Working Memory Guides Action Valuation in Model-based Decision-making Strategy.

Humans use both model-free (or habitual) and model-based (or goal-directed) strategies in sequential decision-making. Working memory (WM) is essential for the model-based strategy; however, its exact role in these processes remains elusive. This study investigates the influence of WM processes on decision-making and the underlying cognitive computing mechanisms. Specifically, we used experimental data from two-stage decision tasks and found that delay and load, two WM-specific variables, impact goal-revisiting behaviors. Then, we proposed possible computational mechanisms by which WM participates in information processing and integrated them into the model-based system. The proposed Hybrid-WM model reproduced the observed experimental effects and fit human behavior better than the classic hybrid reinforcement learning model. These results were verified with independent data sets. Furthermore, differences in model parameters explain the age-related difference in sequential decision-making. Overall, this study suggests that WM guides action valuation in model-based strategies, highlighting the contribution of higher cognitive functions to sequential decision-making.

Test-retest reliability of the attention network test from the perspective of intrinsic network organization.

The attention network test (ANT), developed based on the triple-network taxonomy by Posner and colleagues, has been widely used to examine the efficacy of alerting, orienting and executive control in clinical and developmental neuroscience studies. Recent research suggests the imperfect reliability of the behavioural ANT and its variants. However, the classical ANT fMRI task's test-retest reliability has received little attention. Moreover, it remains ambiguous whether the attention-related intrinsic network components, especially the dorsal attention, ventral attention and frontoparietal network, manifest acceptable reliability. The present study approaches these issues by utilizing an openly available ANT fMRI dataset for participants with Parkinson's disease and healthy elderly. The reproducibility of group-level activations across sessions and participant groups and the test-retest reliability at the individual level were examined at the voxel, region and network levels. The intrinsic network was defined using the Yeo-Schaefer atlas. Our results reveal three critical facets: (1) the overlapping of the group-level contrast map between sessions and between participant groups was unsatisfactory; (2) the reliability of alerting, orienting and executive, defined as a contrast between conditions, was worse than estimates of specific conditions. (3) Dorsal attention, ventral attention, visual and somatomotor networks showed acceptable reliability for the congruent and incongruent conditions. Our results suggest that specific condition estimates might be used instead of the contrast map for individual or group-difference studies.

Split-half reliability estimates of an online card sorting task in a community sample of young and elderly adults.

Executive function is vital for normal social, cognitive, and motor functions. Executive function decline due to aging increases the risk of disability and falls in older adults, which has become an urgent public health issue. Fast and convenient neuropsychological tools are thus needed to identify high-risk groups as early as possible to conduct a timely intervention. Card sorting tasks, such as Wisconsin Card Sorting Task (WCST) and its variants, are popular tools for measuring executive function. This study investigated the reliability of an open-source, self-administered, online, short-version card sorting task with a sample of young (n = 107, 65 females, age: M = 30.1 years, SD = 5.5 years) and elderly Chinese (n = 113, 53 females, age: M = 64.0 years, SD = 6.7 years). We developed an automated scoring and visualization procedure following the recent recommendations on scoring perseverative responses to make the results comparable to the standardized WCST. Reliability estimates of commonly used measures were calculated using the split-half method. All task indices' reliabilities were reasonably good in both old and young groups except for "failure-to-maintain-set." Elderly Chinese adults showed compromised task performance on all measures compared with the young Chinese adults at the group level. The R script of automated scoring and estimation of reliability is publicly available.

In situ Implanting 3D Carbon Network Reinforced Zinc Composite by Powder Metallurgy for Highly Reversible Zn-based Battery Anodes.

