Menara Tommaso, Lisi Giuseppe, Pasqualetti Fabio, Cortese Aurelio
Bourns College of Engineering, University of California Riverside, 900 University Ave, Riverside, California, 92521, UNITED STATES.
Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya, Aichi, 466-8555, JAPAN.
J Neural Eng. 2020 Dec 23. doi: 10.1088/1741-2552/abd684.
Large multi-site neuroimaging datasets have significantly advanced our quest to understand brain-behavior relationships and to develop biomarkers of psychiatric and neurodegenerative disorders. Yet, such data collections come at a cost, as the inevitable differences across samples may lead to biased or erroneous conclusions.
We aim to validate the estimation of individual brain network dynamics fingerprints and appraise sources of variability in large resting-state functional magnetic resonance imaging (rs-fMRI) datasets by providing a novel point of view based on data-driven dynamical models.
Previous work has investigated this critical issue in terms of effects on static measures, such as functional connectivity and brain parcellations. Here, we utilize dynamical models (Hidden Markov models - HMM) to examine how diverse scanning factors in multi-site fMRI recordings affect our ability to infer the brain's spatiotemporal wandering between large-scale networks of activity. Specifically, we leverage a stable HMM trained on the Human Connectome Project (homogeneous) dataset, which we then apply to an heterogeneous dataset of traveling subjects scanned under a multitude of conditions.
Building upon this premise, we first replicate previous work on the emergence of non-random sequences of brain states. We next highlight how these time-varying brain activity patterns are robust subject-specific fingerprints. Finally, we suggest these fingerprints may be used to assess which scanning factors induce high variability in the data.
These results demonstrate that we can i) use large scale dataset to train models that can be then used to interrogate subject-specific data, ii) recover the unique trajectories of brain activity changes in each individual, but also iii) urge caution as our ability to infer such patterns is affected by how, where and when we do so.
大型多中心神经影像数据集极大地推动了我们对脑-行为关系的理解以及对精神疾病和神经退行性疾病生物标志物的开发。然而，此类数据收集是有代价的，因为样本间不可避免的差异可能导致有偏差或错误的结论。
我们旨在通过基于数据驱动的动力学模型提供一种新视角，来验证个体脑网络动力学指纹的估计，并评估大型静息态功能磁共振成像（rs-fMRI）数据集中的变异性来源。
以往的工作从对静态测量（如功能连接和脑分区）的影响方面研究了这个关键问题。在这里，我们利用动力学模型（隐马尔可夫模型 - HMM）来研究多中心功能磁共振成像记录中的各种扫描因素如何影响我们推断大脑在大规模活动网络之间时空游走的能力。具体而言，我们利用在人类连接体计划（同质）数据集上训练的稳定HMM，然后将其应用于在多种条件下扫描的旅行受试者的异质数据集。
基于此前提，我们首先复制了之前关于脑状态非随机序列出现的工作。接下来，我们强调这些随时间变化的脑活动模式是稳健的个体特异性指纹。最后，我们建议这些指纹可用于评估哪些扫描因素会导致数据中的高变异性。
这些结果表明，我们可以：i）使用大规模数据集训练模型，然后将其用于询问个体特异性数据；ii）恢复每个个体脑活动变化的独特轨迹；但同时iii）我们推断此类模式的能力会受到扫描方式、地点和时间的影响，因此要谨慎行事。
