NeuroImage最新文献

One driving factor for attention deployment towards a stimulus is its associated value due to previous experience and learning history. Previous visual search studies found that when looking for a target, distractors associated with higher reward produce more interference (e.g., longer response times). The present study investigated the neural mechanism of such value-driven attention deployment. Specifically, we were interested in which of the three attention sub-processes are responsible for the interference that was repeatedly observed behaviorally: enhancement of relevant information, attentional capture by irrelevant information, or suppression of irrelevant information. We replicated earlier findings showing longer response times and lower accuracy when a target competed with a high-reward compared to a low-reward distractor. We also found a spatial gradient of interference: behavioral performance dropped with increasing proximity to the target. This gradient was steeper for high- than low-reward distractors. Event-related potentials of the EEG signal showed the reason for the reward-induced attentional bias: High-reward distractors required more suppression than low-reward distractors as evident in larger Pd components. This effect was only found for distractors near targets, showing the additional filtering needs required for competing stimuli in close proximity. As a result, fewer attentional resources can be distributed to the target when it competes with a high-reward distractor, as evident in a smaller target-N2pc amplitude. The distractor-N2pc, indicative of attentional capture, was neither affected by distance nor reward, showing that attentional capture alone cannot explain interference by stimuli of high value. In sum our results show that the higher need for suppression of high-value stimuli contributes to reward-modulated attention deployment and increased suppression can prevent attentional capture of high-value stimuli.

Background

Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) are techniques for assessing brain electrical activity and oxygenation. There is evidence of brain electrical activity and oxygenation in preterm/full-term infants by tactile stimuli but none by Reflex Locomotion Therapy. Their knowledge will address the delays in motor development that preterm infants often present. The objective will be to establish the differences in preterm and full-term infants in relation to brain electrical activity and oxygenation, and to test differences between Reflex Locomotion Therapy and massage.

Methods

Randomized clinical trial with healthy preterm and non-preterm infants will be included and will be randomly divided into 3 groups: 2 intervention groups (Reflex Locomotion Therapy/massage therapy) and 1 control group (fake Reflex Locomotion Therapy). Outcome variables will be brain electrical activity and oxygenation changes measured by EEG and fNIRS once after breastfeeding.

Discussion

This study will test the application effects of Reflex Locomotion Therapy and massage therapy in newborn infants in relation to brain electrical activity and oxygenation, and to establish the differences between preterm and full-term infants. Several articles have been identified with different auditory, visual and olfactory stimuli; however, evidence on studies related to tactile stimuli is limited.

Accurate interpretation of in vivo wide-field fluorescent imaging (WFFI) data requires precise separation of raw fluorescence signals into neural and hemodynamic components. The classical Beer-Lambert law-based approach, which uses concurrent 530-nm illumination to estimate relative changes in cerebral blood volume (CBV), fails to account for the scattering and reflection of 530-nm photons from non-neuronal components leading to biased estimates of CBV changes and subsequent misrepresentation of neural activity. This study introduces a novel linear regression approach designed to overcome this limitation. This correction provides a more reliable representation of CBV changes and neural activity in fluorescence data. Our method is validated across multiple datasets, demonstrating its superiority over the classical approach.

As an important biomarker of neural aging, the brain age reflects the integrity and health of the human brain. Accurate prediction of brain age could help to understand the underlying mechanism of neural aging. In this study, a cross-stratified ensemble learning algorithm with staking strategy was proposed to obtain brain age and the derived predicted age difference (PAD) using T1-weighted magnetic resonance imaging (MRI) data. The approach was characterized as by implementing two modules: one was three base learners of 3D-DenseNet, 3D-ResNeXt, 3D-Inception-v4; another was 14 secondary learners of liner regressions. To evaluate performance, our method was compared with single base learners, regular ensemble learning algorithms, and state-of-the-art (SOTA) methods. The results demonstrated that our proposed model outperformed others models, with three metrics of mean absolute error (MAE), root mean-squared error (RMSE), and coefficient of determination (R²) of 2.9405 years, 3.9458 years, and 0.9597, respectively. Furthermore, there existed significant differences in PAD among the three groups of normal control (NC), mild cognitive impairment (MCI) and Alzheimer's disease (AD), with an increased trend across NC, MCI, and AD. It was concluded that the proposed algorithm could be effectively used in computing brain aging and PAD, and offering potential for early diagnosis and assessment of normal brain aging and AD.

