Classifying Cognitive Profiles Using Machine Learning with Privileged Information in Mild Cognitive Impairment.
Classifying Cognitive Profiles Using Machine Learning with Privileged Information in Mild Cognitive Impairment.
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DOI：
10.3389/fncom.2016.00117
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发表时间：
2016
影响因子：
3.2
通讯作者：
Tino P
中科院分区：
文献类型：
--
作者：
Alahmadi HH;Shen Y;Fouad S;Luft CD;Bentham P;Kourtzi Z;Tino P
痴呆症的早期诊断对于评估疾病进展和潜在治疗至关重要。最先进的机器学习技术被越来越多地用于承担这一诊断任务。在这项研究中，我们采用广义矩阵学习向量量化（GMLVQ）分类器根据轻度认知障碍（MCI）患者的认知能力与健康对照进行区分。此外，我们采用了“特权信息学习”的方法，将认知数据和fMRI数据结合起来进行分类任务。所得到的分类器仅对认知数据进行操作，而在训练期间将fMRI数据作为特权信息（PI）合并。这种新颖的分类器具有实际用途，因为患者和老年参与者的脑成像数据收集并不总是可能的。训练MCI患者和年龄匹配的健康对照从时间序列中提取结构。我们询问机器学习分类器是否可以用于区分患者和对照组，以及这些组之间的差异是否与个体认知概况有关。为此，我们对参与者进行了四项认知任务测试：工作记忆、认知抑制、分散注意和选择性注意。我们还收集了概率序列学习任务训练前后的fMRI数据，并提取了fMRI响应和连通性作为机器学习分类器的特征。我们的结果表明，PI引导的GMLVQ分类器优于仅使用认知数据的基线分类器。此外，我们发现对于基线分类器，分散注意力是唯一相关的认知特征。当PI被纳入时，分散注意力仍然是最相关的特征，而认知抑制也与任务相关。有趣的是，对fMRI GMLVQ分类器的分析表明：(1)当整个fMRI信号作为分类器的输入时，训练后的会话是最相关的；(2)当使用反映底层时空fMRI模式的图特征时，预训练最相关。综上所述，这些结果表明，训练前的大脑连通性和训练后的整体fMRI信号都是MCI认知技能的诊断指标。
Early diagnosis of dementia is critical for assessing disease progression and potential treatment. State-or-the-art machine learning techniques have been increasingly employed to take on this diagnostic task. In this study, we employed Generalized Matrix Learning Vector Quantization (GMLVQ) classifiers to discriminate patients with Mild Cognitive Impairment (MCI) from healthy controls based on their cognitive skills. Further, we adopted a “Learning with privileged information” approach to combine cognitive and fMRI data for the classification task. The resulting classifier operates solely on the cognitive data while it incorporates the fMRI data as privileged information (PI) during training. This novel classifier is of practical use as the collection of brain imaging data is not always possible with patients and older participants. MCI patients and healthy age-matched controls were trained to extract structure from temporal sequences. We ask whether machine learning classifiers can be used to discriminate patients from controls and whether differences between these groups relate to individual cognitive profiles. To this end, we tested participants in four cognitive tasks: working memory, cognitive inhibition, divided attention, and selective attention. We also collected fMRI data before and after training on a probabilistic sequence learning task and extracted fMRI responses and connectivity as features for machine learning classifiers. Our results show that the PI guided GMLVQ classifiers outperform the baseline classifier that only used the cognitive data. In addition, we found that for the baseline classifier, divided attention is the only relevant cognitive feature. When PI was incorporated, divided attention remained the most relevant feature while cognitive inhibition became also relevant for the task. Interestingly, this analysis for the fMRI GMLVQ classifier suggests that (1) when overall fMRI signal is used as inputs to the classifier, the post-training session is most relevant; and (2) when the graph feature reflecting underlying spatiotemporal fMRI pattern is used, the pre-training session is most relevant. Taken together these results suggest that brain connectivity before training and overall fMRI signal after training are both diagnostic of cognitive skills in MCI.
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发表时间：
2015-07
期刊：
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影响因子：
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10.1016/j.jalz.2011.03.008
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2011-05
期刊：
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影响因子：
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发表时间：
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影响因子：
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