Multilinear Model Research Articles

Flexibility of Fast Brain Dynamics and Disease Severity in Amyotrophic Lateral Sclerosis.

Amyotrophic lateral sclerosis (ALS) is a multisystem disorder, as supported by clinical, molecular, and neuroimaging evidence. As a consequence, predicting clinical features requires a description of large-scale neuronal dynamics. Normally, brain activity dynamically reconfigures over time, recruiting different brain areas. Brain pathologies induce stereotyped dynamics which, in turn, are linked to clinical impairment. Hence, based on recent evidence showing that brain functional networks become hyperconnected as ALS progresses, we hypothesized that the loss of flexible dynamics in ALS would predict the symptoms severity. To test this hypothesis, we quantified flexibility using the "functional repertoire" (i.e., the number of configurations of active brain areas) as measured from source-reconstructed magnetoencephalography (MEG) in patients with ALS and healthy controls. The activity of brain areas was reconstructed in the classic frequency bands, and the functional repertoire was estimated to quantify spatiotemporal fluctuations of brain activity. Finally, we built a k-fold cross-validated multilinear model to predict the individual clinical impairment from the size of the functional repertoire. Comparing 42 patients with ALS and 42 healthy controls, we found a more stereotyped brain dynamics in patients with ALS (p < 0.05), as conveyed by the smaller functional repertoire. The relationship between the size of the functional repertoire and the clinical scores in the ALS group showed significant correlations in both the delta and the theta frequency bands. Furthermore, through a k-fold cross-validated multilinear regression model, we found that the functional repertoire predicted both clinical staging (p < 0.001 and p < 0.01, in the delta and theta bands, respectively) and symptoms severity (p < 0.001, in both the delta and theta bands). Our work shows that (1) ALS pathology reduces the flexibility of large-scale brain dynamics, (2) subcortical regions play a key role in determining brain dynamics, and (3) reduced brain flexibility predicts disease stage and symptoms severity. Our approach provides a noninvasive tool to quantify alterations in brain dynamics in ALS (and, possibly, other neurodegenerative diseases), thus opening new opportunities in disease management and a framework to test, in the near future, the effects of disease-modifying interventions at the whole-brain level.

Optimization of Vocal Singing Training Methods Based on Multimedia Data Analysis

This paper constructs a vocal singing training optimization model based on multimedia data analysis and conducts in-depth research and analysis on the optimization of vocal singing training methods using multimedia data analysis system. For the problems of poor real-time and large analysis error of the traditional system, a massive real-time multimedia data analysis system based on DSP is designed, with high-speed DSP as the core architecture, multipoint modeling of the experimenter, and data extraction and fusion processing are completed with model association characteristics as the analysis basis. The experimental results show that the real-time performance and accuracy of the system are improved by 15.14% and 13.42%, respectively. To address the time-space domain consistency problem in traditional time series data forecasting, two high-order time series forecasting algorithms based on tensor decomposition are proposed in this paper: the multilinear orthogonal autoregressive model and the multilinear constrained autoregressive model. Using the local structure information of the data to reduce the dimensionality of the data is more beneficial to extract the true low-dimensional representation of the data. Most current supervised cross-modal retrieval methods use a strict binary label matrix (i.e., 0 and 1); however, the interval between labels 0 and 1 is small, which may increase the risk of classification errors. To solve some of the above existing problems, a two-stage hashing method based on smooth matrix decomposition and label relaxation is proposed, which proposes a novel label relaxation strategy that can adaptively control the interval between label intervals and can reduce the quantization loss by about 5.21%. The multimedia data analysis vocal singing training optimization model designed in this paper can improve enhance students’ motivation and accuracy and provide technical support and a rational basis for vocal singing practice and overall musical expression.

