Department of Radiology, First Affiliated Hospital, Guangxi University of Chinese Medicine, Nanning, 530023, Guangxi, China.
School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518060, China.
Alzheimers Res Ther. 2021 Jan 16;13(1):28. doi: 10.1186/s13195-020-00764-6.
The brain's dynamic spontaneous neural activity and dynamic functional connectivity (dFC) are both important in supporting cognition, but how these two types of brain dynamics evolve and co-evolve in subjective cognitive decline (SCD) and mild cognitive impairment (MCI) remain unclear. The aim of the present study was to investigate recurrent and concurrent patterns of two types of dynamic brain states correlated with cognitive decline.
The present study analyzed resting-state functional magnetic resonance imaging data from 62 SCD patients, 75 MCI patients, and 70 healthy controls (HCs). We used the sliding-window and clustering method to identify two types of recurrent brain states from both dFC and dynamic regional spontaneous activity, as measured by dynamic fractional amplitude of low-frequency fluctuations (dfALFF). Then, the occurrence frequency of a dFC or dfALFF state and the co-occurrence frequency of a pair of dFC and dfALFF states among all time points are extracted for each participant to describe their dynamics brain patterns.
We identified a few recurrent states of dfALFF and dFC and further ascertained the co-occurrent patterns of these two types of dynamic brain states (i.e., dfALFF and dFC states). Importantly, the occurrence frequency of a default-mode network (DMN)-dominated dFC state was significantly different between HCs and SCD patients, and the co-occurrence frequencies of a DMN-dominated dFC state and a DMN-dominated dfALFF state were also significantly different between SCD and MCI patients. These two dynamic features were both significantly positively correlated with Mini-Mental State Examination scores.
Our findings revealed novel fMRI-based neural signatures of cognitive decline from recurrent and concurrent patterns of dfALFF and dFC, providing strong evidence supporting SCD as the transition phase between normal aging and MCI. This finding holds potential to differentiate SCD patients from HCs via both dFC and dfALFF as objective neuroimaging biomarkers, which may aid in the early diagnosis and intervention of Alzheimer's disease.
大脑的动态自发神经活动和动态功能连接（dFC）对于支持认知都很重要，但在主观认知下降（SCD）和轻度认知障碍（MCI）中，这两种类型的大脑动力学如何演变和共同演变尚不清楚。本研究旨在探讨与认知下降相关的两种动态脑状态的反复出现和并发模式。
本研究分析了 62 名 SCD 患者、75 名 MCI 患者和 70 名健康对照者（HCs）的静息态功能磁共振成像数据。我们使用滑动窗口和聚类方法从 dFC 和动态区域自发活动中识别两种类型的反复出现的脑状态，这两种活动分别由动态低频波动的分数幅度（dfALFF）来衡量。然后，从每个参与者的所有时间点中提取出 dFC 或 dfALFF 状态的出现频率和一对 dFC 和 dfALFF 状态的共同出现频率，以描述他们的动态脑模式。
我们确定了几个 dfALFF 和 dFC 的反复出现状态，进一步确定了这两种类型的动态脑状态（即 dfALFF 和 dFC 状态）的共同出现模式。重要的是，HC 和 SCD 患者之间默认模式网络（DMN）主导的 dFC 状态的出现频率存在显著差异，SCD 和 MCI 患者之间 DMN 主导的 dFC 状态和 DMN 主导的 dfALFF 状态的共同出现频率也存在显著差异。这两个动态特征都与简易精神状态检查评分显著正相关。
我们的研究结果揭示了基于功能磁共振成像的认知下降的新神经特征，从 dfALFF 和 dFC 的反复出现和并发模式中提供了强有力的证据支持 SCD 是正常衰老和 MCI 之间的过渡阶段。这一发现有可能通过 dFC 和 dfALFF 将 SCD 患者与 HCs 区分开来，作为客观的神经影像学生物标志物，这可能有助于阿尔茨海默病的早期诊断和干预。
