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 β1/α oscillations desynchronize within 0.4 to 0.7 s, consistent with option encoding by competing β1-stabilized subpopulations. Together, outcomes matching locally cached reward representations rapidly update parietal maps, biasing choices toward often-sampled, rewarded options.

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.

Evaluation of costs associated with fertility treatment leading to a live birth after one fresh transfer: A global perspective

Considerable costs are associated with infertility treatment, but little evidence is available on the main drivers of treatment costs. This cost analysis investigated key costs for treatment with assisted reproductive technology (ART) and the proportion of costs attributed to the acquisition of recombinant human follicle-stimulating hormone (r-hFSH) alfa originator for one fresh embryo transfer (ET) leading to a live birth in Spain, Norway, the UK, Germany, Denmark, South Korea, Australia, and New Zealand. The total costs for one ART cycle with a fresh ET leading to a live birth varied between countries (€4108-€12,314). Costs for pregnancy and live birth were the major contributors in European countries, and the costs of oocyte retrieval, monitoring during ovarian stimulation, pregnancy, and live birth were the top contributors in the Asia-Pacific countries, included in this analysis. Acquisition costs for r-hFSH alfa originator contributed to only 5%-17% of the total costs of one ART cycle with one fresh ET leading to a live birth.

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.

Pathogens identified in the internal tissues and placentas of stillbirths: results from the prospective, observational PURPOSe study

OBJECTIVE

To examine internal organ tissues and placentas of stillbirths for various pathogens.

DESIGN

Prospective, observational study.

SETTINGS

Three study hospitals in India and a large maternity hospital in Pakistan.

POPULATION

Stillborn infants delivered in a study hospital.

METHODS

A prospective observational study.

MAIN OUTCOME MEASURES

Organisms identified by pathogen polymerase chain reaction (PCR) in internal organs and placental tissues of stillbirths.

RESULTS

Of 2437 stillbirth internal tissues, 8.3% (95% CI 7.2-9.4) were positive. Organisms were most commonly detected in brain (12.3%), cerebrospinal fluid (CSF) (9.5%) and whole blood (8.4%). Ureaplasma urealyticum/parvum was the organism most frequently detected in at least one internal organ (6.4% of stillbirths and 2% of all tissues). Escherichia coli/Shigella was the next most common (4.1% one or more internal organ tissue sample and 1.3% of tissue samples), followed by Staphylococcus aureus in at least one internal organ tissue (1.9% and 0.9% of all tissues). None of the other organisms was found in more than 1.4% of the tissue samples in stillbirths or more than 0.6% of the internal tissues examined. In the placenta tissue, membrane or cord blood combined, 42.8% (95% CI 40.2-45.3) had at least one organism identified, with U. urealyticum/parvum representing the most commonly identified (27.8%).

CONCLUSIONS

In about 8% of stillbirths, there was evidence of a pathogen in an internal organ. Ureaplasma urealyticum/parvum was the most common organism found in the placenta and in the internal tissues, especially in the fetal brain.

COVID-19 pandemic underscores role of green space in urban carbon dynamics

For Detroit Michigan the arrival of COVID-19 led to intensive measures to prevent further spread of the virus resulting in consequent changes in traffic and energy use. We take advantage of these different emission scenarios to explore CO₂ dynamics in a postindustrial city with a declining population and increasing green space. We present atmospheric CO₂ concentration and net urban ecosystem exchange of CO₂ (NUE) from a typical eddy covariance system and canopy greenness from a field camera on the Wayne State University campus in midtown Detroit. We categorized our study period (January 18, 2020-July 31, 2020) into three subperiods associated with the state-wide shelter-in-place order. Our results support that the city was a net carbon source throughout the period, particularly during the shelter-in-place period, although reduced traffic lowered CO₂ concentrations and NUE. However, during the post-order period when traffic was highest, atmospheric CO₂ concentrations and NUE were lowest, suggesting that the greening of urban vegetation may have greater carbon mitigation potential than lowering anthropogenic carbon emissions through traffic reductions.

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.

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 STATEMENT Humans can adjust their actions in response to contingencies imposed by the environment. Although 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 used 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 of organizational properties of task representations, which are cornerstones of cognitive control theories.

Genomic testing for copy number and single nucleotide variants in spermatogenic failure

PURPOSE

To identify clinically significant genomic copy number (CNV) and single nucleotide variants (SNV) in males with unexplained spermatogenic failure (SPGF).

