Reconfiguration of response-set in task switching: Event-related potential evidence.

In task switching, an interaction between task and response is often observed, with response repetition (RR) benefits in task-repeat trials and RR costs in task-switch trials. The theoretical accounts of the RR effect remain controversial, and neuroscience evidence is scarce. The present study utilized the event-related potentials (ERPs) method to explore the neural mechanism underlying the RR effect by adopting a cued task-switching paradigm. The ERP results revealed the interaction between task and response in the P3b time window, with a response switch positivity under task-repetition conditions and an RR positivity under task-switching conditions. In addition, there were RR positivity in the N2 irrespective of task transition and in the late component (LC, 550-600 ms) that only under the task repetition condition. On the individual level, the RR benefit positively correlated with the RR positivity in the LC, while the RR costs negatively correlated with RR positivity in the N2/P3 component. These results suggest that both response reconfiguration and episodic-retrieval make contributions to the RR effects, which were also discussed in terms of predictive model for a domain-general inference and learning of perceptual categories.

Reporting details of neuroimaging studies on individual traits prediction: A literature survey.

Using machine-learning tools to predict individual phenotypes from neuroimaging data is one of the most promising and hence dynamic fields in systems neuroscience. Here, we perform a literature survey of the rapidly work on phenotype prediction in healthy subjects or general population to sketch out the current state and ongoing developments in terms of data, analysis methods and reporting. Excluding papers on age-prediction and clinical applications, which form a distinct literature, we identified a total 108 papers published since 2007. In these, memory, fluid intelligence and attention were most common phenotypes to be predicted, which resonates with the observation that roughly a quarter of the papers used data from the Human Connectome Project, even though another half recruited their own cohort. Sample size (in terms of training and external test sets) and prediction accuracy (from internal and external validation respectively) did not show significant temporal trends. Prediction accuracy was negatively correlated with sample size of the training set, but not the external test set. While known to be optimistic, leave-one-out cross-validation (LOO CV) was the prevalent strategy for model validation (n = 48). Meanwhile, 27 studies used external validation with external test set. Both numbers showed no significant temporal trends. The most popular learning algorithm was connectome-based predictive modeling introduced by the Yale team. Other common learning algorithms were linear regression, relevance vector regression (RVR), support vector regression (SVR), least absolute shrinkage and selection operator (LASSO), and elastic net. Meanwhile, the amount of data from self-recruiting studies (but not studies using open, shared dataset) was positively correlated with internal validation prediction accuracy. At the same time, self-recruiting studies also reported a significantly higher internal validation prediction accuracy than those using open, shared datasets. Data type and participant age did not significantly influence prediction accuracy. Confound control also did not influence prediction accuracy after adjusted for other factors. To conclude, most of the current literature is probably quite optimistic with internal validation using LOO CV. More efforts should be made to encourage the use of external validation with external test sets to further improve generalizability of the models.

Cross-cohort replicability and generalizability of connectivity-based psychometric prediction patterns.

An increasing number of studies have investigated the relationships between inter-individual variability in brain regions' connectivity and behavioral phenotypes, making use of large population neuroimaging datasets. However, the replicability of brain-behavior associations identified by these approaches remains an open question. In this study, we examined the cross-dataset replicability of brain-behavior association patterns for fluid cognition and openness predictions using a previously developed region-wise approach, as well as using a standard whole-brain approach. Overall, we found moderate similarity in patterns for fluid cognition predictions across cohorts, especially in the Human Connectome Project Young Adult, Human Connectome Project Aging, and Enhanced Nathan Kline Institute Rockland Sample cohorts, but low similarity in patterns for openness predictions. In addition, we assessed the generalizability of prediction models in cross-dataset predictions, by training the model in one dataset and testing in another. Making use of the region-wise prediction approach, we showed that first, a moderate extent of generalizability could be achieved with fluid cognition prediction, and that, second, a set of common brain regions related to fluid cognition across cohorts could be identified. Nevertheless, the moderate replicability and generalizability could only be achieved in specific contexts. Thus, we argue that replicability and generalizability in connectivity-based prediction remain limited and deserve greater attention in future studies.

A Connectivity-Based Psychometric Prediction Framework for Brain-Behavior Relationship Studies.

