{"title":"利用热扩散模型从结构连接组预测静息态大脑功能连接:一种多时间尺度融合方法","authors":"Zhengyuan Lv, Jingming Li, Li Yao, Xiaojuan Guo","doi":"10.1088/1741-2552/ad39a6","DOIUrl":null,"url":null,"abstract":"<italic toggle=\"yes\">Objective.</italic> Understanding the intricate relationship between structural connectivity (SC) and functional connectivity (FC) is pivotal for understanding the complexities of the human brain. To explore this relationship, the heat diffusion model (HDM) was utilized to predict FC from SC. However, previous studies using the HDM have typically predicted FC at a critical time scale in the heat kernel equation, overlooking the dynamic nature of the diffusion process and providing an incomplete representation of the predicted FC. <italic toggle=\"yes\">Approach.</italic> In this study, we propose an alternative approach based on the HDM. First, we introduced a multiple-timescale fusion method to capture the dynamic features of the diffusion process. Additionally, to enhance the smoothness of the predicted FC values, we employed the Wavelet reconstruction method to maintain local consistency and remove noise. Moreover, to provide a more accurate representation of the relationship between SC and FC, we calculated the linear transformation between the smoothed FC and the empirical FC. <italic toggle=\"yes\">Main results.</italic> We conducted extensive experiments in two independent datasets. By fusing different time scales in the diffusion process for predicting FC, the proposed method demonstrated higher predictive correlation compared with method considering only critical time points (Singlescale). Furthermore, compared with other existing methods, the proposed method achieved the highest predictive correlations of 0.6939 <inline-formula>\n<tex-math><?CDATA $ \\pm $?></tex-math>\n<mml:math overflow=\"scroll\"><mml:mrow><mml:mo>±</mml:mo></mml:mrow></mml:math>\n<inline-graphic xlink:href=\"jnead39a6ieqn1.gif\" xlink:type=\"simple\"></inline-graphic>\n</inline-formula> 0.0079 and 0.7302 <inline-formula>\n<tex-math><?CDATA $ \\pm $?></tex-math>\n<mml:math overflow=\"scroll\"><mml:mrow><mml:mo>±</mml:mo></mml:mrow></mml:math>\n<inline-graphic xlink:href=\"jnead39a6ieqn2.gif\" xlink:type=\"simple\"></inline-graphic>\n</inline-formula> 0.0117 on the two datasets respectively. We observed that the visual network at the network level and the parietal lobe at the lobe level exhibited the highest predictive correlations, indicating that the functional activity in these regions may be closely related to the direct diffusion of information between brain regions. <italic toggle=\"yes\">Significance.</italic> The multiple-timescale fusion method proposed in this study provides insights into the dynamic aspects of the diffusion process, contributing to a deeper understanding of how brain structure gives rise to brain function.","PeriodicalId":16753,"journal":{"name":"Journal of neural engineering","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predicting resting-state brain functional connectivity from the structural connectome using the heat diffusion model: a multiple-timescale fusion method\",\"authors\":\"Zhengyuan Lv, Jingming Li, Li Yao, Xiaojuan Guo\",\"doi\":\"10.1088/1741-2552/ad39a6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<italic toggle=\\\"yes\\\">Objective.</italic> Understanding the intricate relationship between structural connectivity (SC) and functional connectivity (FC) is pivotal for understanding the complexities of the human brain. To explore this relationship, the heat diffusion model (HDM) was utilized to predict FC from SC. However, previous studies using the HDM have typically predicted FC at a critical time scale in the heat kernel equation, overlooking the dynamic nature of the diffusion process and providing an incomplete representation of the predicted FC. <italic toggle=\\\"yes\\\">Approach.</italic> In this study, we propose an alternative approach based on the HDM. First, we introduced a multiple-timescale fusion method to capture the dynamic features of the diffusion process. Additionally, to enhance the smoothness of the predicted FC values, we employed the Wavelet reconstruction method to maintain local consistency and remove noise. Moreover, to provide a more accurate representation of the relationship between SC and FC, we calculated the linear transformation between the smoothed FC and the empirical FC. <italic toggle=\\\"yes\\\">Main results.</italic> We conducted extensive experiments in two independent datasets. By fusing different time scales in the diffusion process for predicting FC, the proposed method demonstrated higher predictive correlation compared with method considering only critical time points (Singlescale). Furthermore, compared with other existing methods, the proposed method achieved the highest predictive correlations of 0.6939 <inline-formula>\\n<tex-math><?CDATA $ \\\\pm $?></tex-math>\\n<mml:math overflow=\\\"scroll\\\"><mml:mrow><mml:mo>±</mml:mo></mml:mrow></mml:math>\\n<inline-graphic xlink:href=\\\"jnead39a6ieqn1.gif\\\" xlink:type=\\\"simple\\\"></inline-graphic>\\n</inline-formula> 0.0079 and 0.7302 <inline-formula>\\n<tex-math><?CDATA $ \\\\pm $?