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Combining neuroimaging and behavioral methods to predict developmental trajectory in dyslexic children.

机译:结合神经影像学和行为方法来预测阅读障碍儿童的发展轨迹。

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This research focused on assessing structural (voxel based morphometry, VBM) and functional (functional magnetic resonance imaging, fMRI) neuroimaging, behavioral assessments, and their combination in their predictive ability of future reading skills in dyslexic children and adolescents. It was predicted that the combined approach would yield a more powerful and precise model. Initially, 22 participants were scanned and assessed using behavioral measures, with a 2.5 year behavioral assessment only at follow-up. The average age of the 22 children was 14.25 at Time1, and 16.77 at Time2, with 9 males and 13 females. Neuroimaging scans were done during a rhyme judgment task, which was in line with the phonological processing theory of dyslexia. The outcome variable was the slope (i.e., change over time) of Word Identification standard scores of the Woodcock Reading Mastery Test Revised. Through multiple regression analysis, it was found that behavioral measures (i.e., Word Attack, a measure of phonetic decoding, and Passage Comprehension, a measure of passage reading comprehension) accounted for 19% of the variance. Two neuroimaging predictor variables were found which predicted 55% of the variance (i.e., hyperactivation in right inferior frontal gyrus and the increased gray matter volume of the right parietal lobule). Combination of these variables was able to account for 78% of the variance, thus represented a significantly stronger prediction model than behavioral and neuroimaging models did on their own. In conclusion, a combination of neuroimaging and behavioral approaches lead to a more precise prediction model of change in reading skills in a dyslexic sample.
机译:这项研究的重点是评估结构性(基于体素的形态计量学,VBM)和功能性(功能磁共振成像,fMRI)神经影像学,行为评估以及它们对阅读障碍儿童和青少年未来阅读技能的预测能力的组合。据预测,组合方法将产生一个更强大,更精确的模型。最初,对22位参与者进行了扫描,并使用了行为评估,仅在随访中进行了2.5年的行为评估。 22个孩子的平均年龄在时间1是14.25,在时间2是16.77,其中男性为9,女性为13。在韵律判断任务中进行了神经成像扫描,这与阅读障碍的语音处理理论相符。结果变量是修订的Woodcock阅读掌握测试的单词识别标准分数的斜率(即,随时间变化)。通过多元回归分析,发现行为量度(即Word Attack,语音解码量度和Passage Comprehension量,Passage阅读理解量度)占方差的19%。发现了两个神经影像预测变量,可预测55%的方差(即右下额回的过度激活和右顶叶的灰质体积增加)。这些变量的组合能够解决78%的方差,因此与行为和神经影像模型相比,它们表现出明显更强的预测模型。总之,神经影像学和行为学方法的结合导致了阅读障碍样本中阅读技能变化的更精确的预测模型。

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