Effects of age and sex on eye movement characteristics

Antisaccade error rates in first-episode psychosis, ultra-high risk for psychosis and unaffected relatives of schizophrenia: A systematic review and meta-analysis

BACKGROUND

Antisaccade, which is described as looking at the opposite location of the target, is an eye movements paradigm used for assessing cognitive functions in schizophrenia. Initiation and sustainment of saccades in antisaccade are managed by frontal and parietal cortical areas. Antisaccade abnormalities are well-established findings in schizophrenia. However, studies in the early phases of psychotic disorders and clinical/familial risk for psychosis reported inconsistent findings. The current systematic review aimed to review the results of studies investigating antisaccade error rates in first-episode psychosis (FEP), individuals with ultra-high-risk for psychosis (UHRP), and familial-high-risk for psychosis (FHRP) compared to healthy controls.

METHOD

A meta-analysis of 17 studies was conducted to quantitatively review antisaccade errors in FEP, UHR-P and FHRP. The error rate (Hedges'g) was compared between the total of 860 FEP, UHRP, FHRP, and 817 healthy controls. Hedges' g for effect size, I2 for estimating the percentage of variability, and publication bias were evaluated through the R software.

RESULTS

The outcomes of this meta-analysis suggested that FEP is associated with a robust deficit in the antisaccade error rate (g = 1.16, CI = 0.95-1.38). Additionally, both the clinical and familial high-risk groups showed small but significant increases in AS errors (g = 0.26, CI = 0.02-0.52 and g = 0.34, CI = 0.13-0.55, respectively).

CONCLUSION

The large effect size estimated for FEP was compatible with previously reported results in chronic schizophrenia patients. Additionally, relatives had abnormalities with small to medium effect sizes and significant differences. The current findings suggest that antisaccade errors might be a potential endophenotype for psychotic disorders.

Assessment of oculomotor function after prolonged computer use

To analyze the specific effects of prolonged computer use on oculomotor function, we propose an oculomotor function evaluation system to analyze changes in oculomotor movement function by using an eye tracker to record eye movement data when performing gaze, smooth pursuit, and saccade under normal condition, after one hour and one and a half hours of continuous working at a computer. The captured eye movement data is pre-processed, and then data features are calculated and analyzed to understand the specific effects of continuously using the computer on the oculomotor function. The results show that the oculomotor function decreases as we gaze at the computer screen for longer periods, as evidenced by a decrease in the stability of the gaze function, a reduction in the gaze focus, a reduction in the speed of eye saccades, and a decrease in the smooth pursuit function. In short, the oculomotor function worsens after prolonged working at a computer. This paper presents the effects of continuously using the computer quantificationally for the first time. The proposed oculomotor function evaluation system could also be used to assess patients who have a disability in oculomotor function and specific individuals, e.g. pilots.

Trends in big data analyses by multicenter collaborative translational research in psychiatry

The underlying pathologies of psychiatric disorders, which cause substantial personal and social losses, remain unknown, and their elucidation is an urgent issue. To clarify the core pathological mechanisms underlying psychiatric disorders, in addition to laboratory-based research that incorporates the latest findings, it is necessary to conduct large-sample-size research and verify reproducibility. For this purpose, it is critical to conduct multicenter collaborative research across various fields, such as psychiatry, neuroscience, molecular biology, genomics, neuroimaging, cognitive science, neurophysiology, psychology, and pharmacology. Moreover, collaborative research plays an important role in the development of young researchers. In this respect, the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium and Cognitive Genetics Collaborative Research Organization (COCORO) have played important roles. In this review, we first overview the importance of multicenter collaborative research and our target psychiatric disorders. Then, we introduce research findings on the pathophysiology of psychiatric disorders from neurocognitive, neurophysiological, neuroimaging, genetic, and basic neuroscience perspectives, focusing mainly on the findings obtained by COCORO. It is our hope that multicenter collaborative research will contribute to the elucidation of the pathological basis of psychiatric disorders.

Eye Movement Patterns Can Distinguish Schizophrenia From the Major Affective Disorders and Healthy Control Subjects

Background and hypothesis

No objective tests are currently available to help diagnosis of major psychiatric disorders. This study evaluates the potential of eye movement behavior patterns to predict schizophrenia subjects compared to those with major affective disorders and control groups.

