Arousal evoked by detecting a performance error may provide a mechanism by which error detection leads to either adaptive or maladaptive changes in attention and performance. By pairing EEG data acquisition with simultaneous measurements of pupil diameter, which is thought to reflect norepinephrinergic arousal, this study tested whether transient changes in EEG oscillations in the alpha frequency range (8–12 Hz) following performance mistakes may reflect error‐evoked arousal. In the inter‐trial interval following performance mistakes (approximately 8% of trials), pupil diameter increased and EEG alpha power decreased, compared to the inter‐trial interval following correct responses. Moreover when trials were binned based on pupil diameter on a within‐subjects basis, trials with greater pupil diameter were associated with lower EEG alpha power during the inter‐trial interval. This pattern of association suggests that error‐related alpha suppression, like pupil dilation, reflects arousal in response to error commission. Errors were also followed by worse next‐trial performance, implying that error‐evoked arousal may not always be beneficial for adaptive control.

Highlights We present a new imaging study of 200 adults experiencing depression and anxiety. Quantitative measures of image quality indicate comparable quality to the HCP-YA. In addition, a comprehensive set of assessments measured patients’ symptom profiles. Data will be publicly available through the NIMH Data Archive starting fall 2020.

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Resting state functional connectivity (rsFC) offers promise for individualizing stimulation targets for transcranial magnetic stimulation (TMS) treatments. However, current targeting approaches do not account for non-focal TMS effects or large-scale connectivity patterns. To overcome these limitations, we propose a novel targeting optimization approach that combines whole-brain rsFC and electric-field (e-field) modelling to identify single-subject, symptom-specific TMS targets. In this proof of concept study, we recruited 91 anxious misery (AM) patients and 25 controls. We measured depression symptoms (MADRS/HAMD) and recorded rsFC. We used a PCA regression to predict symptoms from rsFC and estimate the parameter vector, for input into our e-field augmented model. We modeled 17 left dlPFC and 7 M1 sites using 24 equally spaced coil orientations. We computed single-subject predicted ΔMADRS/HAMD scores for each site/orientation using the e-field augmented model, which comprises a linear combination of the following elementwise products (1) the estimated connectivity/symptom coefficients, (2) a vectorized e-field model for site/orientation, (3) rsFC matrix, scaled by a proportionality constant. In AM patients, our connectivity-based model predicted a significant decrease depression for sites near BA9, but not M1 for coil orientations perpendicular to the cortical gyrus. In control subjects, no site/orientation combination showed a significant predicted change. These results corroborate previous work suggesting the efficacy of left dlPFC stimulation for depression treatment, and predict better outcomes with individualized targeting. They also suggest that our novel connectivity-based e-field modelling approach may effectively identify potential TMS treatment responders and individualize TMS targeting to maximize the therapeutic impact.

Introduction: Dimensional psychopathology strives to associate different domains of cognitive dysfunction with brain circuitry. Connectivity patterns as measured by functional magnetic resonance imaging (fMRI) exist at multiple scales, with global networks of connectivity composed of microscale interactions between individual nodes. It remains unclear how separate dimensions of psychopathology might differentially impact these different scales of organization. Methods: Patients experiencing anxious misery symptomology (depression, anxiety and trauma; n = 192) were assessed for symptomology and received resting-state fMRI scans. Three modeling approaches (seed-based correlation analysis [SCA], support vector regression [SVR] and Brain Basis Set Modeling [BSS]), each relying on increasingly dense representations of functional connectivity patterns, were used to associate connectivity patterns with six different dimensions of psychopathology: anxiety sensitivity, anxious arousal, rumination, anhedonia, insomnia and negative affect. Importantly, a full 50 patients were held-out in a testing dataset, leaving 142 patients as training data. Results: Different symptom dimensions were best modeled by different scales of brain connectivity: anhedonia and anxiety sensitivity were best modeled with single connections (SCA), insomnia and anxious arousal by mesoscale patterns (SVR) and negative affect and ruminative thought by broad, cortex-spanning patterns (BBS). Dysfunction within the default mode network was implicated in all symptom dimensions that were best modeled by multivariate models. Conclusion: These results suggest that symptom dimensions differ in the degree to which they impact different scales of brain organization. In addition to advancing our basic understanding of transdiagnostic psychopathology, this has implications for the translation of basic research paradigms to human disorders.