【摘要】 目的 探讨癫痫患者注意功能受损的特点。 方法 2008年12月-2009年12月选取69例癫痫患者作为病例组，35例正常人作为对照组，分别用持续操作任务实验（continuous performance test，CPT）、斯特鲁普实验（Stroop）和双任务实验测查持续注意、选择注意和分散注意功能。 结果 与对照组相比，病例组CPT反应时延长，漏报率增加（Plt;0.05）；Stroop实验冲突条件下反应时延长，冲突、一致和中性条件下错误率增加，冲突和中性条件下的反应时干扰量和错误率干扰量增加（Plt;0.05）；双任务比单任务划销方格数目减少，字符串正确率减少，双任务减退程度增加（Plt;0.05）。 结论 癫痫患者的持续注意、选择注意和分散注意功能均受损。【Abstract】 Objective To observe the damages of attentive function in the patients with epilepsy. Methods From December 2008 to December 2009, 69 patients with epilepsy and 35 healthy people were selected as the patients group and control group. All the selected ones underwent continuous performance test (CPT), Stroop test and dual task, respetviely, to assess their sustained attention, selected attention and divided attention. Results In CPT, the reaction time prolonged and the omission rate increased significantly in the patient group compared with those in the control group (Plt;0.05). In Stroop test, the reaction time in the patients group prolonged under incongruous condition, and error rate increased under incongruous, congruous and neutral conditions. The reaction time interfered effects and error interfered effects increased under incongruous and neutral conditions (Plt;0.05); the boxes crossed and right rate of digit strings decreased and decrement increased during dual task than single task in the patient group (Plt;0.05). Conclusion The sustained attention, selected attention and divided attention of patients with epilepsy are impaired.
Although attention plays an important role in cognitive and perception, there is no simple way to measure one's attention abilities. We identified that the strength of brain functional network in sustained attention task can be used as the physiological indicator to predict behavioral performance. Behavioral and electroencephalogram (EEG) data from 14 subjects during three force control tasks were collected in this paper. The reciprocal of the product of force tolerance and variance were used to calculate the score of behavioral performance. EEG data were used to construct brain network connectivity by wavelet coherence method and then correlation analysis between each edge in connectivity matrices and behavioral score was performed. The linear regression model combined those with significantly correlated network connections into physiological indicator to predict participant's performance on three force control tasks, all of which had correlation coefficients greater than 0.7. These results indicate that brain functional network strength can provide a widely applicable biomarker for sustained attention tasks.