Phenoscreening: a developmental approach to research domain criteria-motivated sampling.

BACKGROUND: To advance early identification efforts, we must detect and characterize neurodevelopmental sequelae of risk among population-based samples early in development. However, variability across the typical-to-atypical continuum and heterogeneity within and across early emerging psychiatric/neurodevelopmental disorders represent fundamental challenges to overcome. Identifying multidimensionally determined profiles of risk, agnostic to DSM categories, via data-driven computational approaches represents an avenue to improve early identification of risk. METHODS: Factor mixture modeling (FMM) was used to identify subgroups and characterize phenotypic risk profiles, derived from multiple parent-report measures of typical and atypical behaviors common to autism spectrum disorder, in a community-based sample of 17- to 25-month-old toddlers (n = 1,570). To examine the utility of risk profile classification, a subsample of toddlers (n = 107) was assessed on a distal, independent outcome examining internalizing, externalizing, and dysregulation at approximately 30 months. RESULTS: FMM results identified five asymmetrically sized subgroups. The putative high- and moderate-risk groups comprised 6% of the sample. Follow-up analyses corroborated the utility of the risk profile classification; the high-, moderate-, and low-risk groups were differentially stratified (i.e., HR > moderate-risk > LR) on outcome measures and comparison of high- and low-risk groups revealed large effect sizes for internalizing (d = 0.83), externalizing (d = 1.39), and dysregulation (d = 1.19). CONCLUSIONS: This data-driven approach yielded five subgroups of toddlers, the utility of which was corroborated by later outcomes. Data-driven approaches, leveraging multiple developmentally appropriate dimensional RDoC constructs, hold promise for future efforts aimed toward early identification of at-risk-phenotypes for a variety of early emerging neurodevelopmental disorders.

Robust nonparametric tests of general linear model coefficients: A comparison of permutation methods and test statistics.

Statistical inference in neuroimaging research often involves testing the significance of regression coefficients in a general linear model. In many applications, the researcher assumes a model of the form Y=α+Xß+Zγ+ε, where Y is the observed brain signal, and X and Z contain explanatory variables that are thought to be related to the brain signal. The goal is to test the null hypothesis H0:ß=0 with the nuisance parameters γ included in the model. Several nonparametric (permutation) methods have been proposed for this problem, and each method uses some variant of the F ratio as the test statistic. However, recent research suggests that the F ratio can produce invalid permutation tests of H0:ß=0 when the ε terms are heteroscedastic (i.e., have non-constant variance), which can occur for a variety of reasons. This study compares the classic F test statistic to the robust W (Wald) test statistic using eight different permutation methods. The results reveal that permutation tests using the F ratio can produce accurate results when the errors are homoscedastic, but high false positive rates when the errors are heteroscedastic. In contrast, permutation tests using the W test statistic produced valid results when the errors were homoscedastic, and asymptotically valid results when the errors were heteroscedastic. In the situation with homoscedastic errors, permutation tests using the W statistic showed slightly reduced power compared to the F statistic, but the difference disappeared as the sample size n increased. Consequently, the W test statistic is recommended for robust nonparametric hypothesis tests of regression coefficients in neuroimaging research.

Exploring individual and group differences in latent brain networks using cross-validated simultaneous component analysis.

Component models such as PCA and ICA are often used to reduce neuroimaging data into a smaller number of components, which are thought to reflect latent brain networks. When data from multiple subjects are available, the components are typically estimated simultaneously (i.e., for all subjects combined) using either tensor ICA or group ICA. As we demonstrate in this paper, neither of these approaches is ideal if one hopes to find latent brain networks that cross-validate to new samples of data. Specifically, we note that the tensor ICA model is too rigid to capture real-world heterogeneity in the component time courses, whereas the group ICA approach is too flexible to uniquely identify latent brain networks. For multi-subject component analysis, we recommend comparing a hierarchy of simultaneous component analysis (SCA) models. Our proposed model hierarchy includes a flexible variant of the SCA framework (the Parafac2 model), which is able to both (i) model heterogeneity in the component time courses, and (ii) uniquely identify latent brain networks. Furthermore, we propose cross-validation methods to tune the relevant model parameters, which reduces the potential of over-fitting the observed data. Using simulated and real data examples, we demonstrate the benefits of the proposed approach for finding credible components that reveal interpretable individual and group differences in latent brain networks.