Study design

Eye movements were recorded from a training set of UK subjects with schizophrenia (SCZ; n = 120), bipolar affective disorder (BPAD; n = 141), major depressive disorder (MDD; n = 136), and healthy controls (CON; n = 142), and from a hold-out set of 133 individuals with proportional group sizes. A German cohort of SCZ (n = 60) and a Scottish cohort of CON subjects (n = 184) acted as a second semi-independent test set. All patients met DSMIV and ICD10 criteria for SCZ, BPAD, and MDD. Data from 98 eye movement features were extracted. We employed a gradient boosted (GB) decision tree multiclass classifier to develop a predictive model. We calculated the area under the curve (AUC) as the primary performance metric.

Study results

Estimates of AUC in one-versus-all comparisons were: SCZ (0.85), BPAD (0.78), MDD (0.76), and CON (0.85). Estimates on part-external validation were SCZ (0.89) and CON (0.65). In all cases, there was good specificity but only moderate sensitivity. The best individual discriminators included free viewing, fixation duration, and smooth pursuit tasks. The findings appear robust to potential confounders such as age, sex, medication, or mental state at the time of testing.

Conclusions

Eye movement patterns can discriminate schizophrenia from major mood disorders and control subjects with around 80% predictive accuracy.

Effects of age and sex on eye movement characteristics

Abnormal eye movements are often associated with psychiatric disorders. Eye movements are sensorimotor functions of the brain, and aging and sex would affect their characteristics. A precise understanding of normal eye movements is required to distinguish disease-related abnormalities from natural differences associated with aging or sex. To date, there is no multicohort study examining age-related dependency and sex effects of eye movements in healthy, normal individuals using large samples to ensure the robustness and reproducibility of the results. In this study, we aimed to provide findings showing the impact of age and sex on eye movement measures. The present study used eye movement measures of more than seven hundred healthy individuals from three large independent cohorts. We herein evaluated eye movement measures quantified by using a set of standard eye movement tests that have been utilized for the examination of patients with schizophrenia. We assessed the statistical significance of the effects of age and sex and its reproducibility across cohorts. We found that 4-18 out of 35 eye movement measures were significantly correlated with age, depending on the cohort, and that 10 of those, which are related to the fixation and motor control of smooth pursuit and saccades, showed high reproducibility. On the other hand, the effects of sex, if any, were less reproducible. The present results suggest that we should take age into account when we evaluate abnormalities in eye movements.

Eye Movement Abnormalities in Major Depressive Disorder

Background: Despite their high lifetime prevalence, major depressive disorder (MDD) is often difficult to diagnose, and there is a need for useful biomarkers for the diagnosis of MDD. Eye movements are considered a non-invasive potential biomarker for the diagnosis of psychiatric disorders such as schizophrenia. However, eye movement deficits in MDD remain unclear. Thus, we evaluated detailed eye movement measurements to validate its usefulness as a biomarker in MDD. Methods: Eye movements were recorded from 37 patients with MDD and 400 healthy controls (HCs) using the same system at five University hospitals. We administered free-viewing, fixation stability, and smooth pursuit tests, and obtained 35 eye movement measurements. We performed analyses of covariance with group as an independent variable and age as a covariate. In 4 out of 35 measurements with significant group-by-age interactions, we evaluated aging effects. Discriminant analysis and receiver operating characteristic (ROC) analysis were conducted. Results: In the free-viewing test, scanpath length was significantly shorter in MDD (p = 4.2 × 10^-3). In the smooth pursuit test, duration of saccades was significantly shorter and peak saccade velocity was significantly lower in MDD (p = 3.7 × 10^-3, p = 3.9 × 10^-3, respectively). In the fixation stability test, there were no significant group differences. There were significant group differences in the older cohort, but not in the younger cohort, for the number of fixations, duration of fixation, number of saccades, and fixation density in the free-viewing test. A discriminant analysis using scanpath length in the free-viewing test and peak saccade velocity in the smooth pursuit showed MDD could be distinguished from HCs with 72.1% accuracy. In the ROC analysis, the area under the curve was 0.76 (standard error = 0.05, p = 1.2 × 10^-7, 95% confidence interval = 0.67-0.85). Conclusion: These results suggest that detailed eye movement tests can assist in differentiating MDD from HCs, especially in older subjects.