Prediction of autism spectrum disorder diagnosis using nonlinear measures of language-related EEG at 6 and 12 months.
Prediction of autism spectrum disorder diagnosis using nonlinear measures of language-related EEG at 6 and 12 months.
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DOI：
10.1186/s11689-021-09405-x
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发表时间：
2021-11-30
影响因子：
4.9
通讯作者：
Nelson CA
中科院分区：
文献类型：
--
作者：
Peck FC;Gabard-Durnam LJ;Wilkinson CL;Bosl W;Tager-Flusberg H;Nelson CA
自闭症谱系障碍（ASD）的早期识别为早期干预和改善发育结果提供了机会。在婴儿期使用脑电图（EEG）已经显示出在预测以后的ASD诊断和识别疾病背后的神经机制方面的前景。鉴于与语言障碍的高共病率，我们和其他人推测，后来被诊断为ASD的婴儿改变了语言学习，包括音素辨别。音素学习在婴儿期迅速发生，因此在生命的第一年改变的神经基质可能作为早期，准确的自闭症诊断指标。使用在ASD家族高风险婴儿的被动音素任务期间在两个不同年龄收集的EEG数据，我们比较了特征选择和机器学习模型组合在6个月（母语音素学习期间）和12个月（母语音素学习后）的预测准确性，我们确定了一个单一模型，该模型对两个年龄段都具有很强的预测准确性（100%）。两个年龄段的样本在大小和诊断上相匹配（n = 14，晚期ASD; n = 40，无ASD）。特征包括10 - 20个导联电极和6个频带的功率和非线性测量组合。通过特征特征和EEG头皮位置比较每个年龄的预测特征。对12个月时收集的所有EEG进行了额外的预测分析;该较大样本包括67名HR婴儿（27名HR-ASD，40名HR-noASD）。使用Pearson相关特征选择和支持向量机分类器的组合，在6个月和12个月时观察到100%的预测诊断准确率。在6个月和12个月的数据上训练的模型之间的预测特征不同。在6个月时，预测特征偏向于来自中央电极的测量、功率测量和α范围内的频率。在12个月时，预测特征更多地分布在功效和非线性测量之间，并且偏向于β范围内的频率。然而，诊断预测准确性大幅下降，在更大的，更多的行为异质性12个月的样本。这些结果表明，语音处理EEG措施可以促进早期识别ASD，但强调需要特定年龄的预测模型，大样本量，以开发临床相关的分类算法。在线版本包含补充材料，可通过10.1186/s11689-021-09405-x获得。
Early identification of autism spectrum disorder (ASD) provides an opportunity for early intervention and improved developmental outcomes. The use of electroencephalography (EEG) in infancy has shown promise in predicting later ASD diagnoses and in identifying neural mechanisms underlying the disorder. Given the high co-morbidity with language impairment, we and others have speculated that infants who are later diagnosed with ASD have altered language learning, including phoneme discrimination. Phoneme learning occurs rapidly in infancy, so altered neural substrates during the first year of life may serve as early, accurate indicators of later autism diagnosis. Using EEG data collected at two different ages during a passive phoneme task in infants with high familial risk for ASD, we compared the predictive accuracy of a combination of feature selection and machine learning models at 6 months (during native phoneme learning) and 12 months (after native phoneme learning), and we identified a single model with strong predictive accuracy (100%) for both ages. Samples at both ages were matched in size and diagnoses (n = 14 with later ASD; n = 40 without ASD). Features included a combination of power and nonlinear measures across the 10‑20 montage electrodes and 6 frequency bands. Predictive features at each age were compared both by feature characteristics and EEG scalp location. Additional prediction analyses were performed on all EEGs collected at 12 months; this larger sample included 67 HR infants (27 HR-ASD, 40 HR-noASD). Using a combination of Pearson correlation feature selection and support vector machine classifier, 100% predictive diagnostic accuracy was observed at both 6 and 12 months. Predictive features differed between the models trained on 6- versus 12-month data. At 6 months, predictive features were biased to measures from central electrodes, power measures, and frequencies in the alpha range. At 12 months, predictive features were more distributed between power and nonlinear measures, and biased toward frequencies in the beta range. However, diagnosis prediction accuracy substantially decreased in the larger, more behaviorally heterogeneous 12-month sample. These results demonstrate that speech processing EEG measures can facilitate earlier identification of ASD but emphasize the need for age-specific predictive models with large sample sizes to develop clinically relevant classification algorithms. The online version contains supplementary material available at 10.1186/s11689-021-09405-x.
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影响因子：
17.1
作者：
Emerson RW;Adams C;Nishino T;Hazlett HC;Wolff JJ;Zwaigenbaum L;Constantino JN;Shen MD;Swanson MR;Elison JT;Kandala S;Estes AM;Botteron KN;Collins L;Dager SR;Evans AC;Gerig G;Gu H;McKinstry RC;Paterson S;Schultz RT;Styner M;IBIS Network;Schlaggar BL;Pruett JR Jr;Piven J
通讯作者：
Piven J
神经影像学中脑部疾病的单受试者预测：前景和陷阱
影响因子：
5.7
作者：
Arbabshirani MR;Plis S;Sui J;Calhoun VD
通讯作者：
Calhoun VD
影响因子：
2.1
作者：
Cantiani, Chiara;Piazza, Caterina;Riva, Valentina
通讯作者：
Riva, Valentina
DOI：
10.1111/j.1469-7610.2009.02101.x
发表时间：
2009-10-01
影响因子：
7.6
作者：
Chawarska, Katarzyna;Klin, Ami;Volkmar, Fred
通讯作者：
Volkmar, Fred
影响因子：
3.5
作者：
Dehaene-Lambertz G
通讯作者：
Dehaene-Lambertz G