Estimating latent trends in multivariate longitudinal data via Parafac2 with functional and structural constraints.

Longitudinal data are inherently multimode in the sense that such data are often collected across multiple modes of variation, for example, time × variables × subjects. In many longitudinal studies, multiple variables are collected to measure some latent construct(s) of interest. In such cases, the goal is to understand temporal trends in the latent variables, as well as individual differences in the trends. Multimode component analysis models provide a powerful framework for discovering latent trends in longitudinal data. However, classic implementations of multimode models do not take into consideration functional information (i.e., the temporal sequence of the collected data) or structural information (i.e., which variables load onto which latent factors) about the study design. In this paper, we reveal how functional and structural constraints can be imposed in multimode models (Parafac and Parafac2) in order to elucidate trends in longitudinal data. As a motivating example, we consider a longitudinal study on per capita alcohol consumption trends conducted from 1970 to 2013 by the U.S. National Institute on Alcohol Abuse and Alcoholism. We demonstrate how functional and structural information about the study design can be incorporated into the Parafac and Parafac2 alternating least squares algorithms to understand temporal and regional trends in three latent constructs: beer consumption, spirits consumption, and wine consumption. Our results reveal that Americans consume more than the recommended amount of alcohol, and total alcohol consumption trends show no signs of decreasing in the last decade.

Computing the real solutions of Fleishman's equations for simulating non-normal data.

Fleishman's power method is frequently used to simulate non-normal data with a desired skewness and kurtosis. Fleishman's method requires solving a system of nonlinear equations to find the third-order polynomial weights that transform a standard normal variable into a non-normal variable with desired moments. Most users of the power method seem unaware that Fleishman's equations have multiple solutions for typical combinations of skewness and kurtosis. Furthermore, researchers lack a simple method for exploring the multiple solutions of Fleishman's equations, so most applications only consider a single solution. In this paper, we propose novel methods for finding all real-valued solutions of Fleishman's equations. Additionally, we characterize the solutions in terms of differences in higher order moments. Our theoretical analysis of the power method reveals that there typically exists two solutions of Fleishman's equations that have noteworthy differences in higher order moments. Using simulated examples, we demonstrate that these differences can have remarkable effects on the shape of the non-normal distribution, as well as the sampling distributions of statistics calculated from the data. Some considerations for choosing a solution are discussed, and some recommendations for improved reporting standards are provided.

Normative cervical spine kinematics of a circumduction task.

Neck pain is a prevalent condition and clinical examination techniques are limited and unable to assess out-of-plane motion. Recent works investigating cervical kinematics during neck circumduction (NC), a dynamic 3D task, has shown the ability to discern those with and without neck pain. The purposes of this study were to establish 1) confidence and prediction intervals of head-to-torso kinematics during NC in a healthy cohort, 2) a baseline summative metric to quantify the duration and magnitude of deviations outside the prediction interval, and 3) the reliability of NC. Thirty-nine participants (25.6 ± 6.3 years, 19F/20M) without neck pain completed left and right NC. A two-way smoothing spline analysis of variance was utilized to determine the mean-fitted values and 90% confidence and prediction intervals for NC. A standardized effect size was calculated and aggregated across all axes (Delta RMSD aggregate), as a summative metric of motion quality. Confidence and prediction intervals were comparable for left and right NC and demonstrated excellent reliability. The average sum of the Delta RMSD aggregate was 2.76 ± 0.55 for left NC and 2.74 ± 0.63 for right NC. The results of this study demonstrate the feasibility of utilizing normative intervals of a NC task to assess head-to-torso kinematics.

The temporal course of over-generalized conditioned threat expectancies in posttraumatic stress disorder.

