Early studies have shown that Tai Chi Chuan (TCC) contributes to the rehabilitation of cognitive disorders and increases blood oxygen concentration levels in the parietal and occipital brain areas; however, the mechanism of TCC training on brain function remains poorly understood. This study hypothesize that TCC has altered brain function and aims to explore the effects of TCC on functional connection and effective connection of the prefrontal cortex (PFC), motor cortex (MC), and occipital cortex (OC). The participants were 23 experienced Chen–style TCC practitioners (TCC group), and 32 demographically matched TCC–naive healthy controls (control group). Functional and effective connections were calculated using wavelet–based coherence analysis and dynamic Bayesian inference method, respectively. Results showed that beyond the intensity of activity in a particular cortical region induced by TCC, significant differences in brain activity and dynamic configuration of connectivity were observed between the TCC and control groups during resting and movement states. These findings suggested that TCC training improved the connection of PFC, MC and OC in myogenic activity, sympathetic nervous system, and endothelial cell metabolic activities; enhanced brain functional connections and relayed the ability of TCC to improve cognition and the anti–memory decline potential.

The present study aimed to evaluate the functional connectivity (FC) in relevant cortex areas during simulated driving with distraction based on functional near-infrared spectroscopy (fNIRS) method. Twelve subjects were recruited to perform three types of driving tasks, namely, straight driving, straight driving with secondary auditory task, and straight driving with secondary visual vigilance task, on a driving simulator. The wavelet amplitude (WA) and wavelet phase coherence (WPCO) of the fNIRS signals were calculated in six frequency intervals: I, 0.6–2 Hz; II, 0.145–0.6 Hz; III, 0.052–0.145 Hz; IV, 0.021–0.052 Hz; and V, 0.0095–0.021 Hz, VI, 0.005–0.0095Hz. Results showed that secondary tasks during driving led to worse driving performance, brain activity changes, and dynamic configuration of the connectivity. The significantly lower WA value in the right motor cortex in interval IV, and higher WPCO values in intervals II, V, and VI were found with additional auditory task. Significant standard deviation of speed and lower WA values in the left prefrontal cortex and right prefrontal cortex in interval VI, and lower WPCO values in intervals I, IV, V, and VI were found under the additional visual vigilance task. The results suggest that the changed FC levels in intervals IV, V, and VI were more likely to reflect the driver’s distraction condition. The present study provides new insights into the relationship between distracted driving behavior and brain activity. The method may be used for the evaluation of drivers’ attention level.

Driving a vehicle is a complex activity that requires high-level brain functions. This study aimed to assess the change in effective connectivity (EC) between the prefrontal cortex (PFC), motor-related areas (MA) and vision-related areas (VA) in the brain network among the resting, simple-driving and car-following states. Twelve young male right-handed adults were recruited to participate in an actual driving experiment. The brain delta [HbO2] signals were continuously recorded using functional near infrared spectroscopy (fNIRS) instruments. The conditional Granger causality (GC) analysis, which is a data-driven method that can explore the causal interactions among different brain areas, was performed to evaluate the EC. The results demonstrated that the hemodynamic activity level of the brain increased with an increase in the cognitive workload. The connection strength among PFC, MA and VA increased from the resting state to the simple-driving state, whereas the connection strength relatively decreased during the car-following task. The PFC in EC appeared as the causal target, while the MA and VA appeared as the causal sources. However, l-MA turned into causal targets with the subtask of car-following. These findings indicate that the hemodynamic activity level of the cerebral cortex increases linearly with increasing cognitive workload. The EC of the brain network can be strengthened by a cognitive workload, but also can be weakened by a superfluous cognitive workload such as driving with subtasks.

To reveal the physiological mechanism of the cognitive decline in subjects with hypertension, the functional connectivity (FC) was assessed by using the wavelet phase coherence (WPCO), and effective connectivity (EC) was assessed by using the coupling strength (CS) of near-infrared spectroscopy (NIRS) signals. NIRS signals were continuously recorded from the prefrontal cortex, sensorimotor cortex, and occipital lobes of 13 hypertensive patients (hypertension group, 70 ± 6.5 years old) and 16 elderly healthy subjects (control group, 71 ± 5.5 years old) in resting and standing periods. WPCO and CS were calculated in four frequency intervals: I, 0.6–2; II, 0.145–0.6; III, 0.052–0.145; and IV, 0.021–0.052 Hz. CS quantifies coupling amplitude. In comparison with the control group, the hypertension group showed significantly decreased (p < 0.05) WPCO and CS in intervals III and IV and in the resting and standing states. WPCO and CS were significantly decreased in the resting state compared with those in the standing state in the hypertension group (p < 0.05). Decreased WPCO and CS indicated a reduced network interaction, suggesting disturbed neurovascular coupling in subjects with hypertension. Compared with the control group, the hypertension group showed significantly lower Mini-Mental State Examination (MMSE) (p = 0.028) and Montreal Cognitive Assessment (MoCA) scores (p = 0.011). In the hypertension group, correlation analysis showed that WPCO and CS were significantly positively correlated with MMSE and MoCA scores, respectively. These findings may provide evidence of impaired cognitive function in hypertension and can enhance the understanding on neurovascular coupling.

Objective This study aimed to reveal the physiological mechanism in subjects with mild cognitive impairment based on effective connectivity method. Methods Effective connectivity was assessed by dynamic Bayesian inference of the oxygenated hemoglobin concentration signals measured through functional near-infrared spectroscopy. The oxygenated hemoglobin concentration signals were recorded from the left prefrontal cortex, right prefrontal cortex, left motor cortex, right motor cortex, left occipital lobe, and right occipital lobe of 26 subjects with mild cognitive impairment (mild cognitive impairment group) and 28 healthy elderly subjects (control group) at resting state. Results The coupling strength of right prefrontal cortex to left prefrontal cortex (F = 7.964, P = 0.007) and left prefrontal cortex to right occipital lobe (F = 4.278, P = 0.044) in interval III as well as left prefrontal cortex to left occipital lobe (F = 5.637, P = 0.021), right occipital lobe to left prefrontal cortex (F = 4.762, P = 0.034), and right prefrontal cortex to left occipital lobe (F = 4.06, P = 0.049) in interval IV in the mild cognitive impairment group were significantly lower than those in the control group. Conclusions The decreased effective connectivity levels among brain regions may be a marker of impaired cognitive function in the mild cognitive impairment group. The constructed effective connectivity network based on functional near-infrared spectroscopy provide a noninvasive method to assess mild cognitive impairment.