NeuroImage
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Comparative Study
Hippocampal volume change measurement: quantitative assessment of the reproducibility of expert manual outlining and the automated methods FreeSurfer and FIRST.
To measure hippocampal volume change in Alzheimer's disease (AD) or mild cognitive impairment (MCI), expert manual delineation is often used because of its supposed accuracy. It has been suggested that expert outlining yields poorer reproducibility as compared to automated methods, but this has not been investigated. ⋯ Quantitative reproducibility values of 1-year microliter and percentage hippocampal volume change were roughly similar between expert manual outlining, FIRST and FreeSurfer, but FreeSurfer reproducibility was statistically significantly superior to both manual outlining and FIRST after exclusion of failed segmentations.
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Parallel imaging methods using multi-coil receiver arrays have been shown to be effective for increasing MRI acquisition speed. However parallel imaging methods for fMRI with 2D sequences show only limited improvements in temporal resolution because of the long echo times needed for BOLD contrast. Recently, Simultaneous Multi-Slice (SMS) imaging techniques have been shown to increase fMRI temporal resolution by factors of four and higher. ⋯ We show that blipped spiral acquisition can achieve almost whole brain coverage at 3mm isotropic resolution in 168 ms. It is also demonstrated that the high temporal resolution allows for dynamic BOLD lag time measurement using visual/motor and retinotopic mapping paradigms. The local BOLD lag time within the visual cortex following the retinotopic mapping stimulation of expanding flickering rings is directly measured and easily translated into an eccentricity map of the cortex.
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Brain extraction is an important procedure in brain image analysis. Although numerous brain extraction methods have been presented, enhancing brain extraction methods remains challenging because brain MRI images exhibit complex characteristics, such as anatomical variability and intensity differences across different sequences and scanners. To address this problem, we present a Locally Linear Representation-based Classification (LLRC) method for brain extraction. ⋯ The International Consortium for Brain Mapping and the Alzheimer's Disease Neuroimaging Initiative databases were used to build a training dataset containing 70 scans. To evaluate the proposed method, we used four publicly available datasets (IBSR1, IBSR2, LPBA40, and ADNI3T, with a total of 241 scans). Experimental results demonstrate that the proposed method outperforms the four common brain extraction methods (BET, BSE, GCUT, and ROBEX), and is comparable to the performance of BEaST, while being more accurate on some datasets compared with BEaST.
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Perception of the external world is based on the integration of inputs from different sensory modalities. Recent experimental findings suggest that this phenomenon is present in lower-level cortical areas at early processing stages. The mechanisms underlying these early processes and the organization of the underlying circuitries are still a matter of debate. ⋯ With these assumptions, the model is able: i) to simulate the sound-induced flash fission illusion; ii) to reproduce psychometric curves assuming a random variability in some parameters; iii) to account for other audio-visual illusions, such as the sound-induced flash fusion and the ventriloquism illusions; and iv) to predict that visual and auditory stimuli are combined optimally in multisensory integration. In sum, the proposed model provides a unifying summary of spatio-temporal audio-visual interactions, being able to both account for a wide set of empirical findings, and be a framework for future experiments. In perspective, it may be used to understand the neural basis of Bayesian audio-visual inference.
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Brain PET scanning plays an important role in the diagnosis, prognostication and monitoring of many brain diseases. Motion artifacts from head motion are one of the major hurdles in brain PET. In this work, we propose to use wireless MR active markers to track head motion in real time during a simultaneous PET-MR brain scan and incorporate the motion measured by the markers in the listmode PET reconstruction. ⋯ The reduction of bias of sphere-to-background PET contrast by active marker based motion correction ranges from 26% to 64% and 17% to 25% for hot (i.e., radioactive) and cold (i.e., non-radioactive) spheres, respectively. The motion correction improved the channelized Hotelling observer signal-to-noise ratio of the spheres by 1.2 to 6.9 depending on their locations and sizes. The proposed wireless MR active marker based motion correction technique removes the motion artifacts in the reconstructed PET images and yields accurate quantitative values.