Top-down and bottom-up neurodynamic evidence in patients with tinnitus

AbstractAlthough a peripheral auditory (bottom-up) deficit is an essential prerequisite for the generation of tinnitus, central cognitive (top-down) impairment has also been shown to be an inherent neuropathological mechanism. Using an auditory oddball paradigm (for top-down analyses) and a passive listening paradigm (for bottom-up analyses) while recording electroencephalograms (EEGs), we investigated whether top-down or bottom-up components were more critical in the neuropathology of tinnitus, independent of peripheral hearing loss. We observed significantly reduced P300 amplitudes (reflecting fundamental cognitive processes such as attention) and evoked theta power (reflecting top-down regulation in memory systems) for target stimuli at the tinnitus frequency of patients with tinnitus but without hearing loss. The contingent negative variation (reflecting top-down expectation of a subsequent event prior to stimulation) and N100 (reflecting auditory bottom-up selective attention) were different between the healthy and patient groups. Interestingly, when tinnitus patients were divided into two subgroups based on their P300 amplitudes, their P170 and N200 components, and annoyance and distress indices to their tinnitus sound were different. EEG theta-band power and its Granger causal neurodynamic results consistently support a double dissociation of these two groups in both top-down and bottom-up tasks. Directed cortical connectivity corroborates that the tinnitus network involves the anterior cingulate and the parahippocampal areas, where higher-order top-down control is generated. Together, our observations provide neurophysiological and neurodynamic evidence revealing a differential engagement of top-down impairment along with deficits in bottom-up processing in patients with tinnitus but without hearing loss

Collision Avoidance with Deep Reinforcement Learning

In the past decade, learning algorithms developed to play video games better than humans have become more common. Google’s DeepMind Technologies developed learning algorithms that could play Atari video games and also demonstrated their famous AlphaGo algorithm which outperformed professional Go players. However, little research has been done on learning algorithms developed to complete the particularly difficult single-player games. In particular, much further research could be done on developing learning algorithms for mechanically challenging games such as “bullet hell” games. We believe that agents could learn to efficiently evade obstacles utilizing deep reinforcement learning. The purpose of this study is to understand how to create such an efficient evasion algorithm. The deep learning model utilized is a convolutional neural network trained with a variant of the Q-learning algorithm. The model is given positional coordinate data and bullet location data as its input and outputs a value function to determine the best following action. The agent controls the directional inputs of the game’s user avatar and its inputs, or actions, are modeled as a two-dimensional Markov decision process. The agent uses the game’s internal score and the amount of time its user avatar avoids being hit by obstacles as its target and experiments with its inputs each episode to increase the maximum reward. Each training episode is reset when the user avatar is hit by the bullets

Ultrasound features of secondary appendicitis in pediatric patients

Purpose The purpose of this study was to evaluate the ultrasonographic findings of secondary appendicitis (SA) and to discuss the differential findings compared with primary appendicitis. Methods In this study, we analyzed the ultrasonographic findings of 94 patients under 15 years old of age treated at our institution from May 2005 to May 2014 who had bowel inflammation and an inflamed appendix with a maximal outer diameter >6 mm that improved with nonsurgical treatment (the SA group). Ninety-nine patients with pathologically proven acute appendicitis (the primary appendicitis [PA] group) from June 2013 to May 2014 and 44 patients with pathologically negative appendectomy results from May 2005 to May 2014 were also included to compare the ultrasonographic features of these conditions. A retrospective review of the ultrasonographic findings was performed by two radiologists. The clinical and laboratory findings were also reviewed. The results were statically analyzed using analysis of variance, the Pearson chi-square test, and the two-tailed Fisher exact test. Results Compared with PA, cases of SA had a smaller diameter (9.8 mm vs. 6.6 mm, P<0.001), and were less likely to show periappendiceal fat inflammation (98% vs. 6%, P<0.001) or an appendicolith (34% vs. 11%, P<0.001). SA showed mural hyperemia on color Doppler ultrasonography as frequently as PA (P=0.887). Conclusion The ultrasonographic features of SA included an increased diameter compared to a healthy appendix and the same level of hyperemia as in PA. However, the diameter was commonly in the equivocal range (mean diameter, 6.6 mm), and periappendiceal fat inflammation was rarely present in SA

Factors influencing sleep quality in the intensive care unit: a descriptive pilot study in Korea

Background As sleep disturbances are common in the intensive care unit (ICU), this study assessed the sleep quality in the ICU and identified barriers to sleep. Methods Patients admitted to the ICUs of a tertiary hospital between June 2022 and December 2022 who were not mechanically ventilated at enrollment were included. The quality of sleep (QoS) at home was assessed on a visual analog scale as part of an eight-item survey, while the QoS in the ICU was evaluated using the Korean version of the Richards-Campbell Sleep Questionnaire (K-RCSQ). Good QoS was defined by a score of ≥50. Results Of the 30 patients in the study, 19 reported a QoS score <50. The Spearman correlation coefficient showed no meaningful relationship between the QoS at home and the overall K-RCSQ QoS score in the ICU (r=0.16, P=0.40). The most common barriers to sleep were physical discomfort (43%), being awoken for procedures (43%), and feeling unwell (37%); environmental factors including noise (30%) and light (13%) were also identified sources of sleep disruption. Physical discomfort (median [interquartile range]: 32 [28.0–38.0] vs. 69 [42.0–80.0], P=0.004), being awoken for procedures (36 [20.0–48.0] vs. 54 [36.0–80.0], P=0.04), and feeling unwell (31 [18.0–42.0] vs. 54 [40.0–76.0], P=0.01) were associated with lower K-RCSQ scores. Conclusions In the ICU, physical discomfort, patient care interactions, and feeling unwell were identified as barriers to sleep