Distinctive modes of cortical communications in tactile temporal order judgment.

Temporal order judgment of two successive tactile stimuli delivered to our hands is often inverted when we cross our hands. The present study aimed to identify time-frequency profiles of the interactions across the cortical network associated with the crossed-hand tactile temporal order judgment task using magnetoencephalography. We found that the interactions across the cortical network were channeled to a low-frequency band (5-10 Hz) when the hands were uncrossed. However, the interactions became activated in a higher band (12-18 Hz) when the hands were crossed. The participants with fewer inverted judgments relied mainly on the higher band, whereas those with more frequent inverted judgments (reversers) utilized both. Moreover, reversers showed greater cortical interactions in the higher band when their judgment was correct compared to when it was inverted. Overall, the results show that the cortical network communicates in two distinctive frequency modes during the crossed-hand tactile temporal order judgment task. A default mode of communications in the low-frequency band encourages inverted judgments, and correct judgment is robustly achieved by recruiting the high-frequency mode.

What Underlies a Greater Reversal in Tactile Temporal Order Judgment When the Hands Are Crossed? A Structural MRI Study.

Our subjective temporal order of two successive tactile stimuli, delivered one to each hand, is often inverted when our hands are crossed. However, there is great variability among different individuals. We addressed the question of why some show almost complete reversal, but others show little reversal. To this end, we obtained structural magnetic resonance imaging data from 42 participants who also participated in the tactile temporal order judgment (TOJ) task. We extracted the cortical thickness and the convoluted surface area as cortical characteristics in 68 regions. We found that the participants with a thinner, larger, and more convoluted cerebral cortex in 10 regions, including the right pars-orbitalis, right and left postcentral gyri, left precuneus, left superior parietal lobule, right middle temporal gyrus, left superior temporal gyrus, right cuneus, left supramarginal gyrus, and right rostral middle frontal gyrus, showed a smaller degree of judgment reversal. In light of major theoretical accounts, we suggest that cortical elaboration in the aforementioned regions improve the crossed-hand TOJ performance through better integration of the tactile stimuli with the correct spatial representations in the left parietal regions, better representation of spatial information in the postcentral gyrus, or improvement of top-down inhibitory control by the right pars-orbitalis.

Statistical Significance Assessment of Phase Synchrony in the Presence of Background Couplings: An ECoG Study.

Statistical significance testing is a necessary step in connectivity analysis. Several statistical test methods have been employed to assess the significance of functional connectivity, but the performance of these methods has not been thoroughly evaluated. In addition, the effects of the intrinsic brain connectivity and background couplings on performance of statistical test methods in task-based studies have not been investigated yet. The background couplings may exist independent of cognitive state and can be observed on both pre- and post-stimulus time intervals. The background couplings may be falsely detected by a statistical test as task-related connections, which can mislead interpretations of the task-related functional networks. The aim of this study was to investigate the relative performance of four commonly used non-parametric statistical test methods-surrogate, demeaned surrogate, bootstrap resampling, and Monte Carlo permutation methods-in the presence of background couplings and noise, with different signal-to-noise ratios (SNRs). Using simulated electrocorticographic (ECoG) datasets and phase locking value (PLV) as a measure of functional connectivity, we evaluated the performances of the statistical test methods utilizing sensitivity, specificity, accuracy, and receiver operating curve (ROC) analysis. Furthermore, we calculated optimal p values for each statistical test method using the ROC analysis, and found that the optimal p values were increased by decreasing the SNR. We also found that the optimal p value of the bootstrap resampling was greater than that of other methods. Our results from the simulation datasets and a real ECoG dataset, as an illustrative case report, revealed that the bootstrap resampling is the most efficient non-parametric statistical test for identifying the significant PLV of ECoG data, especially in the presence of background couplings.

Comparison of statistical tests in effective connectivity analysis of ECoG data.

BACKGROUND: The effects of statistical testing on the results of multivariate autoregressive (MVAR)-based effective connectivity analysis have not been adequately investigated, and it is still unclear which statistical test can provide the most accurate results. NEW METHODS: Using simulated and real electrocorticographic (ECoG) data, we investigated the performance of three nonparametric statistical tests - Monte Carlo permutation, bootstrap resampling, and surrogate data method in MVAR-based effective connectivity analysis. Receiver operating characteristic (ROC) analysis and area under the ROC curve (AUC) were used to assess the performance of each statistical test method. In addition, we found optimal p-values for each method based on ROC analysis. Finally, we investigated the application of statistical tests on partial directed coherence analysis of ECoG data collected in a patient with epilepsy. RESULTS: The bootstrap statistical test performed more accurately than other methods. The surrogate method slightly outperformed the Monte Carlo permutation method. Optimal p-values of statistical tests depended on signal-to-noise ratio (SNR) of data, and its value increased by reducing SNR of data. By considering the typical SNR range of electrophysiological data, we recommended an optimal p-value range for the application of each statistical test method. COMPARISON WITH EXISTING METHODS: Limited studies have investigated the performance of statistical tests for MVAR-based effective connectivity analysis. For the first time, we have investigated the effects of baseline connections on the various performances of statistical tests. CONCLUSIONS: We recommend utilizing the bootstrap statistical test with p-value between 0.05 and 0.1 for effective connectivity analysis of ECoG data.