Effect of Visualization on Muscular Activation for Stability in Adolescent Ballet Students

The purpose of this study is to determine whether commonly used visualization techniques, whose results have been solely anecdotal, produce tangible, scientific results in muscular activation and improvement to ballet balances. Ballet training methods include imagery techniques however, much of this practice is solely based on the experience of the instructor and its results are anecdotal so that there are many gaps between research on imagery and dance instruction. Few published studies focus on the effect of the imagery training for dance students on either motor and nonmotor outcomes (Abraham, 2019). A survey will be administered to ballet instructors to determine the most used visualization cues for stability. Three adolescent female ballet students studying under said instructors will be asked to perform three balances. Surface electromyography data will be taken on the gluteus maximus, hip adductors, and abdominal oblique. The length of balance will also be taken. The dancers will then be exposed to a short visualization session or stimulus of anatomical images with arrows showing bodily adjustments and targeted muscles accompanied by verbal cues developed based on the instructor techniques from the survey. The same balances and data will be taken following the session. Results will be compared to the control data taken prior to the session to reveal whether the visualization training had significant results by determining statistically significant changes in balance times and changes in neuron spikes following spike analysis. Dancers will also be asked for qualitative feedback. Subject 2 yielded a significant increase in length of balance in all three types and the most consistent increase in neuron spikes in all of their muscles. This suggests a positive correlation between an increase in the degree of neuron activation or recruitment of those stability muscles and the ability for an individual to balance. This was also supported by increased confidence they felt in their balances after the visualization session. Subject 1 yielded no significant change in balance time before and after the visualization stimulus and the number of neuron spikes decreased after the session. This suggests that decreased activity in the tested muscles for stability resulted in lower balance times. This lack of muscular activation could be attributed to fatigue as reported by the dancer. The rest of the balances yielded significant increases in lengths of balance which were accompanied by increases in neuron spikes in the gluteus maximus and hip adductors for Degage a la Seconde and in the gluteus maximus for Releve en Retire. Subject 3 yielded insignificant changes in balance times for the first two types of balances but produced increases in the number of neuron spikes in most of the tested muscles in all of the balances. Reports from the dancer of being “less wobbly” the unexpected data to be attributed to an allocation to quality of the balance. The results on length of balances, number of neuron spikes, and confidence/reflection feedback obtained by this study supports the scientific validity of commonly-used visualization techniques in ballet by showcasing a higher degree of activation in the targeted stability muscles and longer average balance lengths should ensue following visualization training. Results also suggest that visualization techniques and stimuli for stability are the most effective when applied to learning unfamiliar movements. Further research could apply such visualization techniques to other movements, and even outside of dance.

Organic Nanoplatforms for Iodinated Contrast Media in CT Imaging

X-ray computed tomography (CT) imaging can produce three-dimensional and high-resolution anatomical images without invasion, which is extremely useful for disease diagnosis in the clinic. However, its applications are still severely limited by the intrinsic drawbacks of contrast media (mainly iodinated water-soluble molecules), such as rapid clearance, serious toxicity, inefficient targetability and poor sensitivity. Due to their high biocompatibility, flexibility in preparation and modification and simplicity for drug loading, organic nanoparticles (NPs), including liposomes, nanoemulsions, micelles, polymersomes, dendrimers, polymer conjugates and polymeric particles, have demonstrated tremendous potential for use in the efficient delivery of iodinated contrast media (ICMs). Herein, we comprehensively summarized the strategies and applications of organic NPs, especially polymer-based NPs, for the delivery of ICMs in CT imaging. We mainly focused on the use of polymeric nanoplatforms to prolong circulation time, reduce toxicity and enhance the targetability of ICMs. The emergence of some new technologies, such as theragnostic NPs and multimodal imaging and their clinical translations, are also discussed.

NIMG-25. IMPROVING THE NONINVASIVE CLASSIFICATION OF GLIOMA GENETIC SUBTYPE WITH DEEP LEARNING AND DIFFUSION-WEIGHTED IMAGING

In contrast to the WHO 2016 guidelines that use genetic alterations to further stratify patients within a designated grade, new recommendations suggest that IDH mutation status, followed by 1p19q-codeletion, should be used before grade when differentiating gliomas. Although most gliomas will be resected and their tissue evaluated with genetic profiling, non-invasive characterization of genetic subgroup can benefit patients where surgery is not otherwise advised or a fast turn-around is required for clinical trial eligibility. Prior studies have demonstrated the utility of using anatomical images and deep learning to distinguish either IDH-mutant from IDH-wildtype tumors or 1p19q-codeleted from non-codeleted lesions separately, but not combined or using the most recent recommendations for stratification. The goal of this study was to evaluate the effects of training strategy and incorporation of Apparent Diffusion Coefficient (ADC) maps from diffusion-weighted imaging on predicting new genetic subgroups with deep learning. Using 414 patients with newly-diagnosed glioma (split 285/50/49 training/validation/test) and optimized training hyperparameters, we found that a 3-class approach with T1-post-contrast, T2-FLAIR, and ADC maps as inputs achieved the best performance for molecular subgroup classification, with overall accuracies of 86.0%[CI:0.839,1.0], 80.0%[CI:0.720,1.0], and 85.7%[CI:0.771,1.0] on training, validation, and test sets, respectively, and final test class accuracies of 95.2%(IDH-wildtype), 88.9%(IDH-mutated,1p19qintact), and 60%(IDHmutated,1p19q-codeleted). Creating an RGB-color image from 3 MRI images and applying transfer learning with a residual network architecture pretrained on ImageNet resulted in an 8% averaged increase in overall accuracy. Although classifying both IDH and 1p19q mutations together was overall advantageous compared with a tiered structure that first classified IDH mutational status, the 2-tiered approach better generalized to an independent multi-site dataset when only anatomical images were used. Including biologically relevant ADC images improved model generalization to our test set regardless of modeling approach, highlighting the utility of incorporating diffusion-weighted imaging in future multi-site analyses of molecular subgroup.