The recent availability of population-based studies with neuroimaging and behavioral measurements opens promising perspectives to investigate the relationships between interindividual variability in brain regions' connectivity and behavioral phenotypes. However, the multivariate nature of connectivity-based prediction model severely limits the insight into brain-behavior patterns for neuroscience. To address this issue, we propose a connectivity-based psychometric prediction framework based on individual regions' connectivity profiles. We first illustrate two main applications: 1) single brain region's predictive power for a range of psychometric variables and 2) single psychometric variable's predictive power variation across brain region. We compare the patterns of brain-behavior provided by these approaches to the brain-behavior relationships from activation approaches. Then, capitalizing on the increased transparency of our approach, we demonstrate how the influence of various data processing and analyses can directly influence the patterns of brain-behavior relationships, as well as the unique insight into brain-behavior relationships offered by this approach.

Functional parcellation of human and macaque striatum reveals human-specific connectivity in the dorsal caudate.

A wide homology between human and macaque striatum is often assumed as in both the striatum is involved in cognition, emotion and executive functions. However, differences in functional and structural organization between human and macaque striatum may reveal evolutionary divergence and shed light on human vulnerability to neuropsychiatric diseases. For instance, dopaminergic dysfunction of the human striatum is considered to be a pathophysiological underpinning of different disorders, such as Parkinson's disease (PD) and schizophrenia (SCZ). Previous investigations have found a wide similarity in structural connectivity of the striatum between human and macaque, leaving the cross-species comparison of its functional organization unknown. In this study, resting-state functional connectivity (RSFC) derived striatal parcels were compared based on their homologous cortico-striatal connectivity. The goal here was to identify striatal parcels whose connectivity is human-specific compared to macaque parcels. Functional parcellation revealed that the human striatum was split into dorsal, dorsomedial, and rostral caudate and ventral, central, and caudal putamen, while the macaque striatum was divided into dorsal, and rostral caudate and rostral, and caudal putamen. Cross-species comparison indicated dissimilar cortico-striatal RSFC of the topographically similar dorsal caudate. We probed clinical relevance of the striatal clusters by examining differences in their cortico-striatal RSFC and gray matter (GM) volume between patients (with PD and SCZ) and healthy controls. We found abnormal RSFC not only between dorsal caudate, but also between rostral caudate, ventral, central and caudal putamen and widespread cortical regions for both PD and SCZ patients. Also, we observed significant structural atrophy in rostral caudate, ventral and central putamen for both PD and SCZ while atrophy in the dorsal caudate was specific to PD. Taken together, our cross-species comparative results revealed shared and human-specific RSFC of different striatal clusters reinforcing the complex organization and function of the striatum. In addition, we provided a testable hypothesis that abnormalities in a region with human-specific connectivity, i.e., dorsal caudate, might be associated with neuropsychiatric disorders.

Accurate nonlinear mapping between MNI volumetric and FreeSurfer surface coordinate systems.

The results of most neuroimaging studies are reported in volumetric (e.g., MNI152) or surface (e.g., fsaverage) coordinate systems. Accurate mappings between volumetric and surface coordinate systems can facilitate many applications, such as projecting fMRI group analyses from MNI152/Colin27 to fsaverage for visualization or projecting resting-state fMRI parcellations from fsaverage to MNI152/Colin27 for volumetric analysis of new data. However, there has been surprisingly little research on this topic. Here, we evaluated three approaches for mapping data between MNI152/Colin27 and fsaverage coordinate systems by simulating the above applications: projection of group-average data from MNI152/Colin27 to fsaverage and projection of fsaverage parcellations to MNI152/Colin27. Two of the approaches are currently widely used. A third approach (registration fusion) was previously proposed, but not widely adopted. Two implementations of the registration fusion (RF) approach were considered, with one implementation utilizing the Advanced Normalization Tools (ANTs). We found that RF-ANTs performed the best for mapping between fsaverage and MNI152/Colin27, even for new subjects registered to MNI152/Colin27 using a different software tool (FSL FNIRT). This suggests that RF-ANTs would be useful even for researchers not using ANTs. Finally, it is worth emphasizing that the most optimal approach for mapping data to a coordinate system (e.g., fsaverage) is to register individual subjects directly to the coordinate system, rather than via another coordinate system. Only in scenarios where the optimal approach is not possible (e.g., mapping previously published results from MNI152 to fsaverage), should the approaches evaluated in this manuscript be considered. In these scenarios, we recommend RF-ANTs (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/registration/Wu2017_RegistrationFusion).

A novel antigenic epitope identified on the accessory protein NS6 of porcine deltacoronavirus.