One key conditioning abnormality in posttraumatic stress disorder (PTSD) is heightened generalization of fear from a conditioned danger-cue (CS+) to similarly appearing safe stimuli. The present work represents the first effort to track the time-course of heightened generalization in PTSD with the prediction of heightened PTSD-related over-generalization in earlier but not later trials. This prediction derives from past discriminative fear-conditioning studies providing incidental evidence that over-generalization in PTSD may be reduced with sufficient learning trials. In the current study, we re-analyzed previously published conditioned fear-generalization data (Kaczkurkin et al., 2017) including combat veterans with PTSD (n = 15) or subthreshold PTSD (SubPTSD: n = 18), and trauma controls (TC: n = 19). This re-analysis aimed to identify the trial-by-trial course of group differences in generalized perceived risk across three classes of safe generalization stimuli (GSs) parametrically varying in similarity to a CS+ paired with shock. Those with PTSD and SubPTSD, relative to TC, displayed significantly elevated generalization to all GSs combined in early but not late generalization trials. Additionally, over-generalization in PTSD and SubPTSD persisted across trials to a greater extent for classes of GSs bearing higher resemblance to CS+. Such results suggest that PTSD-related over-generalization of conditioned threat expectancies can be reduced with sufficient exposure to unreinforced GSs and accentuate the importance of analyzing trial-by-trial changes when assessing over-generalization in clinical populations.

Validation of an automated shape-matching algorithm for biplane radiographic spine osteokinematics and radiostereometric analysis error quantification.

Biplane radiography and associated shape-matching provides non-invasive, dynamic, 3D osteo- and arthrokinematic analysis. Due to the complexity of data acquisition, each system should be validated for the anatomy of interest. The purpose of this study was to assess our system's acquisition methods and validate a custom, automated 2D/3D shape-matching algorithm relative to radiostereometric analysis (RSA) for the cervical and lumbar spine. Additionally, two sources of RSA error were examined via a Monte Carlo simulation: 1) static bead centroid identification and 2) dynamic bead tracking error. Tantalum beads were implanted into a cadaver for RSA and cervical and lumbar spine flexion and lateral bending were passively simulated. A bead centroid identification reliability analysis was performed and a vertebral validation block was used to determine bead tracking accuracy. Our system's overall root mean square error (RMSE) for the cervical spine ranged between 0.21-0.49mm and 0.42-1.80° and the lumbar spine ranged between 0.35-1.17mm and 0.49-1.06°. The RMSE associated with RSA ranged between 0.14-0.69mm and 0.96-2.33° for bead centroid identification and 0.25-1.19mm and 1.69-4.06° for dynamic bead tracking. The results of this study demonstrate our system's ability to accurately quantify segmental spine motion. Additionally, RSA errors should be considered when interpreting biplane validation results.

Color, Music, and Emotion: Bach to the Blues.

When people make cross-modal matches from classical music to colors, they choose colors whose emotional associations fit the emotional associations of the music, supporting the emotional mediation hypothesis. We further explored this result with a large, diverse sample of 34 musical excerpts from different genres, including Blues, Salsa, Heavy metal, and many others, a broad sample of 10 emotion-related rating scales, and a large range of 15 rated music-perceptual features. We found systematic music-to-color associations between perceptual features of the music and perceptual dimensions of the colors chosen as going best/worst with the music (e.g., loud, punchy, distorted music was generally associated with darker, redder, more saturated colors). However, these associations were also consistent with emotional mediation (e.g., agitated-sounding music was associated with agitated-looking colors). Indeed, partialling out the variance due to emotional content eliminated all significant cross-modal correlations between lower level perceptual features. Parallel factor analysis (Parafac, a type of factor analysis that encompasses individual differences) revealed two latent affective factors- arousal and valence -which mediated lower level correspondences in music-to-color associations. Participants thus appear to match music to colors primarily in terms of common, mediating emotional associations.

Influence of proximal trunk borne load on lower limb countermovement joint dynamics.

Vertical jumping involves coordinating the temporal sequencing of angular motion, moment, and power across multiple joints. Studying the biomechanical coordination strategies that differentiates loaded from unloaded vertical jumping may better inform training prescription for athletes needing to jump with load. Common multivariate methods (e.g. Principal Components Analysis) cannot quantify coordination in a dataset with more than two modes. This study aimed to identify coordinative factors across four modes of variation using Parallel Factor (Parafac2) analysis, which may differentiate unloaded (body weight [BW]) from loaded (BW + 20% BW) countermovement jump (CMJ). Thirty-one participants performed unloaded and loaded CMJ. Three-dimensional motion capture with force plate analysis was performed. Inverse dynamics was used to quantify sagittal plane joint angle, velocity, moment, and joint power across the ankle, knee, and hip. The four-mode data were as follows: Mode A was jump cycle (101 cycle points), mode B was participant (31 participants by two load), mode C was joint (two sides by three joints), and mode D was variable (angle, velocity, moment, power). Three factors were extracted, which explained 95.1% of the data's variance. Only factors one (P = 0.001) and three (P < 0.001) significantly differentiated loaded from unloaded jumping. The body augmented hip-dominant at the start, and both hip and ankle dominant behaviors at the end of the ascending phase of the CMJ, but kept knee-dominant behavior invariant, when jumping with a 20% BW load. By studying the variation across all data modes, Parafac2 provides a holistic method of studying jumping coordination.

