Thalamocortical contributions to hierarchical cognitive control.

Cognitive flexibility relies on hierarchically structured task representations that organize task contexts, relevant environmental features, and subordinate decisions. Despite ongoing interest in the human thalamus, its role in cognitive control has been understudied. This study explored thalamic representation and thalamocortical interactions that contribute to hierarchical cognitive control in humans. We found that several thalamic nuclei, including the anterior, mediodorsal, ventrolateral, and pulvinar nuclei, exhibited stronger evoked responses when subjects switch between task contexts. Decoding analysis revealed that thalamic activity preferentially encodes task contexts within the hierarchical task representations. To determine how thalamocortical interactions contribute to task representations, we developed a thalamocortical functional interaction model to predict task-related cortical representation. This data-driven model outperformed comparison models, particularly in predicting activity patterns in cortical regions that encode context representations. Collectively, our findings highlight the significant contribution of thalamic activity and thalamocortical interactions for contextually guided hierarchical cognitive control.

Reward-based option competition in human dorsal stream and transition from stochastic exploration to exploitation in continuous space.

Primates exploring and exploiting a continuous sensorimotor space rely on dynamic maps in the dorsal stream. Two complementary perspectives exist on how these maps encode rewards. Reinforcement learning models integrate rewards incrementally over time, efficiently resolving the exploration/exploitation dilemma. Working memory buffer models explain rapid plasticity of parietal maps but lack a plausible exploration/exploitation policy. The reinforcement learning model presented here unifies both accounts, enabling rapid, information-compressing map updates and efficient transition from exploration to exploitation. As predicted by our model, activity in human frontoparietal dorsal stream regions, but not in MT+, tracks the number of competing options, as preferred options are selectively maintained on the map, while spatiotemporally distant alternatives are compressed out. When valuable new options are uncovered, posterior ß1/α oscillations desynchronize within 0.4 to 0.7 s, consistent with option encoding by competing ß1-stabilized subpopulations. Together, outcomes matching locally cached reward representations rapidly update parietal maps, biasing choices toward often-sampled, rewarded options.

Adverse Clinical Effects Associated With Non-catecholamine Pharmacologic Agents for Treatment of Vasoplegic Syndrome in Adult Cardiac Surgery.

Vasoplegic syndrome is a relatively common complication that can happen during and after major adult cardiac surgery. It is associated with a higher rate of complications, including postoperative renal failure, longer duration of mechanical ventilation, and intensive care unit stay, as well as increased mortality. The underlying pathophysiology of vasoplegic syndrome is that of profound vascular hyporesponsiveness, and involves a complex interplay among inflammatory cytokines, cellular surface receptors, and nitric oxide (NO) production. The pharmacotherapy approaches for the treatment of vasoplegia include medications that increase vascular smooth muscle contraction via increasing cytosolic calcium in myocytes, reduce the vascular effects of NO and inflammation, and increase the biosynthesis of and vascular response to norepinephrine. Clinical trials have demonstrated the clinical efficacy of non-catecholamine pharmacologic agents in the treatment of vasoplegic syndrome. With an increase in their use today, it is important for clinicians to understand the adverse clinical outcomes and patient risk profiles associated with these agents, which will allow better-tailored medical therapy.

Deep Learning Model Compression With Rank Reduction in Tensor Decomposition.

Large neural network models are hard to deploy on lightweight edge devices demanding large network bandwidth. In this article, we propose a novel deep learning (DL) model compression method. Specifically, we present a dual-model training strategy with an iterative and adaptive rank reduction (RR) in tensor decomposition. Our method regularizes the DL models while preserving model accuracy. With adaptive RR, the hyperparameter search space is significantly reduced. We provide a theoretical analysis of the convergence and complexity of the proposed method. Testing our method for the LeNet, VGG, ResNet, EfficientNet, and RevCol over MNIST, CIFAR-10/100, and ImageNet datasets, our method outperforms the baseline compression methods in both model compression and accuracy preservation. The experimental results validate our theoretical findings. For the VGG-16 on CIFAR-10 dataset, our compressed model has shown a 0.88% accuracy gain with 10.41 times storage reduction and 6.29 times speedup. For the ResNet-50 on ImageNet dataset, our compressed model results in 2.36 times storage reduction and 2.17 times speedup. In federated learning (FL) applications, our scheme reduces 13.96 times the communication overhead. In summary, our compressed DL method can improve the image understanding and pattern recognition processes significantly.