Porcine deltacoronavirus (PDCoV) is a novel enteric coronavirus that can cause vomiting, watery diarrhea in pigs and the death of piglets. The open reading frame (ORF) 5 is one of the accessory genes in PDCoV genome and encodes an accessory protein NS6. To date, the function of NS6 is still unclear. In this study, the recombinant NS6 was successfully expressed in prokaryotic expression system and purified. To prepare monoclonal antibody (mAb), six-week-old female BALB/c mice were primed subcutaneously with purified NS6. A novel mouse mAb against NS6 was obtained and designated as 3D5. The isotype of 3D5 is IgG2b with kappa (κ) light chain. 3D5 can specifically recognizes the natural NS6 in swine testis (ST) cells infected with PDCoV and expressed NS6 in human embryonic kidney 293T (HEK 293T) cells transfected with mammalian vector. The minimal linear B cell epitope recognised by 3D5 on NS6 was 25VPELIDPLVK34 determined by peptide scanning and named EP-3D5. The sequence of EP-3D5 is completely conserved among PDCoV strains. Moreover, six to nine residues of EP-3D5 were identified to be conserved in non-PDCoV strains. These results provide valuable insights into the antigenic structure and function of NS6 in virus pathogenesis, and aid for the development of PDCoV epitope-associated diagnostics and vaccine design.

IFITM3 restricts porcine deltacoronavirus infection by targeting its Spike protein.

The discovery of antiviral molecules is crucial for controlling porcine deltacoronavirus (PDCoV). Previous studies have provided evidence that the IFN-inducible transmembrane protein 3 (IFITM3), which is coded by an interferon-stimulated gene, prevents the infections of a number of enveloped viruses. Nevertheless, the involvement of IFITM3 in PDCoV infection remains unexplored. In this study, it was observed that the overexpression of IFITM3 successfully restrictes the infection of PDCoV in cell cultures. Conversely, the suppression of IFITM3 facilitates the infection of PDCoV in IPI-2I and IPEC-J2 cells. Further studies revealed that IFITM3 limits the attachment phase of viral infection by interacting with the S1 subunit of the PDCoV Spike (S) protein. In addition, IFITM3 is verified as a member of the CD225 family, the GxxxG conserved motif of this family is important for it to limit PDCoV infection. In summary, this study reveals the mechanism of IFITM3 as an antiviral molecule to inhibit PDCoV infection, and also provides theoretical supports for screening effective anti-PDCoV drugs.

The challenges and prospects of brain-based prediction of behaviour.

Relating individual brain patterns to behaviour is fundamental in system neuroscience. Recently, the predictive modelling approach has become increasingly popular, largely due to the recent availability of large open datasets and access to computational resources. This means that we can use machine learning models and interindividual differences at the brain level represented by neuroimaging features to predict interindividual differences in behavioural measures. By doing so, we could identify biomarkers and neural correlates in a data-driven fashion. Nevertheless, this budding field of neuroimaging-based predictive modelling is facing issues that may limit its potential applications. Here we review these existing challenges, as well as those that we anticipate as the field develops. We focus on the impacts of these challenges on brain-based predictions. We suggest potential solutions to address the resolvable challenges, while keeping in mind that some general and conceptual limitations may also underlie the predictive modelling approach.

Multilayer meta-matching: translating phenotypic prediction models from multiple datasets to small data.

Resting-state functional connectivity (RSFC) is widely used to predict phenotypic traits in individuals. Large sample sizes can significantly improve prediction accuracies. However, for studies of certain clinical populations or focused neuroscience inquiries, small-scale datasets often remain a necessity. We have previously proposed a "meta-matching" approach to translate prediction models from large datasets to predict new phenotypes in small datasets. We demonstrated large improvement of meta-matching over classical kernel ridge regression (KRR) when translating models from a single source dataset (UK Biobank) to the Human Connectome Project Young Adults (HCP-YA) dataset. In the current study, we propose two meta-matching variants ("meta-matching with dataset stacking" and "multilayer meta-matching") to translate models from multiple source datasets across disparate sample sizes to predict new phenotypes in small target datasets. We evaluate both approaches by translating models trained from five source datasets (with sample sizes ranging from 862 participants to 36,834 participants) to predict phenotypes in the HCP-YA and HCP-Aging datasets. We find that multilayer meta-matching modestly outperforms meta-matching with dataset stacking. Both meta-matching variants perform better than the original "meta-matching with stacking" approach trained only on the UK Biobank. All meta-matching variants outperform classical KRR and transfer learning by a large margin. In fact, KRR is better than classical transfer learning when less than 50 participants are available for finetuning, suggesting the difficulty of classical transfer learning in the very small sample regime. The multilayer meta-matching model is publicly available at GITHUB_LINK.

A theoretical review on the role of positive emotional classroom rapport in preventing EFL students' shame: A control-value theory perspective.

