modulation-neurophysiological-response-to-fearful-stressful-stimuli

Modulation of the Neurophysiological Response to Fearful and Stressful Stimuli Through Repetitive Religious Chanting

In neuropsychological experiments, the late positive potential (LPP) is an event-related potential (ERP) component that reflects the level of one's ...

Shenzhen University

Shenzhen university

The activities of the brain and the heart are dynamic, chaotic, and possibly intrinsically coordinated. This study aims to investigate the effect of Mindfulness-Based Stress Reduction (MBSR) program on the chaoticity of electronic activities of the brain and the heart, and to explore their potential correlation. Electroencephalogram (EEG) and electrocardiogram (ECG) were recorded at the beginning of an 8-week standard MBSR training course and after the course. EEG spectrum analysis was carried out, wavelet entropies (WE) of EEG (together with reconstructed cortical sources) and heart rate were calculated, and their correlation was investigated. We found enhancement of EEG power of alpha and beta waves and lowering of delta waves power during MBSR training state as compared to normal resting state. Wavelet entropy analysis indicated that MBSR mindfulness meditation could reduce the chaotic activities of both EEG and heart rate as a change of state. However, longitudinal change of trait may need more long-term training. For the first time, our data demonstrated that the chaotic activities of the brain and the heart became more coordinated during MBSR training, suggesting that mindfulness training may increase the entrainment between mind and body. The 3D brain regions involved in the change in mental states were identified.

Entrainment of chaotic activities in brain and heart during MBSR mindfulness training.

Chanting and praying are among the most popular religious activities, which are said to be able to alleviate people's negative emotions. However, the neural mechanisms underlying this mental exercise and its temporal course have hardly been investigated. Here, we used event-related potentials (ERPs) to explore the effects of chanting the name of a Buddha (Amitābha) on the brain's response to viewing negative pictures that were fear- and stress-provoking. We recorded and analyzed electroencephalography (EEG) data from 21 Buddhists with chanting experience as they viewed negative and neutral pictures. Participants were instructed to chant the names of Amitābha or Santa Claus silently to themselves or simply remain silent (no-chanting condition) during picture viewing. To measure the physiological changes corresponding to negative emotions, electrocardiogram and galvanic skin response data were also collected. Results showed that viewing negative pictures (vs. neutral pictures) increased the amplitude of the N1 component in all the chanting conditions. The amplitude of late positive potential (LPP) also increased when the negative pictures were viewed under the no-chanting and the Santa Claus condition. However, increased LPP was not observed when chanting Amitābha. The ERP source analysis confirmed this finding and showed that increased LPP mainly originated from the central-parietal regions of the brain. In addition, the participants' heart rates decreased significantly when viewing negative pictures in the Santa Claus condition. The no-chanting condition had a similar decreasing trend although not significant. However, while chanting Amitābha and viewing negative pictures participants' heart rate did not differ significantly from that observed during neutral picture viewing. It is possible that the chanting of Amitābha might have helped the participants to develop a religious schema and neutralized the effect of the negative stimuli. These findings echo similar research findings on Christian religious practices and brain responses to negative stimuli. Hence, prayer/religious practices may have cross-cultural universality in emotion regulation. This study shows for the first time that Buddhist chanting, or in a broader sense, repetition of religious prayers will not modulate brain responses to negative stimuli during the early perceptual stage, but only during the late-stage emotional/cognitive processing.

Repetitive Religious Chanting Modulates the Late-Stage Brain Response to Fear- and Stress-Provoking Pictures.

Independent component analysis (ICA) has been widely used in functional magnetic resonance imaging (fMRI) data analysis to evaluate functional connectivity of the brain; however, there are still some limitations on ICA simultaneously handling neuroimaging datasets with diverse acquisition parameters, e.g., different repetition time, different scanner, etc. Therefore, it is difficult for the traditional ICA framework to effectively handle ever-increasingly big neuroimaging datasets. In this research, a novel feature-map based ICA framework (FMICA) was proposed to address the aforementioned deficiencies, which aimed at exploring brain functional networks (BFNs) at different scales, e.g., the first level (individual subject level), second level (intragroup level of subjects within a certain dataset) and third level (intergroup level of subjects across different datasets), based only on the feature maps extracted from the fMRI datasets. The FMICA was presented as a hierarchical framework, which effectively made ICA and constrained ICA as a whole to identify the BFNs from the feature maps. The simulated and real experimental results demonstrated that FMICA had the excellent ability to identify the intergroup BFNs and to characterize subject-specific and group-specific difference of BFNs from the independent component feature maps, which sharply reduced the size of fMRI datasets. Compared with traditional ICAs, FMICA as a more generalized framework could efficiently and simultaneously identify the variant BFNs at the subject-specific, intragroup, intragroup-specific and intergroup levels, implying that FMICA was able to handle big neuroimaging datasets in neuroscience research.

