Lapses of attention manifest as delayed behavioral responses to salient stimuli. Although they can occur even after a normal night's sleep, they are longer in duration and more frequent after sleep deprivation (SD). To identify changes in task-associated brain activation associated with lapses during SD, we performed functional magnetic resonance imaging during a visual, selective attention task and analyzed the correct responses in a trial-by-trial manner modeling the effects of response time. Separately, we compared the fastest 10% and slowest 10% of correct responses in each state. Both analyses concurred in finding that SD-related lapses differ from lapses of equivalent duration after a normal night's sleep by (1) reduced ability of frontal and parietal control regions to raise activation in response to lapses, (2) dramatically reduced visual sensory cortex activation, and (3) reduced thalamic activation during lapses that contrasted with elevated thalamic activation during nonlapse periods. Despite these differences, the fastest responses after normal sleep and after SD elicited comparable frontoparietal activation, suggesting that performing a task while sleep deprived involves periods of apparently normal neural activation interleaved with periods of depressed cognitive control, visual perceptual functions, and arousal. These findings reveal for the first time some of the neural consequences of the interaction between efforts to maintain wakefulness and processes that initiate involuntary sleep in sleep-deprived persons.

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Most digital still cameras acquire imagery with a color filter array (CFA), sampling only one color value for each pixel and interpolating the other two color values afterwards. The interpolation process is commonly known as demosaicking. In general, a good demosaicking method should preserve the high-frequency information of imagery as much as possible, since such information is essential for image visual quality. We discuss in this paper two key observations for preserving high-frequency information in CFA demosaicking: (1) the high frequencies are similar across three color components, and (2) the high frequencies along the horizontal and vertical axes are essential for image quality. Our frequency analysis of CFA samples indicates that filtering a CFA image can better preserve high frequencies than filtering each color component separately. This motivates us to design an efficient filter for estimating the luminance at green pixels of the CFA image and devise an adaptive filtering approach to estimating the luminance at red and blue pixels. Experimental results on simulated CFA images, as well as raw CFA data, verify that the proposed method outperforms the existing state-of-the-art methods both visually and in terms of peak signal-to-noise ratio, at a notably lower computational cost.

The link between central adiposity and cognition has been established by indirect measures such as body mass index (BMI) or waist–hip ratio. Magnetic resonance imaging (MRI) quantification of central abdominal fat has been linked to elevated risk of cardiovascular and cerebro-vascular disease. However it is not known how quantification of visceral fat correlates with cognitive performance and measures of brain structure. We filled this gap by characterizing the relationships between MRI measures of abdominal adiposity, brain morphometry, and cognition, in healthy elderly. Methods: A total of 184 healthy community dwelling elderly subjects without cognitive impairment participated in this study. Anthropometric and biochemical markers of cardiovascular risk, neuropsychological measurements as well as MRI of the brain and abdomen fat were obtained. Abdominal images were segmented into subcutaneous adipose tissue and visceral adipose tissue (VAT) adipose tissue compartments. Brain MRI measures were analyzed quantitatively to determine total brain volume, hippocampal volume, ventricular volume, and cortical thickness. Results: VAT showed negative association with verbal memory (r = 0.21, p = 0.005) and attention (r = 0.18, p = 0.01). Higher VAT was associated with lower hippocampal volume (F = 5.39, p = 0.02) and larger ventricular volume (F = 6.07, p = 0.02). The participants in the upper quartile of VAT had the lowest hippocampal volume even after adjusting for age, gender, hypertension, and BMI (b = −0.28, p = 0.005). There was a significant age by VAT interaction for cortical thickness in the left prefrontal region. Conclusion: In healthy older adults, elevated VAT is associated with negative effects on cognition, and brain morphometry.

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