The impact of the human thalamus on brain-wide information processing.

The thalamus is a small, bilateral structure in the diencephalon that integrates signals from many areas of the CNS. This critical anatomical position allows the thalamus to influence whole-brain activity and adaptive behaviour. However, traditional research paradigms have struggled to attribute specific functions to the thalamus, and it has remained understudied in the human neuroimaging literature. Recent advances in analytical techniques and increased accessibility to large, high-quality data sets have brought forth a series of studies and findings that (re-)establish the thalamus as a core region of interest in human cognitive neuroscience, a field that otherwise remains cortico-centric. In this Perspective, we argue that using whole-brain neuroimaging approaches to investigate the thalamus and its interaction with the rest of the brain is key for understanding systems-level control of information processing. To this end, we highlight the role of the thalamus in shaping a range of functional signatures, including evoked activity, interregional connectivity, network topology and neuronal variability, both at rest and during the performance of cognitive tasks.

Investigating the Current Status of SARS-CoV-2 Antibodies in Hospital Staff.

The coronavirus disease 2019 (COVID-19) pandemic caused by SARS-CoV-2 had reported over 676 million cases by March 2023. The main aim of this study is to investigate whether the levels of anti-S and anti-N antibodies could precisely indicate the degree of protection against SARS-CoV-2 and affect the probability or time of contracting COVID-19. In this study, a serosurveillance study was conducted in healthcare workers (HCWs) at a regional hospital in Taiwan to evaluate their antibody levels based on infection and vaccination status. Of 245 HCWs enrolled, all have been vaccinated prior to infection. Of these, 85 participants were infected by SARS-CoV-2, while 160 participants were not infected at the time of blood sample collection. The level of anti-SARS-CoV-2 S antibody was significantly higher in the infected HCWs than in the non-infected participants (p < 0.001). It is worth noting that the mean duration between the administration of the last dose of the vaccine and the occurrence of SARS-CoV-2 infection was 5.61 ± 2.95 months. Our follow-up survey revealed that the non-infected group had significantly higher levels of antibodies compared to the infected group (all p < 0.001). In conclusion, this study suggests that the level of antibodies could serve as a reflection of the protective efficacy against SARS-CoV-2 infection. It has the implication for vaccine decision-making policies in the future.

Influence of goals on modular brain network organization during working memory.

Introduction: Top-down control underlies our ability to attend relevant stimuli while ignoring irrelevant, distracting stimuli and is a critical process for prioritizing information in working memory (WM). Prior work has demonstrated that top-down biasing signals modulate sensory-selective cortical areas during WM, and that the large-scale organization of the brain reconfigures due to WM demands alone; however, it is not yet understood how brain networks reconfigure between the processing of relevant versus irrelevant information in the service of WM. Methods: Here, we investigated the effects of task goals on brain network organization while participants performed a WM task that required participants to detect repetitions (e.g., 0-back or 1-back) and had varying levels of visual interference (e.g., distracting, irrelevant stimuli). We quantified changes in network modularity-a measure of brain sub-network segregation-that occurred depending on overall WM task difficulty as well as trial-level task goals for each stimulus during the task conditions (e.g., relevant or irrelevant). Results: First, we replicated prior work and found that whole-brain modularity was lower during the more demanding WM task conditions compared to a baseline condition. Further, during the WM conditions with varying task goals, brain modularity was selectively lower during goal-directed processing of task-relevant stimuli to be remembered for WM performance compared to processing of distracting, irrelevant stimuli. Follow-up analyses indicated that this effect of task goals was most pronounced in default mode and visual sub-networks. Finally, we examined the behavioral relevance of these changes in modularity and found that individuals with lower modularity for relevant trials had faster WM task performance. Discussion: These results suggest that brain networks can dynamically reconfigure to adopt a more integrated organization with greater communication between sub-networks that supports the goal-directed processing of relevant information and guides WM.

Thalamocortical contributions to working memory processes during the n-back task.