A Novel Feature-Map Based ICA Model for Identifying the Individual, Intra/Inter-Group Brain Networks across Multiple fMRI Datasets.

Recently, functional magnetic resonance imaging (fMRI) has been increasingly used to assess brain function. Brain entropy is an effective model for evaluating the alteration of brain complexity. Specifically, the sample entropy (SampEn) provides a feasible solution for revealing the brain's complexity. Occupation is one key factor affecting the brain's activity, but the neuropsychological mechanisms are still unclear. Thus, in this article, based on fMRI and a brain entropy model, we explored the functional complexity changes engendered by occupation factors, taking the seafarer as an example. The whole-brain entropy values of two groups (i.e., the seafarers and the nonseafarers) were first calculated by SampEn and followed by a two-sample t test with AlphaSim correction (p < .05). We found that the entropy of the orbital-frontal gyrus (OFG) and superior temporal gyrus (STG) in the seafarers was significantly higher than that of the nonseafarers. In addition, the entropy of the cerebellum in the seafarers was lower than that of the nonseafarers. We conclude that (1) the lower entropy in the cerebellum implies that the seafarers' cerebellum activity had strong regularity and consistency, suggesting that the seafarer's cerebellum was possibly more specialized by the long-term career training; (2) the higher entropy in the OFG and STG possibly demonstrated that the seafarers had a relatively decreased capability for emotion control and auditory information processing. The above results imply that the seafarer occupation indeed impacted the brain's complexity, and also provided new neuropsychological evidence of functional plasticity related to one's career.

Occupational functional plasticity revealed by brain entropy: A resting-state fMRI study of seafarers.

Many studies have revealed the structural or functional brain changes induced by occupational factors. However, it remains largely unknown how occupation-related connectivity shapes the brain. In this paper, we denote occupational neuroplasticity as the neuroplasticity that takes place to satisfy the occupational requirements by extensively professional training and to accommodate the long-term, professional work of daily life, and a critical review of occupational neuroplasticity related to the changes in brain structure and functional networks has been primarily presented. Furthermore, meta-analysis revealed a neurophysiological mechanism of occupational neuroplasticity caused by professional experience. This meta-analysis of functional neuroimaging studies showed that experts displayed stronger activation in the left precentral gyrus [Brodmann area (BA)6], left middle frontal gyrus (BA6), and right inferior frontal gyrus (BA9) than novices, while meta-analysis of structural studies suggested that experts had a greater gray matter volume in the bilateral superior temporal gyrus (BA22) and right putamen than novices. Together, these findings not only expand the current understanding of the common neurophysiological basis of occupational neuroplasticity across different occupations and highlight some possible targets for neural modulation of occupational neuroplasticity but also provide a new perspective for occupational science research.

Occupational Neuroplasticity in the Human Brain: A Critical Review and Meta-Analysis of Neuroimaging Studies.

Brain tumors are one of the major common causes of cancer-related death, worldwide. Growth prediction of these tumors, particularly gliomas which are the most dominant type, can be quite useful to improve treatment planning, quantify tumor aggressiveness, and estimate patients' survival time towards precision medicine. Studying tumor growth prediction basically requires multiple time points of single or multimodal medical images of the same patient. Recent models are based on complex mathematical formulations that basically rely on a system of partial differential equations, e.g. reaction diffusion model, to capture the diffusion and proliferation of tumor cells in the surrounding tissue. However, these models usually have small number of parameters that are insufficient to capture different patterns and other characteristics of the tumors. In addition, such models consider tumor growth independently for each subject, not being able to get benefit from possible common growth patterns existed in the whole population under study. In this paper, we propose a novel data-driven method via stacked 3D generative adversarial networks (GANs), named GP-GAN, for growth prediction of glioma. Specifically, we use stacked conditional GANs with a novel objective function that includes both l and Dice losses. Moreover, we use segmented feature maps to guide the generator for better generated images. Our generator is designed based on a modified 3D U-Net architecture with skip connections to combine hierarchical features and thus have a better generated image. The proposed method is trained and tested on 18 subjects with 3 time points (9 subjects from collaborative hospital and 9 subjects from BRATS 2014 dataset). Results show that our proposed GP-GAN outperforms state-of-the-art methods for glioma growth prediction and attain average Jaccard index and Dice coefficient of 78.97% and 88.26%, respectively.

