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Mouse movement measures enhance the stop-signal task in adult ADHD assessment

机译：鼠标运动测量增强成人ADHD评估中的停止信号任务

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The accurate detection of attention-deficit/hyperactivity disorder (ADHD) symptoms, such as inattentiveness and behavioral disinhibition, is crucial for delivering timely assistance and treatment. ADHD is commonly diagnosed and studied with specialized questionnaires and behavioral tests such as the stop-signal task. However, in cases of late-onset or mild forms of ADHD, behavioral measures often fail to gauge the deficiencies well-highlighted by questionnaires. To improve the sensitivity of behavioral tests, we propose a novel version of the stop-signal task (SST), which integrates mouse cursor tracking. In two studies, we investigated whether introducing mouse movement measures to the stop-signal task improves associations with questionnaire-based measures, as compared to the traditional (keypress-based) version of SST. We also scrutinized the influence of different parameters of stop-signal tasks, such as the method of stop-signal delay setting or definition of response inhibition failure, on these associations. Our results show that a) SSRT has weak association with impulsivity, while mouse movement measures have strong and significant association with impulsivity; b) machine learning models trained on the mouse movement data from “known” participants using nested cross-validation procedure can accurately predict impulsivity ratings of “unknown” participants; c) mouse movement features such as maximum acceleration and maximum velocity are among the most important predictors for impulsivity; d) using preset stop-signal delays prompts behavior that is more indicative of impulsivity.
机译：准确地检测注意力缺陷/多动障碍（ADHD）症状，例如亲属和行为令人市，对于提供及时的援助和治疗至关重要。 ADHD通常被诊断出，并使用专门的问卷和行为测试等进行研究，例如停止信号任务。然而，在患有晚期或轻度形式的ADHD的情况下，行为措施往往未能衡量问卷突出的缺陷。为了提高行为测试的敏感性，我们提出了一种新颖的停止信号任务（SST），其集成了鼠标光标跟踪。在两项研究中，我们调查了对停止信号任务的引入鼠标运动措施是否改善了基于问卷的措施的关联，与SST的传统（基于KeyPress的）版本相比。我们还在这些关联上仔细审查了停止信号任务的不同参数的影响，例如停止信号延迟设置或响应抑制失败的定义方法。我们的研究结果表明，A）SSRT与冲动的关联薄弱，而小鼠运动措施具有强大而显着的与冲动关系; b）使用嵌套交叉验证程序的“已知”参与者在鼠标移动数据上培训的机器学习模型可以准确地预测“未知”参与者的冲动评级; c）鼠标运动特征如最大加速度和最大速度，是冲动的最重要的预测因子之一; d）使用预设的停止信号延迟提示更大指示冲动的行为。

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