Large-scale dynamic modeling of task-fMRI signals via subspace system identification
Large-scale dynamic modeling of task-fMRI signals via subspace system identification
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DOI：
10.1088/1741-2552/aad8c7
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发表时间：
2018-12-01
影响因子：
4
通讯作者：
Preciado, Victor M.
中科院分区：
文献类型：
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作者：
Becker, Cassiano O.;Bassett, Danielle S.;Preciado, Victor M.
客观的。我们分析基于任务的功能磁共振成像时间序列，以生成大规模动态模型，这些模型能够以良好的精度逼近观察到的信号。方法。我们使用外部输入扩展了确定性和随机状态空间模型的子空间系统识别方法。使用控制理论分析工具来表征生成模型的动态行为。为了验证其有效性，我们通过联合状态输入最大似然估计对已识别的输入输出关系进行概率反演。我们的实验设置探索了使用人类连接组项目最先进的采集和预处理方法生成的大型数据集。主要结果。我们分析了解剖上的分割和空间上密集的时间序列，并提出了一种有效的算法来解决后者产生的高维优化问题。我们的结果能够量化每个任务条件和皮层每个区域之间的输入输出传递函数，例如运动任务。此外，所识别的模型产生任务条件和皮质区域之间的脉冲响应函数，该函数与典型的血液动力学响应函数兼容。然后，我们扩展子空间方法来考虑多受试者实验配置，识别捕获跨受试者共同动态特征的模型。最后，我们表明，通过最大似然的系统反演允许根据观察到的输出来估计任务刺激的发生时间。意义。生成动态输入输出模型的能力可能会对不断扩大的神经反馈领域产生影响。特别是，我们生成的模型允许部分量化外部任务相关输入对大脑代谢反应的影响（以大脑当前状态为条件）。这样的概念为在治疗应用中利用控制理论方法进行神经调节和自我调节提供了基础。
Objective. We analyze task-based fMRI time series to produce large-scale dynamical models that are capable of approximating the observed signal with good accuracy. Approach. We extend subspace system identification methods for deterministic and stochastic state-space models with external inputs. The dynamic behavior of the generated models is characterized using control-theoretic analysis tools. To validate their effectiveness, we perform a probabilistic inversion of the identified input-output relationships via joint state-input maximum likelihood estimation. Our experimental setup explores a large dataset generated using state-of-the-art acquisition and pre-processing methods from the Human Connectome Project. Main results. We analyze both anatomically parcellated and spatially dense time series, and propose an efficient algorithm to address the high-dimensional optimization problem resulting from the latter. Our results enable the quantification of input-output transfer functions between each task condition and each region of the cortex, as exemplified by a motor task. Further, the identified models produce impulse response functions between task conditions and cortical regions that are compatible with typical hemodynamic response functions. We then extend subspace methods to account for multi-subject experimental configurations, identifying models that capture common dynamical characteristics across subjects. Finally, we show that system inversion via maximum-likelihood allows the time-of-occurrence of the task stimuli to be estimated from the observed outputs. Significance. The ability to produce dynamical input-output models might have an impact in the expanding field of neurofeedback. In particular, the models we produce allow the partial quantification of the effect of external task-related inputs on the metabolic response of the brain, conditioned on its current state. Such a notion provides a basis for leveraging control-theoretic approaches to neuromodulation and self-regulation in therapeutic applications.