Modeling for Volume-Fracturing Vertical Wells in Tight Oil Reservoir considering NMR-Based Research on Imbibition

Abstract The lateral broadband fracturing (LBF) technique for vertical well in tight oil reservoir has been proven to provide much larger stimulated reservoir volume (SRV) by widening its width. Although this new fracturing technique has been successfully applied to the development of tight oil reservoirs in Ordos Basin, China, there is still a lack of models and methods to characterize the imbibition of matrix-fracture system, which is heterogeneous permeability distribution. In this paper, a multilinear fractal model considering Nuclear Magnetic Resonance-based research on imbibition (MFMI) is established to characterize the flow characteristics of lateral broadband fracturing vertical wells (LVWs) in tight oil reservoirs by combining the dual-porosity fractal model considering imbibition and the quad-linear flow model. Due to the application of LBF, the nonuniform distribution of fracture system is characterized by fractal porosity and fractal permeability. In addition, the imbibition in SRV of tight oil reservoir is quantitatively characterized by Nuclear Magnetic Resonance (NMR) data. And the production performance of LVWs is quantified by the MFMI. By using the Laplace transformation, Bessel function, iteration, and Stehfest numerical inversion algorithms, the approximate analytic solutions of our established model, including primary hydraulic fractures, SRV, and unstimulated reservoir volume (USRV), are derived. The solutions of pressure and production are used to compare and analyze in order to discuss the influence of parameters related to lateral broadband fracturing (such as fractal parameters, reservoir parameters, and width of SRV) on flow behavior of LVWs in a tight oil reservoir. The modeling results show that the fractal parameters of fracture system have great effect on the fluid flow in LVWs, and LBF contributes to imbibition production.

Research progress in theory and green analytical application of chemical multi-way calibration

<p indent="0mm">The combination of chemical multi-way calibration and modern high-order instruments for accurate quantitative analysis in complex systems has a unique “second-order or higher-order advantage”. This analytical strategy meets the development needs of green analytical chemistry, and has been widely used in the fields of biology, medicine, food, environment, <italic>etc</italic>. This review introduces the latest research progress in theories and applications of chemical multi-way calibration, and the contribution of multi-way calibration methods to green analytical chemistry is emphasized. In the theory part, we focus on the multilinear models and their extensions, multi-way calibration algorithms with second-order or higher-order advantages, and some key issues in the use of multi-way calibration methods. In the application part, the representative applications of different high-order analytical instruments coupled with second-, third-, and fourth-order calibration methods in the past five years are systematically reviewed.

Evaluation of nonhazardous lime requirement estimation methods for Oregon soils

AbstractThe Shoemaker, McLean, and Pratt (SMP) buffer pH method has been used historically in Oregon for estimating lime requirement, however concerns regarding hazardous waste disposal have caused increased interest in nonhazardous methods. The objective was to compare nonhazardous lime requirement estimate (LRE) methods to the SMP method for predicting incubation lime requirement (LRi) for Oregon soils. Twenty‐four soils (pH ≤ 5.5) were incubated with seven rates of CaCO3 ranging from 0 to 22.4 Mg ha–1 and incubated for 90 d at 19 °C. Seven different LRE methods were evaluated and regressed against LRi for pH targets of 5.6, 6.0, and 6.4 using linear regression. The Sikora (r2 = .91–.93), modified Mehlich (r2 = .87–.90), and Sikora‐2 (r2 = .82–.93) buffer methods showed potential as alternatives to SMP (r2 = .91–.93) based on accuracy. A multilinear model combining clay content, KCl‐extractable Al, soil organic matter (SOM), extractable Mg, and pH1:2 also effectively predicted LRi (R2 = .97). The Moore–Sikora method (r2 = .89–.93) showed precision issues due to high replication variability. The single addition of Ca(OH)2 method (r2 = .67–.74) was less accurate than other evaluated methods. In conclusion, there were several viable non‐hazardous LRE method alternatives to the SMP buffer method for Oregon soils, based on accuracy and precision.

