Dynamic Change of Awareness during Meditation Techniques: Neural and Physiological Correlates

Recent fndings illustrate how changes in consciousness accommodated by neural correlates and plasticity of the brain advance a model of perceptual change as a function of meditative practice. During the mindbody response neural correlates of changing awareness illustrate how the autonomic nervous system shifts from a sympathetic dominant to a parasympathetic dominant state. Expansion of awareness during the practice of meditation techniques can be linked to the Default Mode Network (DMN), a network of brain regions that is active when the one is not focused on the outside world and the brain is restful yet awake (Chen et al., 2008). A model is presented illustrating the dynamic mindbody response before and after mindfulness meditation, and connections are made with prefrontal cortex activity, the cardiac and respiratory center, the thalamus and amygdala, the DMN and cortical function connectivity. The default status of the DMN changes corresponding to autonomic modulation resulting from meditation practice

Fractal analysis of resting state functional connectivity of the brain

A variety of resting state neuroimaging data tend to exhibit fractal behavior where its power spectrum follows power-law scaling. Resting state functional connectivity is significantly influenced by fractal behavior which may not directly originate from neuronal population activities of the brain. To describe the fractal behavior, we adopted the fractionally integrated process (FIP) model instead of the fractional Gaussian noise (FGN) since the FIP model covers more general aspects of fractality than the FGN model. We also introduce a novel concept called the nonfractal connectivity which is defined as the correlation of short memory independent of fractal behavior, and compared it with the fractal connectivity which is an asymptotic wavelet correlation. We propose several wavelet-based estimators of fractal connectivity and nonfractal connectivity for a multivariate fractionally integrated noise (mFIN). The performance of these estimators was evaluated through simulation studies and the analyses of resting state functional MRI data of the rat brain.Comment: The 2012 International Joint Conference on Neural Network

Evaluation of denoising strategies to address motion-correlated artifacts in resting-state functional magnetic resonance imaging data from the human connectome roject

Like all resting-state functional connectivity data, the data from the Human Connectome Project (HCP) are adversely affected by structured noise artifacts arising from head motion and physiological processes. Functional connectivity estimates (Pearson's correlation coefficients) were inflated for high-motion time points and for high-motion participants. This inflation occurred across the brain, suggesting the presence of globally distributed artifacts. The degree of inflation was further increased for connections between nearby regions compared with distant regions, suggesting the presence of distance-dependent spatially specific artifacts. We evaluated several denoising methods: censoring high-motion time points, motion regression, the FMRIB independent component analysis-based X-noiseifier (FIX), and mean grayordinate time series regression (MGTR; as a proxy for global signal regression). The results suggest that FIX denoising reduced both types of artifacts, but left substantial global artifacts behind. MGTR significantly reduced global artifacts, but left substantial spatially specific artifacts behind. Censoring high-motion time points resulted in a small reduction of distance-dependent and global artifacts, eliminating neither type. All denoising strategies left differences between high- and low-motion participants, but only MGTR substantially reduced those differences. Ultimately, functional connectivity estimates from HCP data showed spatially specific and globally distributed artifacts, and the most effective approach to address both types of motion-correlated artifacts was a combination of FIX and MGTR

The relationship of genetic susceptibilities for psychosis with physiological fluctuation in functional MRI data

Previously, schizophrenia is found to be related to the variability of the functional magnetic resonance imaging (fMRI) signal in the white matter. However, evidence about the relationship between genetic vulnerabilities and physiological fluctuation in the brain is lacking. We investigated whether familial risk for psychosis (FR) and polygenic risk score for schizophrenia (PRS) are linked with physiological fluctuation in fMRI data. We used data from the Oulu Brain and Mind study (n. = 140-149, aged 20-24 years) that is a substudy of the Northern Finland Birth Cohort 1986. The participants underwent a resting-state fMRI scan. Coefficient of variation (CV) of blood oxygen level dependent (BOLD) signal (CVBOLD) was used as a proxy of physiological fluctuation in the brain. Familial risk was defined to be present if at least one parent had been diagnosed with psychosis previously. PRS was computed based on the results of the prior GWAS by the Schizophrenia Working Group. FR or PRS were not associated with CVBOLD in cerebrospinal fluid, white matter, or grey matter. The findings did not provide evidence for the previous suggestions that genetic vulnerabilities for schizophrenia become apparent in alterations of the variation of the BOLD signal in the brain.Peer reviewe

