Search

Abstract: The abilities to inhibit impulses and withdraw certain responses are critical for human's survival in a fast-changing environment. These processes happen fast, in a complex manner, and sometimes are difficult to capture with fMRI or mean electrophysiological brain signal alone. Therefore, an alternative measure that can reveal the efficiency of the neural mechanism across multiple timescales is needed for the investigation of these brain functions. The present study employs a new approach to analyzing electroencephalography (EEG) signal: the multiscale entropy (MSE), which groups data points with different timescales to reveal any occurrence of repeated patterns, in order to theoretically quantify the complexity (indicating adaptability and efficiency) of neural systems during the process of inhibitory control. From this MSE perspective, EEG signals of successful stop trials are more complex and information rich than that of unsuccessful stop trials. We further applied transcranial direct current stimulation (tDCS), with anodal electrode over presupplementary motor area (preSMA), to test the relationship between behavioral modification with the complexity of EEG signals. We found that tDCS can further increase the EEG complexity of the frontal lobe. Furthermore, the MSE pattern was found to be different between high and low performers (divided by their stop-signal reaction time), where the high-performing group had higher complexity in smaller scales and less complexity in larger scales in comparison to the low-performing group. In addition, this between-group MSE difference was found to interact with the anodal tDCS, where the increase of MSE in low performers benefitted more from the anodal tDCS. Together, the current study demonstrates that participants who suffer from poor inhibitory control can efficiently improve their performance with 10min of electrical stimulation, and such cognitive improvement can be effectively traced back to the complexity within the EEG signals via MSE analysis, thereby offering a theoretical basis for clinical intervention via tDCS for deficits in inhibitory control. [Copyright &y& Elsevier]

Abstract: A recursive scheme aiming at obtaining sparse and focal brain electromagnetic source distribution is proposed based on the interpretation that the weighted minimum norm is the minimum norm estimates of amplitudes on grid points for the source distribution specified by the diagonal elements of the weight matrix. The source distribution is updated so that, at each grid point, the number of current dipoles equals the total source strength estimate of the pre-specified current dipoles. The source strength of a pre-specified current dipole is estimated by projecting the vector of minimum norm estimate to the space spanned by the three column vectors, corresponding to the three amplitudes of the current dipole, of the resolution matrix. The norm of the projected vector yields the source strength estimate of the current dipole. Exact inverse solutions are obtained by this source iteration of minimum norm (SIMN) algorithm for noiseless MEG signals from multi-point sources provided the sources are sufficiently sparse and there are no substantial cancellations among the signals of the sources. For noisy data, a set of “noise sources” is introduced. The diagonal matrix formed by the “noise source numbers” plays the role of regularization matrix and Tikhonov regularization is applied to initialize the “noise source numbers”. Application to the source localization of real EEG data is also presented. [Copyright &y& Elsevier]

Physical activity has been shown to benefit brain and cognition in late adulthood. However, this effect is still unexplored in terms of brain signal complexity, which reflects the level of neural adaptability and efficiency during cognitive processing that cannot be acquired via averaged neuroelectric signals. Here we employed multiscale entropy analysis (MSE) of electroencephalography (EEG), a new approach that conveys important information related to the temporal dynamics of brain signal complexity across multiple time scales, to reveal the association of physical activity with neural adaptability and efficiency in elderly adults. A between-subjects design that included 24 participants (aged 66.63±1.31years; female=12) with high physical activity and 24 age- and gender-matched low physical activity participants (aged 67.29±1.20years) was conducted to examine differences related to physical activity in performance and MSE of EEG signals during a visuo-spatial cognition task. We observed that physically active elderly adults had better accuracy on both visuo-spatial attention and working memory conditions relative to their sedentary counterparts. Additionally, these physically active elderly adults displayed greater MSE values at larger time scales at the Fz electrode in both attention and memory conditions. The results suggest that physical activity may be beneficial for adaptability of brain systems in tasks involving visuo-spatial information. MSE thus might be a promising approach to test the effects of the benefits of exercise on cognition. [ABSTRACT FROM AUTHOR]