Biobehavioral threat sensitivity and amygdala volume: A twin neuroimaging study

Publication date: 1 February 2019

Source: NeuroImage, Volume 186

Author(s): Jens Foell, Isabella M. Palumbo, James R. Yancey, Nathalie Vizueta, Traute Demirakca, Christopher J. Patrick

Abstract

Current literature on the relationship between dispositional fear (or threat sensitivity) and amygdala gray matter volume (GMV) is heterogeneous, with findings including positive, negative, and null correlations. A clearer understanding of this relationship would help to determine the potential utility of amygdala volume as a biomarker of anxious/depressive (internalizing) disorders and contribute to understanding of neural mechanisms for variations in fearfulness. The study reported here used voxel-based morphometry to quantify amygdala GMV scores from structural neuroimaging data in a sample of 44 monozygotic twins (i.e., 22 pairs). Dispositional threat sensitivity (THT) was quantified using a biobehavioral cross-domain score that combined neurophysiological indicators with a psychological scale measure. Analyses revealed expected high concordance for amygdala GMV between co-twins. With respect to the major question of the study, a negative correlation was found between biobehavioral THT scores and amygdala volume – with individuals higher in THT showing smaller amygdala GMV scores. More modest associations of amygdala GMV with symptoms of social phobia, and fear disorder symptomology more broadly, were mediated by THT. These results provide insight into prior mixed findings and support the combined use of biological and behavioral measures to quantify characteristics relevant to mental health problems.

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Connectome-based models predict attentional control in aging adults

Publication date: 1 February 2019

Source: NeuroImage, Volume 186

Author(s): Stephanie Fountain-Zaragoza, Shaadee Samimy, Monica D. Rosenberg, Ruchika Shaurya Prakash

Abstract

There are well-characterized age-related differences in behavioral and neural responses to tasks of attentional control. However, there is also increasing recognition of individual variability in the process of neurocognitive aging. Using connectome-based predictive modeling, a method for predicting individual-level behaviors from whole-brain functional connectivity, a sustained attention connectome-based prediction model (saCPM) has been derived in young adults. The saCPM consists of two large-scale functional networks: a high-attention network whose strength predicts better attention and a low-attention network whose strength predicts worse attention. Here we examined the generalizability of the saCPM for predicting inhibitory control in an aging sample. Forty-two healthy young adults (n = 21, ages 18–30) and older adults (n = 21, ages 60–80) performed a modified Stroop task, on which older adults exhibited poorer performance, indexed by higher reaction time cost between incongruent and congruent trials. The saCPM generalized to predict reaction time cost across age groups, but did not account for age-related differences in performance. Exploratory analyses were conducted to characterize the effects of age on functional connectivity and behavior. We identified subnetworks of the saCPM that exhibited age-related differences in strength. The strength of two low-attention subnetworks, consisting of frontoparietal, medial frontal, default mode, and motor nodes that were more strongly connected in older adults, mediated the effect of age group on performance. These results support the saCPM's ability to capture attention-related patterns reflected in each individual's functional connectivity signature across both task context and age. However, older and younger adults exhibit functional connectivity differences within components of the saCPM networks, and it is these connections that better account for age-related deficits in attentional control.

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Oscillatory signatures of reward prediction errors in declarative learning

Publication date: Available online 2 November 2018

Source: NeuroImage

Author(s): Kate Ergo, Esther De Loof, Clio Janssens, Tom Verguts

Abstract

Reward prediction errors (RPEs) are crucial to learning. Whereas these mismatches between reward expectation and reward outcome are known to drive procedural learning, their role in declarative learning remains underexplored. Earlier work from our lab addressed this, and consistently found that signed reward prediction errors (SRPEs; "better-than-expected" signals) boost declarative learning. In the current EEG study, we sought to explore the neural signatures of SRPEs. Participants studied 60 Dutch-Swahili word pairs while RPE magnitudes were parametrically manipulated. Behaviorally, we replicated our previous findings that SRPEs drive declarative learning, with increased recognition for word pairs accompanied by large, positive RPEs. In the EEG data, at the start of reward feedback processing, we found an oscillatory (theta) signature consistent with unsigned reward prediction errors (URPEs; "different-than-expected" signals). Slightly later during reward feedback processing, we observed oscillatory (high-beta and high-alpha) signatures for SRPEs during reward feedback, similar to SRPE signatures during procedural learning. These findings illuminate the time course of neural oscillations in processing reward during declarative learning, providing important constraints for future theoretical work.

