Background Borderline personality disorder (BPD) is partly characterized by chronic instability in interpersonal relationships, which exacerbates other symptom dimensions of the disorder and can interfere with treatment engagement. Facial emotion recognition paradigms have been used to investigate the bases of interpersonal impairments in BPD, yielding mixed results. We sought to clarify and extend past findings by using the Reading the Mind in the Eyes Test (RMET), a measure of the capacity to discriminate the mental state of others from expressions in the eye region of the face. Method Thirty individuals diagnosed with BPD were compared to 25 healthy controls (HCs) on RMET performance. Participants were also assessed for depression severity, emotional state at the time of assessment, history of childhood abuse, and other Axis I and personality disorders (PDs). Results The BPD group performed significantly better than the HC group on the RMET, particularly for the Total Score and Neutral emotional valences. Effect sizes were in the large range for the Total Score and for Neutral RMET performance. The results could not be accounted for by demographics, co-occurring Axis I or II conditions, medication status, abuse history, or emotional state. However, depression severity partially mediated the relationship between RMET and BPD status. Conclusions Mental state discrimination based on the eye region of the face is enhanced in BPD. An enhanced sensitivity to the mental states of others may be a basis for the social impairments in BPD.

Summary Objective Differentiating pathological and physiological high-frequency oscillations (HFOs) is challenging. In patients with focal epilepsy, HFOs occur during the transitional periods between the up and down state of slow waves. The preferred phase angles of this form of phase-event amplitude coupling are bimodally distributed, and the ripples (80–150 Hz) that occur during the up-down transition more often occur in the seizure onset zone (SOZ). We investigated if bimodal ripple coupling was also evident for faster sleep oscillations, and could identify the SOZ. Methods Using an automated ripple detector, we identified ripple events in 40–60 minute intracranial EEG (iEEG) recordings from 23 patients with medically refractory mesial temporal lobe or neocortical epilepsy. The detector quantified epochs of sleep oscillations and computed instantaneous phase. We utilized a ripple phasor transform, ripple-triggered averaging, and circular statistics to investigate phase event-amplitude coupling. Results We found that at some individual recording sites, ripple event amplitude was coupled with sleep oscillatory phase and the preferred phase angles exhibited two distinct clusters (p<0.05). The distribution of the pooled mean preferred phase angle, defined by combining the means from each cluster at each individual recording site, also exhibited two distinct clusters (p<0.05). Based on the range of preferred phase angles defined by these two clusters, we partitioned each ripple event at each recording site into two groups: depth iEEG peak-trough and trough-peak. The mean ripple rates of the two groups in the SOZ and NSOZ were compared. We found that in the frontal (spindle, p=0.009; theta, p=0.006, slow, p=0.004) and parietal lobe (theta, p=0.007, delta, p=0.002, slow, p=0.001) the SOZ incidence rate for the ripples occurring during the trough-peak transition was significantly increased. Significance Phase-event amplitude coupling between ripples and sleep oscillations may be useful to distinguish pathological and physiological events in patients with frontal and parietal SOZ.

Deficits in attention have been implicated in Obsessive-Compulsive Disorder (OCD), yet their neurobiological bases are poorly understood. In unmedicated adults with OCD (n=30) and healthy controls (n=32), we used resting state functional connectivity MRI (rs-fcMRI) to examine functional connectivity between two neural networks associated with attentional processes: the default mode network (DMN) and the salience network (SN). We then used path analyses to examine putative relationships across three variables of interest: DMN-SN connectivity, attention, and OCD symptoms. In the OCD compared to healthy control participants, there was significantly reduced inverse connectivity between the anterior medial prefrontal cortex (amPFC) and the anterior insular cortex, regions within the DMN and SN, respectively. In OCD, reduced inverse DMN-SN connectivity was associated with both increased OCD symptom severity and decreased sustained attention. Path analyses were consistent with a potential mechanistic explanation: OCD symptoms are associated with an imbalance in DMN-SN networks that subserve attentional processes and this effect of OCD on DMN-SN connectivity is associated with decreased sustained attention. This work builds upon a growing literature suggesting that reduced inverse DMN-SN connectivity may represent a trans-diagnostic marker of attentional processes and suggests a potential mechanistic account of the relationship between OCD and attention. Reduced inverse DMN-SN connectivity may be an important target for treatment development to improve attention in individuals with OCD.

Objective To develop a reliable software method using a topographic analysis of time-frequency plots to distinguish ripple (80–200 Hz) oscillations that are often associated with EEG sharp waves or spikes (RonS) from sinusoid-like waveforms that appear as ripples but correspond with digital filtering of sharp transients contained in the wide bandwidth EEG. Methods A custom algorithm distinguished true from false ripples in one second intracranial EEG (iEEG) recordings using wavelet convolution, identifying contours of isopower, and categorizing these contours into sets of open or closed loop groups. The spectral and temporal features of candidate groups were used to classify the ripple, and determine its duration, frequency, and power. Verification of detector accuracy was performed on the basis of simulations, and visual inspection of the original and band-pass filtered signals. Results The detector could distinguish simulated true from false ripple on spikes (RonS). Among 2934 visually verified trials of iEEG recordings and spectrograms exhibiting RonS the accuracy of the detector was 88.5% with a sensitivity of 81.8% and a specificity of 95.2%. The precision was 94.5% and the negative predictive value was 84.0% (N = 12). Among, 1,370 trials of iEEG recording exhibiting RonS that were reviewed blindly without spectrograms the accuracy of the detector was 68.0%, with kappa equal to 0.01 ± 0.03. The detector successfully distinguished ripple from high spectral frequency ‘fast ripple’ oscillations (200–600 Hz), and characterize ripple duration and spectral frequency and power. The detector was confounded by brief bursts of gamma (30–80 Hz) activity in 7.31 ± 6.09% of trials, and in 30.2 ± 14.4% of the true RonS detections ripple duration was underestimated. Conclusions Characterizing the topographic features of a time-frequency plot generated by wavelet convolution is useful for distinguishing true oscillations from false oscillations generated by filter ringing. Significance Categorizing ripple oscillations and characterizing their properties can improve the clinical utility of the biomarker.