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Background and Aims Falls pose a significant public health risk for community‐dwelling stroke survivors. Current research on biomechanical parameters to trip perturbation in standing tasks often fails to predict fall risk or balance recovery during dynamic tasks such as walking. Moreover, phase‐specific biomechanical adaptations during recovery from perturbations remain underexplored. One‐dimensional statistical parametric mapping (SPM1D) was used in the current research for analyzing time‐series biomechanical data and to investigate joint angular profiles during reactive stepping following trip‐like gait perturbations. The aim was to compare these biomechanical characteristics between stroke survivors and healthy controls. Methods Fourteen participants with stroke and ten healthy controls were assessed using a 16‐camera motion capture system. Participants walked at self‐selected usual walking speeds on a split‐belt treadmill and underwent a simulated trip perturbation, triggered by backward treadmill acceleration during initial foot contact. Biomechanical variables at the reactive step touchdown were derived using the Vicon plug‐in‐gait full‐body model. Biomechanical characteristics (joint angles and moments) showing significant between‐group differences at reactive step touchdown were initially identified, and SPM1D was then utilized to analyze joint angles and moments across three equal phases of the reactive step cycle (initial, middle, and end). Independent‐samples t‐tests complemented SPM1D were used to identify between‐group differences, with significance set at p ≤ 0.05. Results Compared to controls, stroke participants showed increased trunk flexion, knee flexion, and ankle dorsiflexion angles on the perturbed side, along with decreased ankle dorsiflexion moment and altered upper limb movement strategies. SPM1D revealed phase‐specific differences that increased shoulder abduction during the initial phase on the reactive step side, increased shoulder external rotation in the middle phase on the perturbed side, and greater trunk flexion and ankle dorsiflexion angle but reduced ankle dorsiflexion moment on the perturbed side during the end phase. Conclusion Participants with stroke exhibit distinct, phase‐dependent biomechanical adaptations during reactive stepping post‐trip perturbation.

Fatigue is a major injury risk factor. The aim of this study was to investigate the effects of fatigue on lunging during the fatiguing process. The lower extremity joint kinematics and kinetics of fifteen male collegiate badminton players were simultaneously recorded by optical motion-capture and force plate systems during lunging. In addition to statistical analyses of discrete variables, one-dimensional statistical parametric mapping (SPM (1D)) was used to analyze the waveform data. The hypotheses were that the biomechanics of lunging maneuvers would change during the fatiguing process, and the fatigue effects would differ in different periods (I–V) of the stance phase and in different joints. Results showed that the initial contact angles, peak angles, moments, power, and time needed to reach the peak angles at the hip, knee, and ankle in the sagittal plane all decreased post-fatigue. A continuous decreasing tendency was reflected in the moments and power of hip and, in particular, knee joints (mostly p < 0.001). Period IV showed a significant fatigue response. In conclusion, both discrete and waveform data illustrated the effects of fatigue, however, the results of SPM (1D) analysis showed both the key period and body segments affected by the fatigue response.

When investigating the dynamics of three-dimensional multi-body biomechanical systems it is often difficult to derive spatiotemporally directed predictions regarding experimentally induced effects. A paradigm of ‘non-directed’ hypothesis testing has emerged in the literature as a result. Non-directed analyses typically consist of ad hoc scalar extraction, an approach which substantially simplifies the original, highly multivariate datasets (many time points, many vector components). This paper describes a commensurately multivariate method as an alternative to scalar extraction. The method, called ‘statistical parametric mapping’ (SPM), uses random field theory to objectively identify field regions which co-vary significantly with the experimental design. We compared SPM to scalar extraction by re-analyzing three publicly available datasets: 3D knee kinematics, a ten-muscle force system, and 3D ground reaction forces. Scalar extraction was found to bias the analyses of all three datasets by failing to consider sufficient portions of the dataset, and/or by failing to consider covariance amongst vector components. SPM overcame both problems by conducting hypothesis testing at the (massively multivariate) vector trajectory level, with random field corrections simultaneously accounting for temporal correlation and vector covariance. While SPM has been widely demonstrated to be effective for analyzing 3D scalar fields, the current results are the first to demonstrate its effectiveness for 1D vector field analysis. It was concluded that SPM offers a generalized, statistically comprehensive solution to scalar extraction's over-simplification of vector trajectories, thereby making it useful for objectively guiding analyses of complex biomechanical systems.

