Exploring the Lifelong Changes of Interaction between Cingulo-Opercular Network and Other Cognitive Control Related Functional Networks Based on Multiple Connectivity Indices.

BACKGROUND: The cingulo-opercular network (CON) has been proposed to play a central role in cognitive control. The lifetime change mechanism of its integrity and interaction with other cognitive control-related functional networks (CCRNs) is closely associated with developing cognitive control behaviors but needs further elucidation. METHODS: The resting-state functional magnetic resonance imaging data were recorded from 207 subjects, who were divided into three age groups: age 4-20, 21-59, and 60-85 years old. For each group, multiple indices (cross-correlation, total independence, and Granger causality) within CON and between CON and other cognitive control-related functional networks (dorsal attention network, DAN; central executive network, CEN; default mode network, DMN) were calculated and correlated with age to yield maps that delineated the changing pattern of CON-related interaction. RESULTS: We found three main results. (1) The connectivity indices within the CON and between CON and the other three CCRNs showed significant enhancement from childhood to early adulthood (age 4-20 years), (2) mild attenuation within CON from early adulthood to middle age (age 21-59 years), and (3) significant attenuation within CON and between CON and DMN in the elder group (age 60-85 years). CONCLUSIONS: The results indicated the prominently increased integrity of within-CON and CON-CCRNs communication, mildly weakened within-CON communication, and significantly attenuated within-CON and CON-DMN communication, characterizing distinct changing patterns of CON-interaction at three different stages that covered a life-long span.

The impact of National Centralised Drug Procurement policy on the use of policy-related original and generic drugs in China.

AIMS: To evaluate the effects of the first round of National Centralised Drug Procurement pilot (so-called '4+7' policy) on the use of policy-related original and generic drugs. METHODS: This study used drug purchasing order data from the Centralised Drug Procurement Survey in Shenzhen 2019, covering 24 months from January 2018 to December 2019. '4+7' policy-related drugs were selected as study samples, including 25 drugs in the '4+7' procurement list and 69 alternative drugs that have an alternative relationship with '4+7' List drugs in clinical use. '4+7' List drugs were then divided into bid-winning and bid-non-winning products according to the bidding results. Included drugs were sorted into original and generic drugs. Purchase volume, expenditures, and daily costs were selected as outcome variables, and were measured using Defined Daily Doses (DDDs), Chinese Yuan (CNY), and Defined Daily Drug cost (DDDc). A single-group Interrupted Time Series analysis was adopted to quantify policy effect. RESULTS: After policy intervention, the overall policy-related original drugs significantly decreased by 0.39 CNY (95% CI: -0.62 to -0.17, p < 0.01) in DDDc, 5949.36 thousand DDDs (95% CI: -8276.67 to -3622.05, p < 0.001) in volume, and 31,575.08 thousand CNY (95% CI: -41,812.68 to -21,337.49, p < 0.001) in expenditures. The volume proportion of generic drugs increased from 78.6% to 91.0%, and the expenditure proportion of increased from 30.9% to 49.8%. CONCLUSION: '4+7' policy promoted the substitution use of domestic generics against original branded drugs and played positive effects on drug price cut and medication burden reduction. The proportion of original branded drugs and generics that passed generic consistency evaluation significantly increased after policy intervention, indicating the improvement of the overall quality level of drug use in China.

Exploring communication between the thalamus and cognitive control-related functional networks in the cerebral cortex.

It has been suggested by multiple studies (postmortem studies, invasive animal studies, and diffusion tensor imaging in the human brain) that the thalamus is important for communication among cortical regions. Many functional magnetic resonance imaging (fMRI) studies, including noninvasive and whole-brain studies, have reported thalamic co-activation with several cognitive control-related cortical systems. This forms a complex network that may be important for advanced cognitive control-related processes, such as working memory and attention. Nevertheless, how the thalamus communicates with the cognitive control-related network in the intact human brain is an essential question and needs further investigation. To address this question, we conducted a study using dynamic functional connectivity analysis and effective connectivity analysis based on fMRI data from young, healthy adult participants. The results showed that the middle thalamus exhibited both high in- and out-degree regarding the complex network related to cognitive control during both rest and task conditions. Furthermore, intrinsic communication via the middle thalamic regions showed dynamically co-varying patterns, and the thalamic regions showed high flexibility in dynamic community analysis. These results indicated that the mid-thalamic region is an important station for communication between nodes in cognitive control-related networks.

