IEEE J Biomed Health Inform. 2023 Nov;27(11):5449-5458. doi: 10.1109/JBHI.2023.3303470. Epub 2023 Nov 7.
Functional near-infrared spectroscopy (fNIRS) as an emerging optical neuroimaging technique has attracted the interest and attention of many investigators. With the growth of fNIRS data volume, effective data compression methods are urgent. Compressive sensing (CS) has been demonstrated a promising tool to deal with biomedical data. However, whether the compressibility of fNIRS data can discriminate different brain states is unclear. In this study, the fNIRS signals from fifteen attention-deficit/hyperactivity disorder (ADHD) children and fifteen typically developing (TD) children were recorded during an N-back task and a Go/NoGo task respectively. A block sparse Bayesian learning-based CS method was used to reconstruct the compressed fNIRS data. To assess the performance of the CS method, we adopted two metrics, structural similarity index (SSIM) and mean squared error (MSE), both of them effective in evaluating the compressibility of fNIRS data. Then, the two metrics were analyzed to discriminate the brain states of ADHD children and TD children during the two tasks using the multivariate pattern analysis (MVPA) method. As indicated by the results, the CS method could reconstruct the compressed fNIRS data with a high reconstruction quality at different compression ratio ([Formula: see text] and [Formula: see text]). Furthermore, the MVPA method could distinguish different brain states with high accuracy, and identify that the prefrontal cortex is a key brain region for distinguishing ADHD vs. TD or N-back vs. Go/NoGo. These findings indicated that CS is very promising for the storage and transmission of massive fNIRS data, and the compressibility of fNIRS data is a potential biomarker for the diagnosis of ADHD.
功能性近红外光谱（fNIRS）作为一种新兴的光学神经影像学技术，引起了许多研究人员的兴趣和关注。随着 fNIRS 数据量的增长，有效的数据压缩方法迫在眉睫。压缩感知（CS）已被证明是处理生物医学数据的一种很有前途的工具。然而，fNIRS 数据的可压缩性是否可以区分不同的大脑状态尚不清楚。在这项研究中，我们分别在 N-back 任务和 Go/NoGo 任务中记录了 15 名注意缺陷多动障碍（ADHD）儿童和 15 名典型发育（TD）儿童的 fNIRS 信号。采用基于块稀疏贝叶斯学习的 CS 方法对压缩后的 fNIRS 数据进行重建。为了评估 CS 方法的性能，我们采用了两种指标，结构相似性指数（SSIM）和均方误差（MSE），它们都有效地评估了 fNIRS 数据的可压缩性。然后，我们使用多变量模式分析（MVPA）方法，采用这两个指标来分析在两个任务中 CS 方法区分 ADHD 儿童和 TD 儿童的大脑状态的能力。结果表明，CS 方法可以在不同的压缩比（[Formula: see text] 和 [Formula: see text]）下，以较高的重建质量重建压缩后的 fNIRS 数据。此外，MVPA 方法可以以较高的准确率区分不同的大脑状态，并识别出前额叶是区分 ADHD 与 TD 或 N-back 与 Go/NoGo 的关键脑区。这些发现表明 CS 非常适合存储和传输大量的 fNIRS 数据，并且 fNIRS 数据的可压缩性是诊断 ADHD 的潜在生物标志物。
