The Association among Default Mode Network Functional Connectivity, Mentalization, and Psychopathology in a Nonclinical Sample: An eLORETA Study

A model for the diagnosis of anxiety in patients with epilepsy based on phase locking value and Lempel-Ziv complexity features of the electroencephalogram

OBJECTIVE

Anxiety disorders (AD) are critical factors that significantly (about one-fifth) impact the quality of life (QoL) in patients with epilepsy (PWE). Objective diagnostic methods have contributed to the identification of PWE susceptible to AD. This study aimed to identify AD in PWE by constructing a diagnostic model based on the phase locking value (PLV) and Lempel-Ziv Complexity (LZC) features of the electroencephalogram (EEG).

METHODS

EEG data from 131 patients with epilepsy (PWE) were enrolled in this study. Patients were divided into two groups, anxiety disorder (AD, n = 61) and non-anxiety disorder (NAD, n = 70), according to the Hamilton Rating Scale for Anxiety (HAM-A). Support vector machine (SVM) and K-Nearest-Neighbor(KNN) algorithms were used to construct three models - the PLVEEG, LZCEEG, and PLVEEG + LZCEEG feature models. Finally, the area under the receiver operating characteristic curve (AUC) and statistical analyses were performed to evaluate the model performance.

RESULTS

The efficiency of the KNN-based PLCEEG + LZCEEG feature model was the best, and the accuracy, precision, recall, F1-score, and AUC of the model after five-fold cross-validations scores were 87.89 %, 82.27 %, 98.33 %, 88.95 %, and 0.89, respectively. When the model efficiency was optimal, 29 EEG features were suggested. Further analysis of these features indicated 22 EEG features that were significantly different between the two groups, including 50 % features of the alpha (α)-band.

CONCLUSIONS

The PLVEEG + LZCEEG model features can identify AD in PWE. The PLVEEG and LZCEEG characteristics of the α-band may further be explored as potential biomarkers for AD in PWE.

Cooperation within the therapeutic relationship improves metacognitive functioning: preliminary findings

Both clinical observations and empirical data suggest that metacognitive functioning is a factor strongly associated with a good psychotherapeutic outcome. It has been suggested that some interpersonal social motivations (i.e., attachment and cooperation) may be associated with different levels of metacognitive functioning also within the therapeutic relationship. The aim of this study was to explore the relationship between cooperation and metacognitive monitoring within 58 psychotherapy sessions from seven different patients. All patients were initially assessed through a detailed psychiatric interview. Patients' adult attachment styles were evaluated using the Attachment Style Questionnaire. The association between the activation of patients' interpersonal social motivations (e.g., cooperation and attachment) and the modifications of metacognitive abilities during sessions was investigated using the Assessing Interpersonal Motivations in Transcripts method and the Metacognition Assessment Scale have been used. Our results showed that the activation of the patient's cooperative system is positively associated with an increase in metacognitive functioning, while the activation of attachment is not. The results of the present study have important implications for clinicians: they give empirical support for the role of cooperation in fostering metacognition within the therapeutic relationship.

Problematic Use of the Internet Mediates the Association between Reduced Mentalization and Suicidal Ideation: A Cross-Sectional Study in Young Adults

Suicide is a major public health problem, and it is urgent to investigate its underlying clinical and psychological concomitants. It has been suggested that low mentalization skills and problematic use of the internet (PUI) are factors that can play a role in suicidal behaviors. It is possible that poor mentalization skills contribute to leading to forms of PUI, which, in turn, can represent triggers for suicidal ideation (SI). We tested this hypothesis through a quantitative and cross-sectional study on a sample (n = 623) of young adults (age range: 18−34). Self-report measures investigating symptoms related to Social Media Addiction (SMA), Internet Gaming Disorder (IGD), mentalization capacity, and SI were used. A single mediation analysis with two mediators was carried out to evaluate the direct and indirect effects of mentalization on SI through the mediating role of SMA- and IGD-related symptoms, controlling for potential confounding factors (e.g., socio-demographic and addiction-related variables). The four explored variables were significantly associated with each other (all p < 0.001) across all subjects; the mediational model showed that the total effect of mentalization on SI was significant (B = −0.821, SE = 0.092 (95% CI: −1.001; −0.641)) and that both SMA- (B = −0.073, SE = 0.034 (95% CI: −0.145; −0.008)) and IGD-related symptoms (B = 0.046, SE = 0.027 (95% CI: −0.107; −0.001)) were significant mediators of such association. Our findings support the possibility that PUI severity plays a relevant role in mediating the association between low mentalization skills and levels of SI.

Decreased effective connectivity between insula and anterior cingulate cortex during a working memory task after prolonged sleep deprivation

Total sleep deprivation (TSD) causes a decline in almost all cognitive domains, especially working memory. However, we do not have a clear understanding of the degree working memory is impaired under prolonged TSD, nor do we know the underlying neurophysiological mechanism. In this study, we recorded EEG data from 64 subjects while they performed a working memory task during resting wakefulness, after 24 h TSD, and after 30 h TSD. ANOVA was used to verify performance differences between 24 h and 30 h TSD in working memory tasks: (1) reaction time and accuracy hit rates, (2) P200, N200, and P300 amplitude and latency in measurements of event-related potential, as well as (3) effective connectivity strength between brain areas associated with working memory. Compared to 24 h TSD, 30 h TSD significantly decreased accuracy hit rates and induced a larger N200 difference waveform. The effective connectivity analysis showed that 30 h TSD also decreased beta frequency in effective connection strength from the right insular lobe to the left anterior cingulate cortex (ACC). Effective connection from the left ventrolateral prefrontal cortex to the left dorsolateral prefrontal cortex increased in the match condition of the 2-back task. In conclusion, 30 h TSD had a greater negative impact on working memory than 24 h TSD. This impairment of working memory is associated with decreased strength in the effective connection from the right insula to the left ACC.