MATERIALS AND METHODS

Peripheral blood DNA from 97/102 study participants diagnosed with oligozoospermia, severe oligozoospermia, or non-obstructive azoospermia (NOA) was analyzed for CNVs via array comparative genomic hybridization (aCGH) and SNVs using whole-exome sequencing (WES).

RESULTS

Of the 2544 CNVs identified in individuals with SPGF, > 90% were small, ranging from 0.6 to 75 kb. Thirty, clinically relevant genomic aberrations, were detected in 28 patients (~ 29%). These included likely diagnostic CNVs in 3/41 NOA patients (~ 7%): 1 hemizygous, intragenic TEX11 deletion, 1 hemizygous DDX53 full gene deletion, and 1 homozygous, intragenic STK11 deletion. High-level mosaicism for X chromosome disomy (~ 10% 46,XY and ~ 90% 47,XXY) was also identified in 3 of 41 NOA patients who previously tested normal with conventional karyotyping. The remaining 24 CNVs detected were heterozygous, autosomal recessive carrier variants. Follow-up WES analysis confirmed 8 of 27 (30%) CNVs (X chromosome disomy excluded). WES analysis additionally identified 13 significant SNVs and/or indels in 9 patients (~ 9%) including X-linked AR, KAL1, and NR0B1 variants.

CONCLUSION

Using a combined genome-wide aCGH/WES approach, we identified pathogenic and likely pathogenic SNVs and CNVs in 15 patients (15%) with unexplained SPGF. This value equals the detection rate of conventional testing for aneuploidies and is considerably higher than the prevalence of Y chromosome microdeletions. Our results underscore the importance of comprehensive genomic analysis in emerging diagnostic testing of complex conditions like male infertility.

Regional and annual patterns in respiratory virus co-infection etiologies and antibiotic prescriptions for pediatric mycoplasma pneumoniae pneumonia

OBJECTIVE

Mycoplasma pneumoniae (M. pneumoniae) pneumonia is the second-most common cause of community-acquired pneumonia (CAP). This study aimed at investigating into the prevalence of macrolide-resistant M. pneumoniae (MRMP) with respiratory virus co-infection and the antibiotic prescriptions in children with CAP in four provinces in Korea, and to assess the variations in the findings across regions and throughout the year.

PATIENTS AND METHODS

This prospective study was conducted in 29 hospitals in Korea between July 2018 and June 2020. Among the enrolled 1,063 children with CAP, all 451 patients with M. pneumoniae underwent PCR assays of M. pneumoniae and respiratory viruses, and the presence of point mutations of residues 2063 and 2064 was evaluated.

RESULTS

Gwangju-Honam (88.6%) showed the highest prevalence of MRMP pneumonia, while Daejeon-Chungcheong (71.3%) showed the lowest, although the differences in prevalence were not significant (p=0.074). Co-infection of M. pneumoniae pneumonia and respiratory virus was observed in 206 patients (45.4%), and rhinovirus co-infection (101 children; 22.2%) was the most frequent. The prevalence of MRMP pneumonia with respiratory virus co-infection and the antibiotic prescriptions differed significantly among the four provinces (p < 0.05). The monthly rate of MRMP pneumonia cases among all cases of M. pneumoniae pneumonia and tetracycline or quinolone prescriptions did not differ significantly among the four regions (trend p > 0.05) during the study period.

CONCLUSIONS

The prevalence of M. pneumoniae pneumonia with virus co-infection and antibiotic prescriptions could differ according to region, although the MRMP pneumonia rate showed no difference within Korea.

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.

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.

Deep Reinforcement Learning for Edge Service Placement in Softwarized Industrial Cyber-Physical System

Future industrial cyber-physical system (CPS) devices are expected to request a large amount of delay-sensitive services that need to be processed at the edge of a network. Due to limited resources, service placement at the edge of the cloud has attracted significant attention. Although there are many methods of design schemes, the service placement problem in industrial CPS has not been well studied. Furthermore, none of existing schemes can optimize service placement, workload scheduling, and resource allocation under uncertain service demands. To address these issues, we first formulate a joint optimization problem of service placement, workload scheduling, and resource allocation in order to minimize service response delay. We then propose an improved deep Q-network (DQN)-based service placement algorithm. The proposed algorithm can achieve an optimal resource allocation by means of convex optimization where the service placement and workload scheduling decisions are assisted by means of DQN technology. The experimental results verify that the proposed algorithm, compared with existing algorithms, can reduce the average service response time by 8-10%.

