Deep reasoning neural network analysis to predict language deficits from psychometry-driven DWI connectome of young children with persistent language concerns.
Deep reasoning neural network analysis to predict language deficits from psychometry-driven DWI connectome of young children with persistent language concerns.
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DOI：
10.1002/hbm.25437
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发表时间：
2021-07
影响因子：
4.8
通讯作者：
Dong M
中科院分区：
文献类型：
--
作者：
Jeong JW;Banerjee S;Lee MH;O'Hara N;Behen M;Juhász C;Dong M
本研究调查了当前最先进的深度推理网络分析对心理测量驱动的弥散纤维束成像连接体的分析是否可以准确预测具有持续语言问题的幼儿队列（n = 31，年龄：4.25 ± 2.38岁）的表达和接受语言得分。训练一个结合关系网络的扩张卷积神经网络（dilated CNN + RN）来推理“语言网络的扩张CNN特征”与“临床获得的语言成绩”之间的非线性关系。然后使用三重交叉验证来比较扩展CNN + RN预测和实际语言得分之间的Pearson相关性和平均绝对误差（MAE）。扩张的CNN + RN优于其他方法，提供了预测分数和实际分数之间最显著的相关性（即，Pearson R/p值：表达性和接受性语言得分分别为1.00/<.001和.99/<.001），相同得分的MAE分别为0.28和0.28。这种关系的强度表明，表达性和接受性语言得分的预测概率都提高了（即，1.00 1.00）。具体而言，稀疏连接不仅在右中央前回，但也涉及右尾状核的表达和接受语言领域的赤字之间的关系最强。随后的亚组分析推断，基于CNN + RN的扩展语言评分预测的有效性与时间间隔无关。（MRI和语言评估之间）和MRI的年龄，表明使用心理测量驱动的扩散纤维束成像连接体的扩张CNN + RN可能有助于预测语言障碍的存在，并可能提供更好的理解幼儿语言缺陷的神经机制。这项研究调查了目前最先进的深度推理网络分析对心理测量驱动的弥散纤维束成像连接体的分析是否可以准确地预测具有持续语言问题的幼儿群体的表达和接受语言得分。训练了一个结合关系网络的扩张卷积神经网络（扩张CNN+RN）来推理“语言网络的扩张CNN特征”与“临床获得的语言得分”之间的非线性关系。使用心理测量驱动的弥散纤维束成像连接体的拟议扩张CNN+RN可能有助于预测语言障碍的存在，严重程度和类型，并可能更好地了解幼儿语言障碍的神经机制。
This study investigated whether current state‐of‐the‐art deep reasoning network analysis on psychometry‐driven diffusion tractography connectome can accurately predict expressive and receptive language scores in a cohort of young children with persistent language concerns (n = 31, age: 4.25 ± 2.38 years). A dilated convolutional neural network combined with a relational network (dilated CNN + RN) was trained to reason the nonlinear relationship between “dilated CNN features of language network” and “clinically acquired language score”. Three‐fold cross‐validation was then used to compare the Pearson correlation and mean absolute error (MAE) between dilated CNN + RN‐predicted and actual language scores. The dilated CNN + RN outperformed other methods providing the most significant correlation between predicted and actual scores (i.e., Pearson's R/p‐value: 1.00/<.001 and .99/<.001 for expressive and receptive language scores, respectively) and yielding MAE: 0.28 and 0.28 for the same scores. The strength of the relationship suggests elevated probability in the prediction of both expressive and receptive language scores (i.e., 1.00 and 1.00, respectively). Specifically, sparse connectivity not only within the right precentral gyrus but also involving the right caudate had the strongest relationship between deficit in both the expressive and receptive language domains. Subsequent subgroup analyses inferred that the effectiveness of the dilated CNN + RN‐based prediction of language score(s) was independent of time interval (between MRI and language assessment) and age of MRI, suggesting that the dilated CNN + RN using psychometry‐driven diffusion tractography connectome may be useful for prediction of the presence of language disorder, and possibly provide a better understanding of the neurological mechanisms of language deficits in young children. This study investigated whether current state‐of‐the‐art deep reasoning network analysis on psychometry‐driven diffusion tractography connectome can accurately predict expressive and receptive language scores in a cohort of young children with persistent language concerns. A dilated convolutional neural network combined with a relational network (dilated CNN+RN) was trained to reason the nonlinear relationship between “dilated CNN features of language network” and “clinically acquired language score.” The proposed dilated CNN+RN using psychometry‐driven diffusion tractography connectome may be useful for prediction of the presence, severity, and type of language disorder, and possibly provide a better understanding of the neurological mechanisms of language deficits in young children.
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DOI：
10.1073/pnas.0402680101
发表时间：
2004-05-25
影响因子：
11.1
作者：
Gogtay, N;Giedd, JN;Thompson, PM
通讯作者：
Thompson, PM
影响因子：
2.5
作者：
Badcock, Nicholas A.;Bishop, Dorothy V. M.;Hardiman, Mervyn J.;Barry, Johanna G.;Watkins, Kate E.
通讯作者：
Watkins, Kate E.
影响因子：
7.6
作者：
Bishop, Dorothy V. M.;Snowling, Margaret J.;Whitehouse, Andrew
通讯作者：
Whitehouse, Andrew
影响因子：
5.7
作者：
Deoni S;Dean D 3rd;Joelson S;O'Regan J;Schneider N
通讯作者：
Schneider N
影响因子：
5.7
作者：
Fischl, Bruce
通讯作者：
Fischl, Bruce