Effective differentiation between depressed patients and controls using discriminative eye movement features

Transformer-based fusion model for mild depression recognition with EEG and pupil area signals

Early detection and treatment are crucial for the prevention and treatment of depression; compared with major depression, current researches pay less attention to mild depression. Meanwhile, analysis of multimodal biosignals such as EEG, eye movement data, and magnetic resonance imaging provides reliable technical means for the quantitative analysis of depression. However, how to effectively capture relevant and complementary information between multimodal data so as to achieve efficient and accurate depression recognition remains a challenge. This paper proposes a novel Transformer-based fusion model using EEG and pupil area signals for mild depression recognition. We first introduce CSP into the Transformer to construct single-modal models of EEG and pupil data and then utilize attention bottleneck to construct a mid-fusion model to facilitate information exchange between the two modalities; this strategy enables the model to learn the most relevant and complementary information for each modality and only share the necessary information, which improves the model accuracy while reducing the computational cost. Experimental results show that the accuracy of the EEG and pupil area signals of single-modal models we constructed is 89.75% and 84.17%, the precision is 92.04% and 95.21%, the recall is 89.5% and 71%, the specificity is 90% and 97.33%, the F1 score is 89.41% and 78.44%, respectively, and the accuracy of mid-fusion model can reach 93.25%. Our study demonstrates that the Transformer model can learn the long-term time-dependent relationship between EEG and pupil area signals, providing an idea for designing a reliable multimodal fusion model for mild depression recognition based on EEG and pupil area signals.

Age- and Sex-Based Developmental Biomarkers in Eye Movements

BACKGROUND

Eye movement research serves as a critical tool for assessing brain function, diagnosing neurological and psychiatric disorders, and understanding cognition and behavior. Sex differences have largely been under reported or ignored in neurological research. However, eye movement features provide biomarkers that are useful for disease classification with superior accuracy and robustness compared to previous classifiers for neurological diseases. Neurological diseases have a sex specificity, yet eye movement analysis has not been specific to our understanding of sex differences.

METHODS

The study involved subjects recruited from 804 sites equipped with RightEye Vision Systems, primarily located in optometry practices across the United States. Subjects completed six eye movement assessments: circular smooth pursuit (CSP), horizontal smooth pursuit (HSP), vertical smooth pursuit (VSP), horizontal saccades (HS), vertical saccades (VS), and fixation stability (FS). Eye movements were analyzed and classified in accordance with age and sex by multiple t-tests and linear regression models.

RESULTS

This study represented a large sample size of 23,557 subjects, with 11,871 males and 11,686 females representing ages from birth through 80 years of age. We observed statistically significant differences for all eye movement functions between males and females.

CONCLUSIONS

We demonstrate that eye movements are sex-specific and offer normative data to compare sex-specific eye movement function by age. Novel baseline metrics can be compared to individual performance, regardless of sex. This study represents significant progress in linking eye movements with brain function and clinical syndromes, allowing researchers and clinicians to stratify individuals by age and sex.

Pupillary response to cognitive control in depression-prone individuals

Revealing the pupillary correlates of depression-prone individuals is conducive to the early intervention and treatment of depression. This study recruited 31 depression-prone and 31 healthy individuals. They completed an emotional task-switching task combined with a go/no-go task, and task-evoked pupillary responses (TEPR) were recorded. Behavioral results showed no significant differences in behavioral performance in terms of cognitive flexibility and inhibition between the depression-prone group and the healthy control group. The pupillary results revealed that (1) the depression-prone group showed slightly lower TEPRs to positive stimuli than the healthy controls during cue presentation; (2) during target presentation, the depression-prone group did not show an effect of emotional valence on the pupillary response in the task-repeat trials; and (3) compared to the healthy controls, the depression-prone group showed significantly smaller TEPRs to negative no-go stimuli and had a longer latency of the second peak of pupil dilation in no-go trials. These results imply that depression-prone individuals may have slower neural responses in cognitive control tasks and emotion-specific weakened cognitive control than healthy individuals.

Study protocol TransTAM: Transdiagnostic research into emotional disorders and cognitive-behavioral therapy of the adaptive mind

BACKGROUND

Emotional disorders such as depression and anxiety disorders share substantial similarities in their etiology and treatment. In recent decades, these commonalities have been increasingly recognized in classification systems and treatment programs crossing diagnostic boundaries.

METHODS

To examine the prospective effects of different transdiagnostic markers on relevant treatment outcomes, we plan to track a minimum of N = 200 patients with emotional disorders during their routine course of cognitive behavioral therapy at two German outpatient clinics. We will collect a wide range of transdiagnostic markers, ranging from basic perceptual processes and self-report measures to complex behavioral and neurobiological indicators, before entering therapy. Symptoms and psychopathological processes will be recorded before entering therapy, between the 20th and 24th therapy session, and at the end of therapy.

DISCUSSION

Our results could help to identify transdiagnostic markers with high predictive power, but also provide deeper insights into which patient groups with which symptom clusters are less likely to benefit from therapy, and for what reasons.

