NeuroImage
-
Comparative Study
Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains.
This paper evaluates strategies for atlas selection in atlas-based segmentation of three-dimensional biomedical images. Segmentation by intensity-based nonrigid registration to atlas images is applied to confocal microscopy images acquired from the brains of 20 bees. This paper evaluates and compares four different approaches for atlas image selection: registration to an individual atlas image (IND), registration to an average-shape atlas image (AVG), registration to the most similar image from a database of individual atlas images (SIM), and registration to all images from a database of individual atlas images with subsequent multi-classifier decision fusion (MUL). ⋯ The superiority of the MUL strategy over the other three methods is statistically significant (two-sided paired t test, P < 0.001). Both the MUL and AVG strategies performed better than the best possible SIM and IND strategies with optimal a posteriori atlas selection (mean similarity index for optimal SIM, 0.83; for optimal IND, 0.81). Our findings show that atlas selection is an important issue in atlas-based segmentation and that, in particular, multi-classifier techniques can substantially increase the segmentation accuracy.
-
Blood oxygenation level dependent contrast functional MRI (BOLD-fMRI) has been used to define the functional cortices of the brain in preoperative planning for tumor removal. However, some studies have demonstrated false-negative activations in such patients. We compared the evoked-cerebral blood oxygenation (CBO) changes measured by near-infrared spectroscopy (NIRS) and activation mapping of BOLD-fMRI in 12 patients with brain tumors who had no paresis of the upper extremities. ⋯ However, in the Deoxy-increase group, BOLD-fMRI revealed only a small activation area or no activation on the lesion side. Intraoperative brain mapping identified the PSMC on the lesion side that was not demonstrated by BOLD-fMRI. The false-negative activations might have been caused by the atypical evoked-CBO changes (i.e. increases in Deoxy-Hb) and the software employed to calculate the activation maps, which does not regard an increase of Deoxy-Hb (i.e., a decrease in BOLD-fMRI signal) as neuronal activation.
-
The contingent negative variation (CNV) is a long-latency electroencephalography (EEG) surface negative potential with cognitive and motor components, observed during response anticipation. CNV is an index of cortical arousal during orienting and attention, yet its functional neuroanatomical basis is poorly understood. We used functional magnetic resonance imaging (fMRI) with simultaneous EEG and recording of galvanic skin response (GSR) to investigate CNV-related central neural activity and its relationship to peripheral autonomic arousal. ⋯ In a subset of subjects in whom we acquired simultaneous EEG and fMRI data, we observed activity in bilateral thalamus, anterior cingulate, and supplementary motor cortex that was modulated by trial-by-trial amplitude of CNV. These findings provide a likely functional neuroanatomical substrate for the CNV and demonstrate modulation of components of this neural circuitry by peripheral autonomic arousal. Moreover, these data suggest a mechanistic model whereby thalamocortical interactions regulate CNV amplitude.
-
Carbon dioxide is a potent cerebral vasodilator. We have identified a significant source of low-frequency variation in blood oxygen level-dependent (BOLD) magnetic resonance imaging (MRI) signal at 3 T arising from spontaneous fluctuations in arterial carbon dioxide level in volunteers at rest. Fluctuations in the partial pressure of end-tidal carbon dioxide (Pet(CO(2))) of +/-1.1 mm Hg in the frequency range 0-0.05 Hz were observed in a cohort of nine volunteers. ⋯ Doppler ultrasound suggests that a component of low-frequency BOLD signal fluctuations is mediated by CO(2)-induced changes in cerebral blood flow (CBF). These fluctuations are a source of physiological noise and a potentially important confounding factor in fMRI paradigms that modify breathing. However, they can also be used for mapping regional vascular responsiveness to CO(2).
-
FMRI modelling requires flexible haemodynamic response function (HRF) modelling, with the HRF being allowed to vary spatially and between subjects. To achieve this flexibility, voxelwise parameterised HRFs have been proposed; however, inference on such models is very slow. An alternative approach is to use basis functions allowing inference to proceed in the more manageable General Linear Model (GLM) framework. ⋯ Here we extend the work of Penny et al. to give inference on the GLM with constrained HRF basis functions and with spatial Markov Random Fields on the autoregressive noise parameters. Constraining the subspace spanned by the basis set allows for far superior separation of activating voxels from nonactivating voxels in FMRI data. We use spatial mixture modelling to produce final probabilities of activation and demonstrate increased sensitivity on an FMRI dataset.