Numerical simulation of the damage progression of pultruded GFRP beam-to-column connections under monotonic and cyclic loads

This paper presents a numerical investigation on the behaviour of GFRP beam-to-column cleated connections, using stainless steel cleats. Finite element (FE) models were developed, using ABAQUS, simulating four different connection series, which had previously been object of experimental tests. The stainless steel materials were modelled with a built-in multi-linear isotropic hardening model, while the GFRP materials were modelled, via UMAT, with a damage propagation model previously proposed by the authors. The results show that these FE models are well able to predict the monotonic behaviour observed in the experiments, indicating very reasonable predictions of the connections’ strength and failure modes. The hysteretic behaviour of two connections was also simulated with these FE models and the results compared very well with their experimental counterparts. Moreover, for both monotonic and cyclic loading, the models were able to simulate the behaviour for deformation levels well above those required by the higher ductility class of Eurocode 8. Overall, these results show the feasibility of using these FE models in the design of FRP structures, reducing the need of experimental tests, to determine the monotonic and hysteretic behaviour of the connections, which can then be used as input in the full structure design models.

Finite element simulation of hardness testing with different material models

Finite element modelling is a modern and efficient way to perform stress-strain analyses on both simple components and complex structures. If the material properties are properly set, the software can also model the deformation of parts. A very important criterion of accurate finite element calculations is an appropriate material model. The stress–strain function recorded during the tensile testing of standard specimens shows that stress is not constant during deformation and so it can be specified with a function in finite element software. Often, in finite element modelling, a bilinear model is used to simplify this function. The aim of this paper is to show the difference between the bilinear model, the multilinear model (the model split into several sections the stress-strain curve) and the real function in the analysis of deformation in hardness testing. We also analyse the minimum density of meshing which still provides acceptable accuracy in hardness testing calculations.

Nonlinear extended blind end-member and abundance extraction for hyperspectral images

Hyperspectral images had become an essential tool in different application frameworks, such as mineral exploration, food inspection, and medical assessment, among others. However, the interpretability of these images involves an initial processing stage to model the optical interaction and analyze the spectral information. In this work, we study nonlinear unmixing of hyperspectral images by a multilinear mixture model (MMM). In this sense, we propose a nonlinear version of the extended blind end-member and abundance extraction (NEBEAE) method for blind unmixing, i.e. estimation of the end-members, their abundances, and the nonlinear interaction levels. In the problem formulation, we include a normalization step in the hyperspectral measurements for the end-members and abundances to improve robustness. The blind unmixing process can be separated into three estimation subproblems for each component in the model, which are solved by a cyclic coordinate descent algorithm and quadratic constrained optimizations. Each problem is mathematically formulated and derived to construct the general nonlinear iterative unmixing technique. We evaluated our proposal with synthetic and experimental datasets from the remote sensing literature (Cuprite, Urban and Pavia University scene datasets) and a biomedical hyperspectral imaging application. In the validation stage, we compared NEBEAE with three state-of-the-art methods to show its advantages in terms of precision and computational time.

IMG-14. Cognitive deficits and altered functional network organization in pediatric brain tumor patients

INTRODUCTION: Survivors of pediatric brain tumors experience significant cognitive deficits from their diagnosis and treatment. Mechanisms of cognitive injury are poorly understood, and predictors of long-term outcome are lacking. Large-scale, distributed brain systems provide a window into brain organization and function that may yield insight into these mechanisms and outcomes. Recent evidence suggests that systems-level changes are related to cognitive performance across the lifespan. Here, we evaluated brain network architecture, cognitive performance, and brain-behavior relationships in pediatric brain tumor patients. METHODS: Forty-nine patients (ages 7-18y.o.) with any brain tumor diagnosis underwent resting state functional Magnetic Resonance Imaging (rsfMRI) during regularly scheduled clinical visits. All patients were tested with the NIH Toolbox Cognition Battery. One-hundred thirty-nine age- and sex-matched typically developing children were used as controls. All data were processed to minimize artifactual sources of variance. Functional brain networks were created for each patient via rsfMRI data from 300 regions of interest that sample the whole brain. Multilinear models were implemented to examine brain-behavior relationships, while accounting for demographic and clinical factors. RESULTS: Functional network organization was significantly altered in patients compared to controls (p<0.001). Network organization was more affected in patients who received whole-brain radiation therapy than those who did not (t=2.52, p<0.015). Patients demonstrated significant impairments in multiple domains of cognitive performance, e.g. attention (p<0.0001). Weak relationships were found between cognitive performance and network organization, none of which survived multiple comparison correction. CONCLUSIONS: Brain network architecture is significantly altered in pediatric brain tumor patients. Whole-brain radiation was related to the largest changes. Most network and cognitive changes were significant with large effect sizes, yet brain-behavior relationships were weak. Our results suggest that systems-level changes in brain organization may provide insight into long-term changes in brain function in pediatric brain tumor patients.