Mentalization Impairment Is Associated with Problematic Alcohol Use in a Sample of Young Adults: A Cross-Sectional Study

Background: Alcohol is one of the most widely used drugs among adolescents and young people, and problematic alcohol use (PAU) is related to significant long-term biological, clinical, and psychosocial sequelae. Although preliminary reports have linked deficits in mentalization to increased vulnerability to addiction, no studies have specifically explored this phenomenon in relation to PAU. Methods: The association between mentalization impairment and PAU severity was investigated in a sample of 271 young adults (183 females, 65.9%; mean age: 23.20 ± 3.55 years; range: 18–34). Self-report measures investigating PAU and mentalization were administered to all participants. Results: Individuals with PAU reported a more frequent use of tobacco and illicit drugs in the last 12 months. PAU severity was negatively associated with mentalization capacity (rho = −0.21; p < 0.001), and also, when possible, confounding variables (i.e., gender, age, occupation, education, tobacco and illegal drugs use) were controlled for (rho = −0.17; p = 0.004). Conclusion: The present data showed that mentalization impairment is significantly associated with PAU among young adults, suggesting that it may have a role in the development and/or maintenance of alcohol use.

Predicting individual improvement in schizophrenia symptom severity at 1-year follow-up: Comparison of connectomic, structural, and clinical predictors

In a machine learning setting, this study aims to compare the prognostic utility of connectomic, brain structural, and clinical/demographic predictors of individual change in symptom severity in individuals with schizophrenia. Symptom severity at baseline and 1-year follow-up was assessed in 30 individuals with a schizophrenia-spectrum disorder using the Brief Psychiatric Rating Scale. Structural and functional neuroimaging was acquired in all individuals at baseline. Machine learning classifiers were trained to predict whether individuals improved or worsened with respect to positive, negative, and overall symptom severity. Classifiers were trained using various combinations of predictors, including regional cortical thickness and gray matter volume, static and dynamic resting-state connectivity, and/or baseline clinical and demographic variables. Relative change in overall symptom severity between baseline and 1-year follow-up varied markedly among individuals (interquartile range: 55%). Dynamic resting-state connectivity measured within the default-mode network was the most accurate single predictor of change in positive (accuracy: 87%), negative (83%), and overall symptom severity (77%) at follow-up. Incorporating predictors based on regional cortical thickness, gray matter volume, and baseline clinical variables did not markedly improve prediction accuracy and the prognostic utility of these predictors in isolation was moderate (<70%). Worsening negative symptoms at 1-year follow-up were predicted by hyper-connectivity and hypo-dynamism within the default-mode network at baseline assessment, while hypo-connectivity and hyper-dynamism predicted worsening positive symptoms. Given the modest sample size investigated, we recommend giving precedence to the relative ranking of the predictors investigated in this study, rather than the prediction accuracy estimates.

Altered brain network topology related to working memory in internet addiction

BACKGROUND AND AIMS

The working memory (WM) ability of internet addicts and the topology underlying the WM processing in internet addiction (IA) are poorly understood. In this study, we employed a graph theoretical framework to characterize the topological properties of the IA brain network in the source cortical space during WM task.

METHODS

A sample of 24 subjects with IA and 23 matched healthy controls (HCs) performed visual 2-back task. Exact Low Resolution Electromagnetic Tomography was adopted to project the pre-processed EEG signals into source space. Subsequently, Lagged phase synchronization was calculated between all pairs of Brodmann areas, the graph theoretical approaches were then employed to estimate the brain topological properties of all participants during the WM task.

RESULTS

We found better WM behavioral performance in IA subjects compared with the HCs. Moreover, compared to the HC group, more integrated and hierarchical brain network was revealed in the IA subjects in alpha band. And altered regional centrality was mainly resided in frontal and limbic lobes. In addition, significant relationships between the IA severity and the significant altered graph indices were found.

CONCLUSIONS

In conclusion, these findings provide evidence to support the notion that altered topological configuration may underline changed WM function observed in IA.

Default mode network alterations in individuals with high-trait-anxiety: An EEG functional connectivity study

BACKGROUND

Although several researches investigated Default Mode Network (DMN) alterations in individuals with anxiety disorders, up to now no studies have investigated DMN functional connectivity in non-clinical individuals with high-trait-anxiety using quantitative electroencephalography (EEG). Here, the main aim was to extend previous findings investigating the association between trait anxiety and DMN EEG functional connectivity.

METHODS

Twenty-three individuals with high-trait-anxiety and twenty-four controls were enrolled. EEG was recorded during 5 min of resting state (RS). EEG analyses were conducted by means of the exact Low-Resolution Electromagnetic Tomography software (eLORETA).

RESULTS

Compared to controls, individuals with high-trait-anxiety showed a decrease of theta connectivity between right medial prefrontal cortex (mPFC) and right posterior cingulate/retrosplenial cortex. A decrease of beta connectivity was also observed between right mPFC and right anterior cingulate cortex. Furthermore, DMN functional connectivity strength was negatively related with STAI-T total score (i.e., lower connectivity was associated with higher trait anxiety), even when controlling for potential confounding variables (i.e., sex, age, and general psychopathology).

LIMITATIONS

Small sample size makes it difficult to draw definitive conclusions. Furthermore, we did not assess state variation of anxiety, which make our interpretation specific to trait anxiety.

CONCLUSIONS

Taken together, our results suggest that high-trait-anxiety individuals fail to synchronize DMN during RS, reflecting a possible top-down cognitive control deficit. These results may help in the understanding of the individual differences in functional brain networks associated with trait anxiety, a crucial aim in the prevention and in the early etiology understanding of clinical anxiety and related sequelae.