Social brain network correlates with real-life social network in individuals with schizophrenia and social anhedonia

Analysis of functional connectivity using machine learning and deep learning in different data modalities from individuals with schizophrenia

Objective. Schizophrenia(SCZ) is a severe mental disorder associated with persistent or recurrent psychosis, hallucinations, delusions, and thought disorders that affect approximately 26 million people worldwide, according to the World Health Organization. Several studies encompass machine learning (ML) and deep learning algorithms to automate the diagnosis of this mental disorder. Others study SCZ brain networks to get new insights into the dynamics of information processing in individuals suffering from the condition. In this paper, we offer a rigorous approach with ML and deep learning techniques for evaluating connectivity matrices and measures of complex networks to establish an automated diagnosis and comprehend the topology and dynamics of brain networks in SCZ individuals.Approach.For this purpose, we employed an functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) dataset. In addition, we combined EEG measures, i.e. Hjorth mobility and complexity, with complex network measurements to be analyzed in our model for the first time in the literature.Main results.When comparing the SCZ group to the control group, we found a high positive correlation between the left superior parietal lobe and the left motor cortex and a positive correlation between the left dorsal posterior cingulate cortex and the left primary motor. Regarding complex network measures, the diameter, which corresponds to the longest shortest path length in a network, may be regarded as a biomarker because it is the most crucial measure in different data modalities. Furthermore, the SCZ brain networks exhibit less segregation and a lower distribution of information. As a result, EEG measures outperformed complex networks in capturing the brain alterations associated with SCZ.Significance. Our model achieved an area under receiver operating characteristic curve (AUC) of 100% and an accuracy of 98.5% for the fMRI, an AUC of 95%, and an accuracy of 95.4% for the EEG data set. These are excellent classification results. Furthermore, we investigated the impact of specific brain connections and network measures on these results, which helped us better describe changes in the diseased brain.

The Relationship between Social Anhedonia and Perceived Pleasure from Food-An Exploratory Investigation on a Consumer Segment with Depression and Anxiety

Anhedonia, the diminished ability to experience pleasure, is a key symptom of a range of mental and neurobiological disorders and is associated with altered eating behavior. This research study investigated the concept of anhedonia in relation to mental disorders and the perception of pleasure from food to better understand the link between anhedonia and eating behavior. A consumer survey (n = 1051), including the Food Pleasure Scale, the Chapman Revised Social Anhedonia Scale, the Patient Health Questionnaire, and the Generalized Anxiety Disorder scale, was conducted to explore the perception of pleasure from food among people with anhedonic traits. Comparative analyses were performed between people with symptoms of depression and/or anxiety and people with no symptoms of these conditions. A segmentation analysis was furthermore performed based on three levels of anhedonia: Low, Intermediate and High anhedonia. Thus, insights into how food choice and eating habits may be affected by different levels of anhedonia are provided for the first time. Our findings showed that the 'Low anhedonia' segment found pleasure in all aspects of food pleasure, except for the aspect 'eating alone'. 'Eating alone' was, however, appreciated by the 'Intermediate anhedonia' and 'High anhedonia' segments. Both the 'Intermediate anhedonia' and 'High anhedonia' segments proved that their perceptions of food pleasure in general were affected by anhedonia, wherein the more complex aspects in particular, such as 'product information' and 'physical sensation', proved to be unrelated to food pleasure. For the 'High anhedonia' segment, the sensory modalities of food were also negatively associated with food pleasure, indicating that at this level of anhedonia the food itself is causing aversive sensations and expectations. Thus, valuable insights into the food pleasure profiles of people with different levels of anhedonia have been found for future research in the fields of mental illness, (food) anhedonia, and consumer behaviors.

