Mass-univariate Analysis Research Articles

Effects of Inversion and Fixation Location on the Processing of Face and House Stimuli - A Mass Univariate Analysis.

Most Event Related Potential studies investigating the time course of visual processing have focused mainly on the N170 component. Stimulus orientation affects the N170 amplitude for faces but not for objects, a finding interpreted as reflecting holistic/configural processing for faces and featural processing for objects. Furthermore, while recent studies suggest where on the face people fixate impacts the N170, fixation location effects have not been investigated in objects. A data-driven mass univariate analysis (all time points and electrodes) was used to investigate the time course of inversion and fixation location effects on the neural processing of faces and houses. Strong and widespread orientation effects were found for both faces and houses, from 100-350ms post-stimulus onset, including P1 and N170 components, and later, a finding arguing against a lack of holistic processing for houses. While no clear fixation effect was found for houses, fixation location strongly impacted face processing early, reflecting retinotopic mapping around the C2 and P1 components, and during the N170-P2 interval. Face inversion effects were also largest for nasion fixation around 120ms. The results support the view that facial feature integration (1) depends on which feature is being fixated and where the other features are situated in the visual field, (2) occurs maximally during the P1-N170 interval when fixation is on the nasion and (3) continues past 200ms, suggesting the N170 peak, where weak effects were found, might be an inflexion point between processes rather than the end of a feature integration into a whole process.

Stimulus-related oscillatory brain activity discriminates hoarding disorder from OCD and healthy controls

Hoarding disorder (HD) and obsessive-compulsive disorder (OCD) are highly comorbid and genetically related, but their similarities and differences at the neural level are not well characterized. The present study examined the time-frequency information contained in stimulus-related EEG data as participants worked on a visual flanker task. Three groups were included: participants diagnosed with HD (N = 33), OCD (N = 26), and healthy controls (N = 35). Permutation-controlled mass-univariate analyses found no differences between groups in terms of the magnitude of the oscillatory responses. Differences between groups were found selectively for phase-based measures (phase-locking across trials and across sensors) in time ranges well after those consistent with initial visuocortical processes, in the alpha (10 Hz) as well as theta and beta frequency bands, centered around 6 Hz and 15 Hz, respectively. Specifically, HD showed attenuated phase locking in theta and alpha compared to OCD and HC, while OCD showed heightened inter-site phase locking in alpha/beta. Including age as a covariate attenuated, but did not eliminate, the group differences. These findings point to signatures of cortical dynamics and cortical communication task processing that are unique to HD, and which are specifically present during higher-order visual cognition such as stimulus-response mapping, response selection, and action monitoring.

No association between error-related ERPs and trait anxiety in a nonclinical sample: Convergence across analytical methods including mass-univariate statistics.

Enhanced error monitoring, as indexed by increased amplitude of the error-related negativity (ERN) event-related potential (ERP) component, has been suggested to reflect a vulnerability neuro-marker of anxiety disorders. Another error-related ERP component is the error positivity (Pe), which reflects late-stage error processing. The associations between heightened ERN and Pe amplitudes and anxiety levels in the nonclinical population have been inconsistent. In this preregistered study, we examined the association between anxiety, ERN, and Pe, using different analytical methods (mass-univariate analyses, MUAs and conventional analyses), self-reported anxiety scales (STAI and STICSA), and trial numbers (all correct trials and equal numbers of correct and error trials). In a sample of 82 healthy adults, both conventional and MUAs demonstrated a robust enhancement of the ERN and Pe to errors relative to the correct-response ERPs. However, the mass-univariate approach additionally unveiled a wider array of electrodes and a longer effect duration for this error enhancement. Across the analytic methods, the results showed a lack of consistent correlation between trait anxiety and error-related ERPs. Findings were not modulated by trial numbers, analyses, or anxiety scales. The present results suggest a lack of enhancement of error monitoring by anxious traits in individuals with subclinical anxiety and those with clinical anxiety but without a clinical diagnosis. Importantly, the absence of such correlation questions the validity of the ERN as a neural marker for anxiety disorders. Future studies that investigate neuro-markers of anxiety may explore alternative task designs and employ robust statistics to provide a more comprehensive understanding of anxiety vulnerability.

