Localizing Spectral Interactions in the Resting State Network Using the Hilbert-Huang Transform

被引:3
作者
Hsu, Ai-Ling [1 ,2 ]
Li, Chia-Wei [3 ]
Qin, Pengmin [4 ,5 ,6 ,7 ]
Lo, Men-Tzung [8 ]
Wu, Changwei W. [9 ,10 ]
机构
[1] Chang Gung Univ, Bachelor Program Artificial Intelligence, Taoyuan 33305, Taiwan
[2] Chang Gung Mem Hosp Linkou, Dept Psychiat, Taoyuan 33305, Taiwan
[3] Taipei Med Univ, Wan Fang Hosp, Dept Radiol, Taipei 11696, Taiwan
[4] South China Normal Univ, Key Lab Brain Cognit & Educ Sci, Minist Educ, Ctr Studies Psychol Applicat, Guangzhou 510631, Peoples R China
[5] South China Normal Univ, Guangdong Key Lab Mental Hlth & Cognit Sci, Guangzhou 510631, Peoples R China
[6] Pazhou Lab, Guangzhou 510335, Peoples R China
[7] South China Normal Univ, Sch Psychol, Guangzhou 510631, Peoples R China
[8] Natl Cent Univ, Dept Biomed Sci & Engn, Taoyuan 32049, Taiwan
[9] Taipei Med Univ, Grad Inst Mind Brain & Consciousness, Taipei 11031, Taiwan
[10] Taipei Med Univ, Shuang Ho Hosp, Brain & Consciousness Res Ctr, New Taipei 23561, Taiwan
基金
美国国家科学基金会;
关键词
resting-state fMRI; ensemble spectral interaction; Hilbert-Huang transform; amplitude-to-amplitude coupling; time-frequency map; wavelet analysis; DYNAMIC FUNCTIONAL CONNECTIVITY; EMPIRICAL MODE DECOMPOSITION; BRAIN OSCILLATIONS; FREQUENCY; FMRI; NONSTATIONARY; SPECIFICITY; SIGNALS; CORTEX; NOISE;
D O I
10.3390/brainsci12020140
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Brain synchronizations are orchestrated from neuronal oscillations through frequency interactions, such as the alpha rhythm during relaxation. Nevertheless, how the intrinsic interaction forges functional integrity across brain segregations remains elusive, thereby motivating recent studies to localize frequency interactions of resting-state fMRI (rs-fMRI). To this point, we aim to unveil the fMRI-based spectral interactions using the time-frequency (TF) analysis; however, Fourier-based TF analyses impose restrictions on revealing frequency interactions given the limited time points in fMRI signals. Instead of using the Fourier-based wavelet analysis to identify the fMRI frequency of interests, we employed the Hilbert-Huang transform (HHT) for probing the specific frequency contribution to the functional integration, called ensemble spectral interaction (ESI). By simulating data with time-variant frequency changes, we demonstrated the Hilbert TF maps with high spectro-temporal resolution and full accessibility in comparison with the wavelet TF maps. By detecting amplitude-to-amplitude frequency couplings (AAC) across brain regions, we elucidated the ESI disparity between the eye-closed (EC) and eye-open (EO) conditions in rs-fMRI. In the visual network, the strength of the spectral interaction within 0.03-0.04 Hz was amplified in EC compared with that in EO condition, whereas a canonical connectivity analysis did not present differences between conditions. Collectively, leveraging from the instantaneous frequency of HHT, we firstly addressed the ESI technique to map the fMRI-based functional connectivity in a brand-new AAC perspective. The ESI possesses potential in elucidating the functional connectivity at specific frequency bins, thereby providing additional diagnostic merits for future clinical neuroscience.
引用
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页数:14
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