Reproducibility of functional network metrics and network structure: A comparison of task-related BOLD, resting ASL with BOLD contrast, and resting cerebral blood flow

Network analysis is an emerging approach to functional connectivity in which the brain is construed as a graph and its connectivity and information processing estimated by mathematical characterizations of graphs. There has been little to no work examining the reproducibility of network metrics derived from different types of functional magnetic resonance imaging (fMRI) data (e.g., resting vs. task related, or pulse sequences other than standard blood oxygen level dependent [BOLD] data) or of measures of network structure at levels other than summary statistics. Here, we take up these questions, comparing the reproducibility of graphs derived from resting arterial spin-labeling perfusion fMRI with those derived from BOLD scans collected while the participant was performing a task. We also examine the reproducibility of the anatomical connectivity implied by the graph by investigating test–retest consistency of the graphs’ edges. We compare two measures of graph-edge consistency both within versus between subjects and across data types. We find a dissociation in the reproducibility of network metrics, with metrics from resting data most reproducible at lower frequencies and metrics from task-related data most reproducible at higher frequencies; that same dissociation is not recapitulated, however, in network structure, for which the task-related data are most consistent at all frequencies. Implications for the practice of network analysis are discussed.     

Internal and external control: a two-factor model of amplitude change of event-related potentials

The present paper critically examines the contributions of Event-Related Potential (ERP) measures in mental chronometry research. It is argued that amplitude variations in ERP components may provide valuable information regarding the intensity and timing of information processes, and that these amplitude changes are related to energetical rather than to computational processes. It is also suggested that amplitude variations of ERP components in visual discrimination and selective attention tasks are caused by two different processing modes, denoted as external and internal control that are associated with different neural structures. It is further assumed that these two control systems converge upon thalamic neurons that regulate the sensory input to cortex, and that the direction of sustained ERP amplitude changes reflects which system is dominant. Recent ERP studies have shown that the effects of task variables related to motor control are manifested in a surprisingly early phase of the ERP waveform, and that these effects overlap in time with the effects of task variables related to input control. These findings suggest that at least in visual discrimination and selective attention tasks external and internal modes of processing may be activated in parallel.     

Biased processing of sad faces: An ERP marker candidate for depression susceptibility

Depression has been associated with task-relevant increased attention toward negative information, reduced attention toward positive information, or reduced inhibition of task-irrelevant negative information. This study employed behavioural and psychophysiological measures (event-related potentials; ERP) to examine whether groups with risk factors for depression (past depression, current dysphoria) would show attentional biases or inhibitory deficits related to viewing facial expressions. In oddball task blocks, young adult participants responded to an infrequently presented target emotion (e.g., sad) and inhibited responses to an infrequently presented distracter emotion (e.g., happy) in the context of frequently presented neutral stimuli. Previous depression was uniquely associated with greater P3 ERP amplitude following sad targets, reflecting a selective attention bias. Also, dysphoric individuals less effectively inhibited responses to sad distracters than non-dysphoric individuals according to behavioural data, but not psychophysiological data. Results suggest that depression risk may be most reliably characterised by increased attention toward others' depressive facial emotion.     

Median method for eliminating infrequent artifacts and identifying the signals blurred by latency jitter and uncertain occurrence

Most researchers have employed the average method in the identification of event-related brain potentials (ERPs) because they have assumed that the averaging process results in the cancellation of any type of noise. However, the average method is less effective than is the median method for unexpected, infrequent artifacts. Furthermore (and significantly), the average method does not work well for detecting the endogenous, psychological signals that are noninvariant across every trial. Ruchkin (1988) described two types of possible signal variation in amplitude and in latency. The median method is effective for extreme cases of these variations (i.e., the lack of some signals), even if the amount of data is small and their distribution is non-Gaussian. Importantly, in the latter situation, the trial-to-trial latency jitter of the signal is very difficult to eliminate, which introduces obscure errors into the average ERP measurements. We found that the waveform that was generated by the median method was less affected by this jittering than was that generated by the average method. This effect was demonstrated by simulating the signals of artificial and actual ERP data, when the distribution of latency variation was Gaussian.     

Evaluation of Multi-parameter Test Statistics for Multiple Imputation

In Ordinary Least Square regression, researchers often are interested in knowing whether a set of parameters is different from zero. With complete data, this could be achieved using the gain in prediction test, hierarchical multiple regression, or an omnibus F test. However, in substantive research scenarios, missing data often exist. In the context of multiple imputation, one of the current state-of-art missing data strategies, there are several different analogous multi-parameter tests of the joint significance of a set of parameters, and these multi-parameter test statistics can be referenced to various distributions to make statistical inferences. However, little is known about the performance of these tests, and virtually no research study has compared the Type 1 error rates and statistical power of these tests in scenarios that are typical of behavioral science data (e.g., small to moderate samples, etc.). This paper uses Monte Carlo simulation techniques to examine the performance of these multi-parameter test statistics for multiple imputation under a variety of realistic conditions. We provide a number of practical recommendations for substantive researchers based on the simulation results, and illustrate the calculation of these test statistics with an empirical example.     