Brain2GAN: Feature-disentangled neural encoding and decoding of visual perception in the primate brain.

A challenging goal of neural coding is to characterize the neural representations underlying visual perception. To this end, multi-unit activity (MUA) of macaque visual cortex was recorded in a passive fixation task upon presentation of faces and natural images. We analyzed the relationship between MUA and latent representations of state-of-the-art deep generative models, including the conventional and feature-disentangled representations of generative adversarial networks (GANs) (i.e., z- and w-latents of StyleGAN, respectively) and language-contrastive representations of latent diffusion networks (i.e., CLIP-latents of Stable Diffusion). A mass univariate neural encoding analysis of the latent representations showed that feature-disentangled w representations outperform both z and CLIP representations in explaining neural responses. Further, w-latent features were found to be positioned at the higher end of the complexity gradient which indicates that they capture visual information relevant to high-level neural activity. Subsequently, a multivariate neural decoding analysis of the feature-disentangled representations resulted in state-of-the-art spatiotemporal reconstructions of visual perception. Taken together, our results not only highlight the important role of feature-disentanglement in shaping high-level neural representations underlying visual perception but also serve as an important benchmark for the future of neural coding.

Spatial functional reorganizations can serve as potential biomarkers of post facial palsy synkinesis.

Facial palsy can result in a serious complication known as facial synkinesis, causing both physical and psychological harm to the patients. There is growing evidence that patients with facial synkinesis have brain abnormalities, but the brain mechanisms and underlying imaging biomarkers remain unclear. Here, we employed functional magnetic resonance imaging (fMRI) to investigate brain function in 31 unilateral post facial palsy synkinesis patients and 25 healthy controls during different facial expression movements and at rest. Combining surface-based mass-univariate analysis and multivariate pattern analysis, we identified diffused activation and intrinsic connection patterns in the primary motor cortex and the somatosensory cortex on the patient's affected side. Further, we classified post facial palsy synkinesis patients from healthy subjects with favorable accuracy using the support vector machine based on both task-related and resting-state functional magnetic resonance imaging data. Together, these findings indicate the potential of the identified functional reorganizations to serve as neuroimaging biomarkers for facial synkinesis diagnosis.

High-density ear-EEG for understanding ear-centered EEG

Background. Mobile ear-EEG provides the opportunity to record EEG unobtrusively in everyday life. However, in real-life, the EEG data quickly becomes difficult to interpret, as the neural signal is contaminated by other, non-neural signal contributions. Due to the small number of electrodes in ear-EEG devices, the interpretation of the EEG becomes even more difficult. For meaningful and reliable ear-EEG, it is crucial that the brain signals we wish to record in real life are well-understood and that we make optimal use of the available electrodes. Their placement should be guided by prior knowledge about the characteristics of the signal of interest. Objective. We want to understand the signal we record with ear-EEG and make recommendations on how to optimally place a limited number of electrodes. Approach. We built a high-density ear-EEG with 31 channels spaced densely around one ear. We used it to record four auditory event-related potentials (ERPs): the mismatch negativity, the P300, the N100 and the N400. With this data, we gain an understanding of how different stages of auditory processing are reflected in ear-EEG. We investigate the electrode configurations that carry the most information and use a mass univariate ERP analysis to identify the optimal channel configuration. We additionally use a multivariate approach to investigate the added value of multi-channel recordings. Main results. We find significant condition differences for all ERPs. The different ERPs vary considerably in their spatial extent and different electrode positions are necessary to optimally capture each component. In the multivariate analysis, we find that the investigation of the ERPs benefits strongly from multi-channel ear-EEG. Significance. Our work emphasizes the importance of a strong theoretical and practical background when building and using ear-EEG. We provide recommendations on finding the optimal electrode positions. These results will guide future research employing ear-EEG in real-life scenarios.

