Identifying predictors of within-person variance in MRI-based brain volume estimates.

Adequate reliability of measurement is a precondition for investigating individual differences and age-related changes in brain structure. One approach to improve reliability is to identify and control for variables that are predictive of within-person variance. To this end, we applied both classical statistical methods and machine-learning-inspired approaches to structural magnetic resonance imaging (sMRI) data of six participants aged 24-31 years gathered at 40-50 occasions distributed over 6-8 months from the Day2day study. We explored the within-person associations between 21 variables covering physiological, affective, social, and environmental factors and global measures of brain volume estimated by VBM8 and FreeSurfer. Time since the first scan was reliably associated with Freesurfer estimates of grey matter volume and total cortex volume, in line with a rate of annual brain volume shrinkage of about 1 percent. For the same two structural measures, time of day also emerged as a reliable predictor with an estimated diurnal volume decrease of, again, about 1 percent. Furthermore, we found weak predictive evidence for the number of steps taken on the previous day and testosterone levels. The results suggest a need to control for time-of-day effects in sMRI research. In particular, we recommend that researchers interested in assessing longitudinal change in the context of intervention studies or longitudinal panels make sure that, at each measurement occasion, (a) a given participant is measured at the same time of day; (b) all participants are measured at about the same time of day. Furthermore, the potential effects of physical activity, including moderate amounts of aerobic exercise, and testosterone levels on MRI-based measures of brain structure deserve further investigation.

Using within-subject pattern classification to understand lifespan age differences in oscillatory mechanisms of working memory selection and maintenance.

In lifespan studies, large within-group heterogeneity with regard to behavioral and neuronal data is observed. This casts doubt on the validity of group-statistics-based approaches to understand age-related changes on cognitive and neural levels. Recent progress in brain-computer interface research demonstrates the potential of machine learning techniques to derive reliable person-specific models, representing brain behavior mappings. The present study now proposes a supervised learning approach to derive person-specific models for the identification and quantification of interindividual differences in oscillatory EEG responses related to working memory selection and maintenance mechanisms in a heterogeneous lifespan sample. EEG data were used to discriminate different levels of working memory load and the focus of visual attention. We demonstrate that our approach leads to person-specific models with better discrimination performance compared to classical person-nonspecific models. We show how these models can be interpreted both on an individual as well as on a group level. One of the key findings is that, with regard to the time dimension, the between-person variance of the obtained person-specific models is smaller in older than in younger adults. This is contrary to what we expected because of increased behavioral and neuronal heterogeneity in older adults.

A Comparison of Measures for Assessing Profile Similarity in Dyads.

Profile similarity measures are used to quantify the similarity of two sets of ratings on multiple variables. Yet, it remains unclear how different measures are distinct or overlap and what type of information they precisely convey, making it unclear what measures are best applied under varying circumstances. With this study, we aim to provide clarity with respect to how existing measures interrelate and provide recommendations for their use by comparing a wide range of profile similarity measures. We have taken four steps. First, we reviewed 88 similarity measures by applying them to multiple cross-sectional and intensive longitudinal data sets on emotional experience and retained 43 useful profile similarity measures after eliminating duplicates, complements, or measures that were unsuitable for the intended purpose. Second, we have clustered these 43 measures into similarly behaving groups, and found three general clusters: one cluster with difference measures, one cluster with product measures that could be split into four more nuanced groups and one miscellaneous cluster that could be split into two more nuanced groups. Third, we have interpreted what unifies these groups and their subgroups and what information they convey based on theory and formulas. Last, based on our findings, we discuss recommendations with respect to the choice of measure, propose to avoid using the Pearson correlation, and suggest to center profile items when stereotypical patterns threaten to confound the computation of similarity.

A comparison of different measures of the proportion of explained variance in multiply imputed data sets.

The proportion of explained variance is an important statistic in multiple regression for determining how well the outcome variable is predicted by the predictors. Earlier research on 20 different estimators for the proportion of explained variance, including the exact Olkin-Pratt estimator and the Ezekiel estimator, showed that the exact Olkin-Pratt estimator produced unbiased estimates, and was recommended as a default estimator. In the current study, the same 20 estimators were studied in incomplete data, with missing data being treated using multiple imputation. In earlier research on the proportion of explained variance in multiply imputed data sets, an estimator called R ̂ SP 2 $$ {\hat{R}}_{\mathrm{SP}}^2 $$ was shown to be the preferred pooled estimator for regular R 2 $$ {R}^2 $$ . For each of the 20 estimators in the current study, two pooled estimators were proposed: one where the estimator was the average across imputed data sets, and one where R ̂ SP 2 $$ {\hat{R}}_{\mathrm{SP}}^2 $$ was used as input for the calculation of the specific estimator. Simulations showed that estimates based on R ̂ SP 2 $$ {\hat{R}}_{\mathrm{SP}}^2 $$ performed best regarding bias and accuracy, and that the Ezekiel estimator was generally the least biased. However, none of the estimators were unbiased at all times, including the exact Olkin-Pratt estimator based on R ̂ SP 2 $$ {\hat{R}}_{\mathrm{SP}}^2 $$ .

Nocebo hyperalgesia and other expectancy-related factors in daily fibromyalgia pain: Combining experimental and electronic diary methods.