Research on the Impact of Inventory Management Practice on Organizational Performance in Telecommunication Companies

Abstract: This research study examined the impact of inventory management on organizational performance in telecom companies. The study used a descriptive study design, and SPSS was used to conduct statistical analysis. In order to choose the samples, the researcher used both purposive and simplerandom sampling methods. Data for the study was collected from 130 respondents out of which 92 responded making the research valid. The relationships and hypothesis proposed in theconceptual framework were tested using multi-linear regression equation model. The research results indicate that higher levels of inventory management practice can lead to an enhanced competitive advantage and improved organizational performance. The study also accepted the hypothesis that was tested that inventory management and organizational performance have a significant positive relationship. Therefore, it is recommended that telecom organizations in Uganda should adopt Vendor management inventory, maintain good communication with the suppliers as this helps to eventually transfer inventory management responsibilities from the company’s procurement function to the vendors thus revamping organizational performance and increasing their competitiveness. They should also update their inventory management system in order to improve effectiveness by investing more money in the latest IT systems and utilizing sophisticated technologies such as purchasing fuel sensors for inventory monitoring and management so as to minimize on downtime and as well as cost reduction. The research study alsosuggests that management should take a more proactive approach to the problem of inappropriate inventory management strategies being used by constantly exposing its employees to training ona regular basis in order to strengthen their inventory management skills and allow them to understand current inventory management systems thus if implemented it can enable the companyin lowering costs by ensuring that stock records are maintained accurately and kept in order to prevent the risk of overstocking or understocking. Key words: Inventory management, Organizational Performance, Telecommunication companies

Kinematic Hardening Model Comparison of Square Hollow Section Under Cyclic Bending

This study compares the different linearity of the kinematic hardening model of the Square Hollow Section (SHS) under cyclic bending loading. Four specimens of a simple support beam cyclically tested in previous research are listed as hot-rolled, hot-finished, and two cold-formed. Using the bilinear, multilinear, and Chaboche models, each specimen is modeled in kinematic hardening. The variables or node sets for each linearity model are estimated using tensile test data, and Chaboche variables are obtained using the least-square fitting method. Each linearity model for each specimen is built-in FEA using a shell model. The numerical model applied the same cyclic loading history as the previous test. The numerical analysis comparison concluded that Chaboche and the multilinear kinematic model generate the expected result fitted to test hysteresis of cold-formed one and cold-formed 2 SHS, but the bilinear models are not fitted. Moreover, all kinematic models are not fit for the hot-rolled and hot-finished SHS compared to the test hysteresis. So, for hot-rolled and hot-finished SHS, the combined hardening is suggested; there is a possibility it is because of the lower yield ratio that both sections have. Overall, during a cyclic bending analysis of cold-formed SHS, multilinear or Chaboche models are preferable if the data is limited.

Application of Gap Metric to LADRC Design in Multilinear Model of SDR

The solid ducted rocket ramjet (SDR) system faces many disturbances in the process of operation, and the linear active disturbance rejection controller (LADRC) has been widely used in engineering to solve such problems. However, the SDR also has strong nonlinearity, which brings a great challenge to the application of the LADRC in the gas flow regulation of the SDR. And the problem of fast adjustment of the “compensation factor” is one of the main difficulties in LADRC. In this paper, under the LADRC frame, the gas generator system’s closed-loop stability of the SDR was analyzed and the range of compensation factors had been calculated, and then, the “gap factor” was introduced and the “cross-iteration” method was used to quickly map out the “compensation factor” in the multilinear model controller based on the variation of the zero-point position of the system and the gap metric between adjacent set points. This greatly simplifies the parameter tuning process of the LADRC when it was applied to strongly nonlinear systems. Finally, through the comparison of simulation with adaptive PI controller and model-assisted LADRC (M-LADRC), the results have shown that the control method designed in this paper can obtain satisfactory performance and has a good engineering application prospect.