Grey matter structure within the visual networks in migraine with aura: multivariate and univariate analyses.

The visual cortex is involved in the generation of migraine aura. Voxel-based multivariate analyses applied to this region may provide complementary information about aura mechanisms relative to the commonly used mass-univariate analyses. Structural images constrained within the functional resting-state visual networks were obtained in migraine patients with (n = 50) and without (n = 50) visual aura and healthy controls (n = 50). The masked images entered a multivariate analysis in which Gaussian process classification was used to generate pairwise models. Generalizability was assessed by five-fold cross-validation and non-parametric permutation tests were used to estimate significance levels. A univariate voxel-based morphometry analysis was also performed. A multivariate pattern of grey matter voxels within the ventral medial visual network contained significant information related to the diagnosis of migraine with visual aura (aura vs. healthy controls: classification accuracy = 78%, p < 0.001; area under the curve = 0.84, p < 0.001; migraine with aura vs. without aura: classification accuracy = 71%, p < 0.001; area under the curve = 0.73, p < 0.003). Furthermore, patients with visual aura exhibited increased grey matter volume in the medial occipital cortex compared to the two other groups. Migraine with visual aura is characterized by multivariate and univariate patterns of grey matter changes within the medial occipital cortex that have discriminative power and may reflect pathological mechanisms.

LmeEEG: Mass linear mixed-effects modeling of EEG data with crossed random effects

BackgroundMixed-effects models are the current standard for the analysis of behavioral studies in psycholinguistics and related fields, given their ability to simultaneously model crossed random effects for subjects and items. However, they are hardly applied in neuroimaging and psychophysiology, where the use of mass univariate analyses in combination with permutation testing would be too computationally demanding to be practicable with mixed models. New methodHere, we propose and validate an analytical strategy that enables the use of linear mixed models (LMM) with crossed random intercepts in mass univariate analyses of EEG data (lmeEEG). It avoids the unfeasible computational costs that would arise from massive permutation testing with LMM using a simple solution: removing random-effects contributions from EEG data and performing mass univariate linear analysis and permutations on the obtained marginal EEG. ResultslmeEEG showed excellent performance properties in terms of power and false positive rate. Comparison with existing methodslmeEEG overcomes the computational costs of standard available approaches (our method was indeed more than 300 times faster). ConclusionslmeEEG allows researchers to use mixed models with EEG mass univariate analyses. Thanks to the possibility offered by the method described here, we anticipate that LMM will become increasingly important in neuroscience. Data and codes are available at osf.io/kw87a. The codes and a tutorial are also available at github.com/antovis86/lmeEEG.

Mass-univariate analysis of scalp ERPs reveals large effects of gaze fixation location during face processing that only weakly interact with face emotional expression

Decoding others’ facial expressions is critical for social functioning. To clarify the neural correlates of expression perception depending on where we look on the face, three combined gaze-contingent ERP experiments were analyzed using robust mass-univariate statistics. Regardless of task, fixation location impacted face processing from 50 to 350 ms, maximally around 120 ms, reflecting retinotopic mapping around C2 and P1 components. Fixation location also impacted majorly the N170-P2 interval while weak effects were seen at the face-sensitive N170 peak. Results question the widespread assumption that faces are processed holistically into an indecomposable perceptual whole around the N170. Rather, face processing is a complex and view-dependent process that continues well beyond the N170. Expression and fixation location interacted weakly during the P1-N170 interval, supporting a role for the mouth and left eye in fearful and happy expression decoding. Expression effects were weakest at the N170 peak but strongest around P2, especially for fear, reflecting task-independent affective processing. Results suggest N170 reflects a transition between processes rather than the maximum of a holistic face processing stage. Focus on this peak should be replaced by data-driven analyses of the epoch using robust statistics to fully unravel the early visual processing of faces and their affective content.

