Analysis of arm elevation muscle activity through different movement planes and speeds during in-water and dry-land exercise

Objective The objectives of this cross-sectional, analytical inference analysis were to compare shoulder muscle activation at arm elevations of 0° to 90° through different movement planes and speeds during in-water and dry-land exercise and to extrapolate this information to a clinical rehabilitation model. Methods Six muscles of right-handed adult subjects (n = 16; males/females: 50%; age: 26.1 ± 4.5 years) were examined with surface electromyography during arm elevation in water and on dry land. Participants randomly performed 3 elevation movements (flexion, abduction, and scaption) through 0° to 90°. Three movement speeds were used for each movement as determined by a metronome (30°/sec, 45°/sec, and 90°/sec). Dry-land maximal voluntary contraction tests were used to determine movement normalization. Results Muscle activity levels were significantly lower in water compared with dry land at 30°/sec and 45°/sec but significantly higher at 90°/sec. This sequential progressive activation with increased movement speed was proportionally higher on transition from gravity-based on-land activity to water-based isokinetic resistance. The pectoralis major and latissimus dorsi muscles showed higher activity during abduction and scaption. Conclusions These findings on muscle activation suggest protocols in which active flexion is introduced first at low speeds (30°/sec) in water, then at medium speeds (45°/sec) in water or on dry land, and finally at high speeds (90°/sec) on dry land before in water. Abduction requires higher stabilization, necessitating its introduction after flexion, with scaption introduced last. This model of progressive sequential movement ensures that early active motion and then stabilization are appropriately introduced. This should reduce rehabilitation time and improve therapeutic goals without compromising patient safety or introducing inappropriate muscle recruitment or movement speed.

A comparison land-water environment of maximal voluntary isometric contraction during manual muscle testing through surface electromyography

Background The aim of this study was to compare through surface electromyographic (sEMG) recordings of the maximum voluntary contraction (MVC) on dry land and in water by manual muscle test (MMT). Method Sixteen healthy right-handed subjects (8 males and 8 females) participated in measurement of muscle activation of the right shoulder. The selected muscles were the cervical erector spinae, trapezius, pectoralis, anterior deltoid, middle deltoid, infraspinatus and latissimus dorsi. The MVC test conditions were random with respect to the order on the land/in water. Results For each muscle, the MVC test was performed and measured through sEMG to determine differences in muscle activation in both conditions. For all muscles except the latissimus dorsi, no significant differences were observed between land and water MVC scores (p = 0.063–0.679) and precision (%Diff = 7–10%) were observed between MVC conditions in the muscles trapezius, anterior deltoid and middle deltoid. Conclusions If the procedure for data collection is optimal, under MMT conditions it appears that comparable MVC sEMG values were achieved on land and in water and the integrity of the EMG recordings were maintained during wáter immersion.

Analysis of the neuromuscular activity during rising from a chair in water and on dry land

PURPOSE: The purpose of the present study was to analyze the neuromuscular responses during the performance of a sit to stand [STS] task in water and on dry land. SCOPE: 10 healthy subjects, five males and five females were recruited for study. Surface electromyography sEMG was used for lower limb and trunk muscles maximal voluntarty contraction [MVC] and during the STS task. RESULTS: Muscle activity was significantly higher on dry land than in water normalized signals by MVC from the quadriceps-vastus medialis [17.3%], the quadriceps - rectus femoris [5.3%], the long head of the biceps femoris [5.5%], the tibialis anterior [13.9%], the gastrocnemius medialis [3.4%], the soleus [6.2%]. However, the muscle activity was higher in water for the rectus abdominis [-26.6%] and the erector spinae [-22.6%]. CONCLUSIONS: This study for the first time describes the neuromuscular responses in healthy subjects during the performance of the STS task in water. The differences in lower limb and trunk muscle activity should be considered when using the STS movement in aquatic rehabilitation.

A tensor-based morphometry study of genetic influences on brain structure using a new fluid registration method

We incorporated a new Riemannian fluid registration algorithm into a general MRI analysis method called tensor-based morphometry to map the heritability of brain morphology in MR images from 23 monozygotic and 23 dizygotic twin pairs. All 92 3D scans were fluidly registered to a common template. Voxelwise Jacobian determinants were computed from the deformation fields to assess local volumetric differences across subjects. Heritability maps were computed from the intraclass correlations and their significance was assessed using voxelwise permutation tests. Lobar volume heritability was also studied using the ACE genetic model. The performance of this Riemannian algorithm was compared to a more standard fluid registration algorithm: 3D maps from both registration techniques displayed similar heritability patterns throughout the brain. Power improvements were quantified by comparing the cumulative distribution functions of the p-values generated from both competing methods. The Riemannian algorithm outperformed the standard fluid registration.