Aqueous Zn-based batteries have emerged as compelling candidates for grid-scale energy storage, owing to their intrinsic safety, remarkable theoretical energy density and cost-effectiveness. Nonetheless, the dendrite formation, side reactions, and corrosion on anode have overshadowed their practical applications. Herein, we present an in situ grown carbon network reinforcing Zn matrix anode prepared by powder metallurgy. This carbon network provides an uninterrupted internal electron transport pathway and optimize the surface electric field distribution, thereby enabling highly reversible Zn deposition. Consequently, symmetrical cells demonstrate impressive stability, running for over 880 h with a low voltage hysteresis (≈32 mV). Furthermore, this Zn matrix composite anode exhibits enhanced performance in both the aqueous Zn-ion and the Zn-air batteries. Notably, Zn//MnO2 cells display superior rate capabilities, while Zn-air batteries deliver high power density and impressive Zn utilization rate (84.9 %). This work provides a new idea of powder metallurgy method for modified Zn anodes, showcasing potential for large-scale production.

Explainable Alzheimer's Disease Detection Using Linguistic Features from Automatic Speech Recognition.

INTRODUCTION: Alzheimer's disease (AD) is the most prevalent type of dementia and can cause abnormal cognitive function and progressive loss of essential life skills. Early screening is thus necessary for the prevention and intervention of AD. Speech dysfunction is an early onset symptom of AD patients. Recent studies have demonstrated the promise of automated acoustic assessment using acoustic or linguistic features extracted from speech. However, most previous studies have relied on manual transcription of text to extract linguistic features, which weakens the efficiency of automated assessment. The present study thus investigates the effectiveness of automatic speech recognition (ASR) in building an end-to-end automated speech analysis model for AD detection. METHODS: We implemented three publicly available ASR engines and compared the classification performance using the ADReSS-IS2020 dataset. Besides, the SHapley Additive exPlanations algorithm was then used to identify critical features that contributed most to model performance. RESULTS: Three automatic transcription tools obtained mean word error rate texts of 32%, 43%, and 40%, respectively. These automated texts achieved similar or even better results than manual texts in model performance for detecting dementia, achieving classification accuracies of 89.58%, 83.33%, and 81.25%, respectively. CONCLUSION: Our best model, using ensemble learning, is comparable to the state-of-the-art manual transcription-based methods, suggesting the possibility of an end-to-end medical assistance system for AD detection with ASR engines. Moreover, the critical linguistic features might provide insight into further studies on the mechanism of AD.

Dose-dependent early white matter alterations in patients with brain metastases after radiotherapy.

PURPOSE: Previous diffusion tensor imaging (DTI) studies have mainly focused on dose-dependent white matter (WM) alterations 1 month to 1 year after radiation therapy (RT) with a tract-average method. However, WM alterations immediately after RT are subtle, resulting in early WM alterations that cannot be detected by tract-average methods. Therefore, we performed a study with an along-tract method in patients with brain metastases to explore the early dose-response pattern of WM alterations after RT. METHODS: Sixteen patients with brain metastases underwent DTI before and 1-3 days after brain RT. DTI metrics, such as fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD), were calculated. Along-tract statistics were then used to resample WM fibre streamlines and generate a WM skeleton fibre tract. DTI metric alterations (post_RT-pre_RT DTI metrics) and the planned doses (max or mean doses) were mapped to 18 WM tracts. A linear fixed model was performed to analyse the main effect of dose on DTI metric alterations. RESULTS: AD alterations in the left hemispheric uncinated fasciculus (UNC_L) were associated with max doses, in which decreased AD alterations were associated with higher doses. CONCLUSION: Our findings may provide pathological insight into early dose-dependent WM alterations and may contribute to the development of max dose-constrained RT techniques to protect brain microstructure in the UNC_L.

Early-Onset Micromorphological Changes of Neuronal Fiber Bundles During Radiotherapy.