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A framework for multi-component analysis of diffusion MRI data over the neonatal period

Publication date: Available online 2 November 2018

Source: NeuroImage

Author(s): Maximilian Pietsch, Daan Christiaens, Jana Hutter, Lucilio Cordero-Grande, Anthony N. Price, Emer Hughes, A. David Edwards, Joseph V. Hajnal, Serena J. Counsell, J-Donald Tournier

Abstract

We describe a framework for creating a time-resolved group average template of the developing brain using advanced multi-shell high angular resolution diffusion imaging data, for use in group voxel or fixel-wise analysis, atlas-building, and related applications. This relies on the recently proposed multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD) technique. We decompose the signal into one isotropic component and two anisotropic components, with response functions estimated from cerebrospinal fluid and white matter in the youngest and oldest participant groups, respectively. We build an orientationally-resolved template of those tissue components from data acquired from 113 babies between 33 and 44 weeks postmenstrual age, imaged as part of the Developing Human Connectome Project. These data were split into weekly groups, and registered to the corresponding group average templates using a previously-proposed non-linear diffeomorphic registration framework, designed to align orientation density functions (ODF). This framework was extended to allow the use of the multiple contrasts provided by the multi-tissue decomposition, and shown to provide superior alignment. Finally, the weekly templates were registered to the same common template to facilitate investigations into the evolution of the different components as a function of age. The resulting multi-tissue atlas provides insights into brain development and accompanying changes in microstructure, and forms the basis for future longitudinal investigations into healthy and pathological white matter maturation.

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Consistent pre-stimulus influences on auditory perception across the lifespan

Publication date: Available online 2 November 2018

Source: NeuroImage

Author(s): Steven W. McNair, Stephanie J. Kayser, Christoph Kayser

Abstract

As we get older, perception in cluttered environments becomes increasingly difficult as a result of changes in peripheral and central neural processes. Given the aging society, it is important to understand the neural mechanisms constraining perception in the elderly. In young participants, the state of rhythmic brain activity prior to a stimulus has been shown to modulate the neural encoding and perceptual impact of this stimulus – yet it remains unclear whether, and if so, how, the perceptual relevance of pre-stimulus activity changes with age. Using the auditory system as a model, we recorded EEG activity during a frequency discrimination task from younger and older human listeners. By combining single-trial EEG decoding with linear modelling we demonstrate consistent statistical relations between pre-stimulus power and the encoding of sensory evidence in short-latency EEG components, and more variable relations between pre-stimulus phase and subjects' decisions in longer-latency components. At the same time, we observed a significant slowing of auditory evoked responses and a flattening of the overall EEG frequency spectrum in the older listeners. Our results point to mechanistically consistent relations between rhythmic brain activity and sensory encoding that emerge despite changes in neural response latencies and the relative amplitude of rhythmic brain activity with age.

