Computational approaches to fMRI analysis.
Computational approaches to fMRI analysis.
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DOI：
10.1038/nn.4499
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发表时间：
2017-02-23
影响因子：
25
通讯作者：
Willke TL
中科院分区：
文献类型：
--
作者：
Cohen JD;Daw N;Engelhardt B;Hasson U;Li K;Niv Y;Norman KA;Pillow J;Ramadge PJ;Turk-Browne NB;Willke TL
认知神经科学中的分析方法并不总是与功能性磁共振成像（fMRI）数据的丰富性相匹配。早期的方法侧重于估计单个体素或区域内的神经活动，对多次试验或组块取平均值，并在每个参与者中分别建模。这种方法大多忽略了神经表征在体素上的分布式特性、任务期间神经活动的连续动态、对多个参与者进行联合推断的统计学优势以及使用预测模型约束分析的价值。最近的一些探索性和理论驱动的方法已经开始利用这些机会。这些方法凸显了计算技术在fMRI分析中的重要性，尤其是机器学习、算法优化和平行计算。这些技术的应用促成了新一代的实验和分析，可能会改变我们对大脑中一些最复杂且具有人类独特性的信号的理解：诸如思想、意图和记忆等认知行为。
Analysis methods in cognitive neuroscience have not always matched the richness of fMRI data. Early methods focused on estimating neural activity within individual voxels or regions, averaged over trials or blocks and modeled separately in each participant. This approach mostly neglected the distributed nature of neural representations over voxels, the continuous dynamics of neural activity during tasks, the statistical benefits of performing joint inference over multiple participants and the value of using predictive models to constrain analysis. Several recent exploratory and theory-driven methods have begun to pursue these opportunities. These methods highlight the importance of computational techniques in fMRI analysis, especially machine learning, algorithmic optimization and parallel computing. Adoption of these techniques is enabling a new generation of experiments and analyses that could transform our understanding of some of the most complex—and distinctly human—signals in the brain: acts of cognition such as thoughts, intentions and memories.