Decomposing coronary-prone behavior: Dimensions of type a behavior in the Videotaped Structured Interview

This study examined the factorial structure of data generated in the Videotaped Structured Interview for assessment of Type A behavior. Based on the literature, it was argued that there are at least three distinct concepts of Type A behavior, focused on competitiveness and pressured drive, speech characteristics, and hostility, respectively. These three concepts were clearly represented among the factors from factor analyses based on data from 282 subjects. Three factors represented speech and psychomotor characteristics, where the first reflected interactions between subject and interviewer (e.g., response latency, interruptions), the second tempo of speech (accelerating, dysrhythmic and rapid speech), and the third oral gestures (e.g., expiratory sighs). Hostility was reflected in two factors, one defined by emotional intensity (e.g., anger when recalling paat event) and the other hostility expressed towards the interviewer. Pressured drive dominated a content factor reflecting self-awareness of Type A behavior. Finally, there was one factor related to psychomotor tension. Through further factor and item analyses, it was possible to combine these factors into three homogeneous and moderately intercorrelated subscales of Type A behavior reflecting Hostility, Speech Characteristics, and Self-Awareness of Type A behavior. These scales showed good interrater agreement and stability over two years. The Self-Awareness Scale was highly correlated with self-report measures of Type A behavior. The Hostility Scale, on the other hand, was moderately related to measures of Type A behavior, anger and hostility, but was unrelated to anxiety. The Speech Characteristic Scale, finally, was virtually unrelated to psychometric measures.     

Time-specific Errors in Growth Curve Modeling: Type-1 Error Inflation and a Possible Solution with Mixed-Effects Models

Growth curve modeling (GCM) has been one of the most popular statistical methods to examine participants’ growth trajectories using longitudinal data. In spite of the popularity of GCM, little attention has been paid to the possible influence of time-specific errors, which influence all participants at each timepoint. In this article, we demonstrate that the failure to take into account such time-specific errors in GCM produces considerable inflation of type-1 error rates in statistical tests of fixed effects (e.g., coefficients for the linear and quadratic terms). We propose a GCM that appropriately incorporates time-specific errors using mixed-effects models to address the problem. We also provide an applied example to illustrate that GCM with and without time-specific errors would lead to different substantive conclusions about the true growth trajectories. Comparisons with other models in longitudinal data analysis and potential issues of model misspecification are discussed.     

Statistical Issues in the Study of Temporal Data: Daily Experiences

This article reviews statistical issues that arise in temporal data, particularly with respect to daily experience data. Issues related to nonindependence of observations, the nature of data structures, and claims of causality are considered. Through the analysis of data from a single subject, we illustrate concomitant time-series analysis, a general method of examining relationships between two or more series having 50 or more observations. We also discuss detection of and remedies for the problems of trend, cycles, and serial dependency that frequently plague temporal data, and present methods of combining the results of concomitant time series across subjects. Issues that arise in pooling cross-sectional and time-series data and statistical models for addressing these issues are considered for the case in which there are appreciably fewer than 50 observations and a moderate number of subjects. We discuss the possibility of using structural equation modeling to analyze data structures in which there are a large number (e.g., 200) of subjects, but relatively few time points, emphasizing the different causal status of synchronous and lagged effects and the types of models that can be specified for longitudinal data structures. Our conclusion highlights some of the issues raised by temporal data for statistical models, notably the important roles of substantive theory, the question being addressed, the properties of the data, and the assumptions underlying each technique in determining the optimal approach to statistical analysis.     

Solutions to the problems of incremental validity testing in relationship science

Incremental validity testing (i.e., testing whether a focal predictor is associated with an outcome above and beyond a covariate) is common (e.g., 57% of Personal Relationships articles in 2017), yet it is fraught with conceptual and statistical problems. First, researchers often use it to overemphasize the novelty or counterintuitiveness of findings, which hinders cumulative understanding. Second, incremental validity testing requires that the focal predictor and the covariate represent separate constructs; researchers risk committing the “jangle fallacy” without such evidence. Third, the most common approach to incremental validity testing (i.e., standard multiple regression, 88% of articles) inflates Type I error and can produce invalid conclusions. This article also discusses the relevance of these issues to dyadic/longitudinal designs and offers concrete solutions.     

