The human brain can be conceptualized as a dynamical system. Utilizing
resting state fMRI time series imaging, we can study the underlying dynamics at
ear-marked Regions of Interest (ROIs) to understand structure or lack thereof.
This differential behavior could be key to understanding the neurodegeneration
and also to classify between healthy and Mild Cognitive Impairment (MCI)
subjects. In this study, we consider 6 brain networks spanning over 160 ROIs
derived from Dosenbach template, where each network consists of 25-30 ROIs.
Recurrence plot, extensively used to understand evolution of time series, is
employed. Representative time series at each ROI is converted to its
corresponding recurrence plot visualization, which is subsequently condensed to
low-dimensional feature embeddings through Autoencoders. The performance of the
proposed method is shown on fMRI volumes of 100 subjects (balanced data), taken
from publicly available ADNI dataset. Results obtained show peak classification
accuracy of 93% among the 6 brain networks, mean accuracy of 89.3% thereby
illustrating promise in the proposed approach.

通过使用脑部网络的静息态功能磁共振成像技术，本研究采用复发图方法对 100 名受试者的脑部网络进行分析，并通过自编码器得出低维特征嵌入，得到 93% 的分类准确率，证明了该方法的潜力。