Ciuciu Philippe, Wendt Herwig, Combrexelle Sebastien, Abry Patrice
Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:3769-3772. doi: 10.1109/EMBC.2017.8037677.
Scale-free dynamics is nowadays a massively used paradigm to model infraslow macroscopic brain activity. Multifractal analysis is becoming the standard tool to characterize scale-free dynamics. It is commonly used on various modalities of neuroimaging data to evaluate whether arrhythmic fluctuations in ongoing or evoked brain activity are related to pathologies (Alzheimer, epilepsy) or task performance. The success of multifractal analysis in neurosciences remains however so far contrasted: While it lead to relevant findings on M/EEG data, less clear impact was shown when applied to fMRI data. This is mostly due to their poor time resolution and very short duration as well as to the fact that analysis remains performed voxelwise. To take advantage of the large amount of voxels recorded jointly in fMRI, the present contribution proposes the use of a recently introduced Bayesian formalism for multifractal analysis, that regularizes the estimation of the multifractality parameter of a given voxel using information from neighbor voxels. The benefits of this regularized multifractal analysis are illustrated by comparison against classical multifractal analysis on fMRI data collected on one subject, at rest and during a working memory task: Though not yet statistically significant, increased multifractality is observed in task-negative and task-positive networks, respectively.
无标度动力学如今是一种被大量使用的范式，用于对超慢宏观脑活动进行建模。多重分形分析正成为表征无标度动力学的标准工具。它通常用于各种神经成像数据模态，以评估持续或诱发脑活动中的无节律波动是否与病理学（阿尔茨海默病、癫痫）或任务表现相关。然而，多重分形分析在神经科学中的成功至今仍存在反差：虽然它在脑磁图/脑电图（M/EEG）数据上得出了相关结果，但应用于功能磁共振成像（fMRI）数据时，其影响却不太明显。这主要是由于fMRI数据的时间分辨率较差、持续时间非常短，以及分析仍然是逐体素进行的。为了利用fMRI中联合记录的大量体素，本文提出使用一种最近引入的用于多重分形分析的贝叶斯形式，该形式利用来自相邻体素的信息对给定体素的多重分形参数估计进行正则化。通过将这种正则化多重分形分析与对一名受试者在静息和工作记忆任务期间收集的fMRI数据进行的经典多重分形分析进行比较，说明了这种正则化多重分形分析的好处：尽管尚未达到统计学显著性，但分别在任务负性网络和任务正性网络中观察到了多重分形性增加。
