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Brain spontaneous fluctuations in sensorimotor regions were directly related to eyes open and eyes closed: evidences from a machine learning approach

机译:感觉运动区域的大脑自发波动与睁眼和闭眼直接相关:来自机器学习方法的证据

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摘要

Previous studies have demonstrated that the difference between resting-state brain activations depends on whether the subject was eyes open (EO) or eyes closed (EC). However, whether the spontaneous fluctuations are directly related to these two different resting states are still largely unclear. In the present study, we acquired resting-state functional magnetic resonance imaging data from 24 healthy subjects (11 males, 20.17 ± 2.74 years) under the EO and EC states. The amplitude of the spontaneous brain activity in low-frequency band was subsequently investigated by using the metric of fractional amplitude of low frequency fluctuation (fALFF) for each subject under each state. A support vector machine (SVM) analysis was then applied to evaluate whether the category of resting states could be determined from the brain spontaneous fluctuations. We demonstrated that these two resting states could be decoded from the identified pattern of brain spontaneous fluctuations, predominantly based on fALFF in the sensorimotor module. Specifically, we observed prominent relationships between increased fALFF for EC and decreased fALFF for EO in sensorimotor regions. Overall, the present results indicate that a SVM performs well in the discrimination between the brain spontaneous fluctuations of distinct resting states and provide new insight into the neural substrate of the resting states during EC and EO.
机译:先前的研究表明,静止状态的大脑激活之间的差异取决于受试者是睁眼(EO)还是闭眼(EC)。然而,自发性波动是否直接与这两个不同的静止状态直接相关,目前尚不清楚。在本研究中,我们从EO和EC状态下的24名健康受试者(11名男性,20.17±2.74岁)中获得了静息状态功能磁共振成像数据。随后,通过使用每种状态下每个受试者的低频波动幅度幅值(fALFF)的度量,研究低频自发性大脑活动的幅度。然后应用支持向量机(SVM)分析来评估是否可以从大脑自发性波动确定静息状态的类别。我们证明了这两个静止状态可以从确定的大脑自发性波动模式中解码,主要是基于感觉运动模块中的fALFF。具体来说,我们观察到在感觉运动区域EC的fALFF增加与EO的fALFF减少之间存在显着的关系。总体而言,目前的结果表明,SVM在区分不同静止状态的大脑自发性波动方面表现良好,并为EC和EO期间静止状态的神经底物提供了新的见识。

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