Remapping the cognitive and neural profiles of children who struggle at school.
Remapping the cognitive and neural profiles of children who struggle at school.
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DOI：
10.1111/desc.12747
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发表时间：
2019-01
影响因子：
3.7
通讯作者：
Holmes J
中科院分区：
文献类型：
--
作者：
Astle DE;Bathelt J;CALM Team;Holmes J
我们对学习困难的理解很大程度上来自于有特定诊断的儿童或根据严格的纳入标准从社区/临床样本中选出的个人。应用严格的排除标准过分强调组内同质性和组间差异，未能捕获共病。在这里，我们使用人工神经网络形式的无监督机器学习，在大量异构的学习者样本中识别认知特征。健康和教育专业人员将儿童转介到注意力学习和记忆中心（CALM），无论诊断或合并症，注意力，记忆力，语言或学习成绩差的问题（n = 530）。孩子们完成了一系列认知和学习评估，接受了结构性MRI扫描，他们的父母完成了行为问卷。在该网络中，我们可以确定四组儿童：（a）有广泛认知困难，以及严重阅读、拼写和数学问题的儿童;（B）具有典型年龄认知能力和学习特征的儿童;（c）有工作记忆问题的儿童;（d）有语音困难的儿童。尽管他们的认知能力形成鲜明对比，但后两组的学习能力没有差异：在所有学习指标上，两组都比年龄预期水平低1个标准差。重要的是，儿童的认知状况不能通过诊断或转诊原因来预测。我们还为这四组儿童（n = 184）构建了全脑结构连接组，以及另一组典型发育儿童（n = 36），并确定了每组的大脑组织的不同模式。这项研究代表了一个新的举动，以确定数据驱动的神经认知维度的基础上学习相关的困难，在一个有代表性的样本学习困难的学习者。
Our understanding of learning difficulties largely comes from children with specific diagnoses or individuals selected from community/clinical samples according to strict inclusion criteria. Applying strict exclusionary criteria overemphasizes within group homogeneity and between group differences, and fails to capture comorbidity. Here, we identify cognitive profiles in a large heterogeneous sample of struggling learners, using unsupervised machine learning in the form of an artificial neural network. Children were referred to the Centre for Attention Learning and Memory (CALM) by health and education professionals, irrespective of diagnosis or comorbidity, for problems in attention, memory, language, or poor school progress (n = 530). Children completed a battery of cognitive and learning assessments, underwent a structural MRI scan, and their parents completed behavior questionnaires. Within the network we could identify four groups of children: (a) children with broad cognitive difficulties, and severe reading, spelling and maths problems; (b) children with age-typical cognitive abilities and learning profiles; (c) children with working memory problems; and (d) children with phonological difficulties. Despite their contrasting cognitive profiles, the learning profiles for the latter two groups did not differ: both were around 1 SD below age-expected levels on all learning measures. Importantly a child’s cognitive profile was not predicted by diagnosis or referral reason. We also constructed whole-brain structural connectomes for children from these four groupings (n = 184), alongside an additional group of typically developing children (n = 36), and identified distinct patterns of brain organization for each group. This study represents a novel move toward identifying data-driven neurocognitive dimensions underlying learning-related difficulties in a representative sample of poor learners.
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DOI：
10.1016/j.jaac.2018.01.014
发表时间：
2018-04-01
影响因子：
13.3
作者：
Bathelt, Joe;Holmes, Joni;Astle, Duncan E.
通讯作者：
Astle, Duncan E.
DOI：
10.1111/j.1460-6984.2012.00164.x
发表时间：
2012-09-01
影响因子：
2.4
作者：
Duinmeijer, Iris;de Jong, Jan;Scheper, Annette
通讯作者：
Scheper, Annette
DOI：
10.1073/pnas.1115365109
发表时间：
2012-04-24
影响因子：
11.1
作者：
Fair, Damien A.;Bathula, Deepti;Nigg, Joel T.
通讯作者：
Nigg, Joel T.
影响因子：
5.7
作者：
Avants BB;Tustison NJ;Song G;Cook PA;Klein A;Gee JC
通讯作者：
Gee JC
影响因子：
3.7
作者：
Clayden, Jonathan D.;Jentschke, Sebastian;Vargha-Khadem, Faraneh
通讯作者：
Vargha-Khadem, Faraneh