In this study, we present a technique that spans multi-scale views (global
scale- meaning brain network-level and local scale- examining each individual
ROI that constitutes the network) applied to resting-state fMRI volumes. Deep
learning based classification is utilized in understanding neurodegeneration.
The novelty of the proposed approach lies in utilizing two extreme scales of
analysis. One branch considers the entire network within graph-analysis
framework. Concurrently, the second branch scrutinizes each ROI within a
network independently, focusing on evolution of dynamics. For each subject,
graph-based approach employs partial correlation to profile the subject in a
single graph where each ROI is a node, providing insights into differences in
levels of participation. In contrast, non-linear analysis employs recurrence
plots to profile a subject as a multichannel 2D image, revealing distinctions
in underlying dynamics. The proposed approach is employed for classification of
a cohort of 50 healthy control (HC) and 50 Mild Cognitive Impairment (MCI),
sourced from ADNI dataset. Results point to: (1) reduced activity in ROIs such
as PCC in MCI (2) greater activity in occipital in MCI, which is not seen in HC
(3) when analysed for dynamics, all ROIs in MCI show greater predictability in
time-series.

本研究提出了一种应用于静息状态 fMRI 体积的跨多尺度视角（全局尺度 - 脑网络水平和局部尺度 - 检查构成网络的每个个体 ROI）的技术，并利用深度学习的分类方法来理解神经退行性问题。提出的方法的创新之处在于利用了两种极端尺度的分析。一种分支考虑了整个网络在图分析框架内，同时第二种分支分别审查网络中的每个 ROI，重点关注动态演变。该方法用于对 50 个健康对照组（HC）和 50 个轻度认知损害（MCI）人群进行分类，结果表明：（1）MCI 组中的 ROI（如 PCC）活动减少，（2）MCI 组中的枕叶活动增加，这在 HC 组中未见，（3）在动态分析中，MCI 组中的所有 ROI 显示出更大的时间序列可预测性。

多尺度功能磁共振成像时间序列分析用于理解轻度认知功能障碍的神经退行性变化

Multi-scale fMRI time series analysis for understanding  neurodegeneration in MCI

Complex networks are an important paradigm of modern complex systems sciences
which allows quantitatively assessing the structural properties of systems
composed of different interacting entities. During the last years, intensive
efforts have been spent on applying network-based concepts also for the
analysis of dynamically relevant higher-order statistical properties of time
series. Notably, many corresponding approaches are closely related with the
concept of recurrence in phase space. In this paper, we review recent
methodological advances in time series analysis based on complex networks, with
a special emphasis on methods founded on recurrence plots. The potentials and
limitations of the individual methods are discussed and illustrated for
paradigmatic examples of dynamical systems as well as for real-world time
series. Complex network measures are shown to provide information about
structural features of dynamical systems that are complementary to those
characterized by other methods of time series analysis and, hence,
substantially enrich the knowledge gathered from other existing (linear as well
as nonlinear) approaches.

本文概述了基于复杂网络的时间序列分析方法，着重介绍了以复归图为基础的方法，探讨了各个方法的潜力和局限性，并通过动力系统和实际时间序列的典型例子说明了复杂网络测量可以提供其他线性和非线性方法所未能提供的结构特征信息。