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隐藏马尔可夫模型揭示急性轻度创伤性脑损伤全脑功能连接的异常动态特性。

Aberrant dynamic properties of whole-brain functional connectivity in acute mild traumatic brain injury revealed by hidden Markov models.

机构信息

Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, China.

Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China.

出版信息

CNS Neurosci Ther. 2024 Mar;30(3):e14660. doi: 10.1111/cns.14660.

Abstract

OBJECTIVES

This study aimed to investigate the temporal dynamics of brain activity and characterize the spatiotemporal specificity of transitions and large-scale networks on short timescales in acute mild traumatic brain injury (mTBI) patients and those with cognitive impairment in detail.

METHODS

Resting-state functional magnetic resonance imaging (rs-fMRI) was acquired for 71 acute mTBI patients and 57 age-, sex-, and education-matched healthy controls (HCs). A hidden Markov model (HMM) analysis of rs-fMRI data was conducted to identify brain states that recurred over time and to assess the dynamic patterns of activation states that characterized acute mTBI patients and those with cognitive impairment. The dynamic parameters (fractional occupancy, lifetime, interval time, switching rate, and probability) between groups and their correlation with cognitive performance were analyzed.

RESULTS

Twelve HMM states were identified in this study. Compared with HCs, acute mTBI patients and those with cognitive impairment exhibited distinct changes in dynamics, including fractional occupancy, lifetime, and interval time. Furthermore, the switching rate and probability across HMM states were significantly different between acute mTBI patients and patients with cognitive impairment (all p < 0.05). The temporal reconfiguration of states in acute mTBI patients and those with cognitive impairment was associated with several brain networks (including the high-order cognition network [DMN], subcortical network [SUB], and sensory and motor network [SMN]).

CONCLUSIONS

Hidden Markov models provide additional information on the dynamic activity of brain networks in patients with acute mTBI and those with cognitive impairment. Our results suggest that brain network dynamics determined by the HMM could reinforce the understanding of the neuropathological mechanisms of acute mTBI patients and those with cognitive impairment.

摘要

目的

本研究旨在探究急性轻度创伤性脑损伤(mTBI)患者和伴有认知障碍患者脑活动的时间动态,并详细描述其在短时间尺度上的转变和大规模网络的时空特异性。

方法

对 71 例急性 mTBI 患者和 57 例年龄、性别和教育程度匹配的健康对照者(HCs)进行静息态功能磁共振成像(rs-fMRI)采集。采用隐马尔可夫模型(HMM)对 rs-fMRI 数据进行分析,以识别随时间重复出现的脑状态,并评估以激活状态为特征的急性 mTBI 患者和伴有认知障碍患者的动态模式。分析组间的动态参数(分数占据、寿命、间隔时间、切换率和概率)及其与认知表现的相关性。

结果

本研究共确定了 12 个 HMM 状态。与 HCs 相比,急性 mTBI 患者和伴有认知障碍患者的动力学表现出明显的变化,包括分数占据、寿命和间隔时间。此外,急性 mTBI 患者和伴有认知障碍患者之间 HMM 状态的切换率和概率差异有统计学意义(均 P<0.05)。急性 mTBI 患者和伴有认知障碍患者的状态时间重构与多个脑网络有关(包括高级认知网络[DMN]、皮质下网络[SUB]和感觉运动网络[SMN])。

结论

隐马尔可夫模型为急性 mTBI 患者和伴有认知障碍患者脑网络动态活动提供了额外信息。我们的研究结果表明,由 HMM 确定的脑网络动力学可能有助于加深对急性 mTBI 患者和伴有认知障碍患者神经病理学机制的理解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4799/10912843/e4ef269deadb/CNS-30-e14660-g005.jpg

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