A new registration method based on Log-Euclidean tensor metrics and its application to genetic studies

In structural brain MRI, group differences or changes in brain structures can be detected using Tensor-Based Morphometry (TBM). This method consists of two steps: (1) a non-linear registration step, that aligns all of the images to a common template, and (2) a subsequent statistical analysis. The numerous registration methods that have recently been developed differ in their detection sensitivity when used for TBM, and detection power is paramount in epidemological studies or drug trials. We therefore developed a new fluid registration method that computes the mappings and performs statistics on them in a consistent way, providing a bridge between TBM registration and statistics. We used the Log-Euclidean framework to define a new regularizer that is a fluid extension of the Riemannian elasticity, which assures diffeomorphic transformations. This regularizer constrains the symmetrized Jacobian matrix, also called the deformation tensor. We applied our method to an MRI dataset from 40 fraternal and identical twins, to revealed voxelwise measures of average volumetric differences in brain structure for subjects with different degrees of genetic resemblance.

Statistically assisted fluid registration algorithm - SAFIRA

In this paper, we develop and validate a new Statistically Assisted Fluid Registration Algorithm (SAFIRA) for brain images. A non-statistical version of this algorithm was first implemented in [2] and re-formulated using Lagrangian mechanics in [3]. Here we extend this algorithm to 3D: given 3D brain images from a population, vector fields and their corresponding deformation matrices are computed in a first round of registrations using the non-statistical implementation. Covariance matrices for both the deformation matrices and the vector fields are then obtained and incorporated (separately or jointly) in the regularizing (i.e., the non-conservative Lagrangian) terms, creating four versions of the algorithm. We evaluated the accuracy of each algorithm variant using the manually labeled LPBA40 dataset, which provides us with ground truth anatomical segmentations. We also compared the power of the different algorithms using tensor-based morphometry -a technique to analyze local volumetric differences in brain structure- applied to 46 3D brain scans from healthy monozygotic twins.

A lagrangian formulation for statistical fluid registration

We defined a new statistical fluid registration method with Lagrangian mechanics. Although several authors have suggested that empirical statistics on brain variation should be incorporated into the registration problem, few algorithms have included this information and instead use regularizers that guarantee diffeomorphic mappings. Here we combine the advantages of a large-deformation fluid matching approach with empirical statistics on population variability in anatomy. We reformulated the Riemannian fluid algorithmdeveloped in [4], and used a Lagrangian framework to incorporate 0 th and 1st order statistics in the regularization process. 92 2D midline corpus callosum traces from a twin MRI database were fluidly registered using the non-statistical version of the algorithm (algorithm 0), giving initial vector fields and deformation tensors. Covariance matrices were computed for both distributions and incorporated either separately (algorithm 1 and algorithm 2) or together (algorithm 3) in the registration. We computed heritability maps and two vector and tensorbased distances to compare the power and the robustness of the algorithms.

A nonconservative Lagrangian framework for statistical fluid registration - SAFIRA

In this paper, we used a nonconservative Lagrangian mechanics approach to formulate a new statistical algorithm for fluid registration of 3-D brain images. This algorithm is named SAFIRA, acronym for statistically-assisted fluid image registration algorithm. A nonstatistical version of this algorithm was implemented, where the deformation was regularized by penalizing deviations from a zero rate of strain. In, the terms regularizing the deformation included the covariance of the deformation matrices Σ and the vector fields (q). Here, we used a Lagrangian framework to reformulate this algorithm, showing that the regularizing terms essentially allow nonconservative work to occur during the flow. Given 3-D brain images from a group of subjects, vector fields and their corresponding deformation matrices are computed in a first round of registrations using the nonstatistical implementation. Covariance matrices for both the deformation matrices and the vector fields are then obtained and incorporated (separately or jointly) in the nonconservative terms, creating four versions of SAFIRA. We evaluated and compared our algorithms' performance on 92 3-D brain scans from healthy monozygotic and dizygotic twins; 2-D validations are also shown for corpus callosum shapes delineated at midline in the same subjects. After preliminary tests to demonstrate each method, we compared their detection power using tensor-based morphometry (TBM), a technique to analyze local volumetric differences in brain structure. We compared the accuracy of each algorithm variant using various statistical metrics derived from the images and deformation fields. All these tests were also run with a traditional fluid method, which has been quite widely used in TBM studies. The versions incorporating vector-based empirical statistics on brain variation were consistently more accurate than their counterparts, when used for automated volumetric quantification in new brain images. This suggests the advantages of this approach for large-scale neuroimaging studies.