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Altered dynamic electroencephalography connectome phase-space features of emotion regulation in social anxiety

Publication date: Available online 2 November 2018

Source: NeuroImage

Author(s): Mengqi Xing, Hyekyoung Lee, Zachery Morrissey, Moo K. Chung, K. Luan Phan, Heide Klumpp, Alex Leow, Olusola Ajilore

Abstract

Emotion regulation deficits are commonly observed in social anxiety disorder (SAD). We used manifold-learning to learn the phase-space connectome manifold of EEG brain dynamics in twenty SAD participants and twenty healthy controls. The purpose of the present study was to utilize manifold-learning to understand EEG brain dynamics associated with emotion regulation processes. Our emotion regulation task (ERT) contains three conditions: Neutral, Maintain and Reappraise. For all conditions and subjects, EEG connectivity data was converted into series of temporally-consecutive connectomes and aggregated to yield this phase-space manifold. As manifold geodesic distances encode intrinsic geometry, we visualized this space using its geodesic-informed minimum spanning tree and compared neurophysiological dynamics across conditions and groups using the corresponding trajectory length. Results showed that SAD participants had significantly longer trajectory lengths during Neutral and Maintain. Further, trajectory lengths during Reappraise were significantly associated with the habitual use of reappraisal strategies, while Maintain trajectory lengths were significantly associated with the negative affective state during Maintain. In sum, an unsupervised connectome manifold-learning approach can reveal emotion regulation associated phase-space features of brain dynamics.

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Stability of representational geometry across a wide range of fMRI activity levels

Publication date: Available online 2 November 2018

Source: NeuroImage

Author(s): Spencer A. Arbuckle, Atsushi Yokoi, Andrew Pruszynski, Jörn Diedrichsen

Abstract

Fine-grained activity patterns, as measured with functional magnetic resonance imaging (fMRI), are thought to reflect underlying neural representations. Multivariate analysis techniques, such as representational similarity analysis (RSA), can be used to test models of brain representation by quantifying the representational geometry (the collection of pair-wise dissimilarities between activity patterns). One important caveat, however, is that non-linearities in the coupling between neural activity and the fMRI signal may lead to significant distortions in the representational geometry estimated from fMRI activity patterns. Here we tested the stability of representational dissimilarity measures in primary sensory-motor (S1 and M1) and early visual regions (V1/V2) across a large range of activation levels. Participants were visually cued with different letters to perform single finger presses with one of the 5 fingers at a rate of 0.3–2.6 Hz. For each stimulation frequency, we quantified the difference between the 5 activity patterns in M1, S1, and V1/V2. We found that the representational geometry remained relatively stable, even though the average activity increased over a large dynamic range. These results indicate that the representational geometry of fMRI activity patterns can be reliably assessed, largely independent of the average activity in the region. This has important methodological implications for RSA and other multivariate analysis approaches that use the representational geometry to make inferences about brain representations.

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Optimizing fMRI experimental design for MVPA-based BCI control: Combining the strengths of block and event-related designs

Publication date: Available online 1 November 2018

Source: NeuroImage

Author(s): Giancarlo Valente, Amanda Kaas, Elia Formisano, Rainer Goebel

Abstract

Functional Magnetic Resonance Imaging (fMRI) has been successfully used for Brain Computer Interfacing (BCI) to classify (imagined) movements of different limbs. However, reliable classification of more subtle signals originating from co-localized neural networks in the sensorimotor cortex, e.g. individual movements of fingers of the same hand, has proved to be more challenging, especially when taking into account the requirement for high single trial reliability in the BCI context. In recent years, Multi Voxel Pattern Analysis (MVPA) has gained momentum as a suitable method to disclose such weak, distributed activation patterns. Much attention has been devoted to developing and validating data analysis strategies, but relatively little guidance is available on the choice of experimental design, even less so in the context of BCI-MVPA. When applicable, block designs are considered the safest choice, but the expectations, strategies and adaptation induced by blocking of similar trials can make it a sub-optimal strategy. Fast event-related designs, in contrast, require a more complicated analysis and show stronger dependence on linearity assumptions but allow for randomly alternating trials. However, they lack resting intervals that enable the BCI participant to process feedback.

In this proof-of-concept paper a hybrid blocked fast-event related design is introduced that is novel in the context of MVPA and BCI experiments, and that might overcome these issues by combining the rest periods of the block design with the shorter and randomly alternating trial characteristics of a rapid event-related design. A well-established button-press experiment was used to perform a within-subject comparison of the proposed design with a block and a slow event-related design.