Working memory allows individuals to temporally maintain and manipulate information that is no longer accessible from the sensorium. Whereas prior studies have detailed frontoparietal contributions to working memory processes, less emphasis has been placed on subcortical regions, in particular the human thalamus. The thalamus has a complex anatomy that consists of several distinct nuclei, many of which have dense anatomical connectivity with frontoparietal regions, and thus might play an important yet underspecified role for working memory. The goal of our study is to characterize the detailed functional neuroanatomy of the human thalamus and thalamocortical interactions during the n-back task. To that end, we analyzed an n-back fMRI dataset consisting of 395 subjects from the Human Connectome Project (HCP). We found that thalamic nuclei in the anterior, medial, ventral lateral, and posterior medial thalamus showed stronger evoked responses in response to higher working memory load. Activity in most thalamic nuclei were only modulated by working memory load, but not by categorical membership of the memorized stimuli, suggesting that thalamic function supports domain-general processing for working memory. To determine whether thalamocortical interactions contribute to cortical activity for working memory, we employed an activity flow mapping analysis to test whether thalamocortical interactions can predict cortical task activity patterns. In support, this data-driven thalamocortical interaction model explained a significant amount of variance in the observed cortical activity patterns modulated by working memory load. Our results suggest that the anterior, medial, and posterior medial thalamus, and their associated thalamocortical interactions, contribute to the modulations of distributed cortical activity during working memory.

Thalamocortical contributions to cognitive task activity.

Thalamocortical interaction is a ubiquitous functional motif in the mammalian brain. Previously (Hwang et al., 2021), we reported that lesions to network hubs in the human thalamus are associated with multi-domain behavioral impairments in language, memory, and executive functions. Here, we show how task-evoked thalamic activity is organized to support these broad cognitive abilities. We analyzed functional magnetic resonance imaging (MRI) data from human subjects that performed 127 tasks encompassing a broad range of cognitive representations. We first investigated the spatial organization of task-evoked activity and found a basis set of activity patterns evoked to support processing needs of each task. Specifically, the anterior, medial, and posterior-medial thalamus exhibit hub-like activity profiles that are suggestive of broad functional participation. These thalamic task hubs overlapped with network hubs interlinking cortical systems. To further determine the cognitive relevance of thalamic activity and thalamocortical functional connectivity, we built a data-driven thalamocortical model to test whether thalamic activity can be used to predict cortical task activity. The thalamocortical model predicted task-specific cortical activity patterns, and outperformed comparison models built on cortical, hippocampal, and striatal regions. Simulated lesions to low-dimensional, multi-task thalamic hub regions impaired task activity prediction. This simulation result was further supported by profiles of neuropsychological impairments in human patients with focal thalamic lesions. In summary, our results suggest a general organizational principle of how the human thalamocortical system supports cognitive task activity.

Graph-generative neural network for EEG-based epileptic seizure detection via discovery of dynamic brain functional connectivity.

Dynamic complexity in brain functional connectivity has hindered the effective use of signal processing or machine learning methods to diagnose neurological disorders such as epilepsy. This paper proposed a new graph-generative neural network (GGN) model for the dynamic discovery of brain functional connectivity via deep analysis of scalp electroencephalogram (EEG) signals recorded from various regions of a patient's scalp. Brain functional connectivity graphs are generated for the extraction of spatial-temporal resolution of various onset epilepsy seizure patterns. Our supervised GGN model was substantiated by seizure detection and classification experiments. We train the GGN model using a clinically proven dataset of over 3047 epileptic seizure cases. The GGN model achieved a 91% accuracy in classifying seven types of epileptic seizure attacks, which outperformed the 65%, 74%, and 82% accuracy in using the convolutional neural network (CNN), graph neural networks (GNN), and transformer models, respectively. We present the GGN model architecture and operational steps to assist neuroscientists or brain specialists in using dynamic functional connectivity information to detect neurological disorders. Furthermore, we suggest to merge our spatial-temporal graph generator design in upgrading the conventional CNN and GNN models with dynamic convolutional kernels for accuracy enhancement.

Central precuneus lesions are associated with impaired executive function.

