Unique neurocircuitry activation profiles during fear conditioning and extinction among women with posttraumatic stress disorder

BACKGROUND

Neurocircuitry models of posttraumatic stress disorder (PTSD) suggest specific alterations in brain structures linked with fear conditioning and extinction. Most models assume a unitary pattern of neurocircuitry dysfunction in PTSD and little attention has focused on defining unique profiles of neurocircuitry engagement (i.e., biotypes), despite known clinical heterogeneity in PTSD. Here, we aim to address this gap using a data-driven approach to characterize unique neurocircuitry profiles among women with PTSD.

METHODS

Seventy-six women with PTSD related to assaultive violence exposure competed a task during fMRI that alternated between fear conditioning, where a geometric shape predicted the occurrence of an electric shock, and fear extinction, where the geometric shape no longer predicted electric shock. A multivariate clustering analysis was applied to neurocircuitry patterns constrained within an a priori mask of structures linked with emotion processing. Resulting biotypes were compared on clinical measures of neurocognition, trauma exposure, general mental health symptoms, and PTSD symptoms and on psychophysiological responding during the task.

RESULTS

The clustering analysis identified three biotypes (BT), differentiated by patterns of engagement within salience, default mode, and visual processing networks. BT1 was characterized by higher working memory, fewer general mental health symptoms, and low childhood sexual abuse, and lower PTSD symptom severity. BT2 was characterized by lower verbal IQ but better extinction learning as defined by psychophysiology and threat expectancy. BT3 was characterized by low childhood sexual abuse, anxious arousal, and re-experiencing symptoms.

CONCLUSION

This data demonstrates unique profiles of neurocircuitry engagement in PTSD, each associated with different clinical characteristics, and suggests further research defining distinct biotypes of PTSD. Clinicaltrials.gov, https://clinicaltrials.gov/ct2/home, NCT02560389.

Individual differences in rate of acquiring stable neural representations of tasks in fMRI

Task-related functional magnetic resonance imaging (fMRI) is a widely-used tool for studying the neural processing correlates of human behavior in both healthy and clinical populations. There is growing interest in mapping individual differences in fMRI task behavior and neural responses. By utilizing neuroadaptive task designs accounting for such individual differences, task durations can be personalized to potentially optimize neuroimaging study outcomes (e.g., classification of task-related brain states). To test this hypothesis, we first retrospectively tracked the volume-by-volume changes of beta weights generated from general linear models (GLM) for 67 adult subjects performing a stop-signal task (SST). We then modeled the convergence of the volume-by-volume changes of beta weights according to their exponential decay (ED) in units of half-life. Our results showed significant differences in beta weight convergence estimates of optimal stopping times (OSTs) between go following successful stop trials and failed stop trials for both cocaine dependent (CD) and control group (Con), and between go following successful stop trials and go following failed stop trials for Con group. Further, we implemented support vector machine (SVM) classification for 67 CD/Con labeled subjects and compared the classification accuracies of fMRI-based features derived from (1) the full fMRI task versus (2) the fMRI task truncated to multiples of the unit of half-life. Among the computed binary classification accuracies, two types of task durations based on 2 half-lives significantly outperformed the accuracies using fully acquired trials, supporting this length as the OST for the SST. In conclusion, we demonstrate the potential of a neuroadaptive task design that can be widely applied to personalizing other task-based fMRI experiments in either dynamic real-time fMRI applications or within fMRI preprocessing pipelines.

Common Functional Brain States Encode both Perceived Emotion and the Psychophysiological Response to Affective Stimuli

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.

Brain States That Encode Perceived Emotion Are Reproducible but Their Classification Accuracy Is Stimulus-Dependent

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.

Large-scale brain organization during facial emotion processing as a function of early life trauma among adolescent girls

Background

A wealth of research has investigated the impact of early life trauma exposure on functional brain activation during facial emotion processing and has often demonstrated amygdala hyperactivity and weakened connectivity between amygdala and medial PFC (mPFC). There have been notably limited investigations linking these previous node-specific findings into larger-scale network models of brain organization.

Method

To address these gaps, we applied graph theoretical analyses to fMRI data collected during a facial emotion processing task among 88 adolescent girls (n = 59 exposed to direct physical or sexual assault; n = 29 healthy controls), aged 11-17, during fMRI. Large-scale organization indices of modularity, assortativity, and global efficiency were calculated for stimulus-specific functional connectivity using an 883 region-of-interest parcellation.

Results

Among the entire sample, more severe early life trauma was associated with more modular and assortative, but less globally efficient, network organization across all stimulus categories. Among the assaulted girls, severity of early life trauma and PTSD diagnoses were both simultaneously related to increased modular brain organization. We also found that more modularized network organization was related both to amygdala hyperactivation and weakened connectivity between amygdala and medial PFC.

Conclusions

These results demonstrate that early life trauma is associated with enhanced brain organization during facial emotion processing and that this pattern of brain organization might explain the commonly observed association between childhood trauma and amygdala hyperactivity and weakened connectivity with mPFC. Implications of these results for neurocircuitry models are discussed.