The proposed hybrid blocked fast-event related design showed a decoding accuracy that was close to that of the block design, which showed highest accuracy. It allowed for across-design decoding, i.e. reliable prediction of examples obtained with another design. Finally, it also showed the most stable incremental decoding results, obtaining good performance with relatively few blocks.

Our findings suggest that the blocked fast event-related design could be a viable alternative to block designs in the context of BCI-MVPA, when expectations, strategies and adaptation make blocking of trials of the same type a sub-optimal strategy. Additionally, the blocked fast event-related design is also suitable for applications in which fast incremental decoding is desired, and enables the use of a slow or block design during the test phase.

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High frequency repetitive transcranial magnetic stimulation to the left dorsolateral prefrontal cortex modulates sensorimotor cortex function in the transition to sustained muscle pain

Publication date: Available online 1 November 2018

Source: NeuroImage

Author(s): Enrico DE. Martino, David A. Seminowicz, Siobhan M. Schabrun, Laura Petrini, Thomas Graven-Nielsen

Abstract

Based on reciprocal connections between the dorsolateral prefrontal cortex (DLPFC) and basal-ganglia regions associated with sensorimotor cortical excitability, it was hypothesized that repetitive transcranial magnetic stimulation (rTMS) of the left DLPFC would modulate sensorimotor cortical excitability induced by muscle pain. Muscle pain was provoked by injections of nerve growth factor (end of Day-0 and Day-2) into the right extensor carpi radialis brevis (ECRB) muscle in two groups of 15 healthy participants receiving 5 daily sessions (Day-0 to Day-4) of active or sham rTMS. Muscle pain scores and pressure pain thresholds (PPTs) were collected (Day-0, Day-3, Day-5). Assessment of motor cortical excitability using TMS (mapping cortical ECRB muscle representation) and somatosensory evoked potentials (SEPs) from electrical stimulation of the right radial nerve were recorded at Day-0 and Day-5. At Day-0 versus Day-5, the sham compared to active group showed: Higher muscle pain scores and reduced PPTs (P < 0.04); decreased frontal N30 SEP (P < 0.01); increased TMS map volume (P < 0.03). These results indicate that muscle pain exerts modulatory effects on the sensorimotor cortical excitability and left DLPFC rTMS has analgesic effects and modulates pain-induced sensorimotor cortical adaptations. These findings suggest an important role of prefrontal to basal-ganglia function in sensorimotor cortical excitability and pain processing.

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Brain network disintegration during sedation is mediated by the complexity of sparsely connected regions

Publication date: Available online 1 November 2018

Source: NeuroImage

Author(s): I. Pappas, R.M. Adapa, D.K. Menon, E.A. Stamatakis

Abstract

The precise mechanism of anaesthetic action on a neural level remains unclear. Recent approaches suggest that anaesthetics attenuate the complexity of interactions (connectivity) however evidence remains insufficient. We used tools from network and information theory to show that, during propofol-induced sedation, a collection of brain regions displayed decreased complexity in their connectivity patterns, especially so if they were sparsely connected. Strikingly, we found that, despite their low connectivity strengths, these regions exhibited an inordinate role in network integration. Their location and connectivity complexity delineated a specific pattern of sparse interactions mainly involving default mode regions while their connectivity complexity during the awake state also correlated with reaction times during sedation signifying its importance as a reliable indicator of the effects of sedation on individuals. Contrary to established views suggesting sedation affects only richly connected brain regions, we propose that suppressed complexity of sparsely connected regions should be considered a critical feature of any candidate mechanistic description for loss of consciousness.

