Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA.
Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, NE, USA.
J Neurosci Methods. 2020 May 15;338:108649. doi: 10.1016/j.jneumeth.2020.108649. Epub 2020 Mar 10.
Longitudinal neuroimaging studies have demonstrated that adolescence is a crucial developmental period of continued brain growth and change. Motivated by both achievements in graph signal processing and recent evidence that some brain areas act as hubs connecting functionally specialized systems, we propose an approach to detect these regions from a spectral analysis perspective. In particular, as the human brain undergoes substantial development throughout adolescence, we evaluate functional network difference among age groups from functional magnetic resonance imaging (fMRI) measurements.
We treated these measurements as graph signals defined on the parcellated functional brain regions and proposed a graph Laplacian learning based Fourier transform (GLFT) to transform the original graph signals into the frequency domain. Eigen-analysis was conducted afterwards to study the behaviors of the corresponding brain regions, which enabled the characterization of brain maturation.
We first evaluated our method on the synthetic data and then applied it to resting state and task fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC) dataset, comprised of normally developing adolescents from 8 to 22 years of age. The method provided an accuracy of 94.9% in distinguishing different adolescent stages and we detected 13 hubs from resting state fMRI and 16 hubs from task fMRI related to brain maturation.
The proposed GLFT demonstrated its superiority over conventional graph Fourier transform and alternative graph Fourier transform with high predictive power.
The method provides a powerful approach for extracting brain connectivity patterns and identifying hub regions.
纵向神经影像学研究表明，青春期是大脑持续生长和变化的关键发育阶段。受到图信号处理方面的成就以及一些大脑区域充当连接功能专业化系统的枢纽的最新证据的启发，我们提出了一种从谱分析角度来检测这些区域的方法。特别是，由于人类大脑在青春期经历了大量的发育，我们从功能磁共振成像（fMRI）测量中评估了不同年龄组之间的功能网络差异。
我们将这些测量值视为在分割的功能脑区上定义的图信号，并提出了一种基于图拉普拉斯学习的傅里叶变换（GLFT），将原始图信号转换到频域。随后进行特征分析，以研究相应脑区的行为，从而实现大脑成熟的特征描述。
我们首先在合成数据上评估了我们的方法，然后将其应用于来自费城神经发育队列（PNC）数据集的静息态和任务 fMRI 数据，该数据集由 8 至 22 岁的正常发育青少年组成。该方法在区分不同青少年阶段的准确率达到 94.9%，并从静息态 fMRI 中检测到 13 个与大脑成熟相关的枢纽，从任务 fMRI 中检测到 16 个枢纽。
所提出的 GLFT 证明了其优于传统图傅里叶变换和替代图傅里叶变换的优越性，具有很高的预测能力。
该方法为提取大脑连接模式和识别枢纽区域提供了一种强大的方法。