Fluid registration of diffusion tensor images using information theory

We apply an information-theoretic cost metric, the symmetrized Kullback-Leibler (sKL) divergence, or $J$-divergence, to fluid registration of diffusion tensor images. The difference between diffusion tensors is quantified based on the sKL-divergence of their associated probability density functions (PDFs). Three-dimensional DTI data from 34 subjects were fluidly registered to an optimized target image. To allow large image deformations but preserve image topology, we regularized the flow with a large-deformation diffeomorphic mapping based on the kinematics of a Navier-Stokes fluid. A driving force was developed to minimize the $J$-divergence between the deforming source and target diffusion functions, while reorienting the flowing tensors to preserve fiber topography. In initial experiments, we showed that the sKL-divergence based on full diffusion PDFs is adaptable to higher-order diffusion models, such as high angular resolution diffusion imaging (HARDI). The sKL-divergence was sensitive to subtle differences between two diffusivity profiles, showing promise for nonlinear registration applications and multisubject statistical analysis of HARDI data.

Improving fluid registration through white matter segmentation in a twin study design

Robust and automatic non-rigid registration depends on many parameters that have not yet been systematically explored. Here we determined how tissue classification influences non-linear fluid registration of brain MRI. Twin data is ideal for studying this question, as volumetric correlations between corresponding brain regions that are under genetic control should be higher in monozygotic twins (MZ) who share 100% of their genes when compared to dizygotic twins (DZ) who share half their genes on average. When these substructure volumes are quantified using tensor-based morphometry, improved registration can be defined based on which method gives higher MZ twin correlations when compared to DZs, as registration errors tend to deplete these correlations. In a study of 92 subjects, higher effect sizes were found in cumulative distribution functions derived from statistical maps when performing tissue classification before fluid registration, versus fluidly registering the raw images. This gives empirical evidence in favor of pre-segmenting images for tensor-based morphometry.

Automated 3D mapping & shape analysis of the lateral ventricles via fluid registration of multiple surface-based atlases

We developed and validated a new method to create automated 3D parametric surface models of the lateral ventricles, designed for monitoring degenerative disease effects in clinical neuroscience studies and drug trials. First we used a set of parameterized surfaces to represent the ventricles in a manually labeled set of 9 subjects' MRIs (atlases). We fluidly registered each of these atlases and mesh models to a set of MRIs from 12 Alzheimer's disease (AD) patients and 14 matched healthy elderly subjects, and we averaged the resulting meshes for each of these images. Validation experiments on expert segmentations showed that (1) the Hausdorff labeling error rapidly decreased, and (2) the power to detect disease-related alterations monotonically improved as the number of atlases, N, was increased from 1 to 9. We then combined the segmentations with a radial mapping approach to localize ventricular shape differences in patients. In surface-based statistical maps, we detected more widespread and intense anatomical deficits as we increased the number of atlases, and we formulated a statistical stopping criterion to determine the optimal value of N. Anterior horn anomalies in Alzheimer's patients were only detected with the multi-atlas segmentation, which clearly outperformed the standard single-atlas approach.

Genetics of anisotropy asymmetry: Registration and sample size effects

Brain asymmetry has been a topic of interest for neuroscientists for many years. The advent of diffusion tensor imaging (DTI) allows researchers to extend the study of asymmetry to a microscopic scale by examining fiber integrity differences across hemispheres rather than the macroscopic differences in shape or structure volumes. Even so, the power to detect these microarchitectural differences depends on the sample size and how the brain images are registered and how many subjects are studied. We fluidly registered 4 Tesla DTI scans from 180 healthy adult twins (45 identical and fraternal pairs) to a geometrically-centered population mean template. We computed voxelwise maps of significant asymmetries (left/right hemisphere differences) for common fiber anisotropy indices (FA, GA). Quantitative genetic models revealed that 47-62% of the variance in asymmetry was due to genetic differences in the population. We studied how these heritability estimates varied with the type of registration target (T1- or T2-weighted) and with sample size. All methods consistently found that genetic factors strongly determined the lateralization of fiber anisotropy, facilitating the quest for specific genes that might influence brain asymmetry and fiber integrity.

