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Rahe‐Meyer, Niels; Weilbach, Christian; Karst, Matthias; Pawlak, Maciej; Ahmed, Aminul I.; Piepenbrock, S.; Winterhalter, Michael

doi:10.1186/1475-925x-6-1

Cited by 28 publications

(12 citation statements)

References 32 publications

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“…Previous studies in neuroscience and biomedical engineering have acknowledged that ERP function morphology may change over the course of a task. However, most prior work has focused on controlling for longitudinal trends (Gasser et al, 1983; Turetsky et al, 1989) rather than modeling them; the few works on modeling longitudinal trends have been limited to parametric forms (Rossi et al, 2007; De Silva et al, 2012). We will build our proposed MD-FPCA on data produced by the novel meta-preprocessing step, MAP-ERP, capturing the continuum of longitudinal dynamics.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Dimensional Functional Principal Components Analysis of EEG Data

et al. 2017

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Summary The electroencephalography (EEG) data created in event-related potential (ERP) experiments have a complex high-dimensional structure. Each stimulus presentation, or trial, generates an ERP waveform which is an instance of functional data. The experiments are made up of sequences of multiple trials, resulting in longitudinal functional data and moreover, responses are recorded at multiple electrodes on the scalp, adding an electrode dimension. Traditional EEG analyses involve multiple simplifications of this structure to increase the signal-to-noise ratio, effectively collapsing the functional and longitudinal components by identifying key features of the ERPs and averaging them across trials. Motivated by an implicit learning paradigm used in autism research in which the functional, longitudinal and electrode components all have critical interpretations, we propose a multidimensional functional principal components analysis (MD-FPCA) technique which does not collapse any of the dimensions of the ERP data. The proposed decomposition is based on separation of the total variation into subject and subunit level variation which are further decomposed in a two-stage functional principal components analysis. The proposed methodology is shown to be useful for modeling longitudinal trends in the ERP functions, leading to novel insights into the learning patterns of children with Autism Spectrum Disorder (ASD) and their typically developing peers as well as comparisons between the two groups. Finite sample properties of MD-FPCA are further studied via extensive simulations.

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Section: Introductionmentioning

confidence: 99%

A Multi-Dimensional Functional Principal Components Analysis of EEG Data

et al. 2017

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“…The absence of a significant difference in knee flexion joint angle between the conditions VJFR & HJFR in both jumping and landing phase is a novel finding suggesting that trained athletes may perform VJFR and HJFR with similar knee flexion joint angle. Nagano et al (2007) using computer simulation found that the magnitude of knee flexion joint angle was similar between horizontal and vertical jumps [ 38 ]. In contrast, Fukashiro and colleagues (2005) reported that joint kinematics of the hip, knee and ankle were similar between standing horizontal jump and standing vertical jump during push-off.…”

Section: Discussionmentioning

confidence: 99%

Analysis of Jumping-Landing Manoeuvers after Different Speed Performances in Soccer Players

et al. 2015

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PurposeRunning at high speed and sudden change in direction or activity stresses the knee. Surprisingly, not many studies have investigated the effects of sprinting on knee’s kinetics and kinematics of soccer players. Hence, this study is aimed to investigate indices of injury risk factors of jumping-landing maneuvers performed immediately after sprinting in male soccer players.MethodsTwenty-three collegiate male soccer players (22.1±1.7 years) were tested in four conditions; vertical jump (VJ), vertical jump immediately after slow running (VJSR), vertical jump immediately after sprinting (VJFR) and double horizontal jump immediately after sprinting (HJFR). The kinematics and kinetics data were measured using Vicon motion analyzer (100Hz) and two Kistler force platforms (1000Hz), respectively.ResultsFor knee flexion joint angle, (p = 0.014, η = 0.15) and knee valgus moment (p = 0.001, η = 0.71) differences between condition in the landing phase were found. For knee valgus joint angle, a main effect between legs in the jumping phase was found (p = 0.006, η = 0.31), which suggests bilateral deficit existed between the right and left lower limbs.ConclusionIn brief, the important findings were greater knee valgus moment and less knee flexion joint angle proceeding sprint (HJFR & VJFR) rather than no sprint condition (VJ) present an increased risk for knee injuries. These results seem to suggest that running and sudden subsequent jumping-landing activity experienced during playing soccer may negatively change the knee valgus moment. Thus, sprinting preceding a jump task may increase knee risk factors such as moment and knee flexion joint angle.

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“…Within the basal segments (1-6), the largest difference between the mean T max and the segmental T max happened at segment 2. Within the midwall segments (7)(8)(9)(10)(11)(12), the largest difference was at segment 9, and within apical segments (13)(14)(15)(16), the largest difference was at segment 14. It seems that at these segments (2, 9, and 14) larger errors happen in geometry approximation and fiber orientations in the LV model.…”

Section: Rationale For Segmental Variation Of T Max In the Leftmentioning

confidence: 94%

“…Abnormal myocardial stress leads to hypertrophy of the left ventricle (LV) [10]. However, experimental invasive determination of myocardial stress is not currently a reliable option because errors inherent to the experimental methods are so large that they make the measurements unreliable [11,12].…”

Section: Introductionmentioning

confidence: 99%

Method for Calibration of Left Ventricle Material Properties Using Three-Dimensional Echocardiography Endocardial Strains

Dabiri

Sack

Rebelo³

et al. 2019

Journal of Biomechanical Engineering

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We sought to calibrate mechanical properties of left ventricle (LV) based on three-dimensional (3D) speckle tracking echocardiographic imaging data recorded from 16 segments defined by American Heart Association (AHA). The in vivo data were used to create finite element (FE) LV and biventricular (BV) models. The orientation of the fibers in the LV model was rule based, but diffusion tensor magnetic resonance imaging (MRI) data were used for the fiber directions in the BV model. A nonlinear fiber-reinforced constitutive equation was used to describe the passive behavior of the myocardium, whereas the active tension was described by a model based on tissue contraction (Tmax). isight was used for optimization, which used abaqus as the forward solver (Simulia, Providence, RI). The calibration of passive properties based on the end diastolic pressure volume relation (EDPVR) curve resulted in relatively good agreement (mean error = −0.04 ml). The difference between the experimental and computational strains decreased after segmental strain metrics, rather than global metrics, were used for calibration: for the LV model, the mean difference reduced from 0.129 to 0.046 (circumferential) and from 0.076 to 0.059 (longitudinal); for the BV model, the mean difference nearly did not change in the circumferential direction (0.061) but reduced in the longitudinal direction from 0.076 to 0.055. The calibration of mechanical properties for myocardium can be improved using segmental strain metrics. The importance of realistic fiber orientation and geometry for modeling of the LV was shown.

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Cited by 28 publications

References 32 publications

A Multi-Dimensional Functional Principal Components Analysis of EEG Data

A Multi-Dimensional Functional Principal Components Analysis of EEG Data

Analysis of Jumping-Landing Manoeuvers after Different Speed Performances in Soccer Players

Method for Calibration of Left Ventricle Material Properties Using Three-Dimensional Echocardiography Endocardial Strains

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