Machine learning of material properties: Predictive and interpretable multilinear models.

Machine learning models can provide fast and accurate predictions of material properties but often lack transparency. Interpretability techniques can be used with black box solutions, or alternatively, models can be created that are directly interpretable. We revisit material datasets used in several works and demonstrate that simple linear combinations of nonlinear basis functions can be created, which have comparable accuracy to the kernel and neural network approaches originally used. Linear solutions can accurately predict the bandgap and formation energy of transparent conducting oxides, the spin states for transition metal complexes, and the formation energy for elpasolite structures. We demonstrate how linear solutions can provide interpretable predictive models and highlight the new insights that can be found when a model can be directly understood from its coefficients and functional form. Furthermore, we discuss how to recognize when intrinsically interpretable solutions may be the best route to interpretability.

Post-fire performance of bonding interface in explosion-welded stainless-clad bimetallic steel

Corrosion in structural steel has significantly adverse effects on the durability of steel structures. The use of stainless-clad bimetallic steel (SCBS) produced via the explosive welding compound method was suggested to mitigate these adverse effects of corrosion. The shear resistant properties of the bonding interface had noteworthy effects on the mechanical performance of SCBS. The steel structure in service was often affected by fire, which had lasting effects on the mechanical performance of the structural steel. An experiment was conducted to clarify the effects of the exposure temperature and cooling method on the shear resistant properties of the bonding interface of the SCBS. An apparent heat affected zone (HAZ) was observed in the explosive welding area. The metallographic structure in carbon steel comprised ferrite and pearlite. The HAZ in carbon steel was mainly composed of ferrite. The changes in the microstructure of SCBS for various exposure temperatures and cooling methods were discussed. After performing mechanical tests, load–displacement curves of the SCBS specimens were obtained. Some resistance parameters were selected to clarify the effects of the exposure temperature and cooling method on the shear resistant properties of the bonding interface of the SCBS. Based on the obtained experimental results, predictive equations were suggested to reveal the variation trend of the resistance parameters. After performing a comprehensive comparison of the experimental and predictive results, it was determined that the suggested multilinear model could be used to predict the shear resistant properties of the bonding interface of SCBS effectively.

Speed-Flow-Geometric Relationship for Urban Roads Network

Speed on the urban roads is significantly affected by the surrounding geometric and traffic parameters. Based on this fact, we ascertained the impact of the geometric and traffic parameters on the average travel speed of the urban roads network. Herein, 197 urban road segments in Johor (Malaysia) with diverse features were randomly selected. The average travel speed and volume of the traffic on these road segments were measured using the moving observer method (MOM). Meanwhile, these roads’ features were recorded via the direct visual inspection. Various geometric (density of the traffic calming speed, right-turn driveway, access, and right-turn) and cross-sectional (median, number of lanes, and side friction) parameters were considered. First, 14 multilinear models constructed via multilinear regression analysis were developed for traffic volume scenarios (in veh/h and pcu/h). Then, 10 models were adopted to evaluate the geometric parameters’ influence on the average travel speed for the selected roads. The results revealed a considerable impact of some geometric and traffic parameters on the average travel speed for the studied urban roads. Furthermore, the density of traffic calming speed, driveway, and intersection per 1 km of urban road segment one for each parameter was found to reduce the speed of the vehicles from 1.3 to 0.22 km/h. The combination of the road cross-section features such as median, number of lanes, and side friction strongly affected the observed speed variation. It is asserted that the developed model may facilitate the Malaysian urban roads network management to provide better traffic performance with higher mobility and safer roads design and planning, thereby offering a gateway toward sustainability.