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.

Preventable stillbirths in India and Pakistan: a prospective, observational study

OBJECTIVE

Stillbirths occur 10-20 times more frequently in low-income settings compared with high-income settings. We created a methodology to define the proportion of stillbirths that are potentially preventable in low-income settings and applied it to stillbirths in sites in India and Pakistan.

DESIGN

Prospective observational study.

SETTING

Three maternity hospitals in Davangere, India and a large public hospital in Karachi, Pakistan.

POPULATION

All cases of stillbirth at ≥20 weeks of gestation occurring from July 2018 to February 2020 were screened for participation; 872 stillbirths were included in this analysis.

METHODS

We prospectively defined the conditions and gestational ages that defined the stillbirth cases considered potentially preventable. Informed consent was sought from the parent(s) once the stillbirth was identified, either before or soon after delivery. All information available, including obstetric and medical history, clinical course, fetal heart sounds on admission, the presence of maceration as well as examination of the stillbirth after delivery, histology, and polymerase chain reaction for infectious pathogens of the placenta and various fetal tissues, was used to assess whether a stillbirth was potentially preventable.

MAIN OUTCOME MEASURES

Whether a stillbirth was determined to be potentially preventable and the criteria for assignment to those categories.

RESULTS

Of 984 enrolled, 872 stillbirths at ≥20 weeks of gestation met the inclusion criteria and were included; of these, 55.5% were deemed to be potentially preventable. Of the 649 stillbirths at ≥28 weeks of gestation and ≥1000 g birthweight, 73.5% were considered potentially preventable. The most common conditions associated with a potentially preventable stillbirth at ≥28 weeks of gestation and ≥1000 g birthweight were small for gestational age (SGA) (52.8%), maternal hypertension (50.2%), antepartum haemorrhage (31.4%) and death that occurred after hospital admission (15.7%).

CONCLUSIONS

Most stillbirths in these sites were deemed preventable and were often associated with maternal hypertension, antepartum haemorrhage, SGA and intrapartum demise.

TWEETABLE ABSTRACT

Most stillbirths are preventable by better care for women with hypertension, growth restriction and antepartum haemorrhage.

The Human Intraparietal Sulcus Modulates Task-Evoked Functional Connectivity

Past studies have demonstrated that flexible interactions between brain regions support a wide range of goal-directed behaviors. However, the neural mechanisms that underlie adaptive communication between brain regions are not well understood. In this study, we combined theta-burst transcranial magnetic stimulation (TMS) and functional magnetic resonance imaging to investigate the sources of top-down biasing signals that influence task-evoked functional connectivity. Subjects viewed sequences of images of faces and buildings and were required to detect repetitions (2-back vs. 1-back) of the attended stimuli category (faces or buildings). We found that functional connectivity between ventral temporal cortex and the primary visual cortex (VC) increased during processing of task-relevant stimuli, especially during higher memory loads. Furthermore, the strength of functional connectivity was greater for correct trials. Increases in task-evoked functional connectivity strength were correlated with increases in activity in multiple frontal, parietal, and subcortical (caudate and thalamus) regions. Finally, we found that TMS to superior intraparietal sulcus (IPS), but not to primary somatosensory cortex, decreased task-specific modulation in connectivity patterns between the primary VC and the parahippocampal place area. These findings demonstrate that the human IPS is a source of top-down biasing signals that modulate task-evoked functional connectivity among task-relevant cortical regions.

DeepNetQoE: Self-Adaptive QoE Optimization Framework of Deep Networks

Future advances in deep learning and its impact on the development of artificial intelligence (AI) in all fields depend heavily on data size and computational power. Sacrificing massive computing resources in exchange for better precision rates of the network model is recognized by many researchers. This leads to huge computing consumption, while satisfactory results are not always expected when computing resources are limited. Therefore, it is necessary to find a balance between resources and model performance to achieve satisfactory results. This article proposes a self-adaptive quality of experience (QoE) framework, DeepNetQoE, to guide the training of deep networks. A self-adaptive QoE model is set up that relates the model's accuracy with the computing resources required for training which will allow the experience value of the model to improve. To maximize the experience value, a resource allocation model and solutions need to be established. Finally, we carry out experiments based on four network models to analyze the experience values with respect to the crowd counting example. Experimental results show that the proposed DeepNetQoE is capable of adaptively obtaining a high experience value according to user requirements and therefore guiding users to determine the computational resources allocated to the network models.

