Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona, 08018, Spain.
Institució Catalana de la Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona, 08010, Spain.
Nat Commun. 2019 Feb 4;10(1):583. doi: 10.1038/s41467-018-08186-7.
A key unresolved problem in neuroscience is to determine the relevant timescale for understanding spatiotemporal dynamics across the whole brain. While resting state fMRI reveals networks at an ultraslow timescale (below 0.1 Hz), other neuroimaging modalities such as MEG and EEG suggest that much faster timescales may be equally or more relevant for discovering spatiotemporal structure. Here, we introduce a novel way to generate whole-brain neural dynamical activity at the millisecond scale from fMRI signals. This method allows us to study the different timescales through binning the output of the model. These timescales can then be investigated using a method (poetically named brain songs) to extract the spacetime motifs at a given timescale. Using independent measures of entropy and hierarchy to characterize the richness of the dynamical repertoire, we show that both methods find a similar optimum at a timescale of around 200 ms in resting state and in task data.
神经科学中一个未解决的关键问题是确定理解整个大脑时空动力学的相关时间尺度。虽然静息态 fMRI 揭示了超慢时间尺度（低于 0.1 Hz）的网络，但其他神经影像学模态，如 MEG 和 EEG，则表明更快的时间尺度对于发现时空结构同样重要，甚至更为重要。在这里，我们引入了一种从 fMRI 信号生成毫秒级全脑神经动力学活动的新方法。该方法允许我们通过对模型输出进行分箱来研究不同的时间尺度。然后可以使用一种方法（诗意地命名为“脑之歌”）来提取给定时间尺度的时空模式来研究这些时间尺度。使用熵和层次结构的独立度量来描述动态范围的丰富度，我们表明这两种方法在静息状态和任务数据中都在大约 200 ms 的时间尺度上找到了相似的最佳值。