Methodologies for task-fMRI based prognostic biomarkers in response to aphasia treatment

With the diversity in aphasia coupled with diminished gains at the chronic phase, it is imperative to deliver effective rehabilitation plans. Treatment outcomes have therefore been predicted using lesion-to-symptom mapping, but this method lacks holistic functional information about the language-network. This study, therefore, aims to develop whole-brain task-fMRI multivariate analysis to neurobiologically inspect lesion impacts on the language-network and predict behavioral outcomes in persons with aphasia (PWA) undergoing language therapy. In 14 chronic PWA, semantic fluency task-fMRI and behavioral measures were collected to develop prediction methodologies for post-treatment outcomes. Then, a recently developed imaging-based multivariate method to predict behavior (i.e., LESYMAP) was optimized to intake whole-brain task-fMRI data, and systematically tested for reliability with mass univariate methods. We also accounted for lesion size in both methods. Results showed that both mass univariate and multivariate methods identified unique biomarkers for semantic fluency improvements from baseline to 2-weeks post-treatment. Additionally, both methods demonstrated reliable spatial overlap in task-specific areas including the right middle frontal gyrus when identifying biomarkers of language discourse. Thus whole-brain task-fMRI multivariate analysis has the potential to identify functionally meaningful prognostic biomarkers even for relatively small sample sizes. In sum, our task-fMRI based multivariate approach holistically estimates post-treatment response for both word and sentence production and may serve as a complementary tool to mass univariate analysis in developing brain-behavior relationships for improved personalization of aphasia rehabilitation regimens.

A-46 MMA Athletes Demonstrate Different Brain Vital Sign Profiles Compared to Matched Controls at “Baseline”

Abstract Purpose We compared objective brain vital sign metrics derived from event-related potentials (ERPs) of mixed martial arts (MMA) athletes and matched control participants. Methods We recorded 101 successful EEG scans from 12 MMA athletes (24.8 ± 2.6 yr) and 18 matched control participants (26.5±3.5 yr). For this analysis, only “baseline” scans were considered (n=30). “Baseline” was at least 90 days from a previous fight and 90 days (MMA) or 1 year (control) following medical clearance from a previously diagnosed concussion. We derived objective, clinical metrics (brain vital signs) from ERPs for auditory sensation (N100), basic attention (P300), and cognitive processing (N400) evoked while participants listen to a 6-minute auditory stimulus sequence. EEG data were processed using standard methods. N100, P300, and N400 amplitudes and latencies were manually identified on evoked potential waveforms by a blinded reviewer. Differences between groups at the waveform level were evaluated with a mass-univariate analysis using non-parametric resampling. Brain vital signs were compared across groups with a Kruskal-Wallis H-test for independent samples, with FDR correction for multiple comparisons. Results Mass-univariate waveform analyses showed significant differences (P &amp;lt; 0.05) between MMA athletes and controls in both P300 and N400 ERPs. The Kruskal-Wallis test identified a significant difference in N400 amplitude (H=7.04, P=.008, Padj=.047) between MMA athletes and controls. Conclusions MMA athletes demonstrate significant differences in “baseline” brain vital signs compared to controls, indicating that exposure to repetitive head impacts in MMA may be associated with subconcussive changes in brain function.

EEG Reveals Alterations in Motor Imagery in People With Amnestic Mild Cognitive Impairment.

