Generalized reliability based on distances.

The intraclass correlation coefficient (ICC) is a classical index of measurement reliability. With the advent of new and complex types of data for which the ICC is not defined, there is a need for new ways to assess reliability. To meet this need, we propose a new distance-based ICC (dbICC), defined in terms of arbitrary distances among observations. We introduce a bias correction to improve the coverage of bootstrap confidence intervals for the dbICC, and demonstrate its efficacy via simulation. We illustrate the proposed method by analyzing the test-retest reliability of brain connectivity matrices derived from a set of repeated functional magnetic resonance imaging scans. The Spearman-Brown formula, which shows how more intensive measurement increases reliability, is extended to encompass the dbICC.

A Time-Varying Measure of Dyadic Synchrony for Three-Dimensional Motion.

We propose a novel approach to the analysis of synchronized three-dimensional motion in dyads. Motion recorded at high time resolution, as with a gaming device, is preprocessed in each of the three spatial dimensions by spline smoothing. Synchrony is then defined, at each time point, as the cosine between the two individuals' estimated velocity vectors. The approach is extended to allow a time lag, allowing for the analysis of leader-follower dynamics. Mean square cosine over the time range is proposed as a scalar summary of dyadic synchrony, and this measure is found to be positively associated with cognitive empathy.

Two-way principal component analysis for matrix-variate data, with an application to functional magnetic resonance imaging data.

Many modern neuroimaging studies acquire large spatial images of the brain observed sequentially over time. Such data are often stored in the forms of matrices. To model these matrix-variate data we introduce a class of separable processes using explicit latent process modeling. To account for the size and two-way structure of the data, we extend principal component analysis to achieve dimensionality reduction at the individual level. We introduce necessary identifiability conditions for each model and develop scalable estimation procedures. The method is motivated by and applied to a functional magnetic resonance imaging study designed to analyze the relationship between pain and brain activity.

Cross-sectional versus longitudinal designs for function estimation, with an application to cerebral cortex development.

Motivated by studies of the development of the human cerebral cortex, we consider the estimation of a mean growth trajectory and the relative merits of cross-sectional and longitudinal data for that task. We define a class of relative efficiencies that compare function estimates in terms of aggregate variance of a parametric function estimate. These generalize the classical design effect for estimating a scalar with cross-sectional versus longitudinal data, and are shown to be bounded above by it in certain cases. Turning to nonparametric function estimation, we find that longitudinal fits may tend to have higher aggregate variance than cross-sectional ones, but that this may occur because the former have higher effective degrees of freedom reflecting greater sensitivity to subtle features of the estimand. These ideas are illustrated with cortical thickness data from a longitudinal neuroimaging study.

Pointwise influence matrices for functional-response regression.

We extend the notion of an influence or hat matrix to regression with functional responses and scalar predictors. For responses depending linearly on a set of predictors, our definition is shown to reduce to the conventional influence matrix for linear models. The pointwise degrees of freedom, the trace of the pointwise influence matrix, are shown to have an adaptivity property that motivates a two-step bivariate smoother for modeling nonlinear dependence on a single predictor. This procedure adapts to varying complexity of the nonlinear model at different locations along the function, and thereby achieves better performance than competing tensor product smoothers in an analysis of the development of white matter microstructure in the brain.

Methods for scalar-on-function regression.

Recent years have seen an explosion of activity in the field of functional data analysis (FDA), in which curves, spectra, images, etc. are considered as basic functional data units. A central problem in FDA is how to fit regression models with scalar responses and functional data points as predictors. We review some of the main approaches to this problem, categorizing the basic model types as linear, nonlinear and nonparametric. We discuss publicly available software packages, and illustrate some of the procedures by application to a functional magnetic resonance imaging dataset.

Can school counselors deliver cognitive-behavioral treatment for social anxiety effectively? A randomized controlled trial.

BACKGROUND: Social anxiety disorder (SAD) typically onsets in adolescence and is associated with multiple impairments. Despite promising clinical interventions, most socially anxious adolescents remain untreated. To address this clinical neglect, we developed a school-based, 12-week group intervention for youth with SAD, Skills for Academic and Social Success (SASS). When implemented by psychologists, SASS has been found effective. To promote dissemination and optimize treatment access, we tested whether school counselors could be effective treatment providers. METHOD: We randomized 138, ninth through 11th graders with SAD to one of three conditions: (a) SASS delivered by school counselors (C-SASS), (b) SASS delivered by psychologists (P-SASS), or (c) a control condition, Skills for Life (SFL), a nonspecific counseling program. Blind, independent, evaluations were conducted with parents and adolescents at baseline, post-intervention, and 5 months beyond treatment completion. We hypothesized that C-SASS and P-SASS would be superior to the control, immediately after treatment and at follow-up. No prediction was made about the relative efficacy of C-SASS and P-SASS. RESULTS: Compared to controls, adolescents treated with C-SASS or P-SASS experienced significantly greater improvement and reductions of anxiety at the end of treatment and follow-up. There were no significant differences between SASS delivered by school counselors and psychologists. CONCLUSION: With training, school counselors are effective treatment providers to adolescents with social anxiety, yielding benefits comparable to those obtained by specialized psychologists. Questions remain regarding means to maintain counselors' practice standards without external support.

