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Several sampling strategies, which compress an analog signal and recover it, are attracting attention. Representatively, compressive sensing (CS) can restore a sparse signal from its compressed one sampled at a lower rate than the Nyquist rate. However, CS is not appropriate to non-sparse signals. Recently developed compressive covariance sensing (CCS) methods have received great attention. The CCS can compress a non-sparse signal and recover its second-order statistics, but previous CCSs are not only batch processes but also assume that a signal is wide sense-stationary (WSS). Hence, advanced CCSs need to be developed. In this study, two CCS methods are proposed. One is an associative array-based CCS, which is also a batch processor but requires a lower computational complexity than the previous CCSs. The other is a novel online CCS that sequentially compresses a signal and recovers its covariance. The online CCS can trace the characteristics of non-stationary signal. Mathematical analysis and simulation results showed that the proposed methods have high covariance tracking performance as well as accurate restoration.

Accurately cytopathological diagnosis of Papillary Thyroid Carcinoma (PTC) is of importance for reducing costs and increasing efficiency of treatments. In this paper, we pursue that goal by introducing artificial intelligence (AI) for automatic classification of malignant PTC cell clusters from Fine Needle Aspiration Cytology (FNAC) processed by ThinPrep. High-resolution cytological images obtained with a 40 × objective lens digital camera attached to an Olympus microscope were segmented into fragments and then divided into training, validation, and testing subsets. Fragments are non-overlapped patches containing only regions-of-interest that cover informative tissue structures for making proper diagnoses. Deep learning CNN models were pre-trained and fine-tuned on large-scale ImageNet domain before they were re-trained on cytology fragments. Moreover, we proposed a method to compute certainty of the patient-level prediction that undoubtedly provides additional evidence for reliability and confidence of the prediction. Results showed that the best classification performance on digital FNAC images achieved using DenseNet161, obtaining a mean accuracy of 0.9556 (p

Anesthesia is thought to be mediated by inhibiting the integration of information between different areas of the brain. Long-range thalamo-cortical interaction plays a critical role in inducing anesthesia-related unconsciousness. However, it remains unclear how this interaction change according to anesthetic depth. In this study, we aimed to investigate how different levels of anesthesia affect thalamo-frontal interactions. Prior to the experiment, electrodes were implanted to record local field potentials (LFPs). Isoflurane (ISO) was administered and LFPs were measured in rats from four different brain areas (left frontal, right frontal, left thalamus and right thalamus) at four different anesthesia levels: awake, deep (ISO 2.5 vol%), light (ISO 1 vol%) and recovery. Spectral granger causality (Spectral-GC) were calculated at the measured areas in accordance with anesthetic levels. Anesthesia led to a decrease in connectivity in the thalamo-frontal direction and an increase in connectivity in the frontal-thalamic direction. The changes in thalamo-frontal functional connectivity were prominent during deep anesthesia at high frequency bands. The connection strengths between the thalamus and the frontal area changed depending on the depth of anesthesia. The relationships between anesthetic levels and thalamo-frontal activity may shed light on the neural mechanism by which different levels of anesthesia act.

Currently, one of the most challenging issues in modern neuroscience is learning-induced neural plasticity. Many researchers have identified activation-dependent structural brain plasticity in gray and white matter. The game of Baduk is known to require many cognitive processes, and long-term training in such processes would be expected to cause structural changes in related brain areas. We conducted voxel-based analyses of diffusion-tensor imaging (DTI) data and found that, compared to inexperienced controls, long-term trained Baduk players developed larger regions of white matter with increased fractional anisotropy (FA) values in the frontal, cingulum, and striato-thalamic areas that are related to attentional control, working memory, executive regulation, and problem-solving. In addition, inferior temporal regions with increased FA indicate that Baduk experts tend to develop a task-specific template for the game, as compared to controls. In contrast, decreased FA found in dorsolateral premotor and parietal areas indicate that Baduk experts were less likely than were controls to use structures related to load-dependent memory capacity. Right-side dominance in Baduk experts suggests that the tasks involved are mainly spatial processes. Altogether, long-term Baduk training appears to cause structural brain changes associated with many of the cognitive aspects necessary for game play, and investigation of the mechanism underpinning such changes might be helpful for improving higher-order cognitive capacities, such as learning, abstract reasoning, and self-control, which can facilitate education and cognitive therapies.

We investigated the brain substrate of word repetition effects on the implicit memory task using low-resolution electromagnetic tomography (LORETA) with high-density 128-channel EEG and individual MRI as a realistic head model. Thirteen right-handed, healthy subjects performed a word/non-word discrimination task, in which the words and non-words were presented visually, and some of the words appeared twice with a lag of one or five items. All of the subjects exhibited word repetition effects with respect to the behavioral data, in which a faster reaction time was observed to the repeated word (old word) than to the first presentation of the word (new word). The old words elicited more positive-going potentials than the new words, beginning at 200 ms and lasting until 500 ms post-stimulus. We conducted source reconstruction using LORETA at a latency of 400 ms with the peak mean global field potentials and used statistical parametric mapping for the statistical analysis. We found that the source elicited by the old words exhibited a statistically significant current density reduction in the left inferior frontal gyrus. This is the first study to investigate the generators of word repetition effects using voxel-by-voxel statistical mapping of the current density with individual MRI and high-density EEG.