The functional roles of the precuneus are unclear. Focal precuneus lesions are rare, making it difficult to identify robust brain-behavior relationships. Distinct functional subdivisions of the precuneus have been proposed based on unique connectivity profiles. This includes an association of the anterior division with bodily awareness, the central region with complex cognition, and the posterior division with visual processing. Our goal was to test the hypothesis that the central precuneus is preferentially involved (compared to the other sectors of the precuneus) in executive function, as estimated from performance on the trail-making test (TMT). 35 patients with focal brain lesions involving the precuneus were included from the University of Iowa and Montpellier University. Multivariate lesion symptom mapping of TMT performance was performed to evaluate whether lesion location was associated with impaired task performance. Lesion symptom mapping revealed a statistically significant association of central precuneus lesions with impaired TMT performance (r = 0.43, p < 0.01). Further, a functional network derived from this precuneus region showed connectivity to other cortical areas implicated in executive function, including the dorsolateral prefrontal cortex and inferior parietal lobe. This analysis provides support for the role of the central precuneus in executive function, consistent with the unique connectivity pattern of the central precuneus with a broader network implicated in cognitive control and executive function.

Dynamics of Hierarchical Task Representations.

Task representations are critical for cognitive control and adaptive behavior. The hierarchical organization of task representations allows humans to maintain goals, integrate information across varying contexts, and select potential responses. In this study we characterized the structure and interactive dynamics of task representations that facilitate cognitive control. Human participants (both males and females) performed a hierarchical task that required them to select a response rule while considering the contingencies from different contextual inputs. By applying time- and frequency-resolved representational similarity analysis to human electroencephalography data, we characterized properties of task representations that are otherwise difficult to observe. We found that participants formed multiple representations of task-relevant contexts and features from the presented stimuli, beyond simple stimulus-response mappings. These disparate representations were hierarchically structured, with higher-order contextual representations dominantly influencing subordinate representations of task features and response rules. Furthermore, this cascade of top-down interactions facilitated faster responses. Our results describe key properties of task representations that support hierarchical cognitive control.SIGNIFICANCE STATEMENTHumans can adjust their actions in response to contingencies imposed from the environment. Though it has long been hypothesized that this ability depends on mental representations of tasks, the neural dynamics of task representations have been difficult to characterize. Our study utilized electroencephalography data from human participants to demonstrate the neural organization and interactive dynamics of task representations. Our results revealed a top-down, hierarchically organized representational structure that encoded multiple contexts and features from the environment. To support cognitive control, higher-level contextual representations influenced subordinate representations of task-relevant features and potential responses, facilitating response selection in a context-dependent manner. Our results provide direct evidence on organizational properties of task representations, which are cornerstones of cognitive control theories.

Adjuvant Therapy System of COVID-19 Patient: Integrating Warning, Therapy, Post-Therapy Psychological Intervention.

The 2019 novel coronavirus(COVID-19) spreads rapidly, and the large-scale infection leads to the lack of medical resources. For the purpose of providing more reasonable medical service to COVID-19 patients, we designed an novel adjuvant therapy system integrating warning, therapy, and post-therapy psychological intervention. The system combines data analysis, communication networks and artificial intelligence(AI) to design a guidance framework for the treatment of COVID-19 patients. Specifically, in this system, we first can use blood characteristic data to help make a definite diagnosis and classify the patients. Then, the classification results, together with the blood characteristics and underlying diseases disease characteristics of the patient, can be used to assist the doctor in treat treating the patient according to AI algorithms. Moreover, after the patient is discharged from the hospital, the system can monitor the psychological and physiological state at the data collection layer. And in the data feedback layer, this system can analyze the data and report the abnormalities of the patient to the doctor through communication network. Experiments show the effectiveness of our proposed system.

Focal neural perturbations reshape low-dimensional trajectories of brain activity supporting cognitive performance.

The emergence of distributed patterns of neural activity supporting brain functions and behavior can be understood by study of the brain's low-dimensional topology. Functional neuroimaging demonstrates that brain activity linked to adaptive behavior is constrained to low-dimensional manifolds. In human participants, we tested whether these low-dimensional constraints preserve working memory performance following local neuronal perturbations. We combined multi-session functional magnetic resonance imaging, non-invasive transcranial magnetic stimulation (TMS), and methods translated from the fields of complex systems and computational biology to assess the functional link between changes in local neural activity and the reshaping of task-related low dimensional trajectories of brain activity. We show that specific reconfigurations of low-dimensional trajectories of brain activity sustain effective working memory performance following TMS manipulation of local activity on, but not off, the space traversed by these trajectories. We highlight an association between the multi-scale changes in brain activity underpinning cognitive function.

Neuropsychological evidence of multi-domain network hubs in the human thalamus.