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Intersubject similarity of personality is associated with intersubject similarity of brain connectivity patterns

Publication date: Available online 30 October 2018

Source: NeuroImage

Author(s): Wei Liu, Nils Kohn, Guillén Fernández

Abstract

Personality is a central high-level psychological concept that defines individual human beings and has been associated with a variety of real-world outcomes (e.g., mental health and academic performance). Using 2 h, high resolution, functional magnetic resonance imaging (fMRI) resting state data of 984 (primary dataset N = 801, hold-out dataset N = 183) participants from the Human Connectome Project (HCP), we investigated the relationship between personality (five-factor model, FFM) and intrinsic whole-brain functional connectome. We found a pattern of functional brain connectivity ("global personality network") related to personality traits. Consistent with the heritability of personality traits, the connectivity strength of this global personality network is also heritable (more similar between monozygotic twin pairs compared to the dizygotic twin pairs). Validated by both the repeated family-based 10-fold cross-validation and hold-out dataset, our intersubject network similarity analysis allowed us to identify participants' pairs with similar personality profiles. Across all the identified pairs of participants, we found a positive correlation between the network similarity and personality similarity, supporting our "similar brain, similar personality" hypothesis. Furthermore, the global personality network can be used to predict the individual subject's responses in the personality questionnaire on an item level. In sum, based on individual brain connectivity pattern, we could predict different facets of personality, and this prediction is not based on localized regions, but rather relies on the individual connectivity pattern in large-scale brain networks.

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Voxel-based meta-analysis via permutation of subject images (PSI): Theory and implementation for SDM

Publication date: Available online 30 October 2018

Source: NeuroImage

Author(s): Anton Albajes-Eizagirre, Aleix Solanes, Eduard Vieta, Joaquim Radua

Abstract

Coordinate-based meta-analyses (CBMA) are very useful for summarizing the large number of voxel-based neuroimaging studies of normal brain functions and brain abnormalities in neuropsychiatric disorders. However, current CBMA methods do not conduct common voxelwise tests, but rather a test of convergence, which relies on some spatial assumptions that data may seldom meet, and has lower statistical power when there are multiple findings. Here we present a new algorithm that can use standard voxelwise tests and, importantly, conducts a standard permutation of subject images (PSI). Its main steps are: a) multiple imputation of study images; b) imputation of subject images; and c) subject-based permutation test to control the familywise error rate (FWER). The PSI algorithm is general and we believe that developers might implement it for several CBMA methods. We present here an implementation of PSI for seed-based d mapping (SDM) method, which additionally benefits from the use of effect sizes, random-effects models, Freedman-Lane-based permutations and threshold-free cluster enhancement (TFCE) statistics, among others. Finally, we also provide an empirical validation of the control of the FWER in SDM-PSI, which showed that it might be too conservative. We hope that the neuroimaging meta-analytic community will welcome this new algorithm and method.

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L-DOPA reduces model-free control of behavior by attenuating the transfer of value to action

Publication date: Available online 28 October 2018

Source: NeuroImage

Author(s): Nils B. Kroemer, Ying Lee, Shakoor Pooseh, Ben Eppinger, Thomas Goschke, Michael N. Smolka

Abstract

Dopamine is a key neurotransmitter in action control. However, influential theories of dopamine function make conflicting predictions about the effect of boosting dopamine neurotransmission. Here, we tested if increases in dopamine tone by administration of L-DOPA upregulate reward learning as predicted by reinforcement learning theories, and if increases are specific for deliberative "model-based" control or reflexive "model-free" control. Alternatively, L-DOPA may impair learning as suggested by "value" or "thrift" theories of dopamine. To this end, we employed a two-stage Markov decision-task to investigate the effect of L-DOPA (randomized cross-over) on behavioral control while brain activation was measured using fMRI. L-DOPA led to attenuated model-free control of behavior as indicated by the reduced impact of reward on choice. Increased model-based control was only observed in participants with high working memory capacity. Furthermore, L-DOPA facilitated exploratory behavior, particularly after a stream of wins in the task. Correspondingly, in the brain, L-DOPA decreased the effect of reward at the outcome stage and when the next decision had to be made. Critically, reward-learning rates and prediction error signals were unaffected by L-DOPA, indicating that differences in behavior and brain response to reward were not driven by differences in learning. Taken together, our results suggest that L-DOPA reduces model-free control of behavior by attenuating the transfer of value to action. These findings provide support for the value and thrift accounts of dopamine and call for a refined integration of valuation and action signals in reinforcement learning models.