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%.

Between-module functional connectivity of the salient ventral attention network and dorsal attention network is associated with motor inhibition

The ability to inhibit motor response is crucial for daily activities. However, whether brain networks connecting spatially distinct brain regions can explain individual differences in motor inhibition is not known. Therefore, we took a graph-theoretic perspective to examine the relationship between the properties of topological organization in functional brain networks and motor inhibition. We analyzed data from 141 healthy adults aged 20 to 78, who underwent resting-state functional magnetic resonance imaging and performed a stop-signal task along with neuropsychological assessments outside the scanner. The graph-theoretic properties of 17 functional brain networks were estimated, including within-network connectivity and between-network connectivity. We employed multiple linear regression to examine how these graph-theoretical properties were associated with motor inhibition. The results showed that between-network connectivity of the salient ventral attention network and dorsal attention network explained the highest and second highest variance of individual differences in motor inhibition. In addition, we also found those two networks span over brain regions in the frontal-cingulate-parietal network, suggesting that these network interactions are also important to motor inhibition.

Network Localization of Executive Function Deficits in Patients with Focal Thalamic Lesions

The human thalamus has been suggested to be involved in executive function, based on animal studies and correlational evidence from functional neuroimaging in humans. Human lesion studies, examining behavioral deficits associated with focal brain injuries, can directly test the necessity of the human thalamus for executive function. The goal of our study was to determine the specific lesion location within the thalamus as well as the potential disruption of specific thalamocortical functional networks, related to executive dysfunction. We assessed executive function in 15 patients with focal thalamic lesions and 34 comparison patients with lesions that spared the thalamus. We found that patients with mediodorsal thalamic lesions exhibited more severe impairment in executive function when compared to both patients with thalamic lesions that spared the mediodorsal nucleus and to comparison patients with lesions outside the thalamus. Furthermore, we employed a lesion network mapping approach to map cortical regions that show strong functional connectivity with the lesioned thalamic subregions in the normative functional connectome. We found that thalamic lesion sites associated with more severe deficits in executive function showed stronger functional connectivity with ACC, dorsomedial PFC, and frontoparietal network, compared to thalamic lesions not associated with executive dysfunction. These are brain regions and functional networks whose dysfunction could contribute to impaired executive functioning. In aggregate, our findings provide new evidence that delineates a thalamocortical network for executive function.

Rapid eye movement sleep patterns of brain activation and deactivation occur within unique functional networks

Rapid eye movement (REM) sleep is a paradoxical state where the individual appears asleep while the electroencephalogram pattern resembles that of wakefulness. Regional differences in brain metabolism have been observed during REM sleep compared to wakefulness, but it is not known whether the spatial distribution of metabolic differences corresponds to known functional networks in the brain. Here, we use a combination of techniques to evaluate the networks associated with sites of REM sleep activation and deactivation from previously published positron emission tomography studies. We use seed‐based functional connectivity from healthy adults acquired during quiet rest to show that REM‐activation regions are functionally connected in a network that includes retrosplenial cingulate cortex, parahippocampal gyrus, and extrastriate visual cortices, corresponding to components of the default mode network and visual networks. Regions deactivated during REM sleep localize to right‐lateralized fronto‐parietal and salience networks. A negatively correlated relationship was observed between REM‐activation and deactivation networks. Together, these findings show that regional activation and deactivation patterns of REM sleep tend to occur in distinct functional connectivity networks that are present during wakefulness, providing insights regarding the differential contributions of brain regions to the distinct subjective experiences that occur during REM sleep (dreaming) relative to wakefulness.

Investigation of a Novel Decision Support Metric for Head and Neck Adaptive Radiation Therapy Using a Real-Time In Vivo Portal Dosimetry System