A personal computer programmable sine-wave generator

A programmable sine-wave generator has been developed that permits microcomputer control of both discrete and continuous variations in the frequency and amplitude of auditory, visual, or vibrotactile stimuli. The function and design of the sine-wave generator as a peripheral to the Apple II/FIRST system are detailed. Moreover, adaptations of the basic sine-wave circuit are briefly described for interfacing it with other microcomputers (e.g., the IBM PC and compatibles), and for altering the waveform, range, and resolution of the output. Sample programs in Apple II/FIRST and Applesoft BASIC for controlling signal frequency and amplitude are used to illustrate the simplicity of programmable control. The sine-wave generator has many of the capabilities of commercially available ones, at a fraction of the cost.     

Relationships between event‐related potentials and behavioral and scholastic measures of reading ability: A large‐scale,cross‐sectional study

In the cognitive, computational, neuropsychological, and educational literatures, it is established that children approach text in unique ways, and that even adult readers can differ in the strategies they bring to reading. In the developmental event‐related potential (ERP) literature, however, children with differing degrees of reading ability are, the majority of the time, placed in monolithic groups such as ‘normal’ and ‘dyslexic’ (e.g. Araújo et al., 2012) and analyzed only at the group level. This is likely done due to methodological concerns – such as low sample size or a lack of statistical power – that can make it difficult to perform analysis at the individual level. Here, we collected ERPs and behavior from > 100 children in grades pre‐K–7, as they read unconnected text silently to themselves. This large sample, combined with the statistical power of the Linear Mixed Effects Regression (LMER) technique, enables us to address individual differences in ERP component effects due to reading ability at an unprecedented level of detail. Results indicate that it is possible to predict reading‐related report card scores from ERP component amplitudes – especially that of the N250, a component pertaining to sublexical processing (including phonological decoding). Results also reveal relationships between behavioral measures of reading ability and ERP component effects that have previously been elusive, such as the relationship between vocabulary and N400 mean amplitude (cf. Henderson et al., 2011). We conclude that it is possible to meaningfully examine reading‐related ERP effects at the single subject level in developing readers, and that this type of analysis can provide novel insights into both behavior and scholastic achievement.     

Exposure and Response Prevention and Habit Reversal Training: Commonalities, Differential Use, and Combined Applications

Obsessive-compulsive spectrum disorders (OCSDs) have compulsive (i.e., anxiety reductive) and impulsive (i.e., driven by emotional or involuntary impulses) features. The best established psychological treatments for these disorders are behavioral/cognitive-behavioral in nature. More specifically, exposure and response prevention (ERP) (with or without cognitive therapy) and habit reversal training (HRT) are commonly indicated in the treatment of OCSDs. This paper reviews the use of various components of these therapeutic approaches to treat compulsive and impulsive symptomology in individuals with variants of these disorders. Specifically, ERP monotherapy for compulsive (e.g., obsessive-compulsive, body dysmorphic) and impulsive symptoms (e.g., tics, trichotillomania) is discussed as well as a combined treatment approach that integrates elements of ERP and HRT for individuals displaying mixed symptomology. A case example is also provided that illustrates the successful application of various components of ERP and HRT to treat OCSD symptoms. Lastly, other potential OCSD treatments are discussed.     

Eye movements and brain electric potentials during reading

The development of theories and computational models of reading requires an understanding of processing constraints, in particular of timelines related to word recognition and oculomotor control. Timelines of word recognition are usually determined with event-related potentials (ERPs) recorded under conditions of serial visual presentation (SVP) of words; timelines of oculomotor control are derived from parameters of eye movements (EMs) during natural reading. We describe two strategies to integrate these approaches. One is to collect ERPs and EMs in separate SVP and natural reading experiments for the same experimental material (but different subjects). The other strategy is to co-register EMs and ERPs during natural reading from the same subjects. Both strategies yield data that allow us to determine how lexical properties influence ERPs (e.g., the N400 component) and EMs (e.g., fixation durations) across neighboring words. We review our recent research on the effects of frequency and predictability of words on both EM and ERP measures with reference to current models of eye-movement control during reading. Results are in support of the proposition that lexical access is distributed across several fixations and across brain-electric potentials measured on neighboring words.     

Oops!.. I did it again: an ERP and behavioral study of double-errors

To understand the sequealae of action monitoring failures, most previous studies have focused on neural (e.g., the ERN and Pe) and behavioral (e.g., post-error slowing) measures associated with correct trials that precede and follow errors. However, trials that precede and follow errors are not always correct, and no study to date has examined RT and ERP indices in double-error sequences that could shed additional light on multiple response monitoring failures. In the present study, we examined ERP and behavioral data surrounding double-errors to explore the possibility that double-errors could either result from the failed detection of the first error, or from a reduction in compensatory post-error behavioral adjustments. Results indicate a normal ERN and Pe surrounding double-errors; however, errors that followed errors were characterized by reduced post-error reaction time slowing. These data are discussed in terms of existing response monitoring data, and in terms of the utility of double-errors to shed light on distinct types of response monitoring failures.     

