Neuroticism and conscientiousness respectively positively and negatively correlated with the network characteristic path length in dorsal lateral prefrontal cortex: A resting‐state fNIRS study

Abstract

BackgroundAccumulating evidence shows that the dorsal lateral prefrontal cortex (dlPFC) is implicated in personality traits. In this study, resting‐state functional near infrared spectroscopy (fNIRS) combined with small‐world analysis was utilized to examine the relationship between the network properties of dlPFC and personality traits.MethodsThirty college students (aged between 20 and 29) were recruited from the University of Macau campus, whose personality scores were accessed with the NEO‐FFT questionnaire. Graph theory combined with resting‐state fNIRS data was used to quantify the network properties of dlPFC, whereas Pearson correlation analysis was performed to generate the relationship between the small‐world indicators and personality scores.ResultsCompared to matched random networks, the resting‐state brain networks exhibited a larger clustering coefficient (C p, 0.1–0.66), shorter characteristic path length (L p, 0.1–0.66), and higher global (E g, 0.1–0.66) and local efficiency (E loc, 0.1–0.65). In particular, conscientiousness (r = −0.63) and neuroticism (r = 0.40) respectively showed negative and positive correlation with the L p.ConclusionsThe resting‐state functional brain networks in dlPFC exhibited the small‐world properties. In addition, participants with higher conscientiousness scores showed a shorter L p.

Is There a Difference in Brain Functional Connectivity between Chinese Coal Mine Workers Who Have Engaged in Unsafe Behavior and Those Who Have Not?

“…As a function of network efficiency, the global efficiency ( ), the local efficiency ( ), the clustering coefficient ( ), and the characteristic path length ( ) are depicted in Figure 5 . In general, the parameters of global efficiency ( Figure 5 a) and local efficiency ( Figure 5 b) increased with threshold, which is consistent with the previous findings [ 93 , 100 ]. For brain networks of NUB and EUB, the clustering coefficients increased ( Figure 5 c), but the characteristic path length decreased ( Figure 5 d) as sparsity increased.…”

“…Brain networks are typically compared with random networks to test whether they are configured with significantly non-random topology [ 88 ]. Further, 100 matched random networks were generated to compute the ratios of all these indicators between the real brain functional networks [ 84 , 91 , 92 , 93 ].…”

Functional near-infrared spectroscopy can detect low-frequency hemodynamic oscillations in the prefrontal cortex during steady-state visual evoked potential-inducing periodic facial expression stimuli presentation

“…In addition, resting-state and task-elicited fNIRS recordings without flickering stimuli with SSVEP were also acquired in the frontal area using our CW6 system [ 24 , 25 ]. The two sets of fNIRS data were also preprocessed and analyzed using the same procedure as LFHO during SSVEP-inducing periodic FE stimuli presentation.…”

“…The frequency band of brain oscillations is between 0.05 and 500 Hz [1]. In particular, high-frequency oscillations with frequencies over 1 Hz can be detected using electroencephalography (EEG) or magnetoencephalography, indicating that different canonical frequency bands such as delta (1-4 Hz), theta (4)(5)(6)(7)(8), alpha (8)(9)(10)(11)(12), beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (> 30 Hz) correspond to distinct cognitive functions [2]. However, few studies have been carried out to inspect the cognitive functions associated with low-frequency oscillations, which generally fluctuate below 1 Hz.…”

“…Interestingly, previous fNIRS-based SSVEP studies mainly focused on the investigation of brain computer interfaces or brain activation in the occipital cortex [20,21]. In contrast, the application of SSVEP in the fNIRS field [22][23][24][25] has never been used to explore how FEs relate to brain functions in the prefrontal cortex.…”