Boosting factor‐specific functional historical models for the detection of synchronization in bioelectrical signals

Comparing shallow, deep, and transfer learning in predicting joint moments in running

Joint moments are commonly calculated in biomechanics research and provide an indirect measure of muscular behaviors and joint loads. However, joint moments cannot be easily quantified clinically or in the field, primarily due to challenges measuring ground reaction forces outside the laboratory. The present study aimed to compare the accuracy of three different machine learning (ML) techniques - functional regression [ ML_fregress ], a deep neural network (DNN) built from scratch [ ML_DNN ], and transfer learning [ ML_TL ], in predicting joint moments during running. Data for this study came from an open-source dataset and two studies on running with and without external loads. Three-dimensional (3D) joint moments of the hip, knee, and ankle, were derived using inverse dynamics. 3D joint angle, velocity, and acceleration of the three joints served as predictors for each of the three ML techniques. Prediction performance was generally the best using ML_DNN, and the worse using ML_fregress. Absolute predictive performance was the best for sagittal plane moments, which ranged from a RMSE of 0.16 Nm/kg at the ankle using ML_DNN, to a RMSE of 0.49Nm/kg at the knee using ML_fregress. ML_DNN resulted in the greatest improvement in relative prediction performance (relRMSE) by 20% compared to ML_fregress for the ankle adduction-abduction moment. DNN with or without transfer learning was superior in predicting joint moments using kinematic inputs compared to functional regression. Synergizing ML with kinematic inputs has the potential to solve the constraints of obtaining high fidelity biomechanics data normally only possible during laboratory studies.

Functional Data Analysis of Dynamic PET Data

One application of positron emission tomography (PET), a nuclear imaging technique, in neuroscience involves in vivo estimation of the density of various proteins (often, neuroreceptors) in the brain. PET scanning begins with the injection of a radiolabeled tracer that binds preferentially to the target protein; tracer molecules are then continuously delivered to the brain via the bloodstream. By detecting the radioactive decay of the tracer over time, dynamic PET data are constructed to reflect the concentration of the target protein in the brain at each time. The fundamental problem in the analysis of dynamic PET data involves estimating the impulse response function (IRF), which is necessary for describing the binding behavior of the injected radiotracer. Virtually all existing methods have three common aspects: summarizing the entire IRF with a single scalar measure; modeling each subject separately; and the imposition of parametric restrictions on the IRF. In contrast, we propose a functional data analytic approach that regards each subject's IRF as the basic analysis unit, models multiple subjects simultaneously, and estimates the IRF nonparametrically. We pose our model as a linear mixed effect model in which population level fixed effects and subject-specific random effects are expanded using a B-spline basis. Shrinkage and roughness penalties are incorporated in the model to enforce identifiability and smoothness of the estimated curves, respectively, while monotonicity and non-negativity constraints impose biological information on estimates. We illustrate this approach by applying it to clinical PET data with subjects belonging to three diagnosic groups. We explore differences among groups by means of pointwise confidence intervals of the estimated mean curves based on bootstrap samples.

The Emotion Process: Event Appraisal and Component Differentiation

Much emotion research has focused on the end result of the emotion process, categorical emotions, as reported by the protagonist or diagnosed by the researcher, with the aim of differentiating these discrete states. In contrast, this review concentrates on the emotion process itself by examining how ( a) elicitation, or the appraisal of events, leads to ( b) differentiation, in particular, action tendencies accompanied by physiological responses and manifested in facial, vocal, and gestural expressions, before ( c) conscious representation or experience of these changes (feeling) and ( d) categorizing and labeling these changes according to the semantic profiles of emotion words. The review focuses on empirical, particularly experimental, studies from emotion research and neighboring domains that contribute to a better understanding of the unfolding emotion process and the underlying mechanisms, including the interactions among emotion components.

Brain Networks, Emotion Components, and Appraised Relevance

Modeling emotion processes remains a conceptual and methodological challenge in affective sciences. In responding to the other target articles in this special section on “Emotion and the Brain” and the comments on our article, we address the issue of potentially separate brain networks subserving the functions of the different emotion components. In particular, we discuss the suggested role of component synchronization in producing information integration for the dynamic emergence of a coherent emotion process, as well as the links between incentive salience (“wanting”) and concern-relevance in the elicitation of emotion.