Predicting Perceived Disfigurement from Facial Function in Patients with Unilateral Paralysis.

BACKGROUND: Clinical rating tools such as the electronic, clinician-graded facial function (eFACE) scale provide detailed information about aspects of facial functioning relevant to the assessment and treatment of facial paralysis. Past research has established that eFACE scores significantly relate to expert ratings of facial disfigurement. However, no studies have examined the extent to which eFACE scores relate to casual observers' perceptions of disfigurement in facial paralysis. METHODS: Casual observers (n = 539) were recruited at the 2016 Minnesota State Fair, and were shown short videos of facial expressions made by patients (n = 61) with unilateral facial paralysis. Observer ratings of disfigurement were recorded and related to eFACE scores (total and subscores) using mixed-effect regression models. RESULTS: Patients' eFACE scores were significantly related to observers' disfigurement ratings, such that improved function (as indicated by a higher eFACE score) corresponded to a decreased perception of disfigurement. The resting face of patients, their total movement capability, and their involuntary movement through synkinesis all played a significant role in predicting the casual observers' ratings. CONCLUSIONS: The results establish a clear connection between clinician eFACE ratings of facial function and casual observer judgments of disfigurement. In addition, the findings provide insight into which clinical aspects of facial dysfunction are most salient for casual observers' perceptions of disfigurement. Such insights can help both patients and clinicians better understand the expected social implications of different clinical aspects of facial dysfunction.

The effect of glenohumeral plane of elevation on supraspinatus subacromial proximity.

Shoulder pain is a common clinical problem affecting most individuals in their lifetime. Despite the high prevalence of rotator cuff pathology in these individuals, the pathogenesis of rotator cuff disease remains unclear. Position and motion related mechanisms of rotator cuff disease are often proposed, but poorly understood. The purpose of this study was to determine the impact of systematically altering glenohumeral plane on subacromial proximities across arm elevation as measures of tendon compression risk. Three-dimensional models of the humerus, scapula, coracoacromial ligament, and supraspinatus were reconstructed from MRIs in 20 subjects. Glenohumeral elevation was imposed on the humeral and supraspinatus tendon models for three glenohumeral planes, which were chosen to represent flexion, scapular plane abduction, and abduction based on average values from a previous study of asymptomatic individuals. Subacromial proximity was quantified as the minimum distance between the supraspinatus tendon and coracoacromial arch (acromion and coracoacromial ligament), the surface area of the supraspinatus tendon within 2 mm proximity to the coracoacromial arch, and the volume of intersection between the supraspinatus tendon and coracoacromial arch. The lowest modeled subacromial supraspinatus compression measures occurred during flexion at lower angles of elevation. This finding was consistent across all three measures of subacromial proximity. Knowledge of this range of reduced risk may be useful to inform future studies related to patient education and ergonomic design to prevent the development of shoulder pain and dysfunction.

Dynamic properties of successful smiles.

Facial expression of emotion is a foundational aspect of social interaction and nonverbal communication. In this study, we use a computer-animated 3D facial tool to investigate how dynamic properties of a smile are perceived. We created smile animations where we systematically manipulated the smile's angle, extent, dental show, and dynamic symmetry. Then we asked a diverse sample of 802 participants to rate the smiles in terms of their effectiveness, genuineness, pleasantness, and perceived emotional intent. We define a "successful smile" as one that is rated effective, genuine, and pleasant in the colloquial sense of these words. We found that a successful smile can be expressed via a variety of different spatiotemporal trajectories, involving an intricate balance of mouth angle, smile extent, and dental show combined with dynamic symmetry. These findings have broad applications in a variety of areas, such as facial reanimation surgery, rehabilitation, computer graphics, and psychology.

Smoothing spline analysis of variance models: A new tool for the analysis of cyclic biomechanical data.