Disrupted communication of the temporoparietal junction in patients with major depressive disorder.

Patients with major depressive disorder (MDD) suffer impairment in the transmission and integration of internal and external information sources. Accumulating evidence suggests that the temporoparietal junction (TPJ) is important for multiple cognitive and social functions and may act as a key node for the integration of internal and external information. Therefore, the TPJ's aberrant interaction mechanism may underpin MDD psychopathology. To answer this question, we conducted a comprehensive study using resting-state functional magnetic imaging data recorded from 74 patients with MDD and 69 normal controls. First, we examined whether TPJ was the most prominent region with altered functional/effective connectivity with multiple depression-related regions/networks, based on either zero-lag correlations or temporal mutual information (total interdependence and Granger causality) measurements. Accordingly, we derived a network model that depicts alterations of TPJ-connectivity in patients with MDD. Lastly, we performed a cross-approach comparison demonstrating more conducive indicators in delineating the network alteration model. Functional/effective connectivity between the TPJ and major functional networks that govern internal and external-driven information resources was attenuated in patients with MDD. TPJ acts like a key node for information-inflow and integration of multiple information streams. Therefore, dysfunctional connectivity indicators may serve as effective biomarkers for MDD. MDD is associated with the breakdown of the TPJ interaction model and its connections with the default mode network and the task-positive network.

Exploring Brain Dynamic Functional Connectivity Using Improved Principal Components Analysis Based on Template Matching.

Principle components analysis (PCA) can be used to detect repeating co-variant patterns of resting-state dynamic functional connectivity (DFC) of brain networks, accompanied with sliding-window technique. However, the robustness of PCA-based DFC-state extraction (DFC-PCA) is poorly studied. We investigated the reliability of PCA results and improved the robustness of DFC-PCA for a limited sample size. We first established how PCA-based DFC results varied with sample size and PC order in five rounds of bootstrapping with different sample sizes. The consistency across trials increased with increasing sample size and/or decreasing PC order. We then developed a framework based on PC matching and reordering to obtain a more reliable estimation of co-variant DFC patterns. With either the identical template generated by the surrogate dataset itself or with the external template obtained from existing results, the perceptual hash algorithm was used to reorder PCs according to their patterns. After order correction, reliable results were obtained by averaging across trials within each surrogate dataset. This newly developed framework allowed simultaneous measurement and improvement of DFC-PCA. This consistency could also be used as a criterion for PC selection and interpretation to support the reliability and validity of the conclusion.

The functional hierarchy of the task-positive networks indicates a core control system of top-down regulation in visual attention.

The cingulo-opercular network (CON), dorsal attention network (DAN), and ventral attention network (VAN) are prominently activated during attention tasks. The function of these task-positive networks and their interplay mechanisms in attention is one of the central issues in understanding how the human brain manipulates attention to better adapt to the external environment. This study aimed to clarify the CON, DAN, and VAN's functional hierarchy by assessing causal interactions. Functional magnetic resonance imaging (fMRI) data from human participants performing a visual-spatial attention task and correlating Granger causal influences with behavioral performance revealed that CON exerts behavior-enhancing influences upon DAN and VAN, indicating a higher level of CON in top-down attention control. By contrast, the VAN exerts a behavior-degrading influence on CON, indicating external disruption of the CON's control set.

Granger causality reveals a dominant role of memory circuit in chronic opioid dependence.

