NeuroImage最新文献

Background

Grating orientation discrimination (GOD) is commonly used to assess somatosensory spatial processing. It allows discrimination between parallel and orthogonal orientations of tactile stimuli applied to the fingertip. Despite its widespread application, the underlying mechanisms of GOD, particularly the role of cortico-cortical interactions and local brain activity in this process, remain elusive. Therefore, we aimed to investigate how a specific cortico-cortical network and inhibitory circuits within the primary somatosensory cortex (S1) and secondary somatosensory cortex (S2) contribute to GOD.

Methods

In total, 51 healthy young adults were included in our study. We recorded resting-state magnetoencephalography (MEG) and somatosensory-evoked magnetic field (SEF) in participants with open eyes. We converted the data into a source space based on individual structural magnetic resonance imaging. Next, we estimated S1- and S2-seed resting-state functional connectivity (rs-FC) at the alpha and beta bands through resting-state MEG using the amplitude envelope correlation method across the entire brain (i.e., S1/S2-seeds × 15,000 vertices × two frequencies). We assessed the inhibitory response in the S1 and S2 from SEFs using a paired-pulse paradigm. We automatically measured the GOD task in parallel and orthogonal orientations to the index finger, applying various groove widths with a custom-made device.

Results

We observed a specific association between the GOD threshold (all P < 0.048) and the alpha rs-FC in the S1–superior parietal lobule and S1–adjacent to the parieto-occipital sulcus (i.e., lower rs-FC values corresponded to higher performance). In contrast, no association was observed between the local responses and the threshold.

Discussion

The results of this study underpin the significance of specific cortico-cortical networks in recognizing variations in tactile stimuli.

While the significance of obtaining restful sleep at night and maintaining daytime alertness is well recognized for human performance and overall well-being, substantial variations exist in the development of sleepiness during diurnal waking periods. Despite the established roles of the hypothalamus and striatum in sleep-wake regulation, the specific contributions of this neural circuit in regulating individual sleep homeostasis remain elusive. This study utilized resting-state functional magnetic resonance imaging (fMRI) and mathematical modeling to investigate the role of hypothalamus-striatum connectivity in subjective sleepiness variation in a cohort of 71 healthy adults under strictly controlled in-laboratory conditions. Mathematical modeling results revealed remarkable individual differences in subjective sleepiness accumulation patterns measured by the Karolinska Sleepiness Scale (KSS). Brain imaging data demonstrated that morning hypothalamic connectivity to the dorsal striatum significantly predicts the individual accumulation of subjective sleepiness from morning to evening, while no such correlation was observed for the hypothalamus-ventral striatum connectivity. These findings underscore the distinct roles of hypothalamic connectivity to the dorsal and ventral striatum in individual sleep homeostasis, suggesting that hypothalamus-dorsal striatum circuit may be a promising target for interventions mitigating excessive sleepiness and promoting alertness.

Pain is a complex experience that involves sensory, emotional, and motivational components. It has been suggested that pain arising from the head and orofacial regions evokes stronger emotional responses than pain from the body. Indeed, recent work in rodents reports different patterns of activation in ascending pain pathways during noxious stimulation of the skin of the face when compared to noxious stimulation of the body. Such differences may dictate different activation patterns in higher brain regions, specifically in those areas processing the affective component of pain. We aimed to use ultra-high field functional magnetic resonance imaging (fMRI at 7-Tesla) to determine whether noxious thermal stimuli applied to the surface of the face and body evoke differential activation patterns within the ascending pain pathway in awake humans (n=16). Compared to the body, noxious heat stimulation to the face evoked more widespread signal changes in prefrontal cortical regions and numerous brainstem and subcortical limbic areas. Moreover, facial pain evoked significantly different signal changes in the lateral parabrachial nucleus, substantia nigra, paraventricular hypothalamus, and paraventricular thalamus, to those evoked by body pain. These results are consistent with recent preclinical findings of differential activation in the brainstem and subcortical limbic nuclei and associated cortices during cutaneous pain of the face when compared with the body. The findings suggest one potential mechanism by which facial pain could evoke a greater emotional impact than that evoked by body pain.

Background

Quantitative susceptibility mapping (QSM) is a post-processing technique that creates brain susceptibility maps reflecting metal burden through tissue magnetic susceptibility. We assessed topographic differences in magnetic susceptibility between participants with and without Wilson's disease (WD), correlating these findings with clinical severity, brain volume, and biofluid copper and iron indices.

Methods

A total of 43 patients with WD and 20 unaffected controls, were recruited. QSM images were derived from a 3T MRI scanner. Clinical severity was defined using the minimal Unified Wilson's Disease Rating Scale (M-UWDRS) and Montreal Cognitive Assessment scoring. Differences in magnetic susceptibilities between groups were evaluated using general linear regression models, adjusting for age and sex. Correlations between the susceptibilities and clinical scores were analyzed using Spearman's method.

Results

In age- and sex-adjusted analyses, magnetic susceptibility values were increased in WD patients compared with controls, including caudate nucleus, putamen, globus pallidus, and substantia nigra (all p < 0.01). Putaminal susceptibility was greater with an initial neuropsychiatric presentation (n = 25) than with initial hepatic dysfunction (n = 18; p = 0.04). Susceptibility changes correlated negatively with regional brain volume in almost all topographic regions. Serum ferritin, but not serum copper or ceruloplasmin, correlated positively with magnetic susceptibility level in the caudate nucleus (p = 0.04), putamen (p = 0.04) and the hippocampus (p = 0.03). The dominance of magnetic susceptibility in cortical over subcortical regions correlated with M-UWDRS scores (p < 0.01).

Conclusion

The magnetic susceptibility changes could serve as a surrogate marker for patients with WD.

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.