Frequency‐specific dual‐attention based adversarial network for blood oxygen level‐dependent time series prediction

人类连接体项目 功能磁共振成像 计算机科学 默认模式网络 血氧水平依赖性 大脑活动与冥想 人工智能 机器学习 模式识别(心理学) 神经科学 脑电图 功能连接 心理学
作者
Weihao Zheng,Cong Bao,Renhui Mu,Jun Wang,Tongtong Li,Ziyang Zhao,Zhijun Yao,Bin Hu
出处
期刊:Human Brain Mapping [Wiley]
卷期号:45 (14)
标识
DOI:10.1002/hbm.70032
摘要

Abstract Functional magnetic resonance imaging (fMRI) is currently one of the most popular technologies for measuring brain activity in both research and clinical contexts. However, clinical constraints often result in short fMRI scan durations, limiting the diagnostic performance for brain disorders. To address this limitation, we developed an end‐to‐end frequency‐specific dual‐attention‐based adversarial network (FDAA‐Net) to extend the time series of existing blood oxygen level‐dependent (BOLD) data, enhancing their diagnostic utility. Our approach leverages the frequency‐dependent nature of fMRI signals using variational mode decomposition (VMD), which adaptively tracks brain activity across different frequency bands. We integrated the generative adversarial network (GAN) with a spatial–temporal attention mechanism to fully capture relationships among spatially distributed brain regions and temporally continuous time windows. We also introduced a novel loss function to estimate the upward and downward trends of each frequency component. We validated FDAA‐Net on the Human Connectome Project (HCP) database by comparing the original and predicted time series of brain regions in the default mode network (DMN), a key network activated during rest. FDAA‐Net effectively overcame linear frequency‐specific challenges and outperformed other popular prediction models. Test–retest reliability experiments demonstrated high consistency between the functional connectivity of predicted outcomes and targets. Furthermore, we examined the clinical applicability of FDAA‐Net using short‐term fMRI data from individuals with autism spectrum disorder (ASD) and major depressive disorder (MDD). The model achieved a maximum predicted sequence length of 40% of the original scan durations. The prolonged time series improved diagnostic performance by 8.0% for ASD and 11.3% for MDD compared with the original sequences. These findings highlight the potential of fMRI time series prediction to enhance diagnostic power of brain disorders in short fMRI scans.

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