Resting-state magnetic resonance imaging has uncovered abnormal functional connectivity in heroin-dependent individuals (HDIs). However, it remains unclear how brain regions implicated in addictions are related in baseline state without conditioned cues in heroin dependent individuals during opioid maintenance treatment (HDIs-OMT). Previous connectivity analysis assessed the strength of correlated activity between brain regions but lacked the ability to infer directional neural interactions. In the current study, we employed Granger causality analysis to investigate directional causal influences among the brain circuits in HDIs-OMT and non-opioid users. The results revealed a weaker effective connectivity between the caudate nucleus implicated in mediating the reward circuit and other brain regions and also a weaker connectivity between the anterior cingulate cortex and medial prefrontal cortex implicated in mediating inhibitory control. Conversely, HDIs-OMT exhibited stronger effective connectivity between the hippocampus and amygdala implicated in mediating learning-memory, and the anterior cingulate cortex involved in mediating inhibitory control while the putamen mediated learned habits, suggesting that the hippocampus and amygdala may propel the memory circuit to override the control circuit and drive the learned habit in HDIs-OMT. Alterations in learning-memory and inhibitory control may contribute jointly and form a basis for relapse risk even after a period of heroin abstinence. Sustained neural effect of opioid dependence on methadone maintenance including hyperactivation in the memory circuit and impairment in the control circuit support the role of the memory circuitry in relapse and may help redefine targets for treatment.

Breakdown of Sensorimotor Network Communication in Leukoaraiosis.

BACKGROUND: Leukoaraiosis (LA) patients may suffer from sensorimotor dysfunctions. The relationship between behavioral disturbances and changes in the sensorimotor network (SMN) has not been thoroughly elucidated. OBJECTIVE: This study investigated the hypothesized breakdown of communication of SMN and its behavioral consequences in LA. METHODS: Fluid-attenuated inversion recovery (FLAIR) images, resting-state functional magnetic resonance images (fMRI) and behavioral data were collected from 30 LA patients and 26 healthy individuals (normal controls, NC). The subjects were grouped according to LA severity, as indicated by their FLAIR images. Group independent component analysis was applied to the fMRI data to map the functional connectivity of SMN for NC and LA patients. A whole-brain, voxel-wise analysis was employed to investigate the functional connectivity alteration of SMN in LA. The relationships between LA severity, functional connectivity alteration of the SMN and behavioral clinical symptoms were examined by correlation analysis. RESULTS: The right cingulate motor area (rCMA), left posterior insula and left ventral premotor area showed attenuated functional connectivity in the LA patients. The extent of the attenuation was related to the severity of the disease. Furthermore, the attenuation in the rCMA was associated with worse sensorimotor integration performance. CONCLUSIONS: These results suggest that LA impairs sensorimotor integration by interfering with the communication or coordination of these aforementioned regions related to the SMN.

Top-down regulation of default mode activity in spatial visual attention.

Dorsal anterior cingulate and bilateral anterior insula form a task control network (TCN) whose primary function includes initiating and maintaining task-level cognitive set and exerting top-down regulation of sensorimotor processing. The default mode network (DMN), comprising an anatomically distinct set of cortical areas, mediates introspection and self-referential processes. Resting-state data show that TCN and DMN interact. The functional ramifications of their interaction remain elusive. Recording fMRI data from human subjects performing a visual spatial attention task and correlating Granger causal influences with behavioral performance and blood oxygen level-dependent (BOLD) activity we report three main findings. First, causal influences from TCN to DMN, i.e., TCN → DMN, are positively correlated with behavioral performance. Second, causal influences from DMN to TCN, i.e., DMN → TCN, are negatively correlated with behavioral performance. Third, stronger DMN → TCN are associated with less elevated BOLD activity in TCN, whereas the relationship between TCN → DMN and DMN BOLD activity is unsystematic. These results suggest that, during visual spatial attention, top-down signals from TCN to DMN regulate the activity in DMN to enhance behavioral performance, whereas signals from DMN to TCN, acting possibly as internal noise, interfere with task control, leading to degraded behavioral performance.

The Mediating Role of Psychological Balance on the Effects of Dietary Behavior on Cognitive Impairment in Chinese Elderly.