This paper proposes a data-driven analysis method to accurately partition large-scale resting-state functional brain networks from fMRI data. The method is based on a spectral clustering algorithm and combines eigenvector direction selection with Pearson correlation clustering in the spectral space. The method is an improvement on available spectral clustering methods, capable of robustly identifying active brain networks consistent with those from model-driven methods at different noise levels, even at the noise level of real fMRI data.

Escalated aggression represents a frequent and severe form of violence, sometimes manifesting as antisocial behavior. Driven by the pressures of modern life, escalated aggression is of particular concern due to its rising prevalence and its destructive impact on both individual well-being and socioeconomic stability. However, a consistent neural circuitry underpinning it remains to be definitively identified. Here, we addressed this issue by comparing brain alterations between individuals with escalated aggression and those without such behavioral manifestations. We first conducted a meta-analysis to synthesize previous neuroimaging studies on functional and structural alterations of escalated aggression (325 experiments, 2997 foci, 16,529 subjects). Following-up network and functional decoding analyses were conducted to provide quantitative characterizations of the identified brain regions. Our results revealed that brain regions constantly involved in escalated aggression were localized in the subcortical network (amygdala and lateral orbitofrontal cortex) associated with emotion processing, the default mode network (dorsal medial prefrontal cortex and middle temporal gyrus) associated with mentalizing, and the salience network (anterior cingulate cortex and anterior insula) associated with cognitive control. These findings were further supported by additional meta-analyses on emotion processing, mentalizing, and cognitive control, all of which showed conjunction with the brain regions identified in the escalated aggression. Together, these findings advance the understanding of the risk biomarkers of escalated aggressive populations and refine theoretical models of human aggression.

Brain magnetic resonance imaging (MRI) is widely used in clinical practice for disease diagnosis. However, MRI scans acquired at different sites can have different appearances due to the difference in the hardware, pulse sequence, and imaging parameter. It is important to reduce or eliminate such cross-site variations with brain MRI harmonization so that downstream image processing and analysis is performed consistently. Previous works on the harmonization problem require the data acquired from the sites of interest for model training. But in real-world scenarios there can be test data from a new site of interest after the model is trained, and training data from the new site is unavailable when the model is trained. In this case, previous methods cannot optimally handle the test data from the new unseen site. To address the problem, in this work we explore domain generalization for brain MRI harmonization and propose Site Mix (SiMix). We assume that images of travelling subjects are acquired at a few existing sites for model training. To allow the training data to better represent the test data from unseen sites, we first propose to mix the training images belonging to different sites stochastically, which substantially increases the diversity of the training data while preserving the authenticity of the mixed training images. Second, at test time, when a test image from an unseen site is given, we propose a multiview strategy that perturbs the test image with preserved authenticity and ensembles the harmonization results of the perturbed images for improved harmonization quality. To validate SiMix, we performed experiments on the publicly available SRPBS dataset and MUSHAC dataset that comprised brain MRI acquired at nine and two different sites, respectively. The results indicate that SiMix improves brain MRI harmonization for unseen sites, and it is also beneficial to the harmonization of existing sites.