Towards more realistic simulations of microstructural evolution in oxidic systems

High temperature oxidic systems are encountered in nature (magma chambers) and pyrometallurgical processes. In these systems, the solidification of the oxidic liquids influences the flow, the cooling after tapping, the viscosity and rheological behaviour within the reactor and also the freeze lining behaviour. On the mesoscale, the phase field concept has proved to be a very powerful tool for modeling crystallizing microstructures. However, application of the method to slag solidification is still challenging. In this work, we present a phase field model to simulate the faceted crystallization of Fe3O4 in a quaternary FeO–Fe2O3–Cu2O–SiO2 melt under different partial pressures of oxygen to solve certain problems encountered related to more realistic simulations in oxidic systems. The ratio of FeO/Fe2O3 at the upper boundary is in equilibrium with the oxygen fugacity of the atmosphere, while conserving Fe. Two-dimensional simulations are performed with different varying oxygen fugacity in the atmosphere. For the considered composition range, the growth velocities of the spinel crystals increase with decreasing oxygen fugacity. One of the focus points in creating more realistic phase field models is the incorporation of the thermodynamic driving forces in multicomponent multiphase-field models by coupling to thermodynamic databases. The first part of this work used a tabular method. However, as the number of components in the system increases, the number of thermodynamic data points also increases exponentially, and so do the computational and memory requirements. A possible solution for this might be the use of a canonical polyadic decomposition of the tensors containing the thermodynamic data. In this way, the huge tensors are approximated well by compact multilinear models or decompositions. This promising solution has been applied in the second part of this work on the same oxidic liquid-solid system.

Simple and Multivariate Linear Regression Models for pH Conversion Between Measurement Techniques

ABSTRACT Conversions of soil pH values between different methods are required for the standardization of data from international scientific literature, soil test reports and soil databases. All commonly employed matrices (H2O, 0.01 M CaCl2 and 1 M KCl) and relevant soil:solution ratios (1:1, 1:2.5, 1:4 and 1:5) used for pH measurement have yet to be compared using the same sample dataset. The combined effects of soil physiochemical properties on the accuracy and parsimony of soil pH conversions have also not been thoroughly examined. Therefore, the aim of this study was to investigate the mathematical relation between the most common methods of pH determination. A diverse set of soil samples (n = 57) dominated by low-activity clays were used to calibrate simple and multilinear pH conversion models. The combined effects of soil texture, carbon content and salinity on model parsimony and accuracy were critically evaluated. All pH determination methods were found to be highly reproducible and relatable to one another. It was found that simple linear equations are the most effective means of converting pH values (r2 = 0.77–0.97; RMSE = 0.12–0.32), considering the trade-off between model complexity and accuracy. Simple direct conversion factors are not deemed sufficiently accurate for the purpose of agricultural management recommendations.

Decoding ECoG signal into 3D hand translation using deep learning

Objective. Motor brain-computer interfaces (BCIs) are a promising technology that may enable motor-impaired people to interact with their environment. BCIs would potentially compensate for arm and hand function loss, which is the top priority for individuals with tetraplegia. Designing real-time and accurate BCI is crucial to make such devices useful, safe, and easy to use by patients in a real-life environment. Electrocorticography (ECoG)-based BCIs emerge as a good compromise between invasiveness of the recording device and good spatial and temporal resolution of the recorded signal. However, most ECoG signal decoders used to predict continuous hand movements are linear models. These models have a limited representational capacity and may fail to capture the relationship between ECoG signal features and continuous hand movements. Deep learning (DL) models, which are state-of-the-art in many problems, could be a solution to better capture this relationship. Approach. In this study, we tested several DL-based architectures to predict imagined 3D continuous hand translation using time-frequency features extracted from ECoG signals. The dataset used in the analysis is a part of a long-term clinical trial (ClinicalTrials.gov identifier: NCT02550522) and was acquired during a closed-loop experiment with a tetraplegic subject. The proposed architectures include multilayer perceptron, convolutional neural networks (CNNs), and long short-term memory networks (LSTM). The accuracy of the DL-based and multilinear models was compared offline using cosine similarity. Main results. Our results show that CNN-based architectures outperform the current state-of-the-art multilinear model. The best architecture exploited the spatial correlation between neighboring electrodes with CNN and benefited from the sequential character of the desired hand trajectory by using LSTMs. Overall, DL increased the average cosine similarity, compared to the multilinear model, by up to 60%, from 0.189 to 0.302 and from 0.157 to 0.249 for the left and right hand, respectively. Significance. This study shows that DL-based models could increase the accuracy of BCI systems in the case of 3D hand translation prediction in a tetraplegic subject.