BACKGROUND: Cognitive impairment, a significant problem in older adults, may be associated with diet. This study aims to examine the association between the dietary diversity score (DDS), dietary pattern (DP), and cognitive impairment in elderly Chinese. This research further explored the role of psychological balance (PB) as a mediator in the relationship between diet and cognitive impairment. METHODS: A total of 14,318 older adults from the Chinese Longitudinal Healthy Longevity Study (CLHLS) in 2018 were included. Latent class analysis (LCA) was used to identify patterns in seven food varieties. Binary logistic regression models were used to determine factors associated with the DDS, DP, and cognitive impairment. The multiple mediation effect model was evaluated using model 6 in the PROCESS version 3.5 program. RESULTS: Among the participants, 4294 (29.99%) developed cognitive impairment. Compared to people in food variety group two or lower, people with a high dietary diversity score (DDS) had lower odds of cognitive impairment. Compared to DP1, DP2 (OR = 1.24, 95%CI = 1.09 to 1.40) was associated with a higher risk of cognitive impairment, and DP4 (OR = 0.79, 95%CI = 0.69 to 0.89) was associated with a lower risk of cognitive impairment. PB mediated the relationship between DDS, DP, and cognitive impairment, with a mediating effect of 27.24% and 41.00%. CONCLUSIONS: A DP that is rich in fruits, vegetables, red meat, fish, eggs, beans, nuts, and milk was related to a lower risk of cognitive impairment. PB has an indirect impact on cognitive impairment. Our findings underscore the importance of promoting a diverse diet, which may contribute to a lower risk of cognitive impairment in older adults. The PB of the elderly should also be taken into consideration.

Causal interactions in attention networks predict behavioral performance.

Lesion and functional brain imaging studies have suggested that there are two anatomically nonoverlapping attention networks. The dorsal frontoparietal network controls goal-oriented top-down deployment of attention; the ventral frontoparietal network mediates stimulus-driven bottom-up attentional reorienting. The interaction between the two networks and its functional significance has been considered in the past but no direct test has been carried out. We addressed this problem by recording fMRI data from human subjects performing a trial-by-trial cued visual spatial attention task in which the subject had to respond to target stimuli in the attended hemifield and ignore all stimuli in the unattended hemifield. Correlating Granger causal influences between regions of interest with behavioral performance, we report two main results. First, stronger Granger causal influences from the dorsal attention network (DAN) to the ventral attention network (VAN), i.e., DAN→VAN, are generally associated with enhanced performance, with right intraparietal sulcus (IPS), left IPS, and right frontal eye field being the main sources of behavior-enhancing influences. Second, stronger Granger causal influences from VAN to DAN, i.e., VAN→DAN, are generally associated with degraded performance, with right temporal-parietal junction being the main sources of behavior-degrading influences. These results support the hypothesis that signals from DAN to VAN suppress and filter out unimportant distracter information, whereas signals from VAN to DAN break the attentional set maintained by the dorsal attention network to enable attentional reorienting.

Spatio-temporal Granger causality: a new framework.

That physiological oscillations of various frequencies are present in fMRI signals is the rule, not the exception. Herein, we propose a novel theoretical framework, spatio-temporal Granger causality, which allows us to more reliably and precisely estimate the Granger causality from experimental datasets possessing time-varying properties caused by physiological oscillations. Within this framework, Granger causality is redefined as a global index measuring the directed information flow between two time series with time-varying properties. Both theoretical analyses and numerical examples demonstrate that Granger causality is a monotonically increasing function of the temporal resolution used in the estimation. This is consistent with the general principle of coarse graining, which causes information loss by smoothing out very fine-scale details in time and space. Our results confirm that the Granger causality at the finer spatio-temporal scales considerably outperforms the traditional approach in terms of an improved consistency between two resting-state scans of the same subject. To optimally estimate the Granger causality, the proposed theoretical framework is implemented through a combination of several approaches, such as dividing the optimal time window and estimating the parameters at the fine temporal and spatial scales. Taken together, our approach provides a novel and robust framework for estimating the Granger causality from fMRI, EEG, and other related data.

Altered functional brain networks in Prader-Willi syndrome.

