this aim, individual deviations from a normative model of standard (fractional anisotropy) and advanced (free-water) dMRI measures, were calculated by means of age and sex-adjusted z-scores relative to control data, in 18 white matter regions. Even though larger effect sizes are found when testing for group differences in z-scores than are found with raw values (p < .001), predictions based on summary z-score measures achieved low predictive power (AUC < 0.63). Instead, we find that combining information from the different white matter tracts, while using multiple imaging measures simultaneously, improves prediction performance (the best predictor achieved AUC = 0.726). Our findings suggest that extreme deviations from a normative model are not optimal features for prediction. However, including the complete distribution of deviations across multiple imaging measures improves prediction, and could aid in subject-level classification