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Relative contributions of lesion location and lesion size to predictions of varied language deficits in post-stroke aphasia

机译:病变位置和病变大小对中风后失语症各种语言缺陷的预测的相对贡献

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摘要

Despite the widespread use of lesion-symptom mapping (LSM) techniques to study associations between location of brain damage and language deficits, the prediction of language deficits from lesion location remains a substantial challenge. The present study examined several factors which may impact lesion-symptom prediction by (1) testing the relative predictive advantage of general language deficit scores compared to composite scores that capture specific deficit types, (2) isolating the relative contribution of lesion location compared to lesion size, and (3) comparing standard voxel-based lesion-symptom mapping (VLSM) with a multivariate method (sparse canonical correlation analysis, SCCAN). Analyses were conducted on data from 128 participants who completed a detailed battery of psycholinguistic tests and underwent structural neuroimaging (MRI or CT) to determine lesion location. For both VLSM and SCCAN, overall aphasia severity (Western Aphasia Battery Aphasia Quotient) and object naming deficits were primarily predicted by lesion size, whereas deficits in Speech Production and Speech Recognition were better predicted by a combination of lesion size and location. The implementation of both VLSM and SCCAN raises important considerations regarding controlling for lesion size in lesion-symptom mapping analyses. These findings suggest that lesion-symptom prediction is more accurate for deficits within neurally-localized cognitive systems when both lesion size and location are considered compared to broad functional deficits, which can be predicted by overall lesion size alone.
机译:尽管广泛使用病变症状图谱(LSM)技术来研究脑损伤的位置与语言缺陷之间的关联,但是从病变位置预测语言缺陷仍然是一个巨大的挑战。本研究通过(1)测试通用语言缺陷评分与捕获特定缺陷类型的复合评分相比的相对预测优势,研究了可能影响病变症状预测的几个因素,(2)分离出与病变相比病变位置的相对贡献(3)将基于体素的标准病变症状映射(VLSM)与多元方法(稀疏规范相关分析,SCCAN)进行比较。对来自128位参与者的数据进行了分析,他们完成了详细的心理语言测试,并进行了结构神经成像(MRI或CT)以确定病变部位。对于VLSM和SCCAN,总体失语症严重程度(西方失语症电池失语症患者)和对象命名缺陷主要是由病变大小预测的,而语音产生和语音识别的缺陷可以通过病变大小和位置的组合更好地预测。 VLSM和SCCAN的实现都提出了有关在病变-症状映射分析中控制病变大小的重要考虑因素。这些发现表明,将病变的大小和位置都考虑在内时,对于神经定位的认知系统内的缺陷,病变症状的预测比仅由整体病变大小可以预测的广泛功能缺失更为准确。

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