Motor imagery has been used to investigate the cognitive mechanism of motor control. Although behavioral and electrophysiological changes in motor imagery in people with amnestic mild cognitive impairment (aMCI) have been reported, deficits in different types of imagery remain unclear. To explore this question, we used electroencephalography (EEG) to study neural correlates of visual imagery (VI) and kinesthetic imagery (KI) and their relationship to cognitive function in people with aMCI. A hand laterality judgment task was used to induce implicit motor imagery in 29 people with aMCI and 40 healthy controls during EEG recording. Mass univariate and multivariate EEG analysis was applied to explore group differences in a data-driven manner. Modulation of stimuli orientation to event-related potential (ERP) amplitudes differed significantly between groups at 2 clusters located in the posterior-parietal and frontal areas. Multivariate decoding revealed sufficient representation of VI-related orientation features in both groups. Relative to healthy controls, the aMCI group lacked accurate representation of KI-related biomechanical features, suggesting deficits in automatic activation of KI strategy. Electrophysiological correlates were associated with episodic memory, visuospatial function, and executive function. Higher decoding accuracy of biomechanical features predicted better executive function via longer response time in the imagery task in the aMCI group. These findings reveal electrophysiological correlates related to motor imagery deficits in aMCI, including local ERP amplitudes and large-scale activity patterns. Alterations in EEG activity are related to cognitive function in multiple domains, including episodic memory, suggesting the potential of these EEG indices as biomarkers of cognitive impairment.

Disruption to left inferior frontal cortex modulates semantic prediction effects in reading and subsequent memory: Evidence from simultaneous TMS-EEG.

Readers use prior context to predict features of upcoming words. When predictions are accurate, this increases the efficiency of comprehension. However, little is known about the fate of predictable and unpredictable words in memory or the neural systems governing these processes. Several theories suggest that the speech production system, including the left inferior frontal cortex (LIFC), is recruited for prediction but evidence that LIFC plays a causal role is lacking. We first examined the effects of predictability on memory and then tested the role of posterior LIFC using transcranial magnetic stimulation (TMS). In Experiment 1, participants read category cues, followed by a predictable, unpredictable, or incongruent target word for later recall. We observed a predictability benefit to memory, with predictable words remembered better than unpredictable words. In Experiment 2, participants performed the same task with electroencephalography (EEG) while undergoing event-related TMS over posterior LIFC using a protocol known to disrupt speech production, or over the right hemisphere homologue as an active control site. Under control stimulation, predictable words were better recalled than unpredictable words, replicating Experiment 1. This predictability benefit to memory was eliminated under LIFC stimulation. Moreover, while an a priori ROI-based analysis did not yield evidence for a reduction in the N400 predictability effect, mass-univariate analyses did suggest that the N400 predictability effect was reduced in spatial and temporal extent under LIFC stimulation. Collectively, these results provide causal evidence that the LIFC is recruited for prediction during silent reading, consistent with prediction-through-production accounts.

Attentional capture by fearful faces requires consciousness and is modulated by task-relevancy: A dot-probe EEG study.

In the current EEG study, we used a dot-probe task in conjunction with backward masking to examine the neural activity underlying awareness and spatial processing of fearful faces and the neural processes for subsequent cued spatial targets. We presented face images under different viewing conditions (subliminal and supraliminal) and manipulated the relation between a fearful face in the pair and a subsequent target. Our mass univariate analysis showed that fearful faces elicit the N2-posterior-contralateral, indexing spatial attention capture, only when they are presented supraliminally. Consistent with this, the multivariate pattern analysis revealed a successful decoding of the location of the fearful face only in the supraliminal viewing condition. Additionally, the spatial attention capture by fearful faces modulated the processing of subsequent lateralised targets that were spatially congruent with the fearful face, in both al and electrophysiological data. There was no evidence for nonconscious processing of the fearful faces in the current paradigm. We conclude that spatial attentional capture by fearful faces requires visual awareness and it is modulated by top-down task demands.