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Feasibility of functional MRI at ultralow magnetic field via changes in cerebral blood volume

Publication date: Available online 28 October 2018

Source: NeuroImage

Author(s): Kai Buckenmaier, Anders Pedersen, Paul SanGiorgio, Klaus Scheffler, John Clarke, Ben Inglis

Abstract

We investigate the feasibility of performing functional MRI (fMRI) at ultralow field (ULF) with a Superconducting QUantum Interference Device (SQUID), as used for detecting magnetoencephalography (MEG) signals from the human head. While there is negligible magnetic susceptibility variation to produce blood oxygenation level-dependent (BOLD) contrast at ULF, changes in cerebral blood volume (CBV) may be a sensitive mechanism for fMRI given the five-fold spread in spin-lattice relaxation time (T₁) values across the constituents of the human brain. We undertook simulations of functional signal strength for a simplified brain model involving activation of a primary cortical region in a manner consistent with a blocked task experiment. Our simulations involve measured values of T₁ at ULF and experimental parameters for the performance of an upgraded ULFMRI scanner. Under ideal experimental conditions we predict a functional signal-to-noise ratio of between 3.1 and 7.1 for an imaging time of 30 min, or between 1.5 and 3.5 for a blocked task experiment lasting 7.5 min. Our simulations suggest it may be feasible to perform fMRI using a ULFMRI system designed to perform MRI and MEG in situ.

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Dynamic low frequency EEG phase synchronization patterns during proactive control of task switching

Publication date: Available online 28 October 2018

Source: NeuroImage

Author(s): María Eugenia López, Sandra Pusil, Ernesto Pereda, Fernando Maestú, Francisco Barceló

Abstract

Cognitive flexibility is critical for humans living in complex societies with ever growing multitasking demands. Yet the low frequency neural dynamics of distinct task-specific and domain-general mechanisms sub-serving mental flexibility are still ill defined. Here we estimated phase electroencephalogram synchronization by using inter-trial phase coherence (ITPC) at the source space while twenty six young participants were intermittently cued to switch or repeat their perceptual categorization rule of Gabor gratings varying in color and thickness (switch task). Therefore, the aim of this study was to examine whether proactive control is associated with connectivity only in the frontoparietal theta network, or also involves distinct neural connectivity within the delta band, as distinct neural signatures while preparing to switch or repeat a task set, respectively. To this end, we focused the analysis on late-latencies (from 500 to 800 msec post-cue onset), since they are known to be associated with top-down cognitive control processes. We confirmed that proactive control during a task switch was associated with frontoparietal theta connectivity. But importantly, we also found a distinct role of delta band oscillatory synchronization in proactive control, engaging more posterior frontotemporal regions as opposed to frontoparietal theta connectivity. Additionally, we built a regression model by using the ITPC results in delta and theta bands as predictors, and the behavioral accuracy in the switch task as the criterion, obtaining significant results for both frequency bands. All these findings support the existence of distinct proactive cognitive control processes related to functionally distinct though highly complementary theta and delta frontoparietal and temporoparietal oscillatory networks at late-latency temporal scales.