BACKGROUND: Patients receiving cranial radiation face the risk of delayed brain dysfunction. However, an early medical imaging marker is not available until irreversible morphological changes emerge. PURPOSE: To explore the micromorphological white matter changes during the radiotherapy session by utilizing an along-tract analysis framework. STUDY TYPE: Prospective. POPULATION: Eighteen nasopharyngeal carcinoma (two female) patients receiving cranial radiation. FIELD STRENGTH/SEQUENCE: 3.0 T; Diffusion tensor imaging (DTI) and T1- and T2-weighted images (T1W, T2W); computed tomography (CT). ASSESSMENT: Patients received three DTI imaging scans during the radiotherapy (RT), namely the baseline scan (1-2 days before RT began), the middle scan (the middle of the RT session), and the end scan (1-2 days after RT ended). Twelve fibers were segmented after whole-brain tractography. Then, the fractional anisotropy (FA) values and the cumulative radiation dose received for each fiber streamline were resampled and projected into their center fiber. STATISTICAL TESTS: The contrast among the three scans (P1: middle scan-baseline scan; P2: end scan-middle scan; P3: end scan-baseline scan) were compared using the linear mixed model for each of the 12 center fibers. Then, a dose-responsiveness relationship was performed using Pearson correlation. P < 0.05 was considered statistically significant. RESULTS: Six of the 12 center fibers showed significant changes of FA values during the RT but with heterogeneous patterns. The significant changes along a specific center fiber were associated with their cumulative dose received (Genu: P1 r = -0.6182, P2 r = -0.5907; Splenium: P1 r = 0.4055, P = 0.1063, P2 r = 0.6742; right uncinate fasciculus: P1 r = -0.3865, P2 r = -0.4912, P = 0.0533; right corticospinal tract: P1 r = 0.4273, P = 0.1122, P2 r = -0.6885). DATA CONCLUSION: The along-tract analysis might provide sensitive measures on the early-onset micromorphological changes. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 3.

The radiomic-clinical model using the SHAP method for assessing the treatment response of whole-brain radiotherapy: a multicentric study.

OBJECTIVE: To develop and validate a pretreatment magnetic resonance imaging (MRI)-based radiomic-clinical model to assess the treatment response of whole-brain radiotherapy (WBRT) by using SHapley Additive exPlanations (SHAP), which is derived from game theory, and can explain the output of different machine learning models. METHODS: We retrospectively enrolled 228 patients with brain metastases from two medical centers (184 in the training cohort and 44 in the validation cohort). Treatment responses of patients were categorized as a non-responding group vs. a responding group according to the Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) criteria. For each tumor, 960 features were extracted from the MRI sequence. The least absolute shrinkage and selection operator (LASSO) was used for feature selection. A support vector machine (SVM) model incorporating clinical factors and radiomic features wase used to construct the radiomic-clinical model. SHAP method explained the SVM model by prioritizing the importance of features, in terms of assessment contribution. RESULTS: Three radiomic features and three clinical factors were identified to build the model. Radiomic-clinical model yielded AUCs of 0.928 (95%CI 0.901-0.949) and 0.851 (95%CI 0.816-0.886) for assessing the treatment response in the training cohort and validation cohort, respectively. SHAP summary plot illustrated the feature's value affected the feature's impact attributed to model, and SHAP force plot showed the integration of features' impact attributed to individual response. CONCLUSION: The radiomic-clinical model with the SHAP method can be useful for assessing the treatment response of WBRT and may assist clinicians in directing personalized WBRT strategies in an understandable manner. KEY POINTS: • Radiomic-clinical model can be useful for assessing the treatment response of WBRT. • SHAP could explain and visualize radiomic-clinical machine learning model in a clinician-friendly way.

Redox reaction between solid-phase humins and Fe(III) compounds: Toward a further understanding of the redox properties of humin and its possible environmental effects.