Hands-On Deformation of Volumetric Anatomical Imageson a Touchscreen

In this work, we propose a novel metaphor to interact with volumetric anatomical images, e.g., magnetic resonance imaging or computed tomography scans. Beyond simple visual inspection, we empower users to reach the visible anatomical elements directly with their hands, and then move and deform them through natural gestures, while respecting the mechanical behavior of the underlying anatomy. This interaction metaphor relies on novel technical methods that address three major challenges: selection of anatomical elements in volumetric images, mapping of 2D manipulation gestures to 3D transformations, and real-time deformation of the volumetric images. All components of the interaction metaphor have been designed to capture the user’s intent in an intuitive manner, solving the mapping from the 2D touchscreen to the visible elements of the 3D volume. As a result, users have the ability to interact with medical volume images much like they would do with physical anatomy, directly with their hands.

Automated skull stripping in mouse fMRI analysis using 3D U-Net

Skull stripping is an initial and critical step in the pipeline of mouse fMRI analysis. Manual labeling of the brain usually suffers from intra- and inter-rater variability and is highly time-consuming. Hence, an automatic and efficient skull-stripping method is in high demand for mouse fMRI studies. In this study, we investigated a 3D U-Net based method for automatic brain extraction in mouse fMRI studies. Two U-Net models were separately trained on T2-weighted anatomical images and T2*-weighted functional images. The trained models were tested on both interior and exterior datasets. The 3D U-Net models yielded a higher accuracy in brain extraction from both T2-weighted images (Dice > 0.984, Jaccard index > 0.968 and Hausdorff distance < 7.7) and T2*-weighted images (Dice > 0.964, Jaccard index > 0.931 and Hausdorff distance < 3.3), compared with the two widely used mouse skull-stripping methods (RATS and SHERM). The resting-state fMRI results using automatic segmentation with the 3D U-Net models are identical to those obtained by manual segmentation for both the seed-based and group independent component analysis. These results demonstrate that the 3D U-Net based method can replace manual brain extraction in mouse fMRI analysis.

Medullary vein architecture modulates the white matter BOLD cerebrovascular reactivity signal response to CO2: observations from high-resolution T2* weighted imaging at 7T

ABSTRACTBrain stress testing using blood oxygenation level-dependent (BOLD) MRI to evaluate changes in cerebrovascular reactivity (CVR) is of growing interest for evaluating white matter integrity. However, even under healthy conditions, the white matter BOLD-CVR response differs notably from that observed in the gray matter. In addition to actual arterial vascular control, the venous draining topology may influence the WM-CVR response leading to signal delays and dispersions. These types of alterations in hemodynamic parameters are sometimes linked with pathology, but may also arise from differences in normal venous architecture. In this work, high-resolution T2*weighted anatomical images combined with BOLD imaging during a hypercapnic breathing protocol were acquired using a 7 tesla MRI system. Hemodynamic parameters including base CVR, hemodynamic lag, lag-corrected CVR, response onset and signal dispersion, and finally ΔCVR (corrected CVR minus base CVR) were calculated in 8 subjects. Parameter maps were spatially normalized and correlated against an MNI-registered white matter medullary vein atlas. Moderate correlations (Pearson’s rho) were observed between medullary vessel frequency (MVF) and ΔCVR (0.52; 0.58 for total WM), MVF and hemodynamic lag (0.42; 0.54 for total WM), MVF and signal dispersion (0.44; 0.53 for total WM), and finally MVF and signal onset (0.43; 0.52 for total WM). Results indicate that, when assessed in the context of the WM venous architecture, changes in the response shape may only be partially reflective of the actual vascular reactivity response occurring further upstream by control vessels. This finding may have implications when attributing diseases mechanisms and/or progression to presumed impaired WM BOLD-CVR.