A new combined surface and volume registration

3D registration of brain MRI data is vital for many medical imaging applications. However, purely intensitybased approaches for inter-subject matching of brain structure are generally inaccurate in cortical regions, due to the highly complex network of sulci and gyri, which vary widely across subjects. Here we combine a surfacebased cortical registration with a 3D fluid one for the first time, enabling precise matching of cortical folds, but allowing large deformations in the enclosed brain volume, which guarantee diffeomorphisms. This greatly improves the matching of anatomy in cortical areas. The cortices are segmented and registered with the software Freesurfer. The deformation field is initially extended to the full 3D brain volume using a 3D harmonic mapping that preserves the matching between cortical surfaces. Finally, these deformation fields are used to initialize a 3D Riemannian fluid registration algorithm, that improves the alignment of subcortical brain regions. We validate this method on an MRI dataset from 92 healthy adult twins. Results are compared to those based on volumetric registration without surface constraints; the resulting mean templates resolve consistent anatomical features both subcortically and at the cortex, suggesting that the approach is well-suited for cross-subject integration of functional and anatomic data.

A life cycle assessment approach to quantifying greenhouse gas emissions from land-use change for beef production in eastern Australia

In life cycle assessment studies, greenhouse gas (GHG) emissions from direct land-use change have been estimated to make a significant contribution to the global warming potential of agricultural products. However, these estimates have a high uncertainty due to the complexity of data requirements and difficulty in attribution of land-use change. This paper presents estimates of GHG emissions from direct land-use change from native woodland to grazing land for two beef production regions in eastern Australia, which were the subject of a multi-impact life cycle assessment study for premium beef production. Spatially- and temporally consistent datasets were derived for areas of forest cover and biomass carbon stocks using published remotely sensed tree-cover data and regionally applicable allometric equations consistent with Australia's national GHG inventory report. Standard life cycle assessment methodology was used to estimate GHG emissions and removals from direct land-use change attributed to beef production. For the northern-central New South Wales region of Australia estimates ranged from a net emission of 0.03 t CO2-e ha-1 year-1 to net removal of 0.12 t CO2-e ha-1 year-1 using low and high scenarios, respectively, for sequestration in regrowing forests. For the same period (1990-2010), the study region in southern-central Queensland was estimated to have net emissions from land-use change in the range of 0.45-0.25 t CO2-e ha-1 year-1. The difference between regions reflects continuation of higher rates of deforestation in Queensland until strict regulation in 2006 whereas native vegetation protection laws were introduced earlier in New South Wales. On the basis of liveweight produced at the farm-gate, emissions from direct land-use change for 1990-2010 were comparable in magnitude to those from other on-farm sources, which were dominated by enteric methane. However, calculation of land-use change impacts for the Queensland region for a period starting 2006, gave a range from net emissions of 0.11 t CO2-e ha-1 year-1 to net removals of 0.07 t CO2-e ha-1 year-1. This study demonstrated a method for deriving spatially- and temporally consistent datasets to improve estimates for direct land-use change impacts in life cycle assessment. It identified areas of uncertainty, including rates of sequestration in woody regrowth and impacts of land-use change on soil carbon stocks in grazed woodlands, but also showed the potential for direct land-use change to represent a net sink for GHG.

Implications for sustainable land use in high-density cities: Evidence from Hong Kong

Sustainable land use policies are concerned with the kind of world we want to live in now, and in future, and therefore inevitably involve some form of community involvement or consultation process. Hong Kong's sustainable land use planning system is well developed, involving considerable community participation and therefore serves as a good model for similarly situated cities. However, although there are several recent studies involving aspects of its land use planning system, none has yet examined the system as a whole from the perspective of sustainability. To correct this, this paper describes the land use conditions of Hong Kong from both demand and supply perspectives, reviewing its statutory and administrative procedures of land development and allocation together with the sustainable urban renewal practices involved. Problems in current sustainable land use planning and management, such as difficulties in urban renewal, the inherent shortage of land and the lengthy time involved due to need for coordination and responsiveness to multiple stakeholders, and outdated and overcomplicated administrative processes were also analyzed.