Vitali Zagorodnov

Lapsing during Sleep Deprivation Is Associated with Distributed Changes in Brain Activation

Lapses of attention manifest as delayed behavioral responses to salient stimuli. Although they can occur even after a normal nights 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 nights 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.

Adaptive Filtering for Color Filter Array Demosaicking

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.

Skull stripping using graph cuts.

Removal of non-brain tissues, particularly dura, is an important step in enabling accurate measurement of brain structures. Many popular methods rely on iterative surface deformation to fit the brain boundary and tend to leave residual dura. Similar to other approaches, the method proposed here uses intensity thresholding followed by removal of narrow connections to obtain a brain mask. However, instead of using morphological operations to remove narrow connections, a graph theoretic image segmentation technique was used to position cuts that isolate and remove dura. This approach performed well on both the standardized IBSR test data sets and empirically derived data. Compared to the Hybrid Watershed Algorithm (HWA; (Segonne et al., 2004)) the novel approach achieved an additional 10-30% of dura removal without incurring further brain tissue erosion. The proposed method is best used in conjunction with HWA as the errors produced by the two approaches often occur at different locations and cancel out when their masks are combined. Our experiments indicate that this combination can substantially decrease and often fully avoid cortical surface overestimation in subsequent segmentation.

Improvement of brain segmentation accuracy by optimizing non-uniformity correction using N3

Smoothly varying and multiplicative intensity variations within MR images that are artifactual, can reduce the accuracy of automated brain segmentation. Fortunately, these can be corrected. Among existing correction approaches, the nonparametric non-uniformity intensity normalization method N3 (Sled, J.G., Zijdenbos, A.P., Evans, A.C., 1998. Nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans. Med. Imag. 17, 87-97.) is one of the most frequently used. However, at least one recent study (Boyes, R.G., Gunter, J.L., Frost, C., Janke, A.L., Yeatman, T., Hill, D.L.G., Bernstein, M.A., Thompson, P.M., Weiner, M.W., Schuff, N., Alexander, G.E., Killiany, R.J., DeCarli, C., Jack, C.R., Fox, N.C., 2008. Intensity non-uniformity correction using N3 on 3-T scanners with multichannel phased array coils. NeuroImage 39, 1752-1762.) suggests that its performance on 3 T scanners with multichannel phased-array receiver coils can be improved by optimizing a parameter that controls the smoothness of the estimated bias field. The present study not only confirms this finding, but additionally demonstrates the benefit of reducing the relevant parameter values to 30-50 mm (default value is 200 mm), on white matter surface estimation as well as the measurement of cortical and subcortical structures using FreeSurfer (Martinos Imaging Centre, Boston, MA). This finding can help enhance precision in studies where estimation of cerebral cortex thickness is critical for making inferences.

Sleep deprivation and its effects on object-selective attention

Sleep deprivation (SD) affects attention but it is an open question as to whether all subtypes of attention are similarly affected. We investigated the effects of 24 h of total SD on object-selective attention. 26 healthy, young adults viewed quartets of alternating faces or place scenes and performed selective judgments on faces only, scenes only or both faces and scenes. Volunteers underwent fMRI following a normal night of sleep and again following approximately 24 h of total sleep deprivation in a counterbalanced fashion. Sleep deprivation resulted in slower and less accurate picture classification as well as poorer recognition memory for scenes. Attention strongly modulated activation in the Parahippocampal Place Area (PPA). Task-related activation in the fronto-parietal cortex and PPA was reduced in SD, but the relative modulation of PPA activation by attention was preserved. Psychophysiological interaction between the left intra-parietal sulcus and the PPA that was clearly present after a normal night of sleep was reduced below threshold following SD suggesting that PPI may be a more sensitive method of detecting change in selective attention. Sleep deprivation may affect object-selective attention in addition to exerting a task-independent deficit in attention.

Adverse Associations between Visceral Adiposity, Brain Structure, and Cognitive Performance in Healthy Elderly

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.

Reversing Demosaicking and Compression in Color Filter Array Image Processing: Performance Analysis and Modeling

In the conventional processing chain of single-sensor digital still cameras (DSCs), the images are captured with color filter arrays (CFAs) and the CFA samples are demosaicked into a full color image before compression. To avoid additional data redundancy created by the demosaicking process, an alternative processing chain has been proposed to move the compression process before the demosaicking. Recent empirical studies have shown that the alternative chain can outperform the conventional one in terms of image quality at low compression ratios. To provide a theoretically sound basis for such conclusion, we propose analytical models for the reconstruction errors of the two processing chains. The models developed confirm the results of existing empirical studies and provide better understanding of DSC processing chains. The modeling also allows performance predictions for more advanced compression and demosaicking methods, thus providing important cues for future development in this area

Edge-preserving image denoising via optimal color space projection

Denoising of color images can be done on each color component independently. Recent work has shown that exploiting strong correlation between high-frequency content of different color components can improve the denoising performance. We show that for typical color images high correlation also means similarity, and propose to exploit this strong intercolor dependency using an optimal luminance/color-difference space projection. Experimental results confirm that performing denoising on the projected color components yields superior denoising performance, both in peak signal-to-noise ratio and visual quality sense, compared to that of existing solutions. We also develop a novel approach to estimate directly from the noisy image data the image and noise statistics, which are required to determine the optimal projection

Interactive surface-guided segmentation of brain MRI data

MRI segmentation is a process of deriving semantic information from volume data. For brain MRI data, segmentation is initially performed at a voxel level and then continued at a brain surface level by generating its approximation. While successful most of the time, automated brain segmentation may leave errors which have to be removed interactively by editing individual 2D slices. We propose an approach for correcting these segmentation errors in 3D modeling space. We actively use the brain surface, which is estimated (potentially wrongly) in the automated FreeSurfer segmentation pipeline. It allows us to work with the whole data set at once, utilizing the context information and correcting several slices simultaneously. Proposed heuristic editing support and automatic visual highlighting of potential error locations allow us to substantially reduce the segmentation time. The paper describes the implementation principles of the proposed software tool and illustrates its application.

Color image denoising using wavelets and minimum cut analysis

Wavelet thresholding has proven to be an efficient edge-preserving denoising method for grayscale images, especially when it exploits the interscale correlations of wavelet coefficients. Intrascale correlations can further improve the denoising performance, but the gain for grayscale images is generally small. In this letter, we demonstrate that the gain can become substantial in color image denoising, especially for smooth image color-difference components. We then propose a new denoising method, based on the minimum cut algorithm, to exploit both the interscale and intrascale correlations of wavelet coefficients. The proposed method achieves up to 5-dB gain in peak signal-to-noise ratio for color-difference images and leads to fewer visual color artifacts.