Background: Second/foreign language teaching has been considered as a dialogic and interactive job in which teachers' and students' emotions and behaviors are closely connected to each other. When there is a harmonious and positive relationship between the teacher and students in the classroom, many favorable academic outcomes may emerge. A bulk of research has endorsed the power of positive emotional classroom rapport in EFL contexts. However, its role in preventing negative students' emotions like shame, as an achievement emotion, in terms of perceived control and value tasks has rarely (if any) caught scholarly attention. Objective: This study aimed to provide insights into the role of emotions in L2 education and the way students' shame can be prevented or curbed in light of a positive emotional classroom rapport. Method/Design: This article systematically reviewed the theoretical and empirical underpinnings of EFL teachers' positive emotional classroom rapport and students' shame in light of the control-value theory. Results: In this research, it was asserted that by building a positive emotional classroom rapport EFL teachers can block and even eliminate students' shame. Implications: The study offers practical implications to EFL teachers, trainers, principals, and researchers by increasing their knowledge and abilities in managing psycho-emotional mechanisms and factors and enriching interpersonal aspects of EFL education.

Cognitive control is modulated by hierarchical complexity of task switching: An event-related potential study.

The study aimed to explore the effect of hierarchical complexity on task switching. The participants (n = 36) were asked to perform a magnitude or parity judgement on digits (1-9) in the hierarchical simple or complex block. In the simple block, participants made a numerical judgement on the presented digit (1-9) in each trial, whereas in the complex block, they had to first identify whether the digit in the current trial belonged to a predefined category (e.g., whether it was an even number), then perform a numerical judgement or not respond. The behavioural results revealed a significant interaction between hierarchical complexity and transition type (repeat vs. switch), with greater switch cost in the complex than in the simple block. Event-related potentials (ERPs) locked in the cue stage did not reveal this interaction, whereas the ERPs locked in the target stage revealed this interaction during the N2 and P3 time windows, with a larger switch negativity (switch minus repeat) in the complex than in the simple block. These findings demonstrate that an increase in hierarchical complexity triggers increased reactive control in the inhibition of the old task-set and reconfiguration of the new task-set during task switching.

Reconfiguration of response rule is more difficult than that of task goal: Behavior and electrophysiological evidence.

Previous studies have investigated the neural mechanisms underlying cognitive control by using the task-switching paradigm, but differentiation from response rule switching (RR-switch) remains poorly explored. In this study, a partial voluntary task-switching (VTS) paradigm was used to explore the electrophysiological differences between task switching (T-switch) and RR-switching. Participants were sequentially presented with Arabic numerals colored red or green. If the color in the current trial was the same as that in the previous trial, the participants had to perform the same task following the same response rule. Otherwise, they had to voluntarily switch tasks (e.g., from parity task to magnitude task) or switch response rules (e.g., from "pressing F for odd and J for even number" to "pressing J for odd and F for even number"). The behavioral results indicated that RR-switch was infrequently selected, and the performance was less efficient than that of the T-switch. Event-related potential results showed that both T- and RR-switches elicited a larger switch-positivity in the P2 and P3 time windows than that in the repeat condition. Switch-positivity was larger for RR-switch than for T-switch over the frontal sites, suggesting that more attention and cognitive resources were required to update information for the RR-switch than for the T-switch. These findings suggest that in the VTS, the hierarchical relationship between task goals and response rules is relatively loose, resulting in the neural disassociation of task reconfiguration and response change.

Proactive and reactive control differ between task switching and response rule switching: Event-related potential evidence.

The distinction between task-switching (T-switch) and response-rule switching (RR-switch) has been reported in previous studies. However, it is unclear whether the neural correlates of proactive and reactive control differ between T-switch and RR-switch. In this study, a modified cue-target task was adopted. When the cue in the current trial differed from that in the preceding trial in shape (or color), the participants had to perform a T-switch (or RR-switch). Otherwise, they performed the same task following the same response rule. The behavioral results showed that the switch cost was greater for the RR-switch than for the T-switch. The event-related potential results indicated that (1) for cues, the switch-positivity in the late positive component (LPC) (500-800 ms) was more enhanced for the RR-switch than for the T-switch over the central to parietal regions, reflecting increased proactive control for the RR-switch compared with the T-switch; (2) for targets, the P3 amplitude was more attenuated in the RR-switch than the T-switch over the central and parietal regions, reflecting increased reactive control for the RR-switch; and (3) under the T-switch, the switch-positivity in the cue-LPC was negatively correlated with accuracy cost, while under the RR-switch, the switch negativity in the target-P3 was positively correlated with the reaction time cost. These findings suggest that similar proactive and reactive control are recruited in the T-switch and RR-switch, whereas cognitive control efforts clearly differ between them, perhaps due to different sub-processes.