An adaptive closed-loop ECoG decoder for long-term and stable bimanual control of an exoskeleton by a tetraplegic

Objective. The article aims at addressing 2 challenges to step motor brain-computer interface (BCI) out of laboratories: asynchronous control of complex bimanual effectors with large numbers of degrees of freedom, using chronic and safe recorders, and the decoding performance stability over time without frequent decoder recalibration. Approach. Closed-loop adaptive/incremental decoder training is one strategy to create a model stable over time. Adaptive decoders update their parameters with new incoming data, optimizing the model parameters in real time. It allows cross-session training with multiple recording conditions during closed loop BCI experiments. In the article, an adaptive tensor-based recursive exponentially weighted Markov-switching multi-linear model (REW-MSLM) decoder is proposed. REW-MSLM uses a mixture of expert (ME) architecture, mixing or switching independent decoders (experts) according to the probability estimated by a ‘gating’ model. A Hidden Markov model approach is employed as gating model to improve the decoding robustness and to provide strong idle state support. The ME architecture fits the multi-limb paradigm associating an expert to a particular limb or action. Main results. Asynchronous control of an exoskeleton by a tetraplegic patient using a chronically implanted epidural electrocorticography (EpiCoG) recorder is reported. The stable over a period of six months (without decoder recalibration) eight-dimensional alternative bimanual control of the exoskeleton and its virtual avatar is demonstrated. Significance. Based on the long-term (>36 months) chronic bilateral EpiCoG recordings in a tetraplegic (ClinicalTrials.gov, NCT02550522), we addressed the poorly explored field of asynchronous bimanual BCI. The new decoder was designed to meet to several challenges: the high-dimensional control of a complex effector in experiments closer to real-world behavior (point-to-point pursuit versus conventional center-out tasks), with the ability of the BCI system to act as a stand-alone device switching between idle and control states, and a stable performance over a long period of time without decoder recalibration.

Diffusion Tensor Imaging of the Knee to Predict Childhood Growth.

Background Accurate and precise methods to predict growth remain lacking. Diffusion tensor imaging (DTI) depicts the columnar structure of the physis and metaphyseal spongiosa and provides measures of tract volume and length that may help predict growth. Purpose To validate physeal DTI metrics as predictors of height velocity (1-year height gain from time of MRI examination) and total height gain (height gain from time of MRI examination until growth stops) and compare the prediction accuracy with bone age-based models. Materials and Methods Femoral DTI studies (b values = 0 and 600 sec/mm2; directions = 20) of healthy children who underwent MRI of the knee between February 2012 and December 2016 were retrospectively analyzed. Children with height measured at MRI and either 1 year later (height velocity) or after growth cessation (total height gain, mean = 34 months from MRI) were included. Physeal DTI tract volume and length were correlated with height velocity and total height gain. Multilinear regression was used to assess the potential of DTI metrics in the prediction of both parameters. Bland-Altman plots were used to compare root mean square error (RMSE) and bias in height prediction using DTI versus bone age methods. Results Eighty-nine children (mean age, 13 years ± 3 [SD]; 47 boys) had height velocity measured, and 70 (mean age, 14 years ± 1; 36 girls) had total height gain measured. Tract volumes correlated with height velocity (r2 = 0.49) and total height gain (r2 = 0.46) (P < .001 for both) after controlling for age and sex. Tract volume was the strongest predictor for height velocity and total height gain. An optimal multilinear model including tract volume improved prediction of height velocity (R2 = 0.63, RMSE = 1.7 cm) and total height gain (R2 = 0.59, RMSE = 1.8 cm) compared with bone age-based methods (height velocity: R2 = 0.32, RMSE = 2.9 cm; total height gain: R2 = 0.42, RMSE = 5.0 cm). Conclusion Models using tract volume derived from diffusion tensor imaging may perform better than bone age-based models in children for the prediction of height velocity and total height gain. © RSNA, 2022.