NeuroImage
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Peripheral neuropathy often manifests clinically with symptoms of mechanical and cold allodynia. However, the neuroplastic changes associated with peripheral neuropathic pain and the onset and progression of allodynic symptoms remain unclear. Here, we used a chronic neuropathic pain model (spared nerve injury; SNI) to examine functional and metabolic brain changes associated with the development and maintenance of mechanical and cold hypersensitivity, the latter which we assessed both behaviorally and during a novel acetone application paradigm using functional MRI (fMRI). ⋯ These functional brain changes temporally coincided with early and sustained increases in both mechanical and cold sensitivity. SNI rats also showed increased glutamate within the ACC that correlated with behavioral measures of cold hypersensitivity. Together, our findings suggest that extensive functional reorganization within pain-related brain regions may underlie the development and chronification of allodynic-like behaviors.
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Multi-band echo planar imaging (MB-EPI), a new approach to increase data acquisition efficiency and/or temporal resolution, has the potential to overcome critical limitations of standard acquisition strategies for obtaining high-resolution whole brain perfusion imaging using arterial spin labeling (ASL). However, the use of MB also introduces confounding effects, such as spatially varying amplified thermal noise and leakage contamination, which have not been evaluated to date as to their effect on cerebral blood flow (CBF) estimation. ⋯ These studies revealed that the amplified noise, given by the geometry factor (g-factor), and the leakage contamination, assessed by the total leakage factor (TLF), have a minimal impact on CBF estimation. Furthermore, it is demonstrated that MB-EPI greatly benefits high-resolution whole brain pCASL studies in terms of improved spatial and temporal signal-to-noise ratio efficiencies, and increases compliance with the assumptions of the commonly used single blood compartment model, resulting in improved CBF estimates.
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Resting-state functional connectivity (FC) fMRI (rs-fcMRI) offers an appealing approach to mapping the brain's intrinsic functional organization. Blood oxygen level dependent (BOLD) and arterial spin labeling (ASL) are the two main rs-fcMRI approaches to assess alterations in brain networks associated with individual differences, behavior and psychopathology. While the BOLD signal is stronger with a higher temporal resolution, ASL provides quantitative, direct measures of the physiology and metabolism of specific networks. ⋯ Moreover, we observed positive correlations between regional CBF and FC in core areas of all RBNs indicating a relationship between network connectivity and its baseline metabolism. Taken together, the combination of ASL and BOLD rs-fcMRI provides a powerful tool for characterizing the spatiotemporal and quantitative properties of RBNs. These findings pave the way for future BOLD and ASL rs-fcMRI studies in clinical populations that are carried out across time and scanners.
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We propose an echo planar imaging (EPI) distortion correction method (DR-BUDDI), specialized for diffusion MRI, which uses data acquired twice with reversed phase encoding directions, often referred to as blip-up blip-down acquisitions. DR-BUDDI can incorporate information from an undistorted structural MRI and also use diffusion-weighted images (DWI) to guide the registration, improving the quality of the registration in the presence of large deformations and in white matter regions. DR-BUDDI does not require the transformations for correcting blip-up and blip-down images to be the exact inverse of each other. ⋯ The proposed method is robust and generally outperforms existing approaches. The inclusion of the DWIs in the correction process proves to be important to obtain a reliable correction of distortions in the brain stem. Methods that do not use DWIs may produce a visually appealing correction of the non-diffusion weighted images, but the directionally encoded color maps computed from the tensor reveal an abnormal anatomy of the white matter pathways.
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Recently, there has been a lot of interest in characterising the connectivity of resting state brain networks. Most of the literature uses functional connectivity to examine these intrinsic brain networks. Functional connectivity has well documented limitations because of its inherent inability to identify causal interactions. ⋯ We also simulated group differences and compared the ability of spectral and stochastic DCMs to identify these differences. We show that spectral DCM was not only more accurate but also more sensitive to group differences. Finally, we performed a comparative evaluation using real resting state fMRI data (from an open access resource) to study the functional integration within default mode network using spectral and stochastic DCMs.