Blind separation and localization of dipole sources of MEG

We present a new approach to MEG inverse problem by modeling it into a standard blind source separation problem. In our approach, dipole sources and gain matrix are estimated without any knowledge about the head geometry and conductivity. Given the head model, we can compute dipole locations further. Our matrix pencil method developed before is suitable for this task and is applied in the simulation. Simulation results are presented.published_or_final_versio

Sequential approach to blind source separation using second order statistics

A general result on identifiability for the blind source separation problem, based on second order statistics only, is presented in this paper. The separation principle using second order statistics only is first proposed. This is followed by a discussion on a number of algorithms to separate the sources one by one.published_or_final_versio

The neural correlates of apathy in the context of aging and brain disorders: a meta-analysis of neuroimaging studies

IntroductionApathy is a prevalent mood disturbance that occurs in a wide range of populations, including those with normal cognitive aging, mental disorders, neurodegenerative disorders and traumatic brain injuries. Recently, neuroimaging technologies have been employed to elucidate the neural substrates underlying brain disorders accompanying apathy. However, the consistent neural correlates of apathy across normal aging and brain disorders are still unclear.MethodsThis paper first provides a brief review of the neural mechanism of apathy in healthy elderly individuals, those with mental disorders, neurodegenerative disorders, and traumatic brain injuries. Further, following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, the structural and functional neuroimaging meta-analysis using activation likelihood estimation method is performed on the apathy group with brain disorders and the healthy elderly, aiming at exploring the neural correlates of apathy.ResultsThe structural neuroimaging meta-analysis showed that gray matter atrophy is associated with apathy in the bilateral precentral gyrus (BA 13/6), bilateral insula (BA 47), bilateral medial frontal gyrus (BA 11), bilateral inferior frontal gyrus, left caudate (putamen) and right anterior cingulate, while the functional neuroimaging meta-analysis suggested that the functional connectivity in putamen and lateral globus pallidus is correlated with apathy.DiscussionThrough the neuroimaging meta-analysis, this study has identified the potential neural locations of apathy in terms of brain structure and function, which may offer valuable pathophysiological insights for developing more effective therapeutic interventions for affected patients

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

Introduction: 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. Methodology: Twenty-one participants were recruited for a multi-modal MRI study. Their age range was 40–52 years, 11 were female and all participants had at least 1 year of experience in religious chanting. The participants were asked to view neutral/fearful pictures while practicing religious chanting (i.e., chanting the name of Buddha Amitābha), non-religious chanting (i.e., chanting the name of Santa Claus), or no chanting. A 3.0 T Philips MRI scanner was used to collect the data and SPM12 was used to analyze the imaging data. Voxel-based morphometry (VBM) was used to explore the potential hemispheric asymmetries in practitioners. Results: Compared to non-religious chanting and no chanting, higher brain activity was observed in several brain regions when participants performed religious chanting while viewing fearful images. These brain regions included the fusiform gyrus, left parietal lobule, and prefrontal cortex, as well as subcortical regions such as the amygdala, thalamus, and midbrain. Importantly, significantly more activity was observed in the left than in the right amygdala during religious chanting. VBM showed hemispheric asymmetries, mainly in the thalamus, putamen, hippocampus, amygdala, and cerebellum; areas related to skill learning and biased memory formation. Conclusion: This preliminary study showed that repetitive religious chanting may induce strong brain activity, especially in response to stimuli with negative valence. Practicing religious chanting may structurally lateralize a network of brain areas involved in biased memory formation. These functional and structural results suggest that religious chanting helps to form a positive schema to counterbalance negative emotions. Future randomized control studies are necessary to confirm the neural mechanism related to religious chanting in coping with stress and negative emotions.publishedVersio

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

AbstractThe 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

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

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

Rapid convergence of beam pattern in adaptive digital beam forming

When the SMI algorithm is used in ADBF, the convergence rate of beam pattern can be sped up by diagonal loading. In this paper, the proper loading level is given which is determined by some physical parameters. Both SINR and beam pattern can be rapidly converged with proper loading when strong interferences are impinged on sidelobe. Simulation results are given, which verify the theoretical predictions.link_to_subscribed_fulltex

Blind signal estimation using second order statistics

published_or_final_versionElectrical and Electronic EngineeringDoctoralDoctor of Philosoph