Redox reactions between humic substances and Fe(III) compounds play a critical role in the biogeochemical cycle of pollutants. Most humic substances in soils and sediments are in a solid form (i.e. humin (HM)). In order to assess the contribution of electron shuttling via HM within the electron transfer network in natural environments and to predict environmental fate of pollutants associated with iron oxides, it is necessary to understand the electron transfer processes from HM to the environmentally relevant Fe(III) minerals, and to examine the redox reversibility of HM. The results of this study demonstrated that non-reduced HMs could only donate electrons to dissolved ferric citrate and poorly crystalline ferrihydrite, but reduced HMs could also reduce hematite and magnetite that had high crystallinity. The degree of reduction depended on the difference in redox potential and the crystallinity of the Fe(III) compounds. The electron-accepting capacities of different HMs correlated well with their organic carbon content, and quinones and Fe-bound organic component were important electron-accepting groups in HMs. Furthermore, the redox reversibility experiments demonstrated that HMs could maintain stable electron transfer capacities over three reduction-oxidation cycles, indicating that the HM could be an environmentally sustainable electron shuttle. Our results suggest that (1) HM may play an unrecognized and important role in biogeochemical cycles of pollutants in Fe(III) mineral-rich environments; (2) electron shuttling through HM to ferric citrate and ferrihydrite can occur even in the presence of O2; and (3) HM would be a promising material for environmental remediation.

Heterostructure Engineering of Core-Shelled Sb@Sb2 O3 Encapsulated in 3D N-Doped Carbon Hollow-Spheres for Superior Sodium/Potassium Storage.

In this work, the core-shelled Sb@Sb2 O3 heterostructure encapsulated in 3D N-doped carbon hollow-spheres is fabricated by spray-drying combined with heat treatment. The novel core-shelled heterostructures of Sb@Sb2 O3 possess a mass of heterointerfaces, which formed spontaneously at the core-shell contact via annealing oxidation and can promote the rapid Na+ /K+ transfer. The density functional theory calculations revealed the mechanism and significance of Na/K-storage for the core-shelled Sb@Sb2 O3 heterostructure, which validated that the coupling between the high-conductivity of Sb and the stability of Sb2 O3 can relieve the shortcomings of the individual building blocks, thereby enhancing the Na/K-storage capacity. Furthermore, the core-shell structure embedded in the 3D carbon framework with robust structure can further increase the electrode mechanical strength and thus buffer the severe volume changes upon cycling. As a result, such composite architecture exhibited a high specific capacity of ≈573 mA h g-1 for sodium-ion battery (SIB) anode and ≈474 mA h g-1 for potassium-ion battery (PIB) anode at 100 mA g-1 , and superior rate performance (302 mA h g-1 at 30 A g-1 for SIB anode, while 239 mA h g-1 at 5 A g-1 for PIB anode).

Application of humin-immobilized biocathode in a continuous-flow bioelectrochemical system for nitrate removal at low temperature.

Solid-phase humic substances (humin) can work as an additional electron donor to support the low temperature denitrification but the reducing capacity of its non-reduced form is limited. In this study, a continuous-flow denitrifying BES with a humin-immobilized biocathode (H-BioC) was established. Humin was expected to function as a redox mediator and be persistently reduced on the cathode to provide reducing power to a denitrifying biofilm. Results showed that the H-BioC maintained a stable denitrification capacity with low nitrite accumulation for more than 100 days at 5 °C, and the specific microbial denitrification rate and electron transfer rate were 3.97-fold and 1.75-fold higher than those of the unaltered cathode. The results of repeated cycles of humin reduction and oxidation experiments further suggested that the redox activity of humin was stable. Acidovorax was the most dominant genus in both H-BioC biofilm and unaltered cathodic biofilm, while Rhodocyclaceae (unclassified_f_) was more enriched in H-BioC biofilm. Further Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) analyses indicated that biofilm formation, electron transfer, and nitrate reduction functions were more abundant in H-BioC, suggesting a possible enhancement mechanism by humin. The results of this study raise the possibility that immobilization of solid-phase humin may be a useful strategy for electrostimulated heterotrophic denitrification in groundwater where the indigenous bacteria have poor electroactivity.

Transforming brain signals related to value evaluation and self-control into behavioral choices.