Prader-Willi syndrome (PWS) is a genetic imprinting disorder characterized mainly by hyperphagia and early childhood obesity. Previous functional neuroimaging studies used visual stimuli to examine abnormal activities in the eating-related neural circuitry of patients with PWS. It was found that patients with PWS exhibited both excessive hunger and hyperphagia consistently, even in situations without any food stimulation. In the present study, we employed resting-state functional MRI techniques to investigate abnormal brain networks related to eating disorders in children with PWS. First, we applied amplitude of low-frequency fluctuation analysis to define the regions of interest that showed significant alterations in resting-state brain activity levels in patients compared with their sibling control group. We then applied a functional connectivity (FC) analysis to these regions of interest in order to characterize interactions among the brain regions. Our results demonstrated that patients with PWS showed decreased FC strength in the medial prefrontal cortex (MPFC)/inferior parietal lobe (IPL), MPFC/precuneus, IPL/precuneus and IPL/hippocampus in the default mode network; decreased FC strength in the pre-/postcentral gyri and dorsolateral prefrontal cortex (DLPFC)/orbitofrontal cortex (OFC) in the motor sensory network and prefrontal cortex network, respectively; and increased FC strength in the anterior cingulate cortex/insula, ventrolateral prefrontal cortex (VLPFC)/OFC and DLPFC/VLPFC in the core network and prefrontal cortex network, respectively. These findings indicate that there are FC alterations among the brain regions implicated in eating as well as rewarding, even during the resting state, which may provide further evidence supporting the use of PWS as a model to study obesity and to provide information on potential neural targets for the medical treatment of overeating.

Pain avoidance and functional connectivity between insula and amygdala identifies suicidal attempters in patients with major depressive disorder using machine learning.

Pain avoidance can effectively classify suicide attempters from non-attempters among patients with major depressive disorder (MDD). However, the neural circuits underlying pain processing in suicide attempters have not been described comprehensively. In Study 1, we recruited MDD patients with a history of suicide attempts (MDD-SA), and those without (MDD-NSA) to examine the patterns of psychological pain using the latent profile analysis. Further, in Study 2, participants including the MDD-SA, MDD-NSA, and healthy controls underwent resting-state functional magnetic resonance imaging. We used machine learning that included features of gray matter volume (GMV), the functional connectivity (FC) brain patterns of the region of interest, and behavioral data to identify suicide attempters. The results identified three latent classes of psychological pain in MDD patients: the low pain class (18.9%), the painful feeling class (37.2%), and the pain avoidance class (43.9%). Furthermore, the proportion of suicide attempters with high pain avoidance was the highest. The accuracy of multimodality classifiers (63%-92%) was significantly higher than that of brain-only classifiers (56%-85%) and behavior-only classifiers (64%-73%). Pain avoidance ranked first in the optimal feature set of the suicide attempt classification model. The crucial brain imaging features were FC between the left amygdala and right insula, right orbitofrontal and left thalamus, left anterior cingulate cortex and left insula, right orbitofrontal, amygdala, and the GMV of right thalamus. Additionally, the optimal feature set, including pain avoidance and crucial brain patterns of psychological pain neural circuits, was provided for the identification of suicide attempters.

Fusion of pain avoidance and the contingent negative variation induced by punitive condition predict suicide ideation in a college population.

This study examined behavioral and ERP features involved in pain processing as predictors of suicide ideation. Twenty-seven depressed undergraduates with high suicide ideation (HSI), 23 depressed undergraduates with low suicide ideation (LSI), and 32 healthy controls (HCs) completed the clinical Scales. The amplitudes of LPP, P2, P3, CNV, FRN, power in the beta, theta, and delta bands in the SAID task were multimodal EEG features. A machine learning algorithm known as support vector machine was used to select optimal feature sets for predicting pain avoidance, depression, and suicide ideation. The accuracy of suicide ideation classification was significantly higher for multimodal features (78.16%) which pain avoidance ranked the first and the CNV ranked the fifth than a single ERP feature model (66.62%). Pain avoidance emerged as the most optimal feature of suicide ideation classification than depression. And the CNV elicited by punitive cues may be a biomarker in suicide ideation. Pain avoidance and its related EEG components may improve the efficacy of suicide ideation classification as compared to depression.

The impact of key monitoring policy on the usage of policy-related drugs in Hubei Province, China.