In adaptive radiation therapy of head and neck cancer, any significant anatomical changes observed are used to adapt the treatment plan to maintain target coverage without elevating the risk of xerostomia. However, the additional resources required for adaptive radiation therapy pose a challenge for broad-based implementation. It is hypothesized that a change in transit fluence is associated with volumetric change in the vicinity of the target and therefore can be used as a decision support metric for adaptive radiation therapy. This was evaluated by comparing the fluence with volumetric changes in 12 patients. Transit fluence was measured by an in vivo portal dosimetry system. Weekly cone beam computed tomography was used to determine volume change in the rectangular region of interest from condyloid process to C6. The integrated transit fluence through the region of interest on the day of the cone beam computed tomography scan was calculated with the first treatment as the baseline. The correlation between fluence change and volume change was determined. A logistic regression model was also used to associate the 5% region of interest volume reduction replanning trigger point and the fluence change. The model was assessed by a chi-square test. The area under the receiver-operating characteristic curve was also determined. A total of 46 pairs of measurements were obtained. The correlation between fluence and volumetric changes was found to be -0.776 (P value <.001). The negative correlation is attributed to the increase in the photon fluence transport resulting from the volume reduction. The chi-square of the logistic regression was found to be 17.4 (P value <.001). The area under the receiver-operating characteristic curve was found to be 0.88. Results indicate the change in transit fluence, which can be measured without consuming clinical resources or requiring additional time in the treatment room, can be used as a decision support metric for adaptive therapy.

The Low-Dimensional Neural Architecture of Cognitive Complexity Is Related to Activity in Medial Thalamic Nuclei

Cognitive activity emerges from large-scale neuronal dynamics that are constrained to a low-dimensional manifold. How this low-dimensional manifold scales with cognitive complexity, and which brain regions regulate this process, are not well understood. We addressed this issue by analyzing sub-second high-field fMRI data acquired during performance of a task that systematically varied the complexity of cognitive reasoning. We show that task performance reconfigures the low-dimensional manifold and that deviations from these patterns relate to performance errors. We further demonstrate that individual differences in thalamic activity relate to reconfigurations of the low-dimensional architecture during task engagement.

P14.104 The outcomes of conservatively observed asymptomatic nonfunctioning pituitary adenomas with optic nerve compression

BACKGROUND

We investigated the natural history of asymptomatic nonfunctioning pituitary adenomas (NFPAs) abutting on optic nerve.

MATERIAL AND METHODS

Eighty-three patients with asymptomatic NFPAs with documented optic nerve compression on magnetic resonance imaging (MRI) at the time of detection between 2000 and 2016 were included in this study. Patients were evaluated with a hormone test, visual acuity test, visual field test and MRI at the time of diagnosis, and then, yearly, without any treatment (including surgery and radiation).

RESULTS

The mean age was 57.7±13.6 (range, 15 - 81) years. The mean follow-up duration was 66.6±39.0 (range, 12 - 184) months. Tumor volume growth ≥ 20% was observed in 64 (77.1%) patients. Ten (12.2%) patients experienced any kind of hormonal dysfunction, and gonadotropin deficiency was the most common type of hormonal deficiency [n=9 (10.8%)]. Visual deterioration quantified by Visual Impairment Scale was seen 27 (32.5%) patients. There was no statistically significant factor for tumor growth or visual aggravation. Fourteen (16.9%) patients eventually underwent tumor resection. From multivariate analysis, the significant predictors for eventual surgical intervention were cavernous sinus invasion (OR=20.95; 95% CI, 2.754–159.3; p=0.003) and last follow-up visual field defect score (OR=1.170; 95% CI, 1.049–1.305; p=0.005). All patients who underwent surgery did not experience any neurologic or endocrinological deficits postoperatively.

CONCLUSION

The clinical outcomes of conservatively observed NFPAs with optic nerve compression can be acceptable. The decision for surgical intervention should be made by balancing the risk and benefits.

Causal Evidence for the Role of Neuronal Oscillations in Top-Down and Bottom-Up Attention

Beta and gamma frequency neuronal oscillations have been implicated in top-down and bottom-up attention. In this study, we used rhythmic TMS to modulate ongoing beta and gamma frequency neuronal oscillations in frontal and parietal cortex while human participants performed a visual search task that manipulates bottom-up and top-down attention (single feature and conjunction search). Both task conditions will engage bottom-up attention processes, although the conjunction search condition will require more top-down attention. Gamma frequency TMS to superior precentral sulcus (sPCS) slowed saccadic RTs during both task conditions and induced a response bias to the contralateral visual field. In contrary, beta frequency TMS to sPCS and intraparietal sulcus decreased search accuracy only during the conjunction search condition that engaged more top-down attention. Furthermore, beta frequency TMS increased trial errors specifically when the target was in the ipsilateral visual field for the conjunction search condition. These results indicate that beta frequency TMS to sPCS and intraparietal sulcus disrupted top-down attention, whereas gamma frequency TMS to sPCS disrupted bottom-up, stimulus-driven attention processes. These findings provide causal evidence suggesting that beta and gamma oscillations have distinct functional roles for cognition.