Cyclic biomechanical data are commonplace in orthopedic, rehabilitation, and sports research, where the goal is to understand and compare biomechanical differences between experimental conditions and/or subject populations. A common approach to analyzing cyclic biomechanical data involves averaging the biomechanical signals across cycle replications, and then comparing mean differences at specific points of the cycle. This pointwise analysis approach ignores the functional nature of the data, which can hinder one׳s ability to find subtle differences between experimental conditions and/or subject populations. To overcome this limitation, we propose using mixed-effects smoothing spline analysis of variance (SSANOVA) to analyze differences in cyclic biomechanical data. The SSANOVA framework makes it possible to decompose the estimated function into the portion that is common across groups (i.e., the average cycle, AC) and the portion that differs across groups (i.e., the contrast cycle, CC). By partitioning the signal in such a manner, we can obtain estimates of the CC differences (CCDs), which are the functions directly describing group differences in the cyclic biomechanical data. Using both simulated and experimental data, we illustrate the benefits of using SSANOVA models to analyze differences in noisy biomechanical (gait) signals collected from multiple locations (joints) of subjects participating in different experimental conditions. Using Bayesian confidence intervals, the SSANOVA results can be used in clinical and research settings to reliably quantify biomechanical differences between experimental conditions and/or subject populations.

Habitual wearers of colored lenses adapt more rapidly to the color changes the lenses produce.

The visual system continuously adapts to the environment, allowing it to perform optimally in a changing visual world. One large change occurs every time one takes off or puts on a pair of spectacles. It would be advantageous for the visual system to learn to adapt particularly rapidly to such large, commonly occurring events, but whether it can do so remains unknown. Here, we tested whether people who routinely wear spectacles with colored lenses increase how rapidly they adapt to the color shifts their lenses produce. Adaptation to a global color shift causes the appearance of a test color to change. We measured changes in the color that appeared "unique yellow", that is neither reddish nor greenish, as subjects donned and removed their spectacles. Nine habitual wearers and nine age-matched control subjects judged the color of a small monochromatic test light presented with a large, uniform, whitish surround every 5s. Red lenses shifted unique yellow to more reddish colors (longer wavelengths), and greenish lenses shifted it to more greenish colors (shorter wavelengths), consistent with adaptation "normalizing" the appearance of the world. In controls, the time course of this adaptation contained a large, rapid component and a smaller gradual one, in agreement with prior results. Critically, in habitual wearers the rapid component was significantly larger, and the gradual component significantly smaller than in controls. The total amount of adaptation was also larger in habitual wearers than in controls. These data suggest strongly that the visual system adapts with increasing rapidity and strength as environments are encountered repeatedly over time. An additional unexpected finding was that baseline unique yellow shifted in a direction opposite to that produced by the habitually worn lenses. Overall, our results represent one of the first formal reports that adjusting to putting on or taking off spectacles becomes easier over time, and may have important implications for clinical management.

Bootstrap Enhanced Penalized Regression for Variable Selection with Neuroimaging Data.

Recent advances in fMRI research highlight the use of multivariate methods for examining whole-brain connectivity. Complementary data-driven methods are needed for determining the subset of predictors related to individual differences. Although commonly used for this purpose, ordinary least squares (OLS) regression may not be ideal due to multi-collinearity and over-fitting issues. Penalized regression is a promising and underutilized alternative to OLS regression. In this paper, we propose a nonparametric bootstrap quantile (QNT) approach for variable selection with neuroimaging data. We use real and simulated data, as well as annotated R code, to demonstrate the benefits of our proposed method. Our results illustrate the practical potential of our proposed bootstrap QNT approach. Our real data example demonstrates how our method can be used to relate individual differences in neural network connectivity with an externalizing personality measure. Also, our simulation results reveal that the QNT method is effective under a variety of data conditions. Penalized regression yields more stable estimates and sparser models than OLS regression in situations with large numbers of highly correlated neural predictors. Our results demonstrate that penalized regression is a promising method for examining associations between neural predictors and clinically relevant traits or behaviors. These findings have important implications for the growing field of functional connectivity research, where multivariate methods produce numerous, highly correlated brain networks.

The special sign indeterminacy of the direct-fitting Parafac2 model: some implications, cautions, and recommendations for simultaneous component analysis.