Hubs in the human brain support behaviors that arise from brain network interactions. Previous studies have identified hub regions in the human thalamus that are connected with multiple functional networks. However, the behavioral significance of thalamic hubs has yet to be established. Our framework predicts that thalamic subregions with strong hub properties are broadly involved in functions across multiple cognitive domains. To test this prediction, we studied human patients with focal thalamic lesions in conjunction with network analyses of the human thalamocortical functional connectome. In support of our prediction, lesions to thalamic subregions with stronger hub properties were associated with widespread deficits in executive, language, and memory functions, whereas lesions to thalamic subregions with weaker hub properties were associated with more limited deficits. These results highlight how a large-scale network model can broaden our understanding of thalamic function for human cognition.

Liquid Chicken Oil Could Be a Healthy Dietary Oil.

Liquid chicken oil is similar to the human lipid ratio, and is similar to the ideal fatty acids ratio suggested by Hayes, but its benefits remain unclear (Hwang, K.N.; Tung, H.P.; Shaw, H.M. J. Oleo. Sci. 69, 199-206 (2020)). Using soybean oil as a control, liquid chicken oil, coconut oil, lard oil, and olive oil, were tested on SD rats with the rodent diet 5001 plus 1% of high cholesterol addition and moderate 10 % of test oils. Positive results showed that a 10% liquid chicken oil diet reduced LDL and triglycerides, atherogenic index while increasing superoxide dismutase more than the soybean oil control (0.05 ≦ p < 0.10). Moreover, increment of hepatic endogenous glutathione peroxidase was found to be significantly different from the soybean oil control (p < 0.05). In this study, liquid chicken oil had more benefits than vegetable soybean dietary oil, with little evidence of hyperlipidemia. Comparison of the test oils with categories of fatty acids to the idea ratio SFA : MUFA : PUFA = 1 : 1.5 : 1, scored by its average weight implied a parallel trend of lipidemia and hepatic antioxidant activity to its score. It is difficult to use the test of rat to reflect human physiology, it remain 19% different of the fatty acids ratio from human ratio, however, this study reveal that the healthiness of a dietary oil seems relate well to its compatibility to the idea ratio or the host oil ratio, in this case, it is the human ratio.

Intelligent Task Caching in Edge Cloud via Bandit Learning.

Task caching, based on edge cloud, aims to meet the latency requirements of computation-intensive and data-intensive tasks (such as augmented reality). However, current task caching strategies are generally based on the unrealistic assumption of knowing the pattern of user task requests and ignoring the fact that a task request pattern is more user specific (e.g., the mobility and personalized task demand). Moreover, it disregards the impact of task size and computing amount on the caching strategy. To investigate these issues, in this paper, we first formalize the task caching problem as a non-linear integer programming problem to minimize task latency. We then design a novel intelligent task caching algorithm based on a multiarmed bandit algorithm, called M-adaptive upper confidence bound (M-AUCB). The proposed caching strategy cannot only learn the task patterns of mobile device requests online, but can also dynamically adjust the caching strategy to incorporate the size and computing amount of each task. Moreover, we prove that the M-AUCB algorithm achieves a sublinear regret bound. The results show that, compared with other task caching schemes, the M-AUCB algorithm reduces the average task latency by at least 14.8%.

The development of theta and alpha neural oscillations from ages 3 to 24 years.

Intrinsic, unconstrained neural activity exhibits rich spatial, temporal, and spectral organization that undergoes continuous refinement from childhood through adolescence. The goal of this study was to investigate the development of theta (4-8 Hertz) and alpha (8-12 Hertz) oscillations from early childhood to adulthood (years 3-24), as these oscillations play a fundamental role in cognitive function. We analyzed eyes-open, resting-state EEG data from 96 participants to estimate genuine oscillations separately from the aperiodic (1/f) signal. We examined age-related differences in the aperiodic signal (slope and offset), as well as the peak frequency and power of the dominant posterior oscillation. For the aperiodic signal, we found that both the aperiodic slope and offset decreased with age. For the dominant oscillation, we found that peak frequency, but not power, increased with age. Critically, early childhood (ages 3-7) was characterized by a dominance of theta oscillations in posterior electrodes, whereas peak frequency of the dominant oscillation in the alpha range increased between ages 7 and 24. Furthermore, theta oscillations displayed a topographical transition from dominance in posterior electrodes in early childhood to anterior electrodes in adulthood. Our results provide a quantitative description of the development of theta and alpha oscillations.