Cross-validation and hypothesis testing in neuroimaging: An irenic comment on the exchange between Friston and Lindquist et al.

The "ten ironic rules for statistical reviewers" presented by Friston (2012) prompted a rebuttal by Lindquist et al. (2013), which was followed by a rejoinder by Friston (2013). A key issue left unresolved in this discussion is the use of cross-validation to test the significance of predictive analyses. This note discusses the role that cross-validation-based and related hypothesis tests have come to play in modern data analyses, in neuroimaging and other fields. It is shown that such tests need not be suboptimal and can fill otherwise-unmet inferential needs.

Quantile rank maps: a new tool for understanding individual brain development.

We propose a novel method for neurodevelopmental brain mapping that displays how an individual's values for a quantity of interest compare with age-specific norms. By estimating smoothly age-varying distributions at a set of brain regions of interest, we derive age-dependent region-wise quantile ranks for a given individual, which can be presented in the form of a brain map. Such quantile rank maps could potentially be used for clinical screening. Bootstrap-based confidence intervals are proposed for the quantile rank estimates. We also propose a recalibrated Kolmogorov-Smirnov test for detecting group differences in the age-varying distribution. This test is shown to be more robust to model misspecification than a linear regression-based test. The proposed methods are applied to brain imaging data from the Nathan Kline Institute Rockland Sample and from the Autism Brain Imaging Data Exchange (ABIDE) sample.

A multivariate distance-based analytic framework for connectome-wide association studies.

The identification of phenotypic associations in high-dimensional brain connectivity data represents the next frontier in the neuroimaging connectomics era. Exploration of brain-phenotype relationships remains limited by statistical approaches that are computationally intensive, depend on a priori hypotheses, or require stringent correction for multiple comparisons. Here, we propose a computationally efficient, data-driven technique for connectome-wide association studies (CWAS) that provides a comprehensive voxel-wise survey of brain-behavior relationships across the connectome; the approach identifies voxels whose whole-brain connectivity patterns vary significantly with a phenotypic variable. Using resting state fMRI data, we demonstrate the utility of our analytic framework by identifying significant connectivity-phenotype relationships for full-scale IQ and assessing their overlap with existent neuroimaging findings, as synthesized by openly available automated meta-analysis (www.neurosynth.org). The results appeared to be robust to the removal of nuisance covariates (i.e., mean connectivity, global signal, and motion) and varying brain resolution (i.e., voxelwise results are highly similar to results using 800 parcellations). We show that CWAS findings can be used to guide subsequent seed-based correlation analyses. Finally, we demonstrate the applicability of the approach by examining CWAS for three additional datasets, each encompassing a distinct phenotypic variable: neurotypical development, Attention-Deficit/Hyperactivity Disorder diagnostic status, and L-DOPA pharmacological manipulation. For each phenotype, our approach to CWAS identified distinct connectome-wide association profiles, not previously attainable in a single study utilizing traditional univariate approaches. As a computationally efficient, extensible, and scalable method, our CWAS framework can accelerate the discovery of brain-behavior relationships in the connectome.

Optimally weighted L(2) distance for functional data.

Many techniques of functional data analysis require choosing a measure of distance between functions, with the most common choice being L2 distance. In this article we show that using a weighted L2 distance, with a judiciously chosen weight function, can improve the performance of various statistical methods for functional data, including k-medoids clustering, nonparametric classification, and permutation testing. Assuming a quadratically penalized (e.g., spline) basis representation for the functional data, we consider three nontrivial weight functions: design density weights, inverse-variance weights, and a new weight function that minimizes the coefficient of variation of the resulting squared distance by means of an efficient iterative procedure. The benefits of weighting, in particular with the proposed weight function, are demonstrated both in simulation studies and in applications to the Berkeley growth data and a functional magnetic resonance imaging data set.

Functional changes in sleep-related arousal after ketamine administration in individuals with treatment-resistant depression.