GP-GAN: Brain tumor growth prediction using stacked 3D generative adversarial networks from longitudinal MR Images.

During hard times, religious chanting/praying is widely practiced to cope with negative or stressful emotions. While the underlying neural mechanism has not been investigated to a sufficient extent. A previous event-related potential study showed that religious chanting could significantly diminish the late-positive potential induced by negative stimuli. However, the regulatory role of subcortical brain regions, especially the amygdala, in this process remains unclear. This multi-modal MRI study aimed to further clarify the neural mechanism underlying the effectiveness of religious chanting for emotion regulation.

Repetitive Religious Chanting Invokes Positive Emotional Schema to Counterbalance Fear: A Multi-Modal Functional and Structural MRI Study.

This study explored the differentiated neural correlates of mental rotation (MR) in preschoolers with high and low working memory capacity using functional near-infrared spectroscopy (fNIRS). Altogether 38 Chinese preschoolers ( = 5.0 years,  = 0.69 years) completed the  (WMC) test, the  (MR), and its Control tasks (without MR). They were divided into High-WMC ( = 9) and Low-WMC (  = 18) groups based on the WMC scores. The behavioral and fNIRS results indicated that: (1) there were no significant differences in MR task performance between the High-WMC (  = 23.44,  = 0.88) and Low-WMC group ( = 23.67,  = 0.59); (2) the Low-WMC group activated BA6, BA8, BA 9, and BA 44, whereas the High-WMC group activated BA8, BA10 and BA 44 during mental rotation; (3) significant differences were found in the activation of BA44 and BA9 between the High-WMC and Low-WMC groups during mental rotation; and (4) the High-WMC and Low-WMC groups differed significantly in the activation of BA 9 and BA10 during the control tasks, indicating that both areas might be responsible for the group differences in working memory.

Neural Correlates of Mental Rotation in Preschoolers With High or Low Working Memory Capacity: An fNIRS Study.

This study aims to examine the neural correlates of cognitive shifting during the  task with functional near-infrared spectroscopy. Altogether 49 children completed the  tasks, and 25 children (M = 68.66, SD = 5.3) passing all items were classified into the Switch group. Twenty children (M  = 62.05, SD = 8.13) committing more than one perseverative errors were grouped into the Perseverate group. The Switch group had Brodmann Area (BA) 9 and 10 activated in the pre-switch period and BA 6, 9, 10, 40, and 44 in the post-switch period. In contrast, the Perseverate group had BA 9 and 10 activated in the pre-switch period and BA 8, 9, 10 in the post-switch period. The general linear model results afford strong support to the "V-shape curve" hypothesis by identifying a significant decrease-increase cycle in BA 9 and 44, the neural correlations of cognitive shifting.

A Functional Near-Infrared Spectroscopy Examination of the Neural Correlates of Cognitive Shifting in Dimensional Change Card Sort Task.

Magneto-acousto-electrical tomography (MAET) is designed to produce conductivity images with high spatial resolution for a conducting object. In a previous study, for an irregular conductor, transverse scanning and rotational methods with a focus transducer were combined to collect complete electrical information. This kind of method, however, is time-consuming because of the transverse scanning procedure. In this study, we proposed a novel imaging method based on plane ultrasound waves and a new aspect of projection in rotational MAET. In the proposed method, we achieved the projection in each rotation angle by using plane waves rather than mechanical scanning of the focus waves along the transverse direction. Thus, the imaging time was significantly saved. To verify the proposed method, we derived a measurement formula containing a lateral integration, which built the relationship between the measurement formula and the projection under each rotation angle. Next, we constructed two different numerical models to compute magneto-acousto-electrical signals by using a finite element method and reconstructed the corresponding conductivity parameter images based on a filtered back-projection algorithm. Then, simulated signals under different signal-to-ratios (6, 20, 40, and 60 dB) were generated to test the performance of the proposed algorithm. To improve the image quality, we further analysed the influence of the filters and the frequency scaling factors embedded in the filtered back-projection algorithm. Moreover, we computed the Lnorm of the error in case of different frequency scaling factors and measurement noises. Finally, we conducted a phantom experiment with a 64-element linear phased array transducer (center frequency of 2.7 MHz) and reconstructed the conductivity parameter images of the circular phantom with an elliptical hole. The experimental results demonstrated the feasibility and time-efficiency of the proposed rapid rotational MAET.

