Many cognitive and clinical neuroscience research studies seek to determine how contextual factors modulate cognitive processes. In fMRI, hypotheses about how context modulates distributed patterns of information processing are often tested by comparing functional connectivity between neural regions A and B as a function of task condition X and Y, which is termed context-modulated functional connectivity (FC). There exist two exploratory statistical approaches to testing context-modulated FC: the beta-series method and psychophysiological interaction (PPI) analysis methods. While these approaches are commonly used, their relative power for detecting context-modulated FC is unknown, especially with respect to real-world experimental parameters (e.g., number of stimulus repetitions, inter-trial-interval, stimulus duration, etc). Here, we use simulations to compare power for detecting context-modulated FC between the standard PPI formulation (sPPI), generalized PPI formulation (gPPI), and beta series method. Simulation results demonstrate that gPPI and beta series method are generally more powerful than sPPI. Whether gPPI or beta series method performed more powerfully depended on experiment parameters: block designs favor the gPPI, whereas the beta series method was more powerful for designs with more trial repetitions and it also retained more power under conditions of hemodynamic response function variability. On a real data set of adolescent girls, the PPI methods appeared to have greater sensitivity in detecting task-modulated FC when using a block design and the beta series method appeared to have greater sensitivity when using an event-related design with many trial repetitions. Implications of these performance results are discussed.

The brain state hypothesis of image-induced affect processing, which posits that a one-to-one mapping exists between each image stimulus and its induced functional magnetic resonance imaging (fMRI)-derived neural activation pattern (i.e., brain state), has recently received support from several multivariate pattern analysis (MVPA) studies. Critically, however, classification accuracy differences across these studies, which largely share experimental designs and analyses, suggest that there exist one or more unaccounted sources of variance within MVPA studies of affect processing. To explore this possibility, we directly demonstrated strong inter-study correlations between image-induced affective brain states acquired 4 years apart on the same MRI scanner using near-identical methodology with studies differing only by the specific image stimuli and subjects. We subsequently developed a plausible explanation for inter-study differences in affective valence and arousal classification accuracies based on the spatial distribution of the perceived affective properties of the stimuli. Controlling for this distribution improved valence classification accuracy from 56% to 85% and arousal classification accuracy from 61% to 78%, which mirrored the full range of classification accuracy across studies within the existing literature. Finally, we validated the predictive fidelity of our image-related brain states according to an independent measurement, autonomic arousal, captured via skin conductance response (SCR). Brain states significantly but weakly (r = 0.08) predicted the SCRs that accompanied individual image stimulations. More importantly, the effect size of brain state predictions of SCR increased more than threefold (r = 0.25) when the stimulus set was restricted to those images having group-level significantly classifiable arousal properties.

Neuroimaging methodology predominantly relies on the blood oxygenation level dependent (BOLD) signal. While the BOLD signal is a valid measure of neuronal activity, variance in fluctuations of the BOLD signal are not only due to fluctuations in neural activity. Thus, a remaining problem in neuroimaging analyses is developing methods that ensure specific inferences about neural activity that are not confounded by unrelated sources of noise in the BOLD signal. Here, we develop and test a new algorithm for performing semi-blind (i.e., no knowledge of stimulus timings) deconvolution of the BOLD signal that treats the neural event as an observable, but intermediate, probabilistic representation of the system’s state. We test and compare this new algorithm against three other recent deconvolution algorithms under varied levels of autocorrelated and Gaussian noise, hemodynamic response function (HRF) misspecification, and observation sampling rate (i.e., TR). Further, we compare the algorithms’ performance using two models to simulate BOLD data: a convolution of neural events with a known (or misspecified) HRF versus a biophysically accurate balloon model of hemodynamics. We also examine the algorithms’ performance on real task data. The results demonstrated good performance of all algorithms, though the new algorithm generally outperformed the others (3.0% improvement) under simulated resting state experimental conditions exhibiting multiple, realistic confounding factors (as well as 10.3% improvement on a real Stroop task). The simulations also demonstrate that the greatest negative influence on deconvolution accuracy is observation sampling rate. Practical and theoretical implications of these results for improving inferences about neural activity from fMRI BOLD signal are discussed.

Multivariate pattern analysis (MVPA) of functional magnetic resonance imaging (fMRI) data has critically advanced the neuroanatomical understanding of affect processing in the human brain. Central to these advancements is the brain state, a temporally-succinct fMRI-derived pattern of neural activation, which serves as a processing unit. Establishing the brain state’s central role in affect processing, however, requires that it predicts multiple independent measures of affect. We employed MVPA-based regression to predict the valence and arousal properties of visual stimuli sampled from the International Affective Picture System (IAPS) along with the corollary skin conductance response (SCR) for demographically diverse healthy human participants (n = 19). We found that brain states significantly predicted the normative valence and arousal scores of the stimuli as well as the attendant individual SCRs. In contrast, SCRs significantly predicted arousal only. The prediction effect size of the brain state was more than three times greater than that of SCR. Moreover, neuroanatomical analysis of the regression parameters found remarkable agreement with regions long-established by fMRI univariate analyses in the emotion processing literature. Finally, geometric analysis of these parameters also found that the neuroanatomical encodings of valence and arousal are orthogonal as originally posited by the circumplex model of dimensional emotion.

Current neurocircuitry models of PTSD focus on the neural mechanisms that mediate hypervigilance for threat and fear inhibition/extinction learning. Less focus has been directed towards explaining social deficits and heightened risk of revictimization observed among individuals with PTSD related to physical or sexual assault. The purpose of the present study was to foster more comprehensive theoretical models of PTSD by testing the hypothesis that assault-related PTSD is associated with behavioral impairments in a social trust and reciprocity task and corresponding alterations in the neural encoding of social learning mechanisms. Adult women with assault-related PTSD (n=25) and control women (n=15) completed a multi-trial trust game outside of the MRI scanner. A subset of these participants (15 with PTSD and 14 controls) also completed a social and non-social reinforcement learning task during 3T fMRI. Brain regions that encoded the computationally modeled parameters of value expectation, prediction error, and volatility (i.e., uncertainty) were defined and compared between groups. The PTSD group demonstrated slower learning rates during the trust game and social prediction errors had a lesser impact on subsequent investment decisions. PTSD was also associated with greater encoding of negative expected social outcomes in perigenual anterior cingulate cortex and bilateral middle frontal gyri, and greater encoding of social prediction errors in the left temporoparietal junction. These data suggest mechanisms of PTSD-related deficits in social functioning and heightened risk for re-victimization in assault victims; however, comorbidity in the PTSD group and the lack of a trauma-exposed control group temper conclusions about PTSD specifically.