Voxel-Wise Analysis of Structural and Functional MRI for Lateralization of Handedness in College Students

The brain structural and functional basis of lateralization in handedness is largely unclear. This study aimed to explore this issue by using voxel-mirrored homotopic connectivity (VMHC) measured by resting-state functional MRI (R-fMRI) and gray matter asymmetry index (AI) by high-resolution anatomical images. A total of 50 healthy subjects were included, among them were 13 left-handers, 24 right-handers, and 13 mixed-handers. Structural and R-fMRI data of all subjects were collected. There were significant differences in VMHC among the three groups in lateral temporal-occipital, orbitofrontal, and primary hand motor regions. Meanwhile, there were significant differences in AI that existed in medial prefrontal, superior frontal, and superior temporal regions. Besides, the correlation analysis showed that the closer the handedness score to the extreme of the left-handedness (LH), the stronger the interhemispheric functional connectivity, as well as more leftward gray matter. In general, left/mixed-handedness (MH) showed stronger functional homotopy in the transmodal association regions that depend on the integrity of the corpus callosum, but more variable in primary sensorimotor cortices. Furthermore, the group differences in VMHC largely align with that in AI. We located the specific regions for LH/MH from the perspective of structural specification and functional integration, suggesting the plasticity of hand movement and different patterns of emotional processing.

No evidence for three functionally specialized subregions in the subthalamic nucleus: A model-based 7 T fMRI study

The subthalamic nucleus (STN) is a small, subcortical brain structure. It is a target for deep brain stimulation, an invasive treatment that reduces motor symptoms of Parkinson's disease. Side effects of DBS are commonly explained using the tripartite model of STN organization, which proposes three functionally distinct subregions in the STN specialized in cognitive, limbic, and motor processing. However, evidence for the tripartite model exclusively comes from anatomical studies and functional studies using clinical patients. Here, we provide the first experimental tests of the tripartite model in healthy volunteers using ultra-high field 7 Tesla (T) functional magnetic resonance imaging (fMRI). 34 participants performed a random-dot motion decision-making task with a difficulty manipulation and a choice payoff manipulation aimed to differentially affect cognitive and limbic networks. Moreover, participants responded with their left and right index finger, differentially affecting motor networks. We analysed BOLD signal in three subregions of equal volume of the STN along the dorsolateral-ventromedial axis, identified using manually delineated high resolution anatomical images. Our results indicate that all segments responded equally to the experimental manipulations, and did not support the tripartite model.

DACO: Distortion/artefact correction for diffusion MRI data in an integrated framework

In this paper we propose a registration-based algorithm to correct various distortions or artefacts (DACO) commonly observed in diffusion weighted (DW) magnetic resonance images (MRI). The registration in DACO is proceeded on the basis of a pseudo b_0 image, which is synthesized from the anatomical images such as T1-weighted image or T2-weighted image, and a pseudo diffusion MRI (dMRI) data, which is derived from the Gaussian model of diffusion tensor imaging (DTI) or the Hermite model of MAP-MRI. DACO corrects (1) the susceptibility-induced distortions, (2) the intensity inhomogeneity, and (3) the misalignment between the dMRI data and anatomical images by registering the real b_0 image to the pseudo b_0 image, and corrects (4) the eddy current (EC)-induced distortions and (5) the head motions by registering each of the DW images in the real dMRI data to the corresponding image in the pseudo dMRI data. As the above artefacts interact with each other, DACO models each type of artefact in an integrated framework and estimates these models in an interleaved and iterative manner. The mathematical formulation of the models and the comprehensive estimation procedures are detailed in this paper. The evaluation using the human connectome project data shows that DACO could estimate the model parameters accurately. Furthermore, the evaluation conducted on the real human data acquired from clinical MRI scanners reveals that the method could reduce the artefacts effectively. The DACO method leverages the anatomical image, which is routinely acquired in clinical practice, to correct the artefacts, minimizing the additional acquisitions needed to conduct the algorithm. Therefore, our method is beneficial to most dMRI data, particularly to those without acquiring the field map or blip-up and blip-down images.

Depth Dependent Variations in Human Achilles Tendon as a Result of Active Smoking: An Elastographic Study

Current study was set to determine the impact of active smoking on Achilles Tendon (AT) as soft tissue using an elastographic technique. This study comprises of 54 male individuals having sedentary lifestyle. Volunteers were categorized into two groups of smokers (n = 20) and non-smokers (n = 34). Body composition analysis was performed to evaluate the physiological changes in human body mass indexes. Ultrasound Strain Elastog-raphy (USE) technique was used to find the stiffness along with anatomical images to envisage the anomalous status of Achilles tendon. Statistical analysis of data obtained through body composition, tendon anatomy and Strain Elastography (SE) was used to scrutinize the physiological, anatomical and elasticity variations within the tendon. A reduction in Fat Free Mass Index (FFMI) was observed among smokers with a significant difference (P = 0.042). Further, an increased significant difference (P = 0.029) was found in AT Strain Ratios (SR) of smokers as compared to non-smokers. Lightening in tendon mass and dilution in tendon stiffness indicates that smoking mechanism may generate excessive apoptosis and decrease the density of tenocytes. Nicotine is the key element that inhibits the functional capacity of Tendon Stem Cells and is highly responsible for tendinopathy, eventually leading to tendon rupture and injury.