The glutamatergic modulator ketamine is associated with changes in sleep, depression, and suicidal ideation (SI). This study sought to evaluate differences in arousal-related sleep metrics between 36 individuals with treatment-resistant major depression (TRD) and 25 healthy volunteers (HVs). It also sought to determine whether ketamine normalizes arousal in individuals with TRD and whether ketamine's effects on arousal mediate its antidepressant and anti-SI effects. This was a secondary analysis of a biomarker-focused, randomized, double-blind, crossover trial of ketamine (0.5 mg/kg) compared to saline placebo. Polysomnography (PSG) studies were conducted one day before and one day after ketamine/placebo infusions. Sleep arousal was measured using spectral power functions over time including alpha (quiet wakefulness), beta (alert wakefulness), and delta (deep sleep) power, as well as macroarchitecture variables, including wakefulness after sleep onset (WASO), total sleep time (TST), rapid eye movement (REM) latency, and Post-Sleep Onset Sleep Efficiency (PSOSE). At baseline, diagnostic differences in sleep macroarchitecture included lower TST (p = 0.006) and shorter REM latency (p = 0.04) in the TRD versus HV group. Ketamine's temporal dynamic effects (relative to placebo) in TRD included increased delta power earlier in the night and increased alpha and delta power later in the night. However, there were no significant diagnostic differences in temporal patterns of alpha, beta, or delta power, no ketamine effects on sleep macroarchitecture arousal metrics, and no mediation effects of sleep variables on ketamine's antidepressant or anti-SI effects. These results highlight the role of sleep-related variables as part of the systemic neurobiological changes initiated after ketamine administration. Clinical Trials Identifier: NCT00088699.

Bayesian scalar-on-image regression with application to association between intracranial DTI and cognitive outcomes.

Diffusion tensor imaging (DTI) measures water diffusion within white matter, allowing for in vivo quantification of brain pathways. These pathways often subserve specific functions, and impairment of those functions is often associated with imaging abnormalities. As a method for predicting clinical disability from DTI images, we propose a hierarchical Bayesian "scalar-on-image" regression procedure. Our procedure introduces a latent binary map that estimates the locations of predictive voxels and penalizes the magnitude of effect sizes in these voxels, thereby resolving the ill-posed nature of the problem. By inducing a spatial prior structure, the procedure yields a sparse association map that also maintains spatial continuity of predictive regions. The method is demonstrated on a simulation study and on a study of association between fractional anisotropy and cognitive disability in a cross-sectional sample of 135 multiple sclerosis patients.

Ordinal state-trait regression for intensive longitudinal data.

In many psychological studies, in particular those conducted by experience sampling, mental states are measured repeatedly for each participant. Such a design allows for regression models that separate between- from within-person, or trait-like from state-like, components of association between two variables. But these models are typically designed for continuous variables, whereas mental state variables are most often measured on an ordinal scale. In this paper we develop a model for disaggregating between- from within-person effects of one ordinal variable on another. As in standard ordinal regression, our model posits a continuous latent response whose value determines the observed response. We allow the latent response to depend nonlinearly on the trait and state variables, but impose a novel penalty that shrinks the fit towards a linear model on the latent scale. A simulation study shows that this penalization approach is effective at finding a middle ground between an overly restrictive linear model and an overfitted nonlinear model. The proposed method is illustrated with an application to data from the experience sampling study of Baumeister et al. (2020, Personality and Social Psychology Bulletin, 46, 1631).

Paradoxical results of adaptive false discovery rate procedures in neuroimaging studies.

Adaptive false discovery rate (FDR) procedures, which offer greater power than the original FDR procedure of Benjamini and Hochberg, are often applied to statistical maps of the brain. When a large proportion of the null hypotheses are false, as in the case of widespread effects such as cortical thinning throughout much of the brain, adaptive FDR methods can surprisingly reject more null hypotheses than not accounting for multiple testing at all-i.e., using uncorrected p-values. A straightforward mathematical argument is presented to explain why this can occur with the q-value method of Storey and colleagues, and a simulation study shows that it can also occur, to a lesser extent, with a two-stage FDR procedure due to Benjamini and colleagues. We demonstrate the phenomenon with reference to a published data set documenting cortical thinning in attention deficit/hyperactivity disorder. The paper concludes with recommendations for how to proceed when adaptive FDR results of this kind are encountered in practice.

Extracting information from functional connectivity maps via function-on-scalar regression.

