Individual-specific fMRI-Subspaces improve functional connectivity prediction of behavior
Individual-specific fMRI-Subspaces improve functional connectivity prediction of behavior
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DOI：
10.1016/j.neuroimage.2019.01.069
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发表时间：
2019-04-01
期刊：
影响因子：
5.7
通讯作者：
Yeo, B. T. Thomas
中科院分区：
文献类型：
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作者：
Kashyap, Rajan;Kong, Ru;Yeo, B. T. Thomas
利用静息态功能连接（RSFC）来预测人类行为具有重要意义。好的行为预测理论上应该要求RSFC在参与者之间有足够的差异;如果RSFC在参与者之间是相同的，那么行为预测显然是差的。因此，我们假设，去除参与者之间共享的常见静息态功能性磁共振成像（rs-fMRI）信号将改善行为预测。在这里，我们考虑了803名参与者从人类连接体项目（HCP）与四个rs-fMRI运行。我们采用了共同和正交基提取（COBE）技术分解每个HCP运行到两个子空间：一个共同的（组级）子空间共享所有参与者和特定主题的子空间。我们发现，第一个共同的COBE组件的第一次HCP运行是本地化的视觉皮层，是唯一的运行。另一方面，第一次HCP运行的第二个共同COBE成分和其余HCP运行的第一个共同COBE成分高度相似，并定位于默认网络内的区域，包括后扣带回皮质和楔前叶。总的来说，这表明存在跨参与者共享的特定于运行（特定于状态）的效应。通过从第一次HCP运行中去除第一和第二共同COBE组分，以及从剩余HCP运行中去除第一共同COBE组分，所得到的RSFC在跨越认知、情感和个性的58个行为测量中平均提高了11.7%的行为预测。
There is significant interest in using resting-state functional connectivity (RSFC) to predict human behavior. Good behavioral prediction should in theory require RSFC to be sufficiently distinct across participants; if RSFC were the same across participants, then behavioral prediction would obviously be poor. Therefore, we hypothesize that removing common resting-state functional magnetic resonance imaging (rs-fMRI) signals that are shared across participants would improve behavioral prediction. Here, we considered 803 participants from the human connectome project (HCP) with four rs-fMRI runs. We applied the common and orthogonal basis extraction (COBE) technique to decompose each HCP run into two subspaces: a common (group-level) subspace shared across all participants and a subject-specific subspace. We found that the first common COBE component of the first HCP run was localized to the visual cortex and was unique to the run. On the other hand, the second common COBE component of the first HCP run and the first common COBE component of the remaining HCP runs were highly similar and localized to regions within the default network, including the posterior cingulate cortex and precuneus. Overall, this suggests the presence of run-specific (state-specific) effects that were shared across participants. By removing the first and second common COBE components from the first HCP run, and the first common COBE component from the remaining HCP runs, the resulting RSFC improves behavioral prediction by an average of 11.7% across 58 behavioral measures spanning cognition, emotion and personality.