The processes involved in value evaluation and self-control are critical when making behavioral choices. However, the evidence linking these two types of processes to behavioral choices in intertemporal decision-making remains elusive. As the ventromedial prefrontal cortex (vmPFC), striatum, and dorsolateral prefrontal cortex (dlPFC) have been associated with these two processes, we focused on these three regions. We employed functional magnetic resonance imaging during a delayed discounting task (DDT) using a relatively large sample size, three independent samples. We evaluated how much information about a specific choice could be decoded from local patterns in each brain area using multivoxel pattern analysis (MVPA). To investigate the relationship between the dlPFC and vmPFC/striatum regions, we performed a psychophysiological interaction (PPI) analysis. In Experiment I, we found that the vmPFC and dlPFC, but not the striatum, could determine choices in healthy participants. Furthermore, we found that the dlPFC showed significant functional connectivity with the vmPFC, but not the striatum, when making decisions. These results could be replicated in Experiment II with an independent sample of healthy participants. In Experiment III, the choice-decoding accuracy in the vmPFC and dlPFC was lower in patients with addiction (smokers and participants with Internet gaming disorder) than in healthy participants, and decoding accuracy in the dlPFC was related to impulsivity in addicts. Taken together, our findings may provide neural evidence supporting the hypothesis that value evaluation and self-control processes both guide the intertemporal choices, and might provide potential neural targets for the diagnosis and treatment of impulsivity-related brain disorders.

Chronic nicotine exposure impairs uncertainty modulation on reinforcement learning in anterior cingulate cortex and serotonin system.

Deficits in the computational processes of reinforcement learning have been suggested to underlie addiction. Additionally, environmental uncertainty, which is encoded in the anterior cingulate cortex (ACC), modulates reward prediction errors (RPEs) during reinforcement learning and exacerbates addiction. The present study tested whether and how the ACC would have an essential role in drug addiction by failing to use uncertainty to modulate the RPEs during reinforcement learning. In Experiment I, we found that the ACC/medial prefrontal cortex (MPFC) did not modulate RPE learning according to uncertainty in smokers. The effect of uncertainty × RPE in the ACC/MPFC was correlated with the learning rate of RPEs and the duration of nicotine use. Experiment II demonstrated that serotonin, but not dopamine, receptor mRNA expression significantly decreased in the ACC of the nicotine exposed compared to the control rats. Furthermore, there was a positive correlation between learning rate and serotonin receptor mRNA expression in the ACC. Therefore, all present results suggest that impairments in uncertainty modulation in the ACC disrupt reinforcement learning processes in chronic nicotine users and contribute to maladaptive decision-making. These findings support interventions for pathological decision-making in drug addiction that strongly focus on the serotonin system in ACC.

Neural substrates of updating the prediction through prediction error during decision making.

Learning of prediction error (PE), including reward PE and risk PE, is crucial for updating the prediction in reinforcement learning (RL). Neurobiological and computational models of RL have reported extensive brain activations related to PE. However, the occurrence of PE does not necessarily predict updating the prediction, e.g., in a probability-known event. Therefore, the brain regions specifically engaged in updating the prediction remain unknown. Here, we conducted two functional magnetic resonance imaging (fMRI) experiments, the probability-unknown Iowa Gambling Task (IGT) and the probability-known risk decision task (RDT). Behavioral analyses confirmed that PEs occurred in both tasks but were only used for updating the prediction in the IGT. By comparing PE-related brain activations between the two tasks, we found that the rostral anterior cingulate cortex/ventral medial prefrontal cortex (rACC/vmPFC) and the posterior cingulate cortex (PCC) activated only during the IGT and were related to both reward and risk PE. Moreover, the responses in the rACC/vmPFC and the PCC were modulated by uncertainty and were associated with reward prediction-related brain regions. Electric brain stimulation over these regions lowered the performance in the IGT but not in the RDT. Our findings of a distributed neural circuit of PE processing suggest that the rACC/vmPFC and the PCC play a key role in updating the prediction through PE processing during decision making.

Modeling the association between functional connectivity and lateralization with the activity flow framework.