Using machine learning techniques to predict brain age from multimodal data has become a crucial biomarker for assessing brain development. Among various types of brain imaging data, structural magnetic resonance imaging (sMRI) and diffusion magnetic resonance imaging (dMRI) are the most commonly used modalities. sMRI focuses on depicting macrostructural features of the brain, while dMRI reveals the orientation of major white matter fibers and changes in tissue microstructure. However, their differential capabilities in reflecting newborn age and clinical implications have not been systematically studied. This study aims to explore the impact of sMRI and dMRI on brain age prediction. Comparing predictions based on T2-weighted(T2w) and fractional anisotropy (FA) images, we found their mean absolute errors (MAE) in predicting infant age to be similar. Exploratory analysis revealed for T2w images, areas such as the cerebral cortex and ventricles contribute most significantly to age prediction, whereas FA images highlight the cerebral cortex and regions of the main white matter tracts. Despite both modalities focusing on the cerebral cortex, they exhibit significant region-wise differences, reflecting developmental disparities in macro- and microstructural aspects of the cortex. Additionally, we examined the effects of prematurity, gender, and hemispherical asymmetry of the brain on age prediction for both modalities. Results showed significant differences (p<0.05) in age prediction biases based on FA images across gender and hemispherical asymmetry, whereas no significant differences were observed with T2w images. This study underscores the differences between T2w and FA images in predicting infant brain age, offering new perspectives for studying infant brain development and aiding more effective assessment and tracking of infant development.

Cooperative action involves the simulation of actions and their co-representation by two or more people. This requires the involvement of two complex brain systems: the mirror neuron system (MNS) and the mentalizing system (MENT), both of critical importance for successful social interaction. However, their internal organization and the potential synergy of both systems during joint actions (JA) are yet to be determined. The aim of this study was to examine the role and interaction of these two fundamental systems—MENT and MNS—during continuous interaction. To this hand, we conducted a multiple-brain connectivity analysis in the source domain during a motor cooperation task using high-density EEG dual-recordings providing relevant insights into the roles of MNS and MENT at the intra- and interbrain levels.

In particular, the intra-brain analysis demonstrated the essential function of both systems during JA, as well as the crucial role played by single brain regions of both neural mechanisms during cooperative activities. Specifically, our intra-brain analysis revealed that both neural mechanisms are essential during Joint Action (JA), showing a solid connection between MNS and MENT and a central role of the single brain regions of both mechanisms during cooperative actions. Additionally, our inter-brain study revealed increased inter-subject connections involving the motor system, MENT and MNS. Thus, our findings show a mutual influence between two interacting agents, based on synchronization of MNS and MENT systems. Our results actually encourage more research into the still-largely unknown realm of inter-brain dynamics and contribute to expand the body of knowledge in social neuroscience.

Sleep disturbance is associated with the development of neurodegenerative disease. We aimed to address the effects of sleep quality on brain glucose metabolism measured by ¹⁸F-Fl uorodeoxyglucose (¹⁸F-FDG) positron emission tomography (PET) in healthy middle-aged adults. A total of 378 healthy men (mean age: 42.8±3.6 years) were included in this study. Participants underwent brain ¹⁸F-FDG PET and completed the Korean version of the Pittsburgh Sleep Quality Index (PSQI-K). Additionally, anthropometric measurements were obtained. PETs were spatially normalized to MNI space using PET templates from SPM5 with PMOD. The Automated Anatomical Labeling 2 atlas was used to define regions of interest (ROIs). The mean uptake of each ROI was scaled to the mean of the global cortical uptake of each individual and defined as the standardized uptake value ratio (SUVR). After the logarithmic transformation of the regional SUVR, the effects of the PSQI-K on the regional SUVR were investigated using Bayesian hierarchical modeling. Brain glucose metabolism of the posterior cingulate, precuneus, and thalamus showed a negative association with total PSQI-K scores in the Bayesian model ROI-based analysis. Voxel-based analysis using statistical parametric mapping revealed a negative association between the total PSQI-K scores and brain glucose metabolism of the precuneus, postcentral gyrus, posterior cingulate, and thalamus. Poor sleep quality is negatively associated with brain glucose metabolism in the precuneus, posterior cingulate, and thalamus. Therefore, the importance of sleep should not be overlooked, even in healthy middle-aged adults.