Department of Linguistics and Modern Languages, The Chinese University of Hong Kong, Hong Kong SAR, China.
Brain and Mind Institute, The Chinese University of Hong Kong, Hong Kong SAR, China.
Proc Natl Acad Sci U S A. 2018 Jan 30;115(5):E1022-E1031. doi: 10.1073/pnas.1717603115. Epub 2018 Jan 16.
Although cochlear implantation enables some children to attain age-appropriate speech and language development, communicative delays persist in others, and outcomes are quite variable and difficult to predict, even for children implanted early in life. To understand the neurobiological basis of this variability, we used presurgical neural morphological data obtained from MRI of individual pediatric cochlear implant (CI) candidates implanted younger than 3.5 years to predict variability of their speech-perception improvement after surgery. We first compared neuroanatomical density and spatial pattern similarity of CI candidates to that of age-matched children with normal hearing, which allowed us to detail neuroanatomical networks that were either affected or unaffected by auditory deprivation. This information enables us to build machine-learning models to predict the individual children's speech development following CI. We found that regions of the brain that were unaffected by auditory deprivation, in particular the auditory association and cognitive brain regions, produced the highest accuracy, specificity, and sensitivity in patient classification and the most precise prediction results. These findings suggest that brain areas unaffected by auditory deprivation are critical to developing closer to typical speech outcomes. Moreover, the findings suggest that determination of the type of neural reorganization caused by auditory deprivation before implantation is valuable for predicting post-CI language outcomes for young children.
虽然人工耳蜗植入可以使一些儿童获得与年龄相适应的言语和语言发展,但在其他儿童中仍存在沟通延迟的问题,而且即使是在生命早期植入的儿童,其结果也存在很大的差异,难以预测。为了了解这种变异性的神经生物学基础,我们使用了从 MRI 获得的个体儿科人工耳蜗植入(CI)候选者的术前神经形态学数据,以预测他们术后言语感知改善的变异性。我们首先将 CI 候选者的神经解剖密度和空间模式相似性与具有正常听力的年龄匹配儿童进行比较,这使我们能够详细描述受或不受听觉剥夺影响的神经解剖网络。这些信息使我们能够建立机器学习模型来预测 CI 后个体儿童的言语发展。我们发现,未受听觉剥夺影响的大脑区域,特别是听觉联合和认知大脑区域,在患者分类中具有最高的准确性、特异性和敏感性,并且能够做出最精确的预测结果。这些发现表明,未受听觉剥夺影响的大脑区域对于接近典型言语结果的发展至关重要。此外,这些发现表明,在植入前确定听觉剥夺引起的神经重组类型对于预测幼儿的术后语言结果是有价值的。