TRIAL REGISTRATION

The trial was preregistered at the German Clinical Trial Register (DRKS-ID: DRKS00031206; 2023-05-09).

(The limits of) eye-tracking with iPads

Applications for eye-tracking-particularly in the clinic-are limited by a reliance on dedicated hardware. Here we compare eye-tracking implemented on an Apple iPad Pro 11" (third generation)-using the device's infrared head-tracking and front-facing camera-with a Tobii 4c infrared eye-tracker. We estimated gaze location using both systems while 28 observers performed a variety of tasks. For estimating fixation, gaze position estimates from the iPad were less accurate and precise than the Tobii (mean absolute error of 3.2° ± 2.0° compared with 0.75° ± 0.43°), but fixation stability estimates were correlated across devices (r = 0.44, p < 0.05). For tasks eliciting saccades >1.5°, estimated saccade counts (r = 0.4-0.73, all p < 0.05) were moderately correlated across devices. For tasks eliciting saccades >8° we observed moderate correlations in estimated saccade speed and amplitude (r = 0.4-0.53, all p < 0.05). We did, however, note considerable variation in the vertical component of estimated smooth pursuit speed from the iPad and a catastrophic failure of tracking on the iPad in 5% to 20% of observers (depending on the test). Our findings sound a note of caution to researchers seeking to use iPads for eye-tracking and emphasize the need to properly examine their eye-tracking data to remove artifacts and outliers.

Using Pupil Diameter for Psychological Resilience Assessment in Medical Students Based on SVM and SHAP Model

Effectively assessing psychological resilience for medical students is vital for identifying at-risk individuals and developing tailored interventions. At present, few studies have combined physiological indexes of the human body and machine learning for psychological resilience assessment. This study presents a novel approach that employs pupil diameter features and machine learning to predict psychological resilience risk objectively. Firstly, we designed a stimulus paradigm (via auditory and visual stimuli) and collected pupil diameter data from participants using eye-tracking technology. Secondly, the pupil data was preprocessed, including linear interpolation, blink detection, and subtractive baseline correction. Thirdly, statistical metrics were extracted and optimal feature subsets were obtained by Recursive Feature Elimination with Cross-Validation (RFECV). Subsequently, the classification models, including Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost), were trained. The experimental results show that the SVM model has the best performance, and its balance accuracy, recall, and AUC reach 0.906, 0.89, and 0.932, respectively. Finally, we leveraged the Shapley additive explanation (SHAP) model for interpretability analysis. It revealed auditory stimuli have a more significant effect than visual stimuli in psychological resilience assessment. These findings suggested that pupil diameter could be a vital metric for assessing psychological resilience.

A review of machine learning in scanpath analysis for passive gaze-based interaction

The scanpath is an important concept in eye tracking. It refers to a person's eye movements over a period of time, commonly represented as a series of alternating fixations and saccades. Machine learning has been increasingly used for the automatic interpretation of scanpaths over the past few years, particularly in research on passive gaze-based interaction, i.e., interfaces that implicitly observe and interpret human eye movements, with the goal of improving the interaction. This literature review investigates research on machine learning applications in scanpath analysis for passive gaze-based interaction between 2012 and 2022, starting from 2,425 publications and focussing on 77 publications. We provide insights on research domains and common learning tasks in passive gaze-based interaction and present common machine learning practices from data collection and preparation to model selection and evaluation. We discuss commonly followed practices and identify gaps and challenges, especially concerning emerging machine learning topics, to guide future research in the field.

Are depressive symptoms linked to a reduced pupillary response to novel positive information?-An eye tracking proof-of-concept study

Introduction

Depressive symptoms have been linked to difficulties in revising established negative beliefs in response to novel positive information. Recent predictive processing accounts have suggested that this bias in belief updating may be related to a blunted processing of positive prediction errors at the neural level. In this proof-of-concept study, pupil dilation in response to unexpected positive emotional information was examined as a psychophysiological marker of an attenuated processing of positive prediction errors associated with depressive symptoms.

Methods

Participants (N = 34) completed a modified version of the emotional Bias Against Disconfirmatory Evidence (BADE) task in which scenarios initially suggest negative interpretations that are later either confirmed or disconfirmed by additional information. Pupil dilation in response to the confirmatory and disconfirmatory information was recorded.

Results

Behavioral results showed that depressive symptoms were related to difficulties in revising negative interpretations despite disconfirmatory positive information. The eye tracking results pointed to a reduced pupil response to unexpected positive information among people with elevated depressive symptoms.

Discussion

Altogether, the present study demonstrates that the adapted emotional BADE task can be appropriate for examining psychophysiological aspects such as changes in pupil size along with behavioral responses. Furthermore, the results suggest that depression may be characterized by deviations in both behavioral (i.e., reduced updating of negative beliefs) and psychophysiological (i.e., decreased pupil dilation) responses to unexpected positive information. Future work should focus on a larger sample including clinically depressed patients to further explore these findings.

