Contreras-Chávez Luis Benigno, Arce-Guevara Valdemar Emigdio, Guerrero Luis Fernando, Alba Alfonso, Ramírez-Elías Miguel G, Arce-Santana Edgar Roman, Mendez-Garcia Victor Hugo, Jimenez-Cruz Jorge, Bianchi Anna Maria Maddalena, Mendez Martin O
CI3M Lab, Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, San Luis Potosí 78295, Mexico.
Department of Obstetrics and Prenatal Medicine, Hospital University of Bonn, 53127 Bonn, Germany.
Sensors (Basel). 2025 Aug 21;25(16):5212. doi: 10.3390/s25165212.
Schizophrenia is a complex disorder that affects mental organization and cognitive functions, including concentration and memory. One notable manifestation of cognitive changes in schizophrenia is a diminished ability to scan and perform tasks related to visual inspection. From the three evaluable aspects of the ocular movements (saccadic, smooth pursuit, and fixation) in particular, smooth pursuit eye movement (SPEM) involves the tracking of slow moving objects and is closely related to attention, visual memory, and processing speed. However, evaluating smooth pursuit in clinical settings is challenging due to the technical complexities of detecting these movements, resulting in limited research and clinical application. This pilot study investigates whether the quantitative metrics derived from eye-tracking data can distinguish between patients with schizophrenia under treatment and healthy controls. The study included nine healthy participants and nine individuals receiving treatment for schizophrenia. Gaze trajectories were recorded using an eye tracker during a controlled visual tracking task performed during a clinical visit. Spatiotemporal analysis of gaze trajectories was performed by evaluating three different features: polygonal area, colocalities, and direction difference. Subsequently, a support vector machine (SVM) was used to assess the separability between healthy individuals and those with schizophrenia based on the identified gaze trajectory features. The results show statistically significant differences between the control and subjects with schizophrenia for all the computed indexes ( < 0.05) and a high separability achieving around 90% of accuracy, sensitivity, and specificity. The results suggest the potential development of a valuable clinical tool for the evaluation of SPEM, offering utility in clinics to assess the efficacy of therapeutic interventions in individuals with schizophrenia.
精神分裂症是一种复杂的疾病,会影响心理组织和认知功能,包括注意力和记忆力。精神分裂症认知变化的一个显著表现是扫描和执行与视觉检查相关任务的能力下降。特别是从眼球运动的三个可评估方面(扫视、平稳跟踪和注视)来看,平稳跟踪眼球运动(SPEM)涉及对缓慢移动物体的跟踪,并且与注意力、视觉记忆和处理速度密切相关。然而,由于检测这些运动的技术复杂性,在临床环境中评估平稳跟踪具有挑战性,导致相关研究和临床应用有限。这项初步研究调查了从眼动追踪数据得出的定量指标是否能够区分接受治疗的精神分裂症患者和健康对照者。该研究纳入了9名健康参与者和9名接受精神分裂症治疗的个体。在临床就诊期间进行的受控视觉跟踪任务中,使用眼动仪记录注视轨迹。通过评估三个不同特征:多边形面积、共定位和方向差异,对注视轨迹进行时空分析。随后,基于识别出的注视轨迹特征,使用支持向量机(SVM)评估健康个体和精神分裂症患者之间的可分离性。结果显示,对于所有计算指标,对照组和精神分裂症患者之间存在统计学上的显著差异(<0.05),并且具有较高的可分离性,准确率、敏感性和特异性达到约90%。结果表明,有可能开发一种有价值的临床工具来评估SPEM,在临床上可用于评估精神分裂症患者治疗干预的效果。