Scapular kinematics and EMG are frequently measured as a functional assessment of the shoulder. Previous studies have compared interval averaging for these time series data, but it is not clear whether this method exactly captures the dynamics of scapular kinematics and muscle activity. Statistical parametric mapping (SPM) can be used to compare time series data. The purpose of this study was to investigate whether there is a difference between the results of SPM and interval averaging (every 10° or 30°) in comparing scapular kinematics, EMG, and EMG ratio. Scapular kinematics and EMG of the upper trapezius (UT), middle trapezius (MT), and lower trapezius (LT) and serratus anterior (SA) were measured in 21 healthy males. Tasks included arm raising and lowering with or without load, and we compared scapular kinematics, EMG, and EMG ratio in the loaded and unloaded conditions. Results suggest disagreement between SPM and interval averaging. Characteristic results are that for scapular kinematics during lowering SPM showed a decrease in upward rotation in only the regions 113-65° and 42-30°, while interval averaging showed a decrease in all range. For EMG during lowering, SPM results were not significantly different in SA over 50–48 and 45-30°, while interval averaging suggested increased activity in all ranges. For EMG ratio during raising, SPM showed no significant difference, while interval averaging showed a decrease in UT/LT during the latter period. These results indicate that SPM provides better resolution regarding effect regions than interval averaging, and suggest that SPM may improve shoulder function assessment accuracy.

Background： Cerebral glucose metabolism changes are always observed in patients suffering from malignant tumors. This preliminary study aimed to investigate the brain glucose metabolism changes in patients with lung cancer of different histological types. Methods： One hundred and twenty patients with primary untreated lung cancer, who visited People＇s Hospital of Zhengzhou University from February 2012 to July 2013, were divided into three groups based on histological types confirmed by biopsy or surgical pathology, which included adenocarcinoma （52 cases）, squamous cell carcinoma （43 cases）, and small-cell carcinoma （25 cases）. The whole body ^18F-fiuorodeoxyglucose （^18F-FDG） positron emission tomography （PET）/computed tomography （CT） of these cases was retrospectively studied. The brain PET data of three groups were analyzed individually using statistical parametric maps （SPM） software, with 50 age-matched and gender-matched healthy controls for comparison. Results： The brain resting glucose metabolism in all three lung cancer groups showed regional cerebral metabolic reduction. The hypo-metabolic cerebral regions were mainly distributed at the left superior and middle frontal, bilateral superior and middle temporal and inferior and middle temporal gyrus. Besides, the hypo-metabolic regions were also found in the right inferior parietal lobule and hippocampus in the small-cell carcinoma group. The area of the total hypo-metabolic cerebral regions in the small-cell carcinoma group （total voxel value 3255） was larger than those in the adenocarcinoma group （total voxel value 1217） and squamous cell carcinoma group （total voxel value 1292）. Conclusions： The brain resting glucose metabolism in patients with lung cancer shows regional cerebral metabolic reduction and the brain hypo-metabolic changes are related to the histological types of lung cancer.

Total intracranial volume (TIV/ICV) is an important covariate for volumetric analyses of the brain and brain regions, especially in the study of neurodegenerative diseases, where it can provide a proxy of maximum pre-morbid brain volume. The gold-standard method is manual delineation of brain scans, but this requires careful work by trained operators. We evaluated Statistical Parametric Mapping 12 (SPM12) automated segmentation for TIV measurement in place of manual segmentation and also compared it with SPM8 and FreeSurfer 5.3.0. For T1-weighted MRI acquired from 288 participants in a multi-centre clinical trial in Alzheimer's disease we find a high correlation between SPM12 TIV and manual TIV (R2=0.940, 95% Confidence Interval (0.924, 0.953)), with a small mean difference (SPM12 40.4±35.4ml lower than manual, amounting to 2.8% of the overall mean TIV in the study). The correlation with manual measurements (the key aspect when using TIV as a covariate) for SPM12 was significantly higher (p<0.001) than for either SPM8 (R2=0.577 CI (0.500, 0.644)) or FreeSurfer (R2=0.801 CI (0.744, 0.843)). These results suggest that SPM12 TIV estimates are an acceptable substitute for labour-intensive manual estimates even in the challenging context of multiple centres and the presence of neurodegenerative pathology. We also briefly discuss some aspects of the statistical modelling approaches to adjust for TIV. [Display omitted] •288 T1 MRI from multiple scanners were manually segmented for intracranial volume.•We compare SPM12 with the current methods of estimating intracranial volume.•SPM12 shows a very high correlation with manual measures and little bias.•Newer automated volume measures are more accurate controls for head size variation.

Biomechanical processes are often manifested as one-dimensional (1D) trajectories. It has been shown that 1D confidence intervals (CIs) are biased when based on 0D statistical procedures, and the non-parametric 1D bootstrap CI has emerged in the Biomechanics literature as a viable solution. The primary purpose of this paper was to clarify that, for 1D biomechanics datasets, the distinction between 0D and 1D methods is much more important than the distinction between parametric and non-parametric procedures. A secondary purpose was to demonstrate that a parametric equivalent to the 1D bootstrap exists in the form of a random field theory (RFT) correction for multiple comparisons. To emphasize these points we analyzed six datasets consisting of force and kinematic trajectories in one-sample, paired, two-sample and regression designs. Results showed, first, that the 1D bootstrap and other 1D non-parametric CIs were qualitatively identical to RFT CIs, and all were very different from 0D CIs. Second, 1D parametric and 1D non-parametric hypothesis testing results were qualitatively identical for all six datasets. Last, we highlight the limitations of 1D CIs by demonstrating that they are complex, design-dependent, and thus non-generalizable. These results suggest that (i) analyses of 1D data based on 0D models of randomness are generally biased unless one explicitly identifies 0D variables before the experiment, and (ii) parametric and non-parametric 1D hypothesis testing provide an unambiguous framework for analysis when one׳s hypothesis explicitly or implicitly pertains to whole 1D trajectories.