Superiority of form% over Lambda for research on the Rorschach Comprehensive System

Lambda is an important variable in the Rorschach Comprehensive System. However, because of the way it is calculated it has properties that can produce problems for parametric statistical analyses. We illustrate these difficulties and encourage the use of Form% (i.e., pure form responses/total responses) instead of Lambda in research. Form% is easy to calculate, and it is conceptually and mathematically comparable to Lambda. Because it is much more normally distributed, Form% is suitable to use in parametric analyses (e.g., t tests, analyses of variance, correlations, factor analyses, multiple regressions).     

Feasibility,acceptability, and validity of crowdsourcing for collecting longitudinal alcohol use data

Challenges to conducting longitudinal research include financial, time, and geographic constraints. An emerging sampling method positioned to address these concerns is crowdsourcing. This study evaluated the feasibility, acceptability, and validity of collecting intensive longitudinal alcohol use data with the crowdsourcing platform, Amazon.com 's Mechanical Turk (mTurk). Participants (N = 278) recruited from mTurk provided weekly recordings of daily alcohol and soda use over an 18‐week period. Construct and external validity was evaluated using generalized linear mixed models describing associations of between‐subject (e.g., alcohol use severity) and within‐subject (e.g., day of week) variables with prospectively collected alcohol and soda use. High response rates were observed across the 18‐week period demonstrating feasibility (64.1%‐86.8%). The design was acceptable with 94% of participants indicating they were satisfied with the procedures. Multilevel models supported construct and external validity by replicating expected associations, such as more frequent and heavier drinking by individuals with higher AUDIT scores and on weekends. These effects were specific to alcohol use and did not extend to soda consumption. These data support the feasibility, acceptability, and validity of using mTurk for intensive longitudinal data collection. Future studies may leverage this platform to generate large, geographically diverse samples for prospective behavioral analytic research.     

Detecting Curvilinear Relationships: A Comparison of Scoring Approaches Based on Different Item Response Models

There is a growing use of noncognitive assessments around the world, and recent research has posited an ideal point response process underlying such measures. A critical issue is whether the typical use of dominance approaches (e.g., average scores, factor analysis, and the Samejima's graded response model) in scoring such measures is adequate. This study examined the performance of an ideal point scoring approach (e.g., the generalized graded unfolding model) as compared to the typical dominance scoring approaches in detecting curvilinear relationships between scored trait and external variable. Simulation results showed that when data followed the ideal point model, the ideal point approach generally exhibited more power and provided more accurate estimates of curvilinear effects than the dominance approaches. No substantial difference was found between ideal point and dominance scoring approaches in terms of Type I error rate and bias across different sample sizes and scale lengths, although skewness in the distribution of trait and external variable can potentially reduce statistical power. For dominance data, the ideal point scoring approach exhibited convergence problems in most conditions and failed to perform as well as the dominance scoring approaches. Practical implications for scoring responses to Likert-type surveys to examine curvilinear effects are discussed.     

Continuous measurement of attachment behavior: A multimodal view of the strange situation procedure

Infant attachment is a critical indicator of healthy infant social-emotional functioning, which is typically measured using the gold-standard Strange Situation Procedure (SSP). However, expert-based attachment classifications from the SSP are time-intensive (with respect both to expert training and rating), and do not provide an objective, continuous record of infant behavior. To continuously quantify predictors of key attachment behaviors and dimensions, multimodal movement and audio data were collected during the SSP. Forty-nine 1-year-olds and their mothers participated in the SSP and were tracked in three-dimensional space using five synchronized Kinect sensors; LENA recordings were used to quantify crying duration. Theoretically-informed multimodal measures of attachment-related behavior (e.g., dyadic contact duration, infant velocity of approach toward the mother, and infant crying) were used to predict expert rating scales and dimensional summaries of attachment outcomes. Stepwise regressions identified sets of multimodal objective measures that were significant predictors of eight of nine of the expert ratings of infant attachment behaviors in the SSP’s two reunions. These multimodal measures predicted approximately half of the variance in the summary approach/avoidance and resistance/disorganization attachment dimensions. Incorporating all objective measures as predictors regardless of significance levels, predicted individual ratings within an average of one point on the original Likert scales. The results indicate that relatively inexpensive Kinect and LENA sensors can be harnessed to quantify attachment behavior in a key assessment protocol, suggesting the promise of objective measurement to understanding infant-parent interaction.