Altered neural mechanism of social reward anticipation in individuals with schizophrenia and social anhedonia

Altered social reward anticipation could be found in schizophrenia (SCZ) patients and individuals with high levels of social anhedonia (SA). However, few research investigated the putative neural processing for altered social reward anticipation in these populations on the SCZ spectrum. This study aimed to examine the underlying neural mechanisms of social reward anticipation in these populations. Twenty-three SCZ patients and 17 healthy controls (HC), 37 SA individuals and 50 respective HCs completed the Social Incentive Delay (SID) imaging task while they were undertaking MRI brain scans. We used the group contrast to examine the alterations of BOLD activation and functional connectivity (FC, psychophysiological interactions analysis). We then characterized the beta-series social brain network (SBN) based on the meta-analysis results from NeuroSynth and examined their prediction effects on real-life social network (SN) characteristics using the partial least squared regression analysis. The results showed that SCZ patients exhibited hypo-activation of the left medial frontal gyrus and the negative FCs with the left parietal regions, while individuals with SA showed the hyper-activation of the left middle frontal gyrus when anticipating social reward. For the beta-series SBNs, SCZ patients had strengthened cerebellum-temporal FCs, while SA individuals had strengthened left frontal regions FCs. However, such FCs of the SBN failed to predict the real-life SN characteristics. These preliminary findings suggested that SCZ patients and SA individuals appear to exhibit altered neural processing for social reward anticipation, and such neural activities showed a weakened association with real-life SN characteristics.

Decoupling between hub-connected functional connectivity of the social brain network and real-world social network in individuals with social anhedonia

Altered hub regions in brain network have been consistently reported in patients with schizophrenia. However, it is unclear whether similar altered hub regions of the brain would be exhibited in individuals with subclinical features of schizophrenia such as social anhedonia (SA). In this study, we examined the hub regions of resting-state social brain network (SBN) of 35 participants with SA and 50 healthy controls (HC). We further examined the prediction effect of hub-connected FCs with SBN on the real-life social network characteristics. Our findings showed that the right amygdala, left temporal lobe and right media superior frontal gyrus were the hub regions of SBN both in SA and HC groups. In the SA group, the left temporal lobe connected functional connectivity (FC) did not predict social network characteristics, while the other FCs strengthened the association with social network characteristics. These findings were replicated in an independent sample of 33 SA and 32 HC. These findings suggested that the left temporal lobe as one of the hub regions of SBN exhibited the abnormality of their connected FCs in the association with social network characteristics in individuals with SA.

Hub-connected functional connectivity within social brain network weakens the association with real-life social network in schizophrenia patients

Hubs in the brain network are the regions with high centrality and are crucial in the network communication and information integration. Patients with schizophrenia (SCZ) exhibit wide range of abnormality in the hub regions and their connected functional connectivity (FC) at the whole-brain network level. Study of the hubs in the brain networks supporting complex social behavior (social brain network, SBN) would contribute to understand the social dysfunction in patients with SCZ. Forty-nine patients with SCZ and 27 healthy controls (HC) were recruited to undertake the resting-state magnetic resonance imaging scanning and completed a social network (SN) questionnaire. The resting-state SBN was constructed based on the automatic analysis results from the NeuroSynth. Our results showed that the left temporal lobe was the only hub of SBN, and its connected FCs strength was higher than the remaining FCs in both two groups. SCZ patients showed the lower association between the hub-connected FCs (compared to the FCs not connected to the hub regions) with the real-life SN characteristics. These results were replicated in another independent sample (30 SCZ and 28 HC). These preliminary findings suggested that the hub-connected FCs of SBN in SCZ patients exhibit the abnormality in predicting real-life SN characteristics.

Social brain network predicts real-world social network in individuals with social anhedonia

Social anhedonia (SA) impairs social functioning in schizophrenia. Previous evidence suggested that certain brain regions predict longitudinal change of real-world social outcomes, yet previous study designs have failed to capture the corresponding functional connectivity among the brain regions involved. This study measured the real-world social network in 22 pairs of individuals with high and low levels of SA, and followed up them for 21 months. We further explored whether resting-state social brain network characteristics could predict the longitudinal variations of real-world social network. Our results showed that social brain network characteristics could predict the change of real-world social networks in both the high SA and low SA groups. However, the results differed between the two groups, i.e., the topological characteristics of the social brain network predicted real-world social network change in the high SA group; whereas the functional connectivity within the social brain network predicted real-world social network change in the low SA group. Principal component analysis and linear regression analysis on the entire sample showed that the functional connectivity component centered at the right orbital inferior frontal gyrus could best predict social network change. Our findings support the notion that social brain network characteristics could predict social network development.