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

Neuroimaging in repetitive brain trauma

Sports-related concussions are one of the major causes of mild traumatic brain injury. Although most patients recover completely within days to weeks, those who experience repetitive brain trauma (RBT) may be at risk for developing a condition known as chronic traumatic encephalopathy (CTE). While this condition is most commonly observed in athletes who experience repetitive concussive and/or subconcussive blows to the head, such as boxers, football players, or hockey players, CTE may also affect soldiers on active duty. Currently, the only means by which to diagnose CTE is by the presence of phosphorylated tau aggregations post-mortem. Non-invasive neuroimaging, however, may allow early diagnosis as well as improve our understanding of the underlying pathophysiology of RBT. The purpose of this article is to review advanced neuroimaging methods used to investigate RBT, including diffusion tensor imaging, magnetic resonance spectroscopy, functional magnetic resonance imaging, susceptibility weighted imaging, and positron emission tomography. While there is a considerable literature using these methods in brain injury in general, the focus of this review is on RBT and those subject populations currently known to be susceptible to RBT, namely athletes and soldiers. Further, while direct detection of CTE in vivo has not yet been achieved, all of the methods described in this review provide insight into RBT and will likely lead to a better characterization (diagnosis), in vivo, of CTE than measures of self-report

ANTONIO [Material gráfico]

Copia digital. Madrid : Ministerio de Educación, Cultura y Deporte. Subdirección General de Coordinación Bibliotecaria, 201

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

Neuroimaging in repetitive brain trauma

Sports-related concussions are one of the major causes of mild traumatic brain injury. Although most patients recover completely within days to weeks, those who experience repetitive brain trauma (RBT) may be at risk for developing a condition known as chronic traumatic encephalopathy (CTE). While this condition is most commonly observed in athletes who experience repetitive concussive and/or subconcussive blows to the head, such as boxers, football players, or hockey players, CTE may also affect soldiers on active duty. Currently, the only means by which to diagnose CTE is by the presence of phosphorylated tau aggregations post-mortem. Non-invasive neuroimaging, however, may allow early diagnosis as well as improve our understanding of the underlying pathophysiology of RBT. The purpose of this article is to review advanced neuroimaging methods used to investigate RBT, including diffusion tensor imaging, magnetic resonance spectroscopy, functional magnetic resonance imaging, susceptibility weighted imaging, and positron emission tomography. While there is a considerable literature using these methods in brain injury in general, the focus of this review is on RBT and those subject populations currently known to be susceptible to RBT, namely athletes and soldiers. Further, while direct detection of CTE in vivo has not yet been achieved, all of the methods described in this review provide insight into RBT and will likely lead to a better characterization (diagnosis), in vivo, of CTE than measures of self-report

Thalamo-frontal white matter alterations in chronic schizophrenia

Diffusion tensor imaging (DTI) and fiber tractography are useful tools for reconstructing white matter tracts (WMT) in the brain. Previous tractography studies have sought to segment reconstructed WMT into anatomical structures using several approaches, but quantification has been limited to extracting mean values of diffusion indices. Delineating WMT in schizophrenia is of particular interest because schizophrenia has been hypothesized to be a disorder of disrupted connectivity, especially between frontal and temporal regions of the brain. In this study, we aim to differentiate diffusion properties of thalamo-frontal pathways in schizophrenia from normal controls. We present a quantitative group comparison method, which combines the strengths of both tractography-based and voxel-based studies. Our algorithm extracts white matter pathways using whole brain tractography. Functionally relevant bundles are selected and parsed from the resulting set of tracts, using an internal capsule (IC) region of interest (ROI) as “source”, and different Brodmann area (BA) ROIs as “targets”. The resulting bundles are then longitudinally parameterized so that diffusion properties can be measured and compared along the WMT. Using this processing pipeline, we were able to find altered diffusion properties in male patients with chronic schizophrenia in terms of fractional anisotropy (FA) decreases and mean diffusivity (MD) increases in precise and functionally relevant locations. These findings suggest that our method can enhance the regional and functional specificity of DTI group studies, thus improving our understanding of brain function

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

Neuropsychological Outcome and Diffusion Tensor Imaging in Complicated versus Uncomplicated Mild Traumatic Brain Injury

This study examined whether intracranial neuroimaging abnormalities in those with mild traumatic brain injury (MTBI) (i.e., “complicated” MTBIs) are associated with worse subacute outcomes as measured by cognitive testing, symptom ratings, and/or diffusion tensor imaging (DTI). We hypothesized that (i) as a group, participants with complicated MTBIs would report greater symptoms and have worse neurocognitive outcomes than those with uncomplicated MTBI, and (ii) as a group, participants with complicated MTBIs would show more Diffusion Tensor Imaging (DTI) abnormalities. Participants were 62 adults with MTBIs (31 complicated and 31 uncomplicated) who completed neurocognitive testing, symptom ratings, and DTI on a 3T MRI scanner approximately 6-8 weeks post injury. There were no statistically significant differences between groups on symptom ratings or on a broad range of neuropsychological tests. When comparing the groups using tract-based spatial statistics for DTI, no significant difference was found for axial diffusivity or mean diffusivity. However, several brain regions demonstrated increased radial diffusivity (purported to measure myelin integrity), and decreased fractional anisotropy in the complicated group compared with the uncomplicated group. Finally, when we extended the DTI analysis, using a multivariate atlas based approach, to 32 orthopedic trauma controls (TC), the findings did not reveal significantly more areas of abnormal DTI signal in the complicated vs. uncomplicated groups, although both MTBI groups had a greater number of areas with increased radial diffusivity compared with the trauma controls. This study illustrates that macrostructural neuroimaging changes following MTBI are associated with measurable changes in DTI signal. Of note, however, the division of MTBI into complicated and uncomplicated subtypes did not predict worse clinical outcome at 6-8 weeks post injury

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