MULTI-VOXEL PATTERN ANALYSIS OF fMRI DATA DURING SELF- AND OTHER-REFERENTIAL PROCESSING

The study of brain activity in the processing of self-referential information, as compared to the processing of information related to other people, is based on the application of mass-univariate analysis, based on the assumption that activity in one region is independent of activity in other regions. Recently, there has been a growing interest in neuroimaging to investigate spatially distributed information using multivariate approaches such as multivoxel pattern analysis (MVPA). In this paper, we used MVPA to analyze fMRI data recorded during self-evaluation and evaluation of other people of varying proximity. In all pairwise classifications tested, the number of correct identifications was significantly higher than the level of random matches. Predictively significant structures were widely distributed over different brain regions and included areas of the visual, lateral prefrontal, and many other cortical areas in addition to the cortical midline structures that contributed the most. In the self-other classification, ventral areas of the medial prefrontal and cingulate cortex were the most informative for the self condition, whereas parietal and occipital medial areas were the most informative for the other condition. The combination of brain structures, which included the anterior cingulate cortex and both amygdalae, revealed by principal component analysis, correlated positively with the psychometric scale of sensitivity to reward, and negatively with neuroticism scales. Overall, the results show the fruitfulness of using machine learning methods to analyze data from such kinds of experiments.

Global diversity in individualized cortical network topography.

Individualized cortical network topography (ICNT) varies between people and exhibits great variability in the association networks in the human brain. However, these findings were mainly discovered in Western populations. It remains unclear whether and how ICNT is shaped by the non-Western populations. Here, we leveraged a multisession hierarchical Bayesian model to define individualized functional networks in White American and Han Chinese populations with data from both US and Chinese Human Connectome Projects. We found that both the size and spatial topography of individualized functional networks differed between White American and Han Chinese groups, especially in the heteromodal association cortex (including the ventral attention, control, language, dorsal attention, and default mode networks). Employing a support vector machine, we then demonstrated that ethnicity-related ICNT diversity can be used to identify an individual's ethnicity with high accuracy (74%, pperm < 0.0001), with heteromodal networks contributing most to the classification. This finding was further validated through mass-univariate analyses with generalized additive models. Moreover, we reveal that the spatial heterogeneity of ethnic diversity in ICNT correlated with fundamental properties of cortical organization, including evolutionary cortical expansion, brain myelination, and cerebral blood flow. Altogether, this case study highlights a need for more globally diverse and publicly available neuroimaging datasets.

Predictive modeling of optimism bias using gray matter cortical thickness

People have been shown to be optimistically biased when their future outcome expectancies are assessed. In fact, we display optimism bias (OB) toward our own success when compared to a rival individual’s (personal OB [POB]). Similarly, success expectancies for social groups we like reliably exceed those we mention for a rival group (social OB [SOB]). Recent findings suggest the existence of neural underpinnings for OB. Mostly using structural/functional MRI, these findings rely on voxel-based mass-univariate analyses. While these results remain associative in nature, an open question abides whether MRI information can accurately predict OB. In this study, we hence used predictive modelling to forecast the two OBs. The biases were quantified using a validated soccer paradigm, where personal (self versus rival) and social (in-group versus out-group) forms of OB were extracted at the participant level. Later, using gray matter cortical thickness, we predicted POB and SOB via machine-learning. Our model explained 17% variance (R2 = 0.17) in individual variability for POB (but not SOB). Key predictors involved the rostral-caudal anterior cingulate cortex, pars orbitalis and entorhinal cortex—areas that have been associated with OB before. We need such predictive models on a larger scale, to help us better understand positive psychology and individual well-being.

Machine Learning Based Scoring System to Predict the Risk and Severity of Ataxic Speech Using Different Speech Tasks.

The assessment of speech in Cerebellar Ataxia (CA) is time-consuming and requires clinical interpretation. In this study, we introduce a fully automated objective algorithm that uses significant acoustic features from time, spectral, cepstral, and non-linear dynamics present in microphone data obtained from different repeated Consonant-Vowel (C-V) syllable paradigms. The algorithm builds machine-learning models to support a 3-tier diagnostic categorisation for distinguishing Ataxic Speech from healthy speech, rating the severity of Ataxic Speech, and nomogram-based supporting scoring charts for Ataxic Speech diagnosis and severity prediction. The selection of features was accomplished using a combination of mass univariate analysis and elastic net regularization for the binary outcome, while for the ordinal outcome, Spearman's rank-order correlation criterion was employed. The algorithm was developed and evaluated using recordings from 126 participants: 65 individuals with CA and 61 controls (i.e., individuals without ataxia or neurotypical). For Ataxic Speech diagnosis, the reduced feature set yielded an area under the curve (AUC) of 0.97 (95% CI 0.90-1), the sensitivity of 97.43%, specificity of 85.29%, and balanced accuracy of 91.2% in the test dataset. The mean AUC for severity estimation was 0.74 for the test set. The high C-indexes of the prediction nomograms for identifying the presence of Ataxic Speech (0.96) and estimating its severity (0.81) in the test set indicates the efficacy of this algorithm. Decision curve analysis demonstrated the value of incorporating acoustic features from two repeated C-V syllable paradigms. The strong classification ability of the specified speech features supports the framework's usefulness for identifying and monitoring Ataxic Speech.