Frontoparietal Activity Interacts With Task-Evoked Changes in Functional Connectivity

Flexible interactions between brain regions enable neural systems to adaptively transfer and process information. However, the neural substrates that regulate adaptive communications between brain regions are understudied. In this human fMRI study, we investigated this issue by tracking time-varying, task-evoked changes in functional connectivity between localized occipitotemporal regions while participants performed different tasks on the same visually presented stimuli. We found that functional connectivity between ventral temporal and the primary visual regions selectively increased during the processing of task-relevant information. Further, additional task demands selectively strengthen these targeted connectivity patterns. To identify candidate regions that contribute to this increase in inter-regional coupling, we regressed the task-specific time-varying connectivity strength between primary visual and occipitotemporal regions against voxel-wise activity patterns elsewhere in the brain. This allowed us to identify a set of frontal and parietal regions whose activity increased as a function of task-evoked functional connectivity. These results suggest that frontoparietal regions may provide top-down biasing signals to influence task-specific interactions between brain regions.

High-quality human and rat spermatozoal RNA isolation for functional genomic studies

Sperm RNA is a sensitive monitoring endpoint for male reproductive toxicants, and a potential biomarker to assess male infertility and sperm quality. However, isolation of sperm RNA is a challenging procedure due to the heterogeneous population of cells present in the ejaculate, the low yield of RNA per spermatozoon, and the absence of 18S and 28S ribosomal RNA subunits. The unique biology of spermatozoa has created some uncertainty in the field about RNA isolation methods, indicating the need for rigorous quality control checks to ensure reproducibility of data generated from sperm RNA. Therefore, we developed a reliable and effective protocol for RNA isolation from rat and human spermatozoa that delivers highly purified and intact RNA, verified using RNA-specific electrophoretic chips and molecular biology approaches such as RT-PCR and Western blot analysis. The sperm RNA isolation technique was optimized using rat spermatozoa and then adapted to human spermatozoa. Three steps in the sperm isolation procedure, epididymal fluid collection, sperm purification, and spermatozoon RNA extraction, were evaluated and assessed. The sperm RNA extraction methodology consists of collection of rat epididymal fluid with repeated needle punctures of the epididymis, somatic cell elimination using detergent-based somatic cell lysis buffer (SCLB) and the use of RNA isolation Kit. Rat sperm heads are more resistant to disruption than human spermatozoa, necessitating the addition of mechanical lysis with microbeads and heat in the rat protocol, whereas the human sperm protocol only required lysis buffer. In conclusion, this methodology results in reliable and consistent isolation of high-quality sperm RNA. Using this technique will aid in translation of data collected from animal models, and reproducibility of clinical assessment of male factor fertility using RNA molecular biomarkers.

Frontal preparatory neural oscillations associated with cognitive control: A developmental study comparing young adults and adolescents

Functional magnetic resonance imaging (fMRI) studies suggest that age-related changes in the frontal cortex may underlie developmental improvements in cognitive control. In the present study we used magnetoencephalography (MEG) to identify frontal oscillatory neurodynamics that support age-related improvements in cognitive control during adolescence. We characterized the differences in neural oscillations in adolescents and adults during the preparation to suppress a prepotent saccade (antisaccade trials-AS) compared to preparing to generate a more automatic saccade (prosaccade trials-PS). We found that for adults, AS were associated with increased beta-band (16-38Hz) power in the dorsal lateral prefrontal cortex (DLPFC), enhanced alpha- to low beta-band (10-18Hz) power in the frontal eye field (FEF) that predicted performance, and increased cross-frequency alpha-beta (10-26Hz) amplitude coupling between the DLPFC and the FEF. Developmental comparisons between adults and adolescents revealed similar engagement of DLPFC beta-band power but weaker FEF alpha-band power, and lower cross-frequency coupling between the DLPFC and the FEF in adolescents. These results suggest that lateral prefrontal neural activity associated with cognitive control is adult-like by adolescence; the development of cognitive control from adolescence to adulthood is instead associated with increases in frontal connectivity and strengthening of inhibition signaling for suppressing task-incompatible processes.

The Impact of Maternal Body Mass Index and Gestational Age on the Detection of Uterine Contractions by Tocodynamometry: A Retrospective Study

OBJECTIVE

To examine the impact of maternal body mass index (BMI) and gestational age (GA) on uterine contraction detection by tocodynamometry.