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Reconfiguration of brain networks supporting inhibition of emotional challenge

Publication date: Available online 27 October 2018

Source: NeuroImage

Author(s): Morgan E. Bartholomew, Cindy M. Yee, Wendy Heller, Gregory A. Miller, Jeffrey M. Spielberg

Abstract

Reacting to the salient emotional features of a stimulus is adaptive unless the information is irrelevant or interferes with goal-directed behavior. The ability to ignore salient but otherwise extraneous information involves restructuring of brain networks and is a key impairment in several psychological disorders. Despite the importance of understanding inhibitory control of emotional response, the associated brain network mechanisms remain unknown. Utilizing functional magnetic resonance imaging (fMRI) data obtained from 103 participants performing an emotion-word Stroop (EWS) task, the present study applied graph-theory analysis to identify how brain regions subserving emotion processing and cognitive control are integrated within the global brain network to promote more specialized and efficient processing during successful inhibition of response to emotional distractors. The present study identified two sub-networks associated with emotion inhibition, one involving hyper-connectivity to prefrontal cortex and one involving hyper-connectivity to thalamus. Brain regions typically associated with identifying emotion salience were more densely connected with the thalamic hub, consistent with thalamic amplification of prefrontal cortex control of these regions. Additionally, stimuli high in emotion arousal prompted restructuring of the global network to increase clustered processing and overall communication efficiency. These results provide evidence that inhibition of emotion relies on interactions between cognitive control and emotion salience sub-networks.

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People got lost in solving a set of similar problems

Publication date: Available online 26 October 2018

Source: NeuroImage

Author(s): Furong Huang, Qingbai Zhao, Zhijin Zhou, Jing Luo

Abstract

A mental set generally refers to the human brain's tendency to persist with a familiar solution and stubbornly ignore alternatives. However, if a familiar solution is unable to solve a problem similar to a previous problem, does it continue to hinder alternative solutions, and if so, how and why? To answer these questions, a Chinese character decomposition task was adopted in this study. Participants were asked to perform a practice problem that could be solved by a familiar loose chunk decomposition (LCD) solution followed by a test problem that was similar to the practice problem but could only be solved by an unfamiliar tight chunk decomposition (TCD) solution or were asked to repeatedly perform 3–5 practice problems followed by a test problem; the former is the base-set condition, and the latter is the enhanced-set condition. The results showed that the test problem recruited more activation of the inferior frontal gyrus (IFG), middle occipital cortex (MOG), superior parietal lobule (SPL) and dorsal anterior cingulate cortex (dACC) than the practice problem in the latter operation and verification stage, but almost equal activation of the dACC occurred in the early exploration stage. This likely implied that people did not think that the familiar but currently invalid LCD solution could not be used to solve the test problem; thus, it continuously competed for attention with the unfamiliar TCD solution, which required more executive control to suppress. Moreover, compared with the base-set condition, the test problem in the enhanced-set condition recruited greater activations of the IFG, SPL and dACC in the latter verification stage but less activations of regions in the left IFG and MOG in the early exploration stage. These results revealed that people less actively explored and had to work harder to operate the unfamiliar TCD solution, particularly to resolve competition from the familiar but currently invalid LCD solution. In conclusion, people lost the ability to identify errors in the familiar but currently invalid solution, which in turn decreased the exploration efforts and increased the processing demands associated with alternative solutions in the form of attentional bias and competition. This finding broadly explains the dilemma of creative problem solving.

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Immunological Tolerance After Frequent Rabies Booster Vaccinations

Condition:   Rabies Intervention:   Biological: Blood drawn after rabies vaccine booster vaccination Sponsor:   Queen Saovabha Memorial Institute Not yet recruiting (Source: ClinicalTrials.gov)

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the Efficacy and Safety of Sugammadex in Children 0-2 Years Old

Conditions:   Anesthesia;   Pediatric Cancer;   Pediatric Tumor;   Pediatric Hepatoblastoma;   Beta-Thalassemia;   Hepatic Metastases Intervention:   Drug: Sugammadex Sponsor:   Federal Research Institute of Pediatric Hematology, Oncology and Immunology Not yet recruiting (Source: ClinicalTrials.gov)

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Post-stroke Immunological Changes in Younger Stroke Patients

Conditions:   Stroke, Ischemic;   Cognitive Decline Intervention:   Other: Analysis of T-lymphocytes Sponsor:   University Medicine Greifswald Not yet recruiting (Source: ClinicalTrials.gov)

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