The human brain is localized and distributed. On the one hand, each cognitive function tends to involve one hemisphere more than the other, also known as the principle of lateralization. On the other hand, interactions among brain regions in the form of functional connectivity (FC) are indispensable for intact function. Recent years have seen growing interest in the association between lateralization and FC. However, FC metrics vary from spurious correlation to causal associations. If lateralization manifests local processing and causal network interactions, more causally valid FC metrics should predict lateralization index (LI) better than FC based on simple correlations. The present study directly investigates this hypothesis within the activity flow framework to compare the association between lateralization and four brain connectivity metrics: correlation-based FC, multiple-regression FC, partial-correlation FC, and combinedFC. We propose two modeling approaches: the one-step approach, which models the relationship between LI and FC directly, and the two-step approach, which predicts the brain activation and calculates the LI. Our results indicated that multiple-regression FC, partial-correlation FC, and combinedFC could significantly improve the model prediction compared to correlation-based FC, which was consistent in a spatial working memory task (typically right-lateralized) and a language task (typically left-lateralized). The one-step and two-step approach yielded similar conclusions. In addition, the finding was replicated in a clinical sample of schizophrenia (SZ), bipolar disorder (BP), and attention deficit hyperactivity disorder (ADHD). The present study suggests that the causal interactions among brain regions help shape the lateralization pattern.

TSP-GNN: a novel neuropsychiatric disorder classification framework based on task-specific prior knowledge and graph neural network.

Neuropsychiatric disorder (ND) is often accompanied by abnormal functional connectivity (FC) patterns in specific task contexts. The distinctive task-specific FC patterns can provide valuable features for ND classification models using deep learning. However, most previous studies rely solely on the whole-brain FC matrix without considering the prior knowledge of task-specific FC patterns. Insight by the decoding studies on brain-behavior relationship, we develop TSP-GNN, which extracts task-specific prior (TSP) connectome patterns and employs graph neural network (GNN) for disease classification. TSP-GNN was validated using publicly available datasets. Our results demonstrate that different ND types show distinct task-specific connectivity patterns. Compared with the whole-brain node characteristics, utilizing task-specific nodes enhances the accuracy of ND classification. TSP-GNN comprises the first attempt to incorporate prior task-specific connectome patterns and the power of deep learning. This study elucidates the association between brain dysfunction and specific cognitive processes, offering valuable insights into the cognitive mechanism of neuropsychiatric disease.

Individual cartoon video for alleviating perioperative anxiety and reducing emergence delirium in children: a prospective randomised trial.

BACKGROUND: Perioperative anxiety and emergence delirium (ED) in young children may cause a series of adverse events, which are worth investigating. Pharmacological treatments of anxiety and delirium remain uncertain, while non-pharmacological treatments lack personalization and pertinence. AIMS: The aim of study was to determine whether an individual cartoon video can alleviate perioperative anxiety and reduce ED in young children undergoing adenoidectomy and tonsillectomy. METHODS: Children between 3 and 7 years old undergoing adenoidectomy and tonsillectomy were randomly assigned to an individual cartoon video group (group V) or a control group (group C). In group V, an individual cartoon video of the child' s own choice was played throughout the whole waiting, anaesthesia induction and recovery periods. The children in group C were contacted through verbal conversation. The primary outcomes were anxiety measured by the Modified Yale Preoperative Anxiety Scale and ED assessed by the Paediatric Anaesthesia Emergence Delirium (PAED) scale. The secondary outcomes included cooperation during induction, postoperative pain and adverse events. RESULTS: The incidence of anxiety were comparable in group V and group C at the holding area (T0) (26% vs 22%, p=0.323), but the incidence of anxiety of group V were significantly lower than those of group C at the time of entering the operating room (T1), during the induction of anaesthesia (T2) and leaving the post anaesthesia care unit (T6) (p<0.001, p<0.001, p<0.001) after intervention. The peak PAED score in group V was significantly lower than that in group C (12.00 (9.00-13.00) vs 13.50 (10.00-15.00), p=0.016). We found no significant differences in cooperation during induction, postoperative pain or the incidence of adverse events between the groups (2.00 (0.00-4.00) vs 3.00 (1.25-4.00), p=0.110; F=0.059, Pgroup=0.808; 3 (7.5%) vs 4 (10), p=0.692). CONCLUSIONS: The individual cartoon video is an effective method of reducing perioperative anxiety and alleviating ED in children. TRIAL REGISTRATION NUMBER: ChiCTR2200062300 (https://www.chictr.org.cn/index.aspx).

