Predicting Clinical Course from Subcortical Shape in Provisional Tic Disorder

The ongoing NewTics study examines children who have had tics for less than 9 months (NT group) - a population on which little research exists. Here, we further investigate relationships between subcortical shape and tic symptom outcomes. 138 children were assessed at baseline and a 12-month follow-up: 79 with NT, 27 tic-free healthy controls (HC), and 32 with chronic tic disorder or Tourette syndrome (TS), using T1-weighted MRI and total tic scores (TTS) from the Yale Global Tic Severity Scale to evaluate symptom change. Subcortical surface maps were generated using FreeSurfer-initialized large deformation diffeomorphic metric mapping, and linear regression models were constructed to correlate structural shapes with TTS while accounting for covariates, with relationships mapped onto structure surfaces. When compared to healthy controls, smaller mean volumes were found in the TS group for the caudate, nucleus accumbens, pallidum, and thalamus. NT had smaller mean volumes than controls in the caudate, pallidum, and thalamus. Surface maps illustrate distinct patterns of inward deformation (localized volume loss) in the TS group compared to NT children. In the NT group, a larger hippocampus at baseline significantly correlated with the worsening of tic symptoms at 12 months. Outward deformation in the hippocampus and inward deformation in the accumbens at baseline are also related to worsening tic symptoms at follow-up. Since the NT group has had tics only for a few months, we can rule out the possibility that these subcortical volume differences are caused by living with tics for years; they are more likely related to the cause of tics. These observations constitute some of the first prognostic biomarkers for tic disorders and suggest localized circuitry that may be associated with outcome of tic disorders.

A Deformation-Based Shape Study of the Corpus Callosum in First Episode Schizophrenia

Background: Previous first-episode schizophrenia (FES) studies have reported abnormalities in the volume and mid-sagittal size of the corpus callosum (CC), but findings have been inconsistent. Besides, the CC shape has rarely been analyzed in FES. Therefore, in this study, we investigated FES-related CC shape abnormalities using 198 participants [92 FES patients and 106 healthy controls (HCs)].Methods: We conducted statistical shape analysis of the mid-sagittal CC curve in a large deformation diffeomorphic metric mapping framework. The CC was divided into the genu, body, and splenium (gCC, bCC, and sCC) to target the key CC sub-regions affected by the FES pathology. Gender effects have been investigated.Results: There were significant area differences between FES and HC in the entire CC and gCC but not in bCC nor sCC. In terms of the localized shape morphometrics, significant region-specific shape inward-deformations were detected in the superior portion of gCC and the anterosuperior portion of bCC in FES. These global area and local shape morphometric abnormalities were restricted to female FES but not male FES.Conclusions: gCC was significantly affected in the neuropathology of FES and this finding was specific to female FES. This study suggests that gCC may be a key sub-region that is vulnerable to the neuropathology of FES, specifically in female patients. The morphometrics of gCC may serve as novel and efficient biomarkers for screening female FES patients.

Direct Numerical Simulations of a Great Horn Owl in Flapping Flight

Synopsis The fluid dynamics of owls in flapping flight is studied by coordinated experiments and computations. The great horned owl was selected, which is nocturnal, stealthy, and relatively large sized raptor. On the experimental side, perch-to-perch flight was considered in an open wind tunnel. The owl kinematics was captured with multiple cameras from different view angles. The kinematic extraction was central in driving the computations, which were designed to resolve all significant spatio-temporal scales in the flow with an unprecedented level of resolution. The wing geometry was extracted from the planform image of the owl wing and a three-dimensional model, the reference configuration, was reconstructed. This configuration was then deformed in time to best match the kinematics recorded during flights utilizing an image-registration technique based on the large deformation diffeomorphic metric mapping framework. All simulations were conducted using an eddy-resolving, high-fidelity, solver, where the large displacements/deformations of the flapping owl model were introduced with an immersed boundary formulation. We report detailed information on the spatio-temporal flow dynamics in the near wake including variables that are challenging to measure with sufficient accuracy, such as aerodynamic forces. At the same time, our results indicate that high-fidelity computations over smooth wings may have limitations in capturing the full range of flow phenomena in owl flight. The growth and subsequent separation of the laminar boundary layers developing over the wings in this Reynolds number regime is sensitive to the surface micro-features that are unique to each species.

The Comparison of Diffeomorphic Images Based on the Construction of Persistent Homology

An object shape analysis is a problem that is related to such areas as geometry, topology, image processing and machine learning. For analyzing the form, the deformation between the source and terminal form of the object is estimated. The most used form analysis model is the Large Deformation Diffeomorphic Metric Mapping (LDDMM) model. The LDDMM model can be supplemented with functional non-geometric information about objects (volume, color, formation time). The paper considers algorithms for constructing sets of barcodes for comparing diffeomorphic images, which are real values taken by persistent homology. A distinctive feature of the use of persistent homology with respect to methods of algebraic topology is to obtain more information about the shape of the object. An important direction of the application of persistent homology is the study invariants of big data. A method based on persistent cohomology is proposed that combines persistent homology technologies with embedded non-geometric information presented as functions of simplicial complexes. The proposed structure of extended barcodes using cohomology increases the effectiveness of persistent homology methods. A modification of the Wasserstein method for finding the distance between images by introducing non-geometric information was proposed. The possibility of the formation of barcodes of images invariant to transformations of rotation, shift and similarity is considered.

EM-LDDMM for 3D to 2D registration

We examine the problem of mapping dense 3D atlases onto censored, sparsely sampled 2D target sections at micron and meso scales. We introduce a new class of large deformation diffeomorphic metric mapping (LD-DMM) algorithms for generating dense atlas correspondences onto sparse 2D samples by introducing a field of hidden variables which must be estimated representing a large class of target image uncertainties including (i) unknown parameters representing cross stain contrasts, (ii) censoring of tissue due to localized measurements of target subvolumes and (iii) sparse sampling of target tissue sections. For prediction of the hidden fields we introduce the generalized expectation-maximization algorithm (EM) for which the E-step calculates the conditional mean of the hidden variates simultaneously combined with the diffeomorphic correspondences between atlas and target coordinate systems. The algorithm is run to fixed points guaranteeing estimators satisfy the necessary maximizer conditions when interpreted as likelihood estimators. The dense mapping is an injective correspondence to the sparse targets implying all of the 3D variations are performed only on the atlas side with variation in the targets only 2D manipulations.