Abstract：
OBJECTIVE: Brain areas implicated in semantic memory can be damaged in patients with epilepsy (PWE). However, it is challenging to delineate semantic processing deficits from acoustic, linguistic, and other verbal aspects in current neuropsychological assessments. We developed a new Visual-based Semantic Association Task (ViSAT) to evaluate nonverbal semantic processing in PWE.
METHODS: The ViSAT was adapted from similar predecessors (Pyramids & Palm Trees test, PPT; Camels & Cactus Test, CCT) comprised of 100 unique trials using real-life color pictures that avoid demographic, cultural, and other potential confounds. We obtained performance data from 23 PWE participants and 24 control participants (Control), along with crowdsourced normative data from 54 Amazon Mechanical Turk (Mturk) workers.
RESULTS: ViSAT reached a consensus >90% in 91.3% of trials compared to 83.6% in PPT and 82.9% in CCT. A deep learning model demonstrated that visual features of the stimulus images (color, shape; i.e., non-semantic) did not influence top answer choices (p = 0.577). The PWE group had lower accuracy than the Control group (p = 0.019). PWE had longer response times than the Control group in general and this was augmented for the semantic processing (trial answer) stage (both p < 0.001).
CONCLUSIONS: This study demonstrated performance impairments in PWE that may reflect dysfunction of nonverbal semantic memory circuits, such as seizure onset zones overlapping with key semantic regions (e.g., anterior temporal lobe). The ViSAT paradigm avoids confounds, is repeatable/longitudinal, captures behavioral data, and is open-source, thus we propose it as a strong alternative for clinical and research assessment of nonverbal semantic memory.
METHODS: The ViSAT was adapted from similar predecessors (Pyramids & Palm Trees test, PPT; Camels & Cactus Test, CCT) comprised of 100 unique trials using real-life color pictures that avoid demographic, cultural, and other potential confounds. We obtained performance data from 23 PWE participants and 24 control participants (Control), along with crowdsourced normative data from 54 Amazon Mechanical Turk (Mturk) workers.
RESULTS: ViSAT reached a consensus >90% in 91.3% of trials compared to 83.6% in PPT and 82.9% in CCT. A deep learning model demonstrated that visual features of the stimulus images (color, shape; i.e., non-semantic) did not influence top answer choices (p = 0.577). The PWE group had lower accuracy than the Control group (p = 0.019). PWE had longer response times than the Control group in general and this was augmented for the semantic processing (trial answer) stage (both p < 0.001).
CONCLUSIONS: This study demonstrated performance impairments in PWE that may reflect dysfunction of nonverbal semantic memory circuits, such as seizure onset zones overlapping with key semantic regions (e.g., anterior temporal lobe). The ViSAT paradigm avoids confounds, is repeatable/longitudinal, captures behavioral data, and is open-source, thus we propose it as a strong alternative for clinical and research assessment of nonverbal semantic memory.
摘要：
目的：癫痫（PWE）患者可能会损害与语义记忆有关的大脑区域。然而,从声学中描述语义处理缺陷是具有挑战性的，语言学，以及当前神经心理学评估中的其他言语方面。我们开发了一种新的基于视觉的语义关联任务（ViSAT）来评估PWE中的非语言语义处理。
方法：ViSAT改编自类似的前辈（金字塔和棕榈树测试，PPT;骆驼和仙人掌测试,CCT）由100个独特的试验组成，使用现实生活中的彩色图片，避免了人口统计，文化,和其他潜在的困惑。我们从23名PWE参与者和24名对照参与者(对照)获得了表现数据，以及来自54名亚马逊机械土耳其人(Mturk)工人的众包规范数据。
结果：ViSAT在91.3％的试验中达成共识>90％，而PPT中为83.6％，CCT中为82.9％。深度学习模型证明了刺激图像的视觉特征(颜色，形状；即，非语义）不影响首选答案选择（p=0.577）。PWE组的准确性低于对照组（p=0.019）。总体上，PWE的响应时间比对照组更长，并且在语义处理（试验答案）阶段得到了增强（均p<0.001）。
结论：这项研究表明，PWE的表现障碍可能反映了非语言语义记忆回路的功能障碍，例如癫痫发作发作区与关键语义区域重叠(例如，颞叶前叶）。ViSAT范式避免了混淆，是可重复的/纵向的，捕获行为数据，并且是开源的，因此，我们建议将其作为非语言语义记忆的临床和研究评估的有力替代方案。
方法：ViSAT改编自类似的前辈（金字塔和棕榈树测试，PPT;骆驼和仙人掌测试,CCT）由100个独特的试验组成，使用现实生活中的彩色图片，避免了人口统计，文化,和其他潜在的困惑。我们从23名PWE参与者和24名对照参与者(对照)获得了表现数据，以及来自54名亚马逊机械土耳其人(Mturk)工人的众包规范数据。
结果：ViSAT在91.3％的试验中达成共识>90％，而PPT中为83.6％，CCT中为82.9％。深度学习模型证明了刺激图像的视觉特征(颜色，形状；即，非语义）不影响首选答案选择（p=0.577）。PWE组的准确性低于对照组（p=0.019）。总体上，PWE的响应时间比对照组更长，并且在语义处理（试验答案）阶段得到了增强（均p<0.001）。
结论：这项研究表明，PWE的表现障碍可能反映了非语言语义记忆回路的功能障碍，例如癫痫发作发作区与关键语义区域重叠(例如，颞叶前叶）。ViSAT范式避免了混淆，是可重复的/纵向的，捕获行为数据，并且是开源的，因此，我们建议将其作为非语言语义记忆的临床和研究评估的有力替代方案。