Altered intrinsic functional connectivity in language-related brain regions in association with verbal memory performance in euthymic bipolar patients

Potential abnormalities in the structure and function of the temporal lobes have been studied much less in bipolar disorder than in schizophrenia. This may not be justified because language-related symptoms, such as pressured speech and flight of ideas, and cognitive deficits in the domain of verbal memory are amongst the hallmark of bipolar disorder (BD), and contribution of temporal lobe dysfunction is therefore likely. In the current study, we examined resting-state functional connectivity (FC) between the auditory cortex (Heschl’s gyrus [HG], planum temporale [PT]) and whole brain using seed correlation analysis in n = 21 BD euthymic patients and n = 20 matched healthy controls and associated it with verbal memory performance. In comparison to controls BD patients showed decreased functional connectivity between Heschl’s gyrus and planum temporale and the left superior and middle temporal gyrus. Additionally, fronto-temporal functional connectivity with the right inferior frontal/precentral gyrus and the insula was increased in patients. Verbal episodic memory deficits in the investigated sample of BD patients and language-related symptoms might therefore be associated with a diminished FC within the auditory/temporal gyrus and a compensatory fronto-temporal pathway

Biophysical Modulations of Functional Connectivity

Resting-state low frequency oscillations have been detected in many functional magnetic resonance imaging (MRI) studies and appear to be synchronized between functionally related areas. Converging evidence from MRI and other imaging modalities suggest that this activity has an intrinsic neuronal origin. Multiple consistent networks have been found in large populations, and have been shown to be stable over time. Further, these patterns of functional connectivity have been shown to be altered in healthy controls under various physiological challenges. This review will present the biophysical characterization of functional connectivity, and examine the effects of physical state manipulations (such as anesthesia, fatigue, and aging) in healthy controls.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90432/1/brain-2E2011-2E0039.pd

Complex-Valued Time-Series Correlation Increases Sensitivity in FMRI Analysis

Purpose To develop a linear matrix representation of correlation between complex-valued (CV) time-series in the temporal Fourier frequency domain, and demonstrate its increased sensitivity over correlation between magnitude-only (MO) time-series in functional MRI (fMRI) analysis. Materials and Methods The standard in fMRI is to discard the phase before the statistical analysis of the data, despite evidence of task related change in the phase time-series. With a real-valued isomorphism representation of Fourier reconstruction, correlation is computed in the temporal frequency domain with CV time-series data, rather than with the standard of MO data. A MATLAB simulation compares the Fisher-z transform of MO and CV correlations for varying degrees of task related magnitude and phase amplitude change in the time-series. The increased sensitivity of the complex-valued Fourier representation of correlation is also demonstrated with experimental human data. Since the correlation description in the temporal frequency domain is represented as a summation of second order temporal frequencies, the correlation is easily divided into experimentally relevant frequency bands for each voxel\u27s temporal frequency spectrum. The MO and CV correlations for the experimental human data are analyzed for four voxels of interest (VOIs) to show the framework with high and low contrast-to-noise ratios in the motor cortex and the supplementary motor cortex. Results The simulation demonstrates the increased strength of CV correlations over MO correlations for low magnitude contrast-to-noise time-series. In the experimental human data, the MO correlation maps are noisier than the CV maps, and it is more difficult to distinguish the motor cortex in the MO correlation maps after spatial processing. Conclusions Including both magnitude and phase in the spatial correlation computations more accurately defines the correlated left and right motor cortices. Sensitivity in correlation analysis is important to preserve the signal of interest in fMRI data sets with high noise variance, and avoid excessive processing induced correlation