Testing a prediction is fundamental to scientific experiments. Where biomechanical experiments involve analysis of 1-Dimensional (waveform) data, sample size estimation should consider both 1D variance and hypothesised 1D effects. This study exemplifies 1D sample size estimation using typical biomechanical signals and contrasts this with 0D (discrete) power analysis. For context, biomechanics papers from 2018 and 2019 were reviewed to characterise current practice. Sample size estimation occurred in approximately 4% of 653 papers and reporting practice was mixed. To estimate sample sizes, common biomechanical signals were sourced from the literature and 1D effects were generated artificially using the open-source power1d software. Smooth Gaussian noise was added to the modelled 1D effect to numerically estimate the sample size required. Sample sizes estimated using 1D power procedures varied according to the characteristics of the dataset, requiring only small-to-moderate sample sizes of approximately 5–40 to achieve target powers of 0.8 for reported 1D effects, but were always larger than 0D sample sizes (from N + 1 to >N + 20). The importance of a priori sample size estimation is highlighted and recommendations are provided to improve the consistency of reporting. This study should enable researchers to construct 1D biomechanical effects to address adequately powered, hypothesis-driven, predictive research questions.

A false positive is the mistake of inferring an effect when none exists, and although α controls the false positive (Type I error) rate in classical hypothesis testing, a given α value is accurate only if the underlying model of randomness appropriately reflects experimentally observed variance. Hypotheses pertaining to one-dimensional (1D) (e.g. time-varying) biomechanical trajectories are most often tested using a traditional zero-dimensional (0D) Gaussian model of randomness, but variance in these datasets is clearly 1D. The purpose of this study was to determine the likelihood that analyzing smooth 1D data with a 0D model of variance will produce false positives. We first used random field theory (RFT) to predict the probability of false positives in 0D analyses. We then validated RFT predictions via numerical simulations of smooth Gaussian 1D trajectories. Results showed that, across a range of public kinematic, force/moment and EMG datasets, the median false positive rate was 0.382 and not the assumed α=0.05, even for a simple two-sample t test involving N=10 trajectories per group. The median false positive rate for experiments involving three-component vector trajectories was p=0.764. This rate increased to p=0.945 for two three-component vector trajectories, and to p=0.999 for six three-component vectors. This implies that experiments involving vector trajectories have a high probability of yielding 0D statistical significance when there is, in fact, no 1D effect. Either (a) explicit a priori identification of 0D variables or (b) adoption of 1D methods can more tightly control α.

Breaststroke and butterfly are complex swimming techniques requiring refined motor skills to perform successfully, with coordinated and consistent interaction between propulsive and resistive forces being decisive when considering swimmers expertise. The current study analysed those techniques intercycle kinematic variation in two swimmers cohorts. Twenty elite and 15 national level swimmers performed one 25 m breaststroke and one 25 m butterfly sprints, with an underwater camera recording images at 120 Hz in the sagittal plane. Mean velocity, maximum and minimum velocities, stroke rate and length, intracycle velocity variation and phases relative duration were calculated for consecutive cycles (elite: five breaststroke/butterfly, national level: eight breaststroke/seven butterfly). The two highest peaks and the lower peak in between in breaststroke were also addressed. Intercycle and inter-groups analysis were performed using ANOVA, ANCOVA and Statistical Parametric Mapping. Elite and national level differed regarding breaststroke mean and maximum velocities, 1st and 2nd peaks and minimum between peaks (1.30 ± 0.02 vs 1.15 ± 0.02 m/s, 2.13 ± 0.05 vs 1.88 ± 0.06 m/s, 1.63 ± 0.05 vs 1.48 ± 0.05 m/s, 2.13 ± 0.05 vs 1.86 ± 0.05 m/s, 1.33 ± 0.04 vs 1.23 ± 0.04 m/s), and butterfly mean, maximum and minimum velocities, stroke rate and intracycle velocity variation, respectively (1.65 ± 0.01 vs 1.50 ± 0.01 m/s, 2.20 ± 0.04 vs 2.09 ± 0.04 m/s, 1.12 ± 0.04 vs 0.79 ± 0.04 m/s, (57.9 ± 0.9 vs 54.9 ± 1.0cycles/min, 18.4 ± 1.3 vs 23.7 ± 1.3 %). Elite and national level swimmers showed consistent breaststroke intercycle kinematic variation, but a butterfly mean velocity decay, with the upper limbs release and recovery, and the outsweep phases originating variability between butterfly cycles. Skill levels contrasted in technical and strategic features at sprint breaststroke and butterfly but showed similar velocity variability between consecutive swimming cycles.