A semi-parametric nonlinear model for event-related fMRI

Intranasal oxytocin enhances stress-protective effects of social support in women with negative childhood experiences during a virtual Trier Social Stress Test

Oxytocin is considered a biological mechanism underlying stress-protective effects of positive social interactions. It is assumed to underlie the women-specific tend-and-befriend response to stress, although few studies have tested this assertion with female samples. The aim of the present study was, therefore, to test whether oxytocin enhances stress-protective effects of social support during stress in women, taking into account the moderating role of childhood adversity. The sample consisted of 180 female undergraduate students who had reported on experiences of childhood abuse and how often their mother used love withdrawal as an insensitive disciplinary strategy. Women participated in a virtual version of the Trier Social Stress Test (TSST) and were randomly assigned to receive 24 IU oxytocin or a placebo and to receive support or no support from a female friend (sub-groups N = 45). Results showed that oxytocin reduced heart rate variability during the TSST in participants who received support, possibly indicating that oxytocin increases attention and stimulates a challenge motivational state in the presence of a friend. In addition, we found that, in the presence of a friend, oxytocin reduced state anxiety levels and cortisol levels after the TSST, but only in women with higher levels of adverse childhood experiences. Our findings may indicate that oxytocin is a neurobiological means to attain and benefit from social support under stressful circumstances, which may be particularly adaptive for women with a history of adversity. Thus, oxytocin may function as motivator for affiliative disposition during stress exposure in women with a history of childhood adversity. Results should be replicated in clinical samples.

Double-wavelet transform for multisubject task-induced functional magnetic resonance imaging data

The goal of this article is to model multisubject task-induced functional magnetic resonance imaging (fMRI) response among predefined regions of interest (ROIs) of the human brain. Conventional approaches to fMRI analysis only take into account temporal correlations, but do not rigorously model the underlying spatial correlation due to the complexity of estimating and inverting the high dimensional spatio-temporal covariance matrix. Other spatio-temporal model approaches estimate the covariance matrix with the assumption of stationary time series, which is not always feasible. To address these limitations, we propose a double-wavelet approach for modeling the spatio-temporal brain process. Working with wavelet coefficients simplifies temporal and spatial covariance structure because under regularity conditions, wavelet coefficients are approximately uncorrelated. Different wavelet functions were used to capture different correlation structures in the spatio-temporal model. The main advantages of the wavelet approach are that it is scalable and that it deals with nonstationarity in brain signals. Simulation studies showed that our method could reduce false-positive and false-negative rates by taking into account spatial and temporal correlations simultaneously. We also applied our method to fMRI data to study activation in prespecified ROIs in the prefontal cortex. Data analysis showed that the result using the double-wavelet approach was more consistent than the conventional approach when sample size decreased.

Using Event-Related Potentials and Startle to Evaluate Time Course in Anxiety and Depression

The National Institute of Mental Health's Research Domain Criteria initiative is a research framework designed toward understanding psychopathology as abnormalities of dimensional neurobehavioral constructs rather than in terms of DSM-defined categories. Research Domain Criteria constructs within the negative valence domain are particularly relevant for understanding anxiety and depressive disorders, which are pervasive, debilitating, and characterized by negative processing bias. One important direction for Research Domain Criteria research is investigating processes and parameters related to the time course (or chronometry) of negative valenced constructs. Two reliable methods for assessing chronometry are event-related potentials (ERPs) and startle blink. In this qualitative review, we examine ERP and startle studies of individuals with anxiety or depression or individuals vulnerable to affective disorders. The aim of the review is to highlight how these methods can inform the role of chronometry in the spectrum of anxiety and depression. ERP studies examining different chronometry facets of negative valenced responses have shown that transdiagnostic groups of individuals with internalizing psychopathologies exhibit abnormalities at early stages of processing. Startle reactivity studies have robustly differentiated fear-based disorders (e.g., panic disorder, social phobia) from other anxiety disorders (e.g., generalized anxiety disorder) and have also shown that different internalizing phenotypes exhibit different patterns of habituation. Findings lend support to the value of ERP and startle measures in identifying groups that cut across conventional classification systems. We also highlight methodological issues that can aid in the validity and reproducibility of ERP and startle findings and, ultimately, in the goal of developing more precise models of anxiety and depression.

Using patient-specific hemodynamic response function in epileptic spike analysis of human epilepsy: a study based on EEG-fNIRS

Functional near-infrared spectroscopy (fNIRS) can be combined with electroencephalography (EEG) to continuously monitor the hemodynamic signal evoked by epileptic events such as seizures or interictal epileptiform discharges (IEDs, aka spikes). As estimation methods assuming a canonical shape of the hemodynamic response function (HRF) might not be optimal, we sought to model patient-specific HRF (sHRF) with a simple deconvolution approach for IED-related analysis with EEG-fNIRS data. Furthermore, a quadratic term was added to the model to account for the nonlinearity in the response when IEDs are frequent. Prior to analyzing clinical data, simulations were carried out to show that the HRF was estimable by the proposed deconvolution methods under proper conditions. EEG-fNIRS data of five patients with refractory focal epilepsy were selected due to the presence of frequent clear IEDs and their unambiguous focus localization. For each patient, both the linear sHRF and the nonlinear sHRF were estimated at each channel. Variability of the estimated sHRFs was seen across brain regions and different patients. Compared with the SPM8 canonical HRF (cHRF), including these sHRFs in the general linear model (GLM) analysis led to hemoglobin activations with higher statistical scores as well as larger spatial extents on all five patients. In particular, for patients with frequent IEDs, nonlinear sHRFs were seen to provide higher sensitivity in activation detection than linear sHRFs. These observations support using sHRFs in the analysis of IEDs with EEG-fNIRS data.