></tex-math>\\n<mml:math overflow=\\\"scroll\\\"><mml:mrow><mml:mo>±</mml:mo></mml:mrow></mml:math>\\n<inline-graphic xlink:href=\\\"jnead39a6ieqn2.gif\\\" xlink:type=\\\"simple\\\"></inline-graphic>\\n</inline-formula> 0.0117 on the two datasets respectively. We observed that the visual network at the network level and the parietal lobe at the lobe level exhibited the highest predictive correlations, indicating that the functional activity in these regions may be closely related to the direct diffusion of information between brain regions. <italic toggle=\\\"yes\\\">Significance.</italic> The multiple-timescale fusion method proposed in this study provides insights into the dynamic aspects of the diffusion process, contributing to a deeper understanding of how brain structure gives rise to brain function.\",\"PeriodicalId\":16753,\"journal\":{\"name\":\"Journal of neural engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-04-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of neural engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1088/1741-2552/ad39a6\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of neural engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1088/1741-2552/ad39a6","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0
摘要
目的。了解结构连通性(SC)和功能连通性(FC)之间错综复杂的关系对于理解人类大脑的复杂性至关重要。为了探索这种关系,研究人员利用热扩散模型(HDM)从 SC 预测 FC。然而,以往使用 HDM 的研究通常是在热核方程的临界时间尺度上预测 FC,从而忽略了扩散过程的动态性质,导致预测的 FC 不完整。方法。在本研究中,我们提出了一种基于 HDM 的替代方法。首先,我们引入了一种多时间尺度融合方法,以捕捉扩散过程的动态特征。此外,为了提高预测 FC 值的平滑度,我们采用了小波重建方法来保持局部一致性并去除噪声。此外,为了更准确地表示 SC 和 FC 之间的关系,我们计算了平滑 FC 和经验 FC 之间的线性变换。主要结果。我们在两个独立的数据集上进行了广泛的实验。通过融合扩散过程中的不同时间尺度来预测 FC,与只考虑关键时间点(单一尺度)的方法相比,所提出的方法表现出更高的预测相关性。此外,与其他现有方法相比,所提出的方法在两个数据集上分别达到了 0.6939 ± 0.0079 和 0.7302 ± 0.0117 的最高预测相关性。我们观察到,网络层面的视觉网络和顶叶层面的顶叶表现出最高的预测相关性,这表明这些区域的功能活动可能与脑区之间的直接信息扩散密切相关。意义重大。本研究提出的多时间尺度融合方法有助于深入了解扩散过程的动态方面,有助于加深对大脑结构如何产生大脑功能的理解。
Predicting resting-state brain functional connectivity from the structural connectome using the heat diffusion model: a multiple-timescale fusion method
Objective. Understanding the intricate relationship between structural connectivity (SC) and functional connectivity (FC) is pivotal for understanding the complexities of the human brain. To explore this relationship, the heat diffusion model (HDM) was utilized to predict FC from SC. However, previous studies using the HDM have typically predicted FC at a critical time scale in the heat kernel equation, overlooking the dynamic nature of the diffusion process and providing an incomplete representation of the predicted FC. Approach. In this study, we propose an alternative approach based on the HDM. First, we introduced a multiple-timescale fusion method to capture the dynamic features of the diffusion process. Additionally, to enhance the smoothness of the predicted FC values, we employed the Wavelet reconstruction method to maintain local consistency and remove noise. Moreover, to provide a more accurate representation of the relationship between SC and FC, we calculated the linear transformation between the smoothed FC and the empirical FC. Main results. We conducted extensive experiments in two independent datasets. By fusing different time scales in the diffusion process for predicting FC, the proposed method demonstrated higher predictive correlation compared with method considering only critical time points (Singlescale). Furthermore, compared with other existing methods, the proposed method achieved the highest predictive correlations of 0.6939 ± 0.0079 and 0.7302 ± 0.0117 on the two datasets respectively. We observed that the visual network at the network level and the parietal lobe at the lobe level exhibited the highest predictive correlations, indicating that the functional activity in these regions may be closely related to the direct diffusion of information between brain regions. Significance. The multiple-timescale fusion method proposed in this study provides insights into the dynamic aspects of the diffusion process, contributing to a deeper understanding of how brain structure gives rise to brain function.
期刊介绍:
The goal of Journal of Neural Engineering (JNE) is to act as a forum for the interdisciplinary field of neural engineering where neuroscientists, neurobiologists and engineers can publish their work in one periodical that bridges the gap between neuroscience and engineering. The journal publishes articles in the field of neural engineering at the molecular, cellular and systems levels.
The scope of the journal encompasses experimental, computational, theoretical, clinical and applied aspects of: Innovative neurotechnology; Brain-machine (computer) interface; Neural interfacing; Bioelectronic medicines; Neuromodulation; Neural prostheses; Neural control; Neuro-rehabilitation; Neurorobotics; Optical neural engineering; Neural circuits: artificial & biological; Neuromorphic engineering; Neural tissue regeneration; Neural signal processing; Theoretical and computational neuroscience; Systems neuroscience; Translational neuroscience; Neuroimaging.