NeuroImage
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Although progress has been made in relating neuronal events to changes in brain metabolism and blood flow, the interpretation of functional neuroimaging data in terms of the underlying brain circuits is still poorly understood. Computational modeling of connection patterns both among and within regions can be helpful in this interpretation. We present a neural network model of the ventral visual pathway and its relevant functional connections. ⋯ We then demonstrate that the disconnection may be explained by reduced local recurrent circuitry in frontal cortex. This method extends currently available methods for estimating functional connectivity from human imaging data by including both local circuits and features of interregional connections, such as topography and sparseness, in addition to total connection strengths. Furthermore, our results suggest how fronto-temporal functional disconnection in schizophrenia can result from reduced local synaptic connections within frontal cortex rather than compromised interregional connections.
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Fiber tracking is increasingly used to plan and guide neurosurgical procedures of intracranial tumors in the vicinity of functionally important areas of the brain. However, valid data concerning the reliability of tracking with respect to the actual pathoanatomical situation are lacking. We retrospectively correlated fiber tracking based on magnetic resonance (MR) DT imaging with the histopathological data of 25 patients with WHO grade II and III gliomas. ⋯ In 9 patients we were able to reconstruct brain fiber tracts at biopsy loci (2-32% tumor infiltration) using an FA threshold of 0.15 and 0.2, but not for a threshold of 0.25 or 0.3. The neurological outcome demonstrated potential tumor cell infiltration of functionally intact brain fiber tracts in the range of 2-8%. These findings may be useful in planning therapeutic approaches to gliomas in the vicinity of eloquent brain regions.
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The negative blood oxygenation level-dependent (BOLD) signal following the cessation of stimulation (post-stimulus BOLD undershoot) is observed in functional magnetic resonance imaging (fMRI) studies. However, its spatial characteristics are unknown. To investigate this, gradient-echo BOLD fMRI in response to visual stimulus was obtained in isoflurane-anesthetized cats at 9.4 T. ⋯ The post-stimulus BOLD undershoot was observed within the cortex and near the surface of the cortex, while the prolonged CBV elevation was observed only at the middle of the cortex. Within the cortex, the largest post-stimulus undershoot was detected at the middle of the cortex, similar to the CBV increase during the stimulation period. Our findings demonstrate that, even though there is significant contribution from pial vessel signals, the post-stimulus undershoot BOLD signal is useful to improve the spatial localization of fMRI to active cortical sites.
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In previous work we have described a spatially regularised General Linear Model (GLM) for the analysis of brain functional Magnetic Resonance Imaging (fMRI) data where Posterior Probability Maps (PPMs) are used to characterise regionally specific effects. The spatial regularisation is defined over regression coefficients via a Laplacian kernel matrix and embodies prior knowledge that evoked responses are spatially contiguous and locally homogeneous. In this paper we propose to finesse this Bayesian framework by specifying spatial priors using Sparse Spatial Basis Functions (SSBFs). ⋯ The method includes non-linear wavelet shrinkage as a special case. As compared to Laplacian spatial priors, SSBFs allow for spatial variations in signal smoothness, are more computationally efficient and are robust to heteroscedastic noise. Results are shown on synthetic data and on data from an event-related fMRI experiment.