Clarifying the role of Cortico-Cortical and Amygdalo-Cortical brain dysconnectivity associated with Conduct Problems

A recent meta-analysis of resting-state functional connectivity studies revealed that individuals exhibiting antisocial behaviors or conduct problems may show disrupted brain connectivity in networks underpinning socio-affective and attentional processes. However, studies included in the meta-analysis generally rely on small sample sizes and substantially differ in terms of psychometric scales and neuroimaging methodologies. Therefore, we aimed to identify reliable functional brain connectivity alterations associated with severity of conduct problems using a large sample of adolescents and two measures of conduct problems. In a sample of 1416 children and adolescents, mass-univariate analyses of connectivity measures between 333 cortical parcels were conducted to examine the relationship between resting-state functional cortical-cortical connectome and the severity of conduct problems using the Child Behavior Checklist (CBCL) and the Strengths and Difficulties Questionnaire (SDQ). At a liberal threshold, results showed that the functional brain connectivity significantly associated with conduct problems largely differ between the two scales. Indeed, only 21 pairs of brain regions overlapped between the CBCL and SDQ. Permutation feature importance of these 21 brain connectivity measures revealed that connectivity between precentral/postcentral gyri and lateral prefrontal cortex (both ventral and dorsal) were the most important features in explaining variance in conduct problems. The current study highlights that psychometric measures may yield distinct functional connectivity results. Moreover, severity of conduct problems in children and adolescents was mainly associated with deficient functional connectivity of somatomotor and ventral attention networks indicating potential alterations in motor, cognitive and reward processes.

Rejection Distress Suppresses Medial Prefrontal Cortex in Borderline Personality Disorder

BackgroundBorderline personality disorder (BPD) is characterized by an elevated distress response to social exclusion (i.e., rejection distress), the neural mechanisms of which remain unclear. Functional magnetic resonance imaging studies of social exclusion have relied on the classic version of the Cyberball task, which is not optimized for functional magnetic resonance imaging. Our goal was to clarify the neural substrates of rejection distress in BPD using a modified version of Cyberball, which allowed us to dissociate the neural response to exclusion events from its modulation by exclusionary context. MethodsTwenty-three women with BPD and 22 healthy control participants completed a novel functional magnetic resonance imaging modification of Cyberball with 5 runs of varying exclusion probability and rated their rejection distress after each run. We tested group differences in the whole-brain response to exclusion events and in the parametric modulation of that response by rejection distress using mass univariate analysis. ResultsAlthough rejection distress was higher in participants with BPD (F1,40 = 5.25, p = .027, η2 = 0.12), both groups showed similar neural responses to exclusion events. However, as rejection distress increased, the rostromedial prefrontal cortex response to exclusion events decreased in the BPD group but not in control participants. Stronger modulation of the rostromedial prefrontal cortex response by rejection distress was associated with higher trait rejection expectation, r = −0.30, p = .050. ConclusionsHeightened rejection distress in BPD might stem from a failure to maintain or upregulate the activity of the rostromedial prefrontal cortex, a key node of the mentalization network. Inverse coupling between rejection distress and mentalization-related brain activity might contribute to heightened rejection expectation in BPD.