Introduction: This study evaluated quantitatively the impact of the first batch of the catalog of Key Monitoring and Rational Use Drugs (KMRUD) in Hubei Province on policy-related drug use and expenditures. Methods: This study is aimed to provide a basis for the successful implementation of subsequent catalogs of KMRUD, which may promote the standardization of clinical application of related drugs and effectively reduce drug expenses of the patients. Data on the procurement records of policy-related drugs from January 2018 to June 2021 were obtained from the Drug Centralized Procurement Platform of the Public Resources Trading Center in Hubei Province. Interrupted time-series (ITS) analysis was used in this study. Results: After the implementation of the first batch of the catalog of KMRUD, the consumption of policy-related drugs decreased by 83.29% in 2020. The spending on policy-related drugs decreased by 83.93% in 2020. The introduction of the first batch of the catalog of KMRUD was associated with a significant decrease in the spending on policy-related drugs in the level (p = 0.001). Before the implementation of the KMRUD catalog policy, the Defined Daily Doses (DDDs) (ß1 = -32.26 p < 0.001) and spending (ß1 = -3662.19 p < 0.001) on policy-related drugs showed a downward trend. In the aggregated ITS analysis, the Defined Daily Dose cost (DDDc) of policy-related drugs decreased significantly in the trend (p < 0.001). After the implementation of the KMRUD catalog policy, the monthly procurement volume of 10 policy-related drugs have a significant downward trend (p < 0.05), and 4 policy-related drugs have a significant upward trend (p < 0.05). Conclusion: After the policy intervention, the total DDDc on policy-related drugs indicated sustained reductions. The KMRUD policy overall achieved the goal of limiting policy-related drug use and controlling cost increases. And it is recommended that the health department quantify the usage indicator of adjuvant drugs, uniform standards, and apply prescription reviews and dynamic supervision, and other measures to strengthen supervision.

Dual-functional Network Regulation Underlies the Central Executive System in Working Memory.

The frontoparietal network (FPN) and cingulo-opercular network (CON) may exert top-down regulation corresponding to the central executive system (CES) in working memory (WM); however, contributions and regulatory mechanisms remain unclear. We examined network interaction mechanisms underpinning the CES by depicting CON- and FPN-mediated whole-brain information flow in WM. We used datasets from participants performing verbal and spatial working memory tasks, divided into encoding, maintenance, and probe stages. We used general linear models to obtain task-activated CON and FPN nodes to define regions of interest (ROI); an online meta-analysis defined alternative ROIs for validation. We calculated whole-brain functional connectivity (FC) maps seeded by CON and FPN nodes at each stage using beta sequence analysis. We used Granger causality analysis to obtain the connectivity maps and assess task-level information flow patterns. For verbal working memory, the CON functionally connected positively and negatively to task-dependent and task-independent networks, respectively, at all stages. FPN FC patterns were similar only in the encoding and maintenance stages. The CON elicited stronger task-level outputs. Main effects were: stable CON â†’ FPN, CON â†’ DMN, CON â†’ visual areas, FPN â†’ visual areas, and phonological areas â†’ FPN. The CON and FPN both up-regulated task-dependent and down-regulated task-independent networks during encoding and probing. Task-level output was slightly stronger for the CON. CON â†’ FPN, CON â†’ DMN, visual areas â†’ CON, and visual areas â†’ FPN showed consistent effects. The CON and FPN might together underlie the CES's neural basis and achieve top-down regulation through information interaction with other large-scale functional networks, and the CON may be a higher-level regulatory core in WM.

Unbalanced amygdala communication in major depressive disorder.