Using multisession tDCS stimulation as an early intervention on memory bias processing in subthreshold depression.

Transcranial direct current stimulation (tDCS) as an intervention tool has gained promising results in major depression disorder. However, studies related to subthreshold depression's (SD) cognitive deficits and neuromodulation approaches for the treatment of SD are still rare. We adopted Beck's cognitive model of depression and tested the tDCS stimulation effects on attentional and memory deficits on SD. First, this was a single-blinded, randomized, sham-controlled clinical trial to determine a 13-day tDCS modulation effect on 49 SD (27: Stimulation; 22: Sham) and 17 healthy controls. Second, the intervention effects of the consecutive and single-session tDCS were compared. Furthermore, the attentional and memory biases were explored in SD. Anodal tDCS was administrated over left dorsolateral prefrontal cortex for 13 consecutive days. Attentional and memory bias were assessed through a modified Sternberg task and a dot-probe task on the 1st, 2nd, and 15th day while their EEG was being recorded. After the 13-day tDCS stimulation (not after single-session stimulation), we found reduced memory bias (Stimulation vs. Sham, p = .02, r2  = .09) and decreased mid-frontal alpha power (p < .01, r2  = .13). In contrast, tDCS did not affect any attentional related behavioral or neural indexes (all ps > .15). Finally, reduced depressive symptoms (e.g., BDI score) were found for both groups. The criteria of SD varied across studies; the efficacy of this protocol should be tested in elderly patients. Our study suggests memory bias of SD can be modulated by the multisession tDCS and alpha power could serve as a neural index for intervention.

Combined gene expression and protein interaction analysis of dynamic modularity in glioma prognosis.

Because of the variety of factors affecting glioma prognosis, prediction of patient survival is particularly difficult. Protein-protein interaction (PPI) networks have been considered with regard to how their spatial characteristics relate to glioma. However, the dynamic nature of PPIs in vivo makes them temporally and spatially complex events. Integration of prognosis-specific co-expression information adds further dynamic features to these networks. Although some biomarkers for glioma prognosis have been identified, none is sufficient for accurate prediction of either prognosis or improved survival. We have established co-expressed protein-interaction networks that integrate protein-protein interactions with glioma gene-expression profiles related to different survival times. Biomarkers related to glioma prognosis were identified by comparative analysis of the dynamic features of the glioma prognosis network, particularly subnetworks. Four significantly differently expressed genes (SDEGs) are upregulated and ten SDEGs downregulated as lifetime is extended. In addition, 97 enhanced differently co-expressed protein interactions (DCPIs) and 99 weakened DCPIs were associated with glioma patient lifetime extension. We propose a method for estimating glioma prognosis on the basis of the construction of a dynamic modular network. We have used this method to identify dynamic genes and interactions related to glioma prognosis. Among these, enhanced MYC expression was related to lifetime extension, as were interactions between E2F1 and RB1 and between EGFR and p38. This method is a novel means of studying the molecular mechanisms determining prognosis in glioma.

Sox11 modulates neocortical development by regulating the proliferation and neuronal differentiation of cortical intermediate precursors.

Neural precursor cells play important roles in the neocortical development, but the mechanisms of neural progenitor proliferation, neuronal differentiation, and migration, as well as patterning are still unclear. Sox11, one of SoxC family members, has been reported to be essential for embryonic and adult neurogenesis. But there is no report about the roles of Sox11 in corticogenesis. In order to investigate Sox11 function during cortical development, loss of function experiment was performed in this study. Knockdown of Sox11 by Sox11 siRNA constructs resulted in a diminished neuronal differentiation, but enhanced proliferation of intermediate progenitors. Accompanied with the high expression of Sox11 in the postmitotic neurons, but low expression of Sox11 in the dividing neural progenitors, all the observations indicate that Sox11 induces neuronal differentiation during the neocortical development.