METHODS

Gravidas with preterm labor (PTL) complaints who were evaluated by tocodynamometry, discharged from Labor and Delivery triage, and subsequently readmitted for preterm delivery were studied. Forty-six patients in whom contractions were detected (group 1) were compared to 49 women in whom contractions were not detected (group 2) with respect to BMI and GA at both evaluation and delivery. Multivariable logistic regression was used to adjust for confounders.

RESULTS

Group 2 had a higher mean BMI (31.7 vs 26.1, P < .001), were more likely to be obese (57.1% vs 19.6%, P < .001), and were more likely to have been evaluated in the mid-trimester (36.7% vs 17.4%, P = .04) compared to group 1. Independent risk factors for the inability of the tocodynamometer to detect contractions were obesity (odds ratio [OR] 0.18, 95% confidence interval [CI] 0.07-0.46) and evaluation in the mid-trimester (OR 0.33, 95% CI 0.13-0.84).

CONCLUSION

Our study provides evidence that the effectiveness of tocodynamometry diminishes with increasing maternal BMI. Efficacy of tocodynamometry is also decreased at earlier GA, most pronounced below 25 weeks. To evaluate women with PTL symptoms in the mid-trimester or symptomatic obese women at any GA, a modality other than tocodynamometry could be valuable to more accurately assess uterine activity.

Intra- and inter-individual differences in human sperm DNA methylation

There is growing evidence that sperm DNA methylation is important in maintaining proper sperm health and function. Previous studies have associated sperm DNA methylation levels with sperm quality and function, however, little is known regarding the intra- and inter-individual variability in sperm methylation levels. This study characterizes this variation. Sperm epigenetic differences between successive semen samples from 12 patients were examined to identify the intra- and inter-individual differences globally across the genome, and in specifically defined genomic regions using the Illumina Infinium HumanMethylation450 BeadChips. Methylation analysis identified a bimodal distribution in the methylation levels that were non-uniformly distributed across the different genomic regions. The methylation levels were highly correlated in both the intra- and inter-individual comparisons. The intra-individual methylation levels were more highly correlated than the inter-individual comparison both globally and across the defined genomic regions, demonstrating that sperm DNA methylation levels are relatively stable between semen sample collections.

The Contribution of Network Organization and Integration to the Development of Cognitive Control

Cognitive control, which continues to mature throughout adolescence, is supported by the ability for well-defined organized brain networks to flexibly integrate information. However, the development of intrinsic brain network organization and its relationship to observed improvements in cognitive control are not well understood. In the present study, we used resting state functional magnetic resonance imaging (RS-fMRI), graph theory, the antisaccade task, and rigorous head motion control to characterize and relate developmental changes in network organization, connectivity strength, and integration to inhibitory control development. Subjects were 192 10–26-y-olds who were imaged during 5 min of rest. In contrast to initial studies, our results indicate that network organization is stable throughout adolescence. However, cross-network integration, predominantly of the cingulo-opercular/salience network, increased with age. Importantly, this increased integration of the cingulo-opercular/salience network significantly moderated the robust effect of age on the latency to initiate a correct inhibitory control response. These results provide compelling evidence that the transition to adult-level inhibitory control is dependent upon the refinement and strengthening of integration between specialized networks. Our findings support a novel, two-stage model of neural development, in which networks stabilize prior to adolescence and subsequently increase their integration to support the cross-domain incorporation of information processing critical for mature cognitive control.

This study reveals that although the organization of functional brain networks remains stable during adolescence, between-network integration continues to increase, underlying maturation in cognitive control.

Adolescence is a unique period of brain development, with major changes occurring across the brain at many different levels of brain functioning. At the macroscopic level, the brain is composed of individual regions that collaborate in networks to perform diverse cognitive functions. Some networks of brain regions perform lower-level sensorimotor processing, while other networks orchestrate more complex functions, such as cognitive control. The affiliation of each region to a network is referred to as network organization. Brain regions not only can communicate with other regions belonging to their own network but also with regions in other networks. Brain regions that communicate with regions belonging to other networks display a high level of integration since they link their network with another network. We found that during adolescence, network organization does not change. However, integration continues to increase, underscoring the notion that brain function becomes more distributed and collaborative during this unique period of development. Furthermore, this increased network integration underlies improvements in cognitive control. Thus, we provide a network-based account for improvements in cognitive functioning during adolescence.