OBJECTIVE: Expectancies are known to shape pain experiences, but it remains unclear how different types of expectancies contribute to daily pain fluctuations in fibromyalgia. This combined experimental and diary study aims to provide insights into how experimentally-derived nocebo hyperalgesia and other, diary-derived, expectancy-related factors are associated with each other and with daily pain in fibromyalgia. METHODS: Forty-one female patients with fibromyalgia first participated in a lab procedure measuring nocebo hyperalgesia magnitude, then filled out an electronic diary 3 times a day over 3 weeks regarding the expectancy-related factors of pain expectancy, anxiety, optimism, and pain-catastrophizing thoughts, and current pain intensity. RESULTS: Our results indicate that experimentally-induced nocebo hyperalgesia was not significantly related to diary-assessed expectancy-related factors and did not predict daily fibromyalgia pain. Higher levels of the self-reported expectancy-related factors pain expectancy and pain catastrophizing, but not anxiety and optimism, predicted moment-to-moment pain increases in fibromyalgia, after controlling for current pain, moment-of-day and all other expectancy-related factors. CONCLUSION: Our exploratory research findings indicate that self-reported expectancy-related factors, particularly pain expectancy and pain catastrophizing, are potentially more relevant for predicting daily pain experience than experimentally-induced nocebo hyperalgesia. Further translation of nocebo hyperalgesia is needed from experimental to Ecological Momentary Assessment research. Our findings imply that targeting the decrease in pain expectancy and catastrophizing thoughts e.g., via Cognitive Behavioral Therapy, have potential for improving daily pain levels in fibromyalgia.

Outliers may not be automatically removed.

Researchers often remove outliers when comparing groups. It is well documented that the common practice of removing outliers within groups leads to inflated Type I error rates. However, it was recently argued by André (2022) that if outliers are instead removed across groups, Type I error rates are not inflated. The same study discusses that removing outliers across groups is a specific case of the more general concept of hypothesis-blind removal of outliers, which is consequently recommended. In this paper, I demonstrate that, contrary to this advice, hypothesis-blind outlier removal is problematic. Specifically, it almost always invalidates confidence intervals and biases estimates if there are group differences. It moreover inflates Type I error rates in certain situations, for example, when variances are unequal and data nonnormal. Consequently, a data point may not be removed solely because it is deemed an outlier, whether the procedure used is hypothesis-blind or hypothesis-aware. I conclude by recommending valid alternatives. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Social anxiety is related to reduced face gaze during a naturalistic social interaction.

BACKGROUND: Social anxiety has long been related to reduced eye contact, and this feature is seen as a causal and a maintaining factor of social anxiety disorder. The present research adds to the literature by investigating the relationship between social anxiety and visual avoidance of faces in a reciprocal face-to-face conversation, while taking into account two aspects of conversations as potential moderating factors: conversational role and level of intimacy. METHOD: Eighty-five female students (17-25 years) completed the Leibowitz Social Anxiety Scale and had a face-to-face getting-acquainted conversation with a female confederate. We alternated conversational role (talking versus listening) and manipulated intimacy of the topics (low versus high). Participants' gaze behavior was registered with Tobii eye-tracking glasses. Three dependent measures were extracted regarding fixations on the face of the confederate: total duration, proportion of fixations, and mean duration. RESULTS: The results revealed that higher levels of social anxiety were associated with reduced face gaze on all three measures. The relation with total fixation duration was stronger for low intimate topics. The relation with mean fixation duration was stronger during listening than during speaking. CONCLUSION: The results highlight the importance of studying gaze behavior in a naturalistic social interaction.

Electrophysiological markers for anticipatory processing of nocebo-augmented pain.

Nocebo effects on pain are widely thought to be driven by negative expectations. This suggests that anticipatory processing, or some other form of top-down cognitive activity prior to the experience of pain, takes place to form sensory-augmenting expectations. However, little is known about the neural markers of anticipatory processing for nocebo effects. In this event-related potential study on healthy participants (n = 42), we tested whether anticipatory processing for classically conditioned nocebo-augmented pain differed from pain without nocebo augmentation using stimulus preceding negativity (SPN), and Granger Causality (GC). SPN is a slow-wave ERP component thought to measure top-down processing, and GC is a multivariate time series analysis used to measure functional connectivity between brain regions. Fear of pain was assessed with the Fear of Pain Questionnaire-III and tested for correlation with SPN and GC metrics. We found evidence that both anticipatory processing measured with SPN and functional connectivity from frontal to temporoparietal brain regions measured with GC were increased for nocebo pain stimuli relative to control pain stimuli. Other GC node pairs did not yield significant effects, and a lag in the timing of nocebo pain stimuli limited interpretation of the results. No correlations with trait fear of pain measured after the conditioning procedure were detected, indicating that while differences in neural activity could be detected between the anticipation of nocebo and control pain trials, they likely were not related to fear. These results highlight the role that top-down processes play in augmenting sensory perception based on negative expectations before sensation occurs.

Gaussian Process Panel Modeling-Machine Learning Inspired Analysis of Longitudinal Panel Data.

In this article, we extend the Bayesian nonparametric regression method Gaussian Process Regression to the analysis of longitudinal panel data. We call this new approach Gaussian Process Panel Modeling (GPPM). GPPM provides great flexibility because of the large number of models it can represent. It allows classical statistical inference as well as machine learning inspired predictive modeling. GPPM offers frequentist and Bayesian inference without the need to resort to Markov chain Monte Carlo-based approximations, which makes the approach exact and fast. GPPMs are defined using the kernel-language, which can express many traditional modeling approaches for longitudinal data, such as linear structural equation models, multilevel models, or state-space models but also various commonly used machine learning approaches. As a result, GPPM is uniquely able to represent hybrid models combining traditional parametric longitudinal models and nonparametric machine learning models. In the present paper, we introduce GPPM and illustrate its utility through theoretical arguments as well as simulated and empirical data.