Frequency-specific functional connectivity revealed by wavelet-based coherence analysis in elderly subjects with cerebral infarction using NIRS method.
OBJECTIVE
Resting-state functional connectivity in subjects with cerebral infarction (CI) was assessed using wavelet-based coherence analysis of near-infrared spectroscopy (NIRS) signals.


METHODS
Continuous recordings of NIRS signals were measured from the prefrontal cortex and sensorimotor cortical areas of 12 subjects with CI (CI group) and 16 healthy subjects (healthy group) during the resting state. The channels in these areas were divided into four connection types: homologous connectivity, frontoposterior connectivity, contralateral connectivity, and homolateral connectivity. Wavelet coherence (WCO) and wavelet phase coherence (WPCO) were calculated in six frequency intervals in each channel pair: I, 0.6-2 Hz; II, 0.145-0.6 Hz; III, 0.052-0.145 Hz; IV, 0.021-0.052 Hz; V, 0.0095-0.021 Hz; and VI, 0.005-0.0095 Hz.


RESULTS
WCO in the six frequency intervals was significant for all channels in the healthy group. By contrast, WCO in frequency intervals II-VI showed weakened connectivity in the CI group, especially in terms of frontoposterior connectivity. WCO was significantly lower in the CI group than in the healthy group in the following connectivities and frequency intervals: front-posterior, IV-VI (p < 0.05); homologous, III-V (p < 0.01); motor-contralateral, III-V (p < 0.05); and motor-homolateral, III-V (p < 0.05). WPCO in frequency intervals III (F = 5.032, p = 0.033) and IV (F = 11.95, p = 0.002) in frontoposterior connectivity, as well as in intervals III-V in homologous, motor-contralateral and motor-homolateral connectivities were significantly lower (p < 0.05) in the CI group than in the healthy group. However, WPCO in interval I showed significantly higher levels in motor-homolateral connectivity in the CI group than in the healthy group (F = 4.241, p = 0.049).


CONCLUSIONS
The authors' results suggest that CI causes a frequency-specific disruption in resting-state connectivity. This may be useful for assessing the effectiveness of functional recovery after CI.
Key Laboratory of High Efficiency and Clean Mechanical Manufacture, School of Mechanical Engineering, Shandong University, Jinan 250061, China.
Qitao
Q
Tan
Interdisciplinary Division of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong Special Administrative Region, China.
Ming
M
Zhang
Ji'nan Central Hospital, Jinan 250013, China.
Yi
Y
Wang
Key Laboratory of High Efficiency and Clean Mechanical Manufacture, School of Mechanical Engineering, Shandong University, Jinan 250061, China.
Manyu
M
Zhang
Interdisciplinary Division of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong Special Administrative Region, China.
Yan
Y
Wang
Hospital of Shandong University, Jinan 250061, China.
Qing
Q
Xin
Key Laboratory of High Efficiency and Clean Mechanical Manufacture, School of Mechanical Engineering, Shandong University, Jinan 250061, China.
Bitan
B
Wang
Key Laboratory of High Efficiency and Clean Mechanical Manufacture, School of Mechanical Engineering, Shandong University, Jinan 250061, China.
Zengyong
Z
Li
D000368:Aged; D002544:Cerebral Infarction / Q000503:physiopathology*; D005260:Female; D006801:Humans; D008297:Male; D009415:Nerve Net / Q000503:physiopathology*; D012146:Rest; D019265:Spectroscopy, Near-Infrared; D058067:Wavelet Analysis