Functional connectivity of an individual human brain is often studied by acquiring a resting state functional magnetic resonance imaging scan, and mapping the correlation of each voxel's BOLD time series with that of a seed region. As large collections of such maps become available, including multisite data sets, there is an increasing need for ways to distill the information in these maps in a readily visualized form. Here we propose a two-step analytic strategy. First, we construct connectivity-distance profiles, which summarize the connectivity of each voxel in the brain as a function of distance from the seed, a functional relationship that has attracted much recent interest. Next, these profile functions are regressed on predictors of interest, whether categorical (e.g., acquisition site or diagnostic group) or continuous (e.g., age). This procedure can provide insight into the roles of multiple sources of variation, and detect large-scale patterns not easily available from conventional analyses. We illustrate the proposed methods with a resting state data set pooled across four imaging sites.

L-dopa modulates functional connectivity in striatal cognitive and motor networks: a double-blind placebo-controlled study.

Functional connectivity (FC) analyses of resting-state fMRI data allow for the mapping of large-scale functional networks, and provide a novel means of examining the impact of dopaminergic challenge. Here, using a double-blind, placebo-controlled design, we examined the effect of L-dopa, a dopamine precursor, on striatal resting-state FC in 19 healthy young adults. We examined the FC of 6 striatal regions of interest (ROIs) previously shown to elicit networks known to be associated with motivational, cognitive and motor subdivisions of the caudate and putamen (Di Martino et al., 2008). In addition to replicating the previously demonstrated patterns of striatal FC, we observed robust effects of L-dopa. Specifically, L-dopa increased FC in motor pathways connecting the putamen ROIs with the cerebellum and brainstem. Although L-dopa also increased FC between the inferior ventral striatum and ventrolateral prefrontal cortex, it disrupted ventral striatal and dorsal caudate FC with the default mode network. These alterations in FC are consistent with studies that have demonstrated dopaminergic modulation of cognitive and motor striatal networks in healthy participants. Recent studies have demonstrated altered resting state FC in several conditions believed to be characterized by abnormal dopaminergic neurotransmission. Our findings suggest that the application of similar experimental pharmacological manipulations in such populations may further our understanding of the role of dopaminergic neurotransmission in those conditions.

Functional generalized linear models with images as predictors.

Functional principal component regression (FPCR) is a promising new method for regressing scalar outcomes on functional predictors. In this article, we present a theoretical justification for the use of principal components in functional regression. FPCR is then extended in two directions: from linear to the generalized linear modeling, and from univariate signal predictors to high-resolution image predictors. We show how to implement the method efficiently by adapting generalized additive model technology to the functional regression context. A technique is proposed for estimating simultaneous confidence bands for the coefficient function; in the neuroimaging setting, this yields a novel means to identify brain regions that are associated with a clinical outcome. A new application of likelihood ratio testing is described for assessing the null hypothesis of a constant coefficient function. The performance of the methodology is illustrated via simulations and real data analyses with positron emission tomography images as predictors.

On distance-based permutation tests for between-group comparisons.

Permutation tests based on distances among multivariate observations have found many applications in the biological sciences. Two major testing frameworks of this kind are multiresponse permutation procedures and pseudo-F tests arising from a distance-based extension of multivariate analysis of variance. In this article, we derive conditions under which these two frameworks are equivalent. The methods and equivalence results are illustrated by reanalyzing an ecological data set and by a novel application to functional magnetic resonance imaging data.

Development of anterior cingulate functional connectivity from late childhood to early adulthood.

Human cerebral development is remarkably protracted. Although microstructural processes of neuronal maturation remain accessible only to morphometric post-mortem studies, neuroimaging tools permit the examination of macrostructural aspects of brain development. The analysis of resting-state functional connectivity (FC) offers novel possibilities for the investigation of cerebral development. Using seed-based FC methods, we examined the development of 5 functionally distinct cingulate-based intrinsic connectivity networks (ICNs) in children (n = 14, 10.6 +/- 1.5 years), adolescents (n = 12, 15.4 +/- 1.2) and young adults (n=14, 22.4 +/- 1.2). Children demonstrated a more diffuse pattern of correlation with voxels proximal to the seed region of interest (ROI) ("local FC"), whereas adults exhibited more focal patterns of FC, as well as a greater number of significantly correlated voxels at long distances from the seed ROI. Adolescents exhibited intermediate patterns of FC. Consistent with evidence for different maturational time courses, ICNs associated with social and emotional functions exhibited the greatest developmental effects. Our findings demonstrate the utility of FC for the study of developing functional organization. Moreover, given that ICNs are thought to have an anatomical basis in neuronal connectivity, measures of FC may provide a quantitative index of brain maturation in healthy subjects and those with neurodevelopmental disorders.