Differential Impact of Anxious Misery Psychopathology on Multiple Representations of the Functional Connectome.

Background: One aim of characterizing dimensional psychopathology is associating different domains of affective dysfunction with brain circuitry. The functional connectome, as measured by functional magnetic resonance imaging, can be modeled and associated with psychopathology through multiple methods; some methods assess univariate relationships while others summarize broad patterns of activity. It remains unclear whether different dimensions of psychopathology require different representations of the connectome to generate reproducible associations. Methods: Patients experiencing anxious misery symptomology (depression, anxiety, and trauma; n = 192) received resting-state functional magnetic resonance imaging scans. Three modeling approaches (seed-based correlation analysis, edgewise regression, and brain basis set modeling), each relying on increasingly broader representations of the functional connectome, were used to associate connectivity patterns with six data-driven dimensions of psychopathology: anxiety sensitivity, anxious arousal, rumination, anhedonia, insomnia, and negative affect. To protect against overfitting, 50 participants were held out in a testing dataset, leaving 142 participants as training data. Results: Different modeling approaches varied in the extent to which they could model different symptom dimensions: seed-based correlation analysis failed to reproducibly model any symptoms, subsets of the connectome (edgewise regression) were sufficient to model insomnia and anxious arousal, and broad representations of the entire connectome (brain basis set modeling) were necessary to model negative affect and ruminative thought. Conclusions: These results indicate that different methods of representing the functional connectome differ in the degree that they can model different symptom dimensions, highlighting the potential sufficiency of subsets of connections for some dimensions and the necessity of connectome-wide approaches in others.

Proof of concept study to develop a novel connectivity-based electric-field modelling approach for individualized targeting of transcranial magnetic stimulation treatment.

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.

Effects of post-response arousal on cognitive control: Adaptive or maladaptive?

This study investigated whether detection of a performance mistake is followed by adaptive or detrimental effects on subsequent attention and performance. Using a Stroop task with spatial cueing, along with simultaneous EEG and pupillary measurements, we examined evidence bearing on two alternative hypotheses: maladaptive arousal and adaptive control. Error detection, indexed by the error-related negativity ERP component, was followed by pupil dilation and suppression of EEG oscillations in the alpha band, two indices of arousal that were associated with one another on a trial-by-trial basis. On the trials following errors, there was neural evidence of enhanced spatial cueing, manifested in greater hemispheric activation contralateral to the cued visual field. However, this post-error enhancement was not followed by changes in Stroop or spatial cueing effects in performance, nor by increased attentional cueing effects in ERP responses to targets. Rather, performance tended to be slower and less accurate following errors compared to correct trials, and higher post-response arousal, indexed by larger pupils, predicted next-trial slowing and decreased P2 amplitude to targets. Results favor the maladaptive arousal account of post-error cognitive control and offer only limited support for adaptive control.

Simultaneous EEG and pupillary evidence for post-error arousal during a speeded performance task.

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.

Dimensional connectomics of anxious misery, a human connectome study related to human disease: Overview of protocol and data quality.

Disparate diagnostic categories from the Diagnostic and Statistical Manual of Mental Disorders (DSM), including generalized anxiety disorder, major depressive disorder and post-traumatic stress disorder, share common behavioral and phenomenological dysfunctions. While high levels of comorbidity and common features across these disorders suggest shared mechanisms, past research in psychopathology has largely proceeded based on the syndromal taxonomy established by the DSM rather than on a biologically-informed framework of neural, cognitive and behavioral dysfunctions. In line with the National Institute of Mental Health's Research Domain Criteria (RDoC) framework, we present a Human Connectome Study Related to Human Disease that is intentionally designed to generate and test novel, biologically-motivated dimensions of psychopathology. The Dimensional Connectomics of Anxious Misery study is collecting neuroimaging, cognitive and behavioral data from a heterogeneous population of adults with varying degrees of depression, anxiety and trauma, as well as a set of healthy comparators (to date, n = 97 and n = 24, respectively). This sample constitutes a dataset uniquely situated to elucidate relationships between brain circuitry and dysfunctions of the Negative Valence construct of the RDoC framework. We present a comprehensive overview of the eligibility criteria, clinical procedures and neuroimaging methods of our project. After describing our protocol, we present group-level activation maps from task fMRI data and independent components maps from resting state data. Finally, using quantitative measures of neuroimaging data quality, we demonstrate excellent data quality relative to a subset of the Human Connectome Project of Young Adults (n = 97), as well as comparable profiles of cortical thickness from T1-weighted imaging and generalized fractional anisotropy from diffusion weighted imaging. This manuscript presents results from the first 121 participants of our full target 250 participant dataset, timed with the release of this data to the National Institute of Mental Health Data Archive in fall 2020, with the remaining half of the dataset to be released in 2021.