Parafac2 is the most flexible Simultaneous Component Analysis (SCA) model that produces an essentially unique solution. In this paper, we discuss how Parafac2's special sign indeterminacy affects applications of SCA, and we reveal how an external criterion variable can be used to ensure that estimated Parafac2 weights are meaningfully signed across the levels of the nesting mode. We present an example with real data from clinical psychology that illustrates the importance of Parafac2's special sign indeterminacy, as well as the effectiveness of our proposed solution. We also discuss the implications of our results for general applications of SCA.

A critique of Tensor Probabilistic Independent Component Analysis: implications and recommendations for multi-subject fMRI data analysis.

Tensor Probabilistic Independent Component Analysis (TPICA) is a popular tool for analyzing multi-subject fMRI data (voxels×time×subjects) because of TPICA's supposed robustness. In this paper, we show that TPICA is not as robust as its authors claim. Specifically, we discuss why TPICA's overall objective is questionable, and we present some flaws related to the iterative nature of the TPICA algorithm. To demonstrate the relevance of these issues, we present a simulation study that compares TPICA versus Parallel Factor Analysis (Parafac) for analyzing simulated multi-subject fMRI data. Our simulation results demonstrate that TPICA produces a systematic bias that increases with the spatial correlation between the true components, and that the quality of the TPICA solution depends on the chosen ICA algorithm and iteration scheme. Thus, TPICA is not robust to small-to-moderate deviations from the model's spatial independence assumption. In contrast, Parafac produces unbiased estimates regardless of the spatial correlation between the true components, and Parafac with orthogonality-constrained voxel maps produces smaller biases than TPICA when the true voxel maps are moderately correlated. As a result, Parafac should be preferred for the analysis multi-subject fMRI data where the underlying components may have spatially overlapping voxel activation patterns.

Analyzing individual and group differences in multijoint multiwaveform gait data using the Parafac2 model.

Locomotion research often involves analyzing multiwaveform data (e.g., velocities, accelerations, etc.) from various body locations (e.g., knees, ankles, etc.) of several subjects. Therefore, some multivariate technique such as principal component analysis is often used to examine interrelationships between the many correlated waveforms. Despite its extensive use in locomotion research, principal component analysis is for two-mode data, whereas locomotion data are typically collected in higher mode form. In this paper, we present the benefits of analyzing four-mode locomotion data (subjects × time × joints × waveforms) using the Parafac2 model, which is a component model designed for analyzing variation in multimode data. Using bilateral hip, knee, and ankle angular displacement, velocity, and acceleration waveforms, we demonstrate Parafac2's ability to produce interpretable components describing (i) the fundamental patterns of variation in lower limb angular kinematics during healthy walking and (ii) the fundamental differences between normal and atypical subjects' multijoint multiwaveform locomotive patterns. Also, we illustrate how Parafac2 makes it possible to determine which waveforms best characterize the individual and/or group differences captured by each component. Our results indicate that different waveforms should be used for different purposes, confirming the need for the holistic analysis of multijoint multiwaveform locomotion data, particularly when investigating atypical motion patterns.

Parallel Factor Analysis of gait waveform data: A multimode extension of Principal Component Analysis.

Gait data are typically collected in multivariate form, so some multivariate analysis is often used to understand interrelationships between observed data. Principal Component Analysis (PCA), a data reduction technique for correlated multivariate data, has been widely applied by gait analysts to investigate patterns of association in gait waveform data (e.g., interrelationships between joint angle waveforms from different subjects and/or joints). Despite its widespread use in gait analysis, PCA is for two-mode data, whereas gait data are often collected in higher-mode form. In this paper, we present the benefits of analyzing gait data via Parallel Factor Analysis (Parafac), which is a component analysis model designed for three- or higher-mode data. Using three-mode joint angle waveform data (subjects×time×joints), we demonstrate Parafac's ability to (a) determine interpretable components revealing the primary interrelationships between lower-limb joints in healthy gait and (b) identify interpretable components revealing the fundamental differences between normal and perturbed subjects' gait patterns across multiple joints. Our results offer evidence of the complex interconnections that exist between lower-limb joints and limb segments in both normal and abnormal gaits, confirming the need for the simultaneous analysis of multi-joint gait waveform data (especially when studying perturbed gait patterns).