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Covering more cognitive concepts requires accumulating many brain images.
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Brain extraction, also known as skull stripping, is one of the most important preprocessing steps for many automatic brain image analysis.
This resulted in ∼2 Tb of image data from each brain, allowing us to image many brains, whereas imaging with even 2× higher magnification in each dimension would have led to prohibitively (based on our present processing capacity) large file sizes of ∼20 Tb per brain.
However, coregistration of functional brain images across many subjects offers the experimental advantage of enabling voxel-based analysis over multiple subjects and is therefore widely used in human studies.
As suggested in many studies [12], [19], [24], [28], automated segmentation of neonatal brain images remains a challenging problem due to poor image quality (low spatial resolution and tissue contrast) and high within-tissue intensity variability.
Imaging studies in ADHD [ 2, 27] have suggested many brain structures associated with cognitive/attention networks display functional abnormalities.
Both DNA fingerprints and brain images prominently involve visual representations of biological facts, but scientific representations can take many other forms, not all pictorial or graphic.
But brain images taken during testing showed "hyperactivation and hypoconnectivity" in the retired players' frontal lobes, correlated with how many times the players had been removed from games.
They have taken brain images during hypothetical moral decisions.
Figure 4 Brain images.
The T2 weighted Magnetic Resonance Imaging (MRI) brain images are considered for image segmentation.
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