Altered attentive bias towards interpersonal communication information across phases of schizophrenia: an eye-tracking study

Identifying depression with mixed features: the potential value of eye-tracking features

Objectives

To investigate the utility of eye-tracking features as a neurobiological marker for identifying depression with mixed features (DMF), a psychiatric disorder characterized by the presence of depressive symptoms alongside subsyndromal manic features, thereby complicating both diagnosis and therapeutic intervention.

Methods

A total of 93 participants were included, comprising 41 patients with major depressive disorder (MDD), of whom 20 were classified as DMF, and 52 healthy controls (HC). Eye-tracking features were collected using an infrared-based device, and participants were evaluated using clinical scales including the Montgomery-Åsberg Depression Rating Scale (MADRS), Young Mania Rating Scale (YMRS), and Brief Psychiatric Rating Scale (BPRS). Performance of extreme gradient boosting (XGBoost) model based on demographic and clinical characteristics was compared with that of the model created after adding ocular movement data.

Results

Significant differences were observed in certain eye-tracking features between DMF, MDD, and HC, particularly in orienting saccades and overlapping saccades. Incorporating eye-tracking features into the XGBoost model enhanced the predictive accuracy for DMF, as evidenced by an increase in the area under the curve (AUC) from 0.571 to 0.679 (p < 0.05), representing an 18.9% improvement. This suggests a notable enhancement in the model's ability to distinguish DMF from other groups. The velocity of overlapping saccades and task completion time during free viewing were identified as significant predictive factors.

Conclusion

Eye-tracking features, especially the velocity of overlapping saccades and free viewing task completion time, hold potential as non-invasive biomarkers for the identification of DMF. The integration of these parameters into the XGBoost machine learning model significantly improved the accuracy of DMF diagnosis, offering a promising approach for enhancing clinical decision-making in psychiatric settings.

Attentional biases in facial emotion processing in individuals at clinical high risk for psychosis

Individuals at clinical high risk (CHR) for psychosis exhibit altered facial emotion processing (FEP) and poor social functioning. It is unclear whether FEP deficits result from attentional biases, and further, how these abnormalities are linked to symptomatology (e.g., negative symptoms) and highly comorbid disorders that are also tied to abnormal FEP (e.g., depression). In the present study, we employed an eye-tracking paradigm to assess attentional biases and clinical interviews to examine differences between CHR (N = 34) individuals and healthy controls (HC; N = 46), as well as how such biases relate to symptoms and functioning in CHR individuals. Although no CHR-HC differences emerged in attentional biases, within the CHR group, symptoms and functioning were related to biases. Depressive symptoms were related to some free-view attention switching biases (e.g., to and from fearful faces, r = .50). Negative symptoms were related to more slowly disengaging from happy faces (r = .44), spending less time looking at neutral faces (r =  - .42), and more time looking at no face (Avolition, r = .44). In addition, global social functioning was related to processes that overlapped with both depression and negative symptoms, including time looking at no face (r =  - .68) and free-view attention switching with fearful faces (r =  - .40). These findings are consistent with previous research, indicating that negative symptoms play a prominent role in the CHR syndrome, with distinct mechanisms relative to depression. Furthermore, the results suggest that attentional bias indices from eye-tracking paradigms may be predictive of social functioning.