NeuroImage
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The blood oxygenation level dependent (BOLD) response measured with functional magnetic resonance imaging (fMRI) depends on the evoked changes in cerebral blood flow (CBF) and cerebral metabolic rate of oxygen (CMRO(2)) in response to changes in neural activity. This response is strongly modulated by the CBF/CMRO(2) coupling relationship with activation, defined as n, the ratio of the fractional changes. The reliability of the BOLD signal as a quantitative reflection of underlying physiological changes depends on the stability of n in response to different stimuli. ⋯ Instead, this response ratio was significantly lower for the BOLD response (BOLD response: 0.23 ± 0.13, mean ± SD; CBF response: 0.42 ± 0.18; p=0.0054). This data is consistent with a reduced dynamic range (strongest/weakest response ratio) of the CMRO(2) response (~1.7-fold) compared to that of the CBF response (~2.4-fold) as luminance contrast increases, corresponding to an increase of n from 1.7 at the lowest contrast level to 2.3 at the highest contrast level. The implication of these results for fMRI studies is that the magnitude of the BOLD response does not accurately reflect the magnitude of underlying physiological processes.
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Experientially opening oneself to pain rather than avoiding it is said to reduce the mind's tendency toward avoidance or anxiety which can further exacerbate the experience of pain. This is a central feature of mindfulness-based therapies. Little is known about the neural mechanisms of mindfulness on pain. ⋯ The reduced baseline activation in left aI correlated with lifetime meditation experience. This pattern of low baseline activity coupled with high response in aIns and aMCC was associated with enhanced neural habituation in amygdala and pain-related regions before painful stimulation and in the pain-related regions during painful stimulation. These findings suggest that cultivating experiential openness down-regulates anticipatory representation of aversive events, and increases the recruitment of attentional resources during pain, which is associated with faster neural habituation.
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Noise in fMRI recordings creates uncertainty when mapping functional networks in the brain. Non-neural physiological processes can introduce correlated noise across much of the brain, altering the apparent strength and extent of intrinsic networks. In this work, a new data-driven noise correction, termed "APPLECOR" (for Affine Parameterization of Physiological Large-scale Error Correction), is introduced. ⋯ APPLECOR is shown to achieve greater consistency of the default mode network across time and across subjects than was achieved using global mean regression, respiratory volume and heart rate correction (RVHRCOR (Chang et al., 2009)), or no correction. Combining APPLECOR with RVHRCOR regressors attained greater consistency than either correction alone. Use of the proposed noise-reduction approach may help to better identify and delineate the structure of resting state networks.
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Differing noise variance across study populations has been shown to cause artifactual group differences in functional connectivity measures. In this study, we investigate the use of short echo time functional MRI data to correct for these noise sources in blood oxygenation level dependent (BOLD)-weighted time series. A dual-echo sequence was used to simultaneously acquire data at both a short (TE=3.3 ms) and a BOLD-weighted (TE=35 ms) echo time. ⋯ Finally, functional connectivity maps of the default mode network were constructed using a seed correlation approach. The effects of short TE correction and low-pass filtering on the resulting correlations maps were compared. Results suggest that short TE correction more accurately differentiates artifactual correlations from the correlations of interest in conditions of amplified noise.
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Epigenetic modifications mediated by histone deacetylases (HDACs) play important roles in the mechanisms of different neurologic diseases and HDAC inhibitors (HDACIs) have shown promise in therapy. However, pharmacodynamic profiles of many HDACIs in the brain remain largely unknown due to the lack of validated methods for noninvasive imaging of HDAC expression-activity. ⋯ Immunohistochemical analyses of brain tissue revealed the heterogeneity of expression of individual HDACs in different brain structures and cell types and confirmed that PET/CT/MRI with [(18)F]FAHA reflects the level of expression-activity of HDAC class IIa enzymes. Furthermore, PET/CT/MRI with [(18)F]FAHA enabled non-invasive, quantitative assessment of pharmacodynamics of HDAC inhibitor SAHA in the brain.