Rapid rotational magneto-acousto-electrical tomography with filtered back-projection algorithm based on plane waves.

This study aims to examine the impact of heavy use of tablets on preschoolers' executive function during the Dimensional Change Card Sort (DCCS) task using the functional near-infrared spectroscopy (fNIRS). Altogether, 38 Chinese preschoolers ( = 5.0 years,  = 0.69 years, 17 girls) completed the tasks before the COVID-19 lockdown. Eight children never used tablets, while 16 children were diagnosed as the 'heavy-user'. The results indicated that: (1) the 'non-user' outperformed the 'heavy-user' with a significantly higher correct rate in the DCCS task; (2) the two groups differed significantly in the activation of the prefrontal cortex (BA 9): the 'non-user' pattern is normal and healthy, whereas the 'heavy-user' pattern is not normal and needs further exploration.

Tablet Use Affects Preschoolers' Executive Function: fNIRS Evidence from the Dimensional Change Card Sort Task.

Attention-deficit/hyperactivity disorder (ADHD) is presumed to be heterogeneous, but the best way to characterize this heterogeneity remains unclear. Although considerable evidence suggests that the 2 different types of ADHD, inattention and combined, have different cognitive and behavioral profiles, and underlying neurobiologies, we currently lack information on whether these subtypes reflect separated brain structure changes. Structural magnetic resonance imaging scans (N = 234), diagnostic, and demographic information were obtained from the ADHD-200 database. Of this sample, 138 were Typically Developing people, 37 were ADHD-Combined, and 59 were ADHD-Inattentive patients. Freesurfer segmentation methods were used to measure cortical thickness, area, and volume, subcortical volume and hipposubfield volume. ADHD-Inattentive patients showed milder clinical symptoms but more serious cognitive injury than ADHD-Combined patients. In addition, dissociable structural brain changes were found in different subtypes of ADHD, particularly in terms of decreased subcortical volume in ADHD-Combined patients compared with Typically Developing people. Clinical symptoms were predominantly related to smaller rh_caudalanteriorcingulate thickness and left-Pallidum volume, whereas verbal IQ injury was correlated strongly with smaller rh_insula area. These findings indicate that there are significant differences in clinical symptoms and gray matter damage between ADHD-Combined and -Inattentive patients. This supports the growing evidence of heterogeneity in the ADHD-Inattentive subtype and the evidence of brain structure differences.

Structural Brain Changes and Associated Symptoms of ADHD Subtypes in Children.

Many studies reported that spontaneous fluctuation of the blood oxygen level-dependent signal exists in multiple frequency components and changes over time. By assuming a reliable energy contrast between low- and high-frequency bands for each voxel, we developed a novel spectrum contrast mapping (SCM) method to decode brain activity at the voxel-wise level and further validated it in designed experiments. SCM consists of the following steps: first, the time course of each given voxel is subjected to fast Fourier transformation; the corresponding spectrum is divided into low- and high-frequency bands by given reference frequency points; then, the spectral energy ratio of the low- to high-frequency bands is calculated for each given voxel. Finally, the activity decoding map is formed by the aforementioned energy contrast values of each voxel. Our experimental results demonstrate that the SCM (1) was able to characterize the energy contrast of task-related brain regions; (2) could decode brain activity at rest, as validated by the eyes-closed and eyes-open resting-state experiments; (3) was verified with test-retest validation, indicating excellent reliability with most coefficients > 0.9 across the test sessions; and (4) could locate the aberrant energy contrast regions which might reveal the brain pathology of brain diseases, such as Parkinson's disease. In summary, we demonstrated that the reliable energy contrast feature was a useful biomarker in characterizing brain states, and the corresponding SCM showed excellent brain activity-decoding performance at the individual and group levels, implying its potentially broad application in neuroscience, neuroimaging, and brain diseases.

Chunqi Chang

School of Biomedical Engineering

Shenzhen University

Modulation of the Neurophysiological Response to Fearful and Stressful Stimuli Through Repetitive Religious Chanting

Chunqi Chang

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}School of Biomedical Engineering

Shenzhen University

Modulation of the Neurophysiological Response to Fearful and Stressful Stimuli Through Repetitive Religious Chanting

School of Biomedical Engineering