Differential eye movement features between Alzheimer's disease patients with and without depressive symptoms

BACKGROUND

Accurately diagnosing depressive symptoms in Alzheimer's disease (AD) patients is often challenging. Eye movement parameters have been demonstrated as biomarkers for assessing cognition and psychological conditions.

AIM

To investigate the differences in eye movement between AD patients with and without depressive symptoms.

METHODS

Eye movement data of 65 AD patients were compared between the depressed AD (D-AD) and non-depressed AD (nD-AD) groups. Logistic regression analysis was employed to identify diagnostic biomarkers and the ROC curve was plotted. The correlation between eye movement and HAMD-17 scores was assessed by partial correlation analysis.

RESULTS

The D-AD patients showed longer saccade latency and faster average/peak saccade velocities in the overlap prosaccade test, longer average reaction time and faster average saccade velocity in the gap prosaccade test, longer start-up durations, slower pursuit velocity, more offsets, and larger total offset degrees in the smooth pursuit test, and poorer fixation stability in both the central and lateral fixation tests compared to nD-AD patients. The start-up duration in the smooth pursuit test and the number of offsets in the central fixation test were identified as the diagnostic eye movement parameters for D-AD patients with the area under the ROC curves of 0.8011. Partial correlation analysis revealed that the start-up duration and pursuit velocity in the smooth pursuit test and the total offset degrees in the lateral fixation test were correlated with HAMD-17 scores in D-AD patients.

DISCUSSION AND CONCLUSIONS

Eye movement differences may help to differentiate D-AD patients from nD-AD patients in a non-invasive and cost-effective manner.

Research on a New Intelligent and Rapid Screening Method for Depression Risk in Young People Based on Eye Tracking Technology

Depression is a prevalent mental disorder, with young people being particularly vulnerable to it. Therefore, we propose a new intelligent and rapid screening method for depression risk in young people based on eye tracking technology. We hypothesized that the "emotional perception of eye movement" could characterize defects in emotional perception, recognition, processing, and regulation in young people at high risk for depression. Based on this hypothesis, we designed the "eye movement emotional perception evaluation paradigm" and extracted digital biomarkers that could objectively and accurately evaluate "facial feature perception" and "facial emotional perception" characteristics of young people at high risk of depression. Using stepwise regression analysis, we identified seven digital biomarkers that could characterize emotional perception, recognition, processing, and regulation deficiencies in young people at high risk for depression. The combined effectiveness of an early warning can reach 0.974. Our proposed technique for rapid screening has significant advantages, including high speed, high early warning efficiency, low cost, and high intelligence. This new method provides a new approach to help effectively screen high-risk individuals for depression.

Abnormal eye movement features in patients with depression: Preliminary findings based on eye tracking technology

BACKGROUND

Saccadic eye movement (SEM) has been considered a non-invasive potential biomarker for the diagnosis of depression in recent years, but its application is not yet mature. In this study, we used eye-tracking technology to identify the eye movements of patients with depression to develop a new method for objectively identifying depression.

METHODS

Thirty-six patients with depression as the depression group, while thirty-six matched healthy individuals as the control group were recruited and completed eye movement tests, including two tasks: the prosaccade task and the antisaccade task. iViewX RED 500 eye-tracking instruments from SMI were used to collect eye movement data for both groups.

RESULTS

In the prosaccade task, there was no difference between the depression and control groups(t = 0.019， P > 0.05). In general, with increasing angle, both groups showed significantly higher peak velocity (F = 81.72， P < 0.0001), higher mean velocity (F = 32.83, P = 0.000), and greater SEM amplitude (F = 24.23, P < 0.0001). In the antisaccade task, there were significant differences in correct rate (t = 3.219, P = 0.002) and mean velocity (F = 3.253， P < 0.05) between the depression group and the control group. In the anti-effect analysis, there were significant differences in correct rate (F = 67.44, P < 0.0001) and accuracy (F = 79.02, P < 0.0001) between the depression group and the control group. Both groups showed longer latency and worse correct rate and precision in the antisaccade task compared with the prosaccade task.

CONCLUSION

Patients with depression showed different eye movement features, which could be potential biomarkers for clinical identification. Further studies must validate these results with larger sample sizes and more clinical populations.

Depression and Cognitive Impairment: Current Understanding of Its Neurobiology and Diagnosis

BACKGROUND

Eye movement is critical for obtaining precise visual information and providing sensorimotor processes and advanced cognitive functions to the brain behavioral indicator.

METHODS

In this article, we present a narrative review of the eye-movement paradigms (such as fixation, smooth pursuit eye movements, and memory-guided saccade tasks) in major depression.

RESULTS

Characteristics of eye movement are considered to reflect several aspects of cognitive deficits regarded as an aid to diagnosis. Findings regarding depressive disorders showed differences from the healthy population in paradigms, the characteristics of eye movement may reflect cognitive deficits in depression. Neuroimaging studies have demonstrated the effectiveness of different eye movement paradigms for MDD screening.

CONCLUSION

Depression can be distinguished from other mental illnesses based on eye movements. Eye movement reflects cognitive deficits that can help diagnose depression, and it can make the entire diagnostic process more accurate.