Watters Harrison, Fazili Abia, Daley Lauren, Belden Alex, LaGrow T J, Bolt Taylor, Loui Psyche, Keilholz Shella
Emory University.
Department of Biomedical Engineering, Emory University/Georgia Institute of Technology.
bioRxiv. 2024 Apr 9:2024.04.07.588391. doi: 10.1101/2024.04.07.588391.
The intrinsic dynamics of human brain activity display a recurring pattern of anti-correlated activity between the default mode network (DMN), associated with internal processing and mentation, and task positive regions, associated with externally directed attention. In human functional magnetic resonance imaging (fMRI) data, this anti-correlated pattern is detectable on the infraslow timescale (<0.1 Hz) as a quasi-periodic pattern (QPP). While the DMN is implicated in creativity and musicality in traditional time-averaged functional connectivity studies, no one has yet explored how creative training may alter dynamic spatiotemporal patterns involving the DMN such as QPPs. In the present study, we compare the outputs of two QPP detection approaches, sliding window algorithm and complex principal components analysis (cPCA). We apply both methods to an existing dataset of musicians captured with resting state fMRI, grouped as either classical, improvisational, or minimally trained non-musicians. The original time-averaged functional connectivity (FC) analysis of this dataset used improvisation as a proxy for creative thinking and found that the DMN and visual networks (VIS) display higher connectivity in improvisational musicians. We expand upon this dataset's original study and find that QPP analysis detects convergent results at the group level with both methods. In improvisational musicians, dynamic functional correlation in the group-averaged QPP was found to be increased between the DMN-VIS and DMN-FPN for both the QPP algorithm and complex principal components analysis (cPCA) methods. Additionally, we found an unexpected increase in FC in the group-averaged QPP between the dorsal attention network and amygdala in improvisational musicians; this result was not reported in the original seed-based study of this dataset. The current study represents a novel application of two dynamic FC detection methods with results that replicate and expand upon previous seed-based FC findings. The results show the robustness of both the QPP phenomenon and its detection methods. This study also demonstrates the value of dynamic FC methods in reproducing seed-based findings and their promise in detecting group-wise or individual differences that may be missed by traditional seed-based resting state fMRI studies.
人类大脑活动的内在动力学表现出一种反复出现的模式，即与内部加工和思维相关的默认模式网络（DMN）和与外部定向注意力相关的任务积极区域之间存在反相关活动。在人类功能磁共振成像（fMRI）数据中，这种反相关模式在亚慢时间尺度（<0.1 Hz）上可检测为一种准周期性模式（QPP）。虽然在传统的时间平均功能连接性研究中，DMN与创造力和音乐能力有关，但尚未有人探索创造性训练如何改变涉及DMN的动态时空模式，如QPPs。在本研究中，我们比较了两种QPP检测方法的输出，即滑动窗口算法和复主成分分析（cPCA）。我们将这两种方法应用于一个现有的音乐家静息态fMRI数据集，这些音乐家分为古典音乐家、即兴演奏音乐家或训练最少的非音乐家。该数据集最初的时间平均功能连接性（FC）分析使用即兴演奏作为创造性思维的代理指标，发现DMN和视觉网络（VIS）在即兴演奏音乐家中表现出更高的连接性。我们在该数据集原始研究的基础上进行扩展，发现QPP分析在组水平上用这两种方法都检测到了趋同的结果。对于QPP算法和复主成分分析（cPCA）方法，在即兴演奏音乐家中，组平均QPP中DMN-VIS和DMN-FPN之间的动态功能相关性均增加。此外，我们发现即兴演奏音乐家中组平均QPP的背侧注意网络和杏仁核之间的FC意外增加；在该数据集基于种子的原始研究中未报告此结果。当前研究代表了两种动态FC检测方法的新应用，其结果复制并扩展了先前基于种子的FC研究结果。结果显示了QPP现象及其检测方法的稳健性。本研究还证明了动态FC方法在重现基于种子的研究结果方面的价值，以及它们在检测传统基于种子的静息态fMRI研究可能遗漏的组间或个体差异方面的前景。
