Joram Soch

Trait anxiety modulates fronto-limbic processing of emotional interference in borderline personality disorder

Previous studies of cognitive alterations in borderline personality disorder (BPD) have yielded conflicting results. Given that a core feature of BPD is affective instability, which is characterized by emotional hyperreactivity and deficits in emotion regulation, it seems conceivable that short-lasting emotional distress might exert temporary detrimental effects on cognitive performance. Here we used functional magnetic resonance imaging (fMRI) to investigate how task-irrelevant emotional stimuli (fearful faces) affect performance and fronto-limbic neural activity patterns during attention-demanding cognitive processing in 16 female, unmedicated BPD patients relative to 24 age-matched healthy controls. In a modified flanker task, emotionally negative, socially salient pictures (fearful vs. neutral faces) were presented as distracters in the background. Patients, but not controls, showed an atypical response pattern of the right amygdala with increased activation during emotional interference in the (difficult) incongruent flanker condition, but emotion-related amygdala deactivation in the congruent condition. A direct comparison of the emotional conditions between the two groups revealed that the strongest diagnosis-related differences could be observed in the dorsal and, to a lesser extent, also in the rostral anterior cingulate cortex (dACC, rACC) where patients exhibited an increased neural response to emotional relative to neutral distracters. Moreover, in the incongruent condition, both the dACC and rACC fMRI responses during emotional interference were negatively correlated with trait anxiety in the patients, but not in the healthy controls. As higher trait anxiety was also associated with longer reaction times (RTs) in the BPD patients, we suggest that in BPD patients the ACC might mediate compensatory cognitive processes during emotional interference and that such neurocognitive compensation that can be adversely affected by high levels of anxiety.

How to avoid mismodelling in GLM-based fMRI data analysis: cross-validated Bayesian model selection.

Voxel-wise general linear models (GLMs) are a standard approach for analyzing functional magnetic resonance imaging (fMRI) data. An advantage of GLMs is that they are flexible and can be adapted to the requirements of many different data sets. However, the specification of first-level GLMs leaves the researcher with many degrees of freedom which is problematic given recent efforts to ensure robust and reproducible fMRI data analysis. Formal model comparisons that allow a systematic assessment of GLMs are only rarely performed. On the one hand, too simple models may underfit data and leave real effects undiscovered. On the other hand, too complex models might overfit data and also reduce statistical power. Here we present a systematic approach termed cross-validated Bayesian model selection (cvBMS) that allows to decide which GLM best describes a given fMRI data set. Importantly, our approach allows for non-nested model comparison, i.e. comparing more than two models that do not just differ by adding one or more regressors. It also allows for spatially heterogeneous modelling, i.e. using different models for different parts of the brain. We validate our method using simulated data and demonstrate potential applications to empirical data. The increased use of model comparison and model selection should increase the reliability of GLM results and reproducibility of fMRI studies.