BACKGROUND: Previous studies suggested an association between functional alteration of the amygdala and typical major depressive disorder (MDD) symptoms. Examining whether and how the interaction between the amygdala and regions/functional networks is altered in patients with MDD is important for understanding its neural basis. METHODS: Resting-state functional magnetic resonance imaging data were recorded from 67 patients with MDD and 74 age- and sex-matched healthy controls (HCs). A framework for large-scale network analysis based on seed mappings of amygdala sub-regions, using a multi-connectivity-indicator strategy (cross-correlation, total interdependencies (TI), Granger causality (GC), and machine learning), was employed. Multiple indicators were compared between the two groups. The altered indicators were ranked in a supporting-vector machine-based procedure and associated with the Hamilton Rating Scale for Depression scores. RESULTS: The amygdala connectivity with the default mode network and ventral attention network regions was enhanced and that with the somatomotor network, dorsal frontoparietal network, and putamen regions in patients with MDD was reduced. The machine learning analysis highlighted altered indicators that were most conducive to the classification between the two groups. LIMITATIONS: Most patients with MDD received different pharmacological treatments. It is difficult to illustrate the medication state's effect on the alteration model because of its complex situation. CONCLUSION: The results indicate an unbalanced interaction model between the amygdala and functional networks and regions essential for various emotional and cognitive functions. The model can help explain potential aberrancy in the neural mechanisms that underlie the functional impairments observed across various domains in patients with MDD.

A quasi-experimental study of the volume-based procurement (VBP) effect on antiviral medications of hepatitis B virus in China.

Background: The Pilot Plan of National Centralized Volume-Based Procurement (NCVBP) was adopted to cope with the rapid increase in drug expenditures. This research aimed to quantitatively evaluate the impact of the NCVBP on antiviral medications for the hepatitis B virus. Methods: Data on nucleoside analogs (NAs) medications of hepatitis B virus monthly procurement records in the pilot cities from January 2018 to December 2019 were extracted from the China Drug Supply Information Platform (CDSIP). The impacts of the NCVBP on purchased volumes, expenditures, and pre-defined daily dose costs were evaluated by interrupted time-series (ITS) analysis using Stata 16.0. We constructed two segments with one interruptive point (March 2019). Results: Compared to the same period between pre-and post-intervention, the purchased volume of NAs medications were increased by 92.85%, and selected medications were increased by 119.09%. Analysis of changes in the level of NAs medication followed a decrease in purchased expenditure (coefficient: 5364.88, p < 0.001), meanwhile, the purchased volume was increased with statistical significance (coefficient:605.49, p < 0.001). The Defined Daily Dose cost (DDDc) of NAs medication followed a decrease (coefficient: 8.90, p < 0.001). The NCVBP reform was followed by an increase of 618.41 ten thousand Defined Daily Dose (DDD) (p < 0.001) in purchased volume and a reduction of 5273.84 ten thousand Chinese Yuan (CNY) (p < 0.001) in the purchased expenditure of selected medications in the level. The DDDc of selected medications decreased in the level (coefficient: 9.87, p < 0.001), while the DDDc of alternative medications increased in the slope (coefficient:0.07, p = 0.030). The purchased volume and expenditure of bid-winning products increased by 964.08 ten thousand DDD and 637.36 ten thousand CNY in the level (p < 0.001). An increase of 633.46 ten thousand DDD (p < 0.001) in purchased volume and a reduction of 4285.32 ten thousand CNY (p < 0.001) in the purchased expenditure of generic drugs in the level was observed. Conclusion: The NCVBP reduced the DDDc of NAs medication, improved the utilization of the selected medications, and promoted the usage of generic products.

Exploring resting-state functional connectivity with total interdependence.

Resting-state fMRI has become a powerful tool for studying network mechanisms of normal brain functioning and its impairments by neurological and psychiatric disorders. Analytically, independent component analysis and seed-based cross correlation are the main methods for assessing the connectivity of resting-state fMRI time series. A feature common to both methods is that they exploit the covariation structures of contemporaneously (zero-lag) measured data but ignore temporal relations that extend beyond the zero-lag. To examine whether data covariations across different lags can contribute to our understanding of functional brain networks, a measure that can uncover the overall temporal relationship between two resting-state BOLD signals is needed. In this paper we propose such a measure referred as total interdependence (TI). Comparing TI with zero-lag cross correlation (CC) we report three results. First, when combined with a random permutation procedure, TI can reveal the amount of temporal relationship between two resting-state BOLD time series that is not captured by CC. Second, comparing resting-state data with task-state data recorded in the same scanning session, we demonstrate that the resting-state functional networks constructed with TI match more precisely the networks activated by the task. Third, TI is shown to be more statistically sensitive than CC and provides better feature vectors for network clustering analysis.