NeuroImage最新文献

Persuasive communication is fundamental to information propagation and human social interaction. However, prior work has predominantly focused on immediate persuasive process, neglecting how decision-preferences updating following persuasion and its underlying neural reorganization. Using a naturalistic dyadic persuasion task and functional near-infrared spectroscopy (fNIRS) hyperscanning, we examined how distinct persuasion models-Role-Differentiated Leadership and Egalitarian-Reciprocity-shape decision-preference updating and group decision consensus at both behavioral and neural levels. Behaviorally, the Role-Differentiated Leadership model, rather than Egalitarian-reciprocity model, as the predominant form of persuasive communication, wherein persuadees significantly updated their decision-preferences while persuaders remained relatively stable. Intra-brain network revealed that persuadees exhibited pronounced reorganization in both global and nodal network metrics (including global efficiency, small-worldness, degree centrality, and nodal efficiency), particularly in the left temporo-parietal junction and frontoparietal regions. These neural changes predicted the magnitude of individual decision-preference updating. Furthermore, inter-brain network synchronization in fronto-temporo-parietal circuits such as rDLPFC-lSFG, lSTG-lDLPFC, and lITG-AG increased in post-ranking session compared to pre-ranking session and robustly predicted group decision consensus through support vector regression. Together, these findings provide converging neurobehavioral evidence that structured persuasive roles shape decision-preference updating through coordinated intra- and inter-brain network reorganizations, offering novel insights into how interpersonal persuasion operates in real-time social influence.

Visceral signals, such as cardiac oscillations, are considered a significant source influencing ongoing cortical activity. Research has shown that perceptual and cognitive functions fluctuate with the heart cycle. Seminal studies proposed that upstream signals tied to cardiac contraction (i.e., systole) inhibit brain activity. However, a clear relationship between cardiac phases and cortical excitability, measured by motor-evoked potentials (MEPs) via transcranial magnetic stimulation (TMS), is not yet established. To examine the link between cardiac signals and corticospinal excitability (CSE), we combined electrophysiological measures with TMS targeting the left motor cortex (lM1) in healthy individuals. Input-output (I/O) curves of MEPs were modelled relative to cardiac phases, assessing CSE variations between systole and diastole. We also investigated how different cardiac output affect MEP amplitudes on a trial-by-trial basis. Overall, I/O curves highlighted a greater inhibition of CSE during systoles, characterized by decreased MEP amplitudes at maximal stimulation intensities and a diminished corticomotor gain. Trial-by-trial assessment also indicated that MEPs amplitude may be negatively affected by the strength of cardiac output, indexed by the length of interbeat-intervals (IBIs). These findings suggest that cardiac signals actively modulate brain excitability, which holds significant implications. Accounting for the cardiac cycle can reduce variability in TMS and electrophysiological studies, improving reproducibility. Clinically, aligning non-invasive brain stimulation or neurorehabilitation protocols with phases of higher excitability (e.g., diastole) may enhance treatment efficacy and motor recovery. More broadly, the results contribute to models of brain-body interaction and may provide a physiological marker of altered heart-brain coupling in clinical populations.

Electroencephalogram (EEG) source imaging (ESI) is highly underdetermined, which poses a long-standing challenge in neuroimaging. Traditional methods typically rely on predefined priors to constrain the solution space; however, the need for manual parameter adjustments often makes it difficult to achieve optimal integration of prior information. Although recent deep learning methods can automatically update parameters in a data-driven manner, their black-box characteristics lead to a lack of interpretability and the need for extensive training sets. To integrate the advantages of these two types of methods, we propose a novel neural network model based on deep unfolding, called variation sparse source imaging network (VSSI_2p-Net). Specifically, we introduce variation sparsity and ℓ_2,p norm (02p-Net can optimize all parameters, including the critical p in ℓ_2,p-norm and the variation sparsity operator, in an end-to-end manner with a reasonably sized training set. In this way, VSSI_2p-Net achieves more flexible prior information integration while retaining the interpretability of traditional methods, so that a more accurate and efficient solution for ESI can be obtained. We compared the performance of VSSI_2p-Net with several traditional baseline methods and state-of-the-art deep learning methods on synthetic and real datasets. The results show that VSSI_2p-Net significantly outperforms existing methods in source localization accuracy, spatial range estimation, and imaging speed across various source configurations.

The dynamic changes occurring in the brain to adapt to the environment are crucial for human survival. Extensive research has demonstrated that the Tibetan population, indigenous to the plateau, has evolved unique physiological adaptations to hypoxia. However, the neurocognitive basis of these adaptive strategies remains incompletely understood. This study employs a multimodal approach (behavioral testing, event-related potentials, and time-frequency analysis) to systematically examine the effects of long-term high-altitude hypoxic exposure (3,680 m) on working memory function in indigenous Tibetans. The aim is to determine whether this impact stems from energy-constrained adaptive functional adjustments or irreversible neurofunctional impairment. Participants included high-altitude native Tibetans, Tibetan migrants residing at plain for 1 and 3 years, and low-altitude Han Chinese controls. Results revealed that spatial working memory remained unaffected in native Tibetans, while verbal working memory accuracy (ACC) showed statistically significant decline. Following relocation to the plains, verbal working memory progressively recovered with increasing duration of residence, with the 3-year group reaching control levels. Neurophysiological data further revealed compensatory increases in late positive potential (LPP) amplitude and beta-band oscillatory power among high-altitude natives, both of which exhibited linear decline with residence duration in individuals relocated to the plains. These findings indicate that high-altitude hypoxia does not cause permanent impairment of verbal working memory function. Instead, it induces selective inhibition of energy-intensive verbal processing systems under energy-constrained conditions. This inhibition is environmentally dependent and reversibly restores upon improved oxygen supply. This study confirms at the cognitive neural mechanism level that functional changes induced by high-altitude hypoxia are fundamentally energy-optimization-driven adaptive reorganization, providing crucial empirical evidence for understanding human brain plasticity under extreme conditions.

Users of hearing aids (HAs) and cochlear implants (CIs) experience significant difficulty understanding a target speaker in multi-talker environments or when other background noise is present. Segregation of a particular voice from background noise occurs partly through enhanced cortical tracking of amplitude fluctuations in the target signal. Measuring a person's cortical tracking allows decoding their focus of attention and may be used for neurofeedback in hearing devices, potentially aiding their users with speech-in-noise comprehension. Most studies on cortical speech tracking have employed typical hearing (TH) individuals, whereas studies in people with hearing impairment whose cortical tracking may differ are still scarce. The objective of this study was to compare cortical speech tracking of HA (n=29) and CI users (n=24) to that of age-matched TH individuals (n=29). We recorded EEG data while the participants attended one of two competing talkers (one with a female and one with a male voice), in a free-field acoustic environment. Importantly, HA users as well as CI users used their personal, clinically-fitted devices. Cortical speech tracking was assessed through linear backward and forward models that related the EEG data to the speech envelope. For the CI users, electrical artifacts stemming from the implant were addressed through a bespoke method for artifact rejection. We found that the HA group exhibited cortical tracking and attentional modulation that were largely comparable to those of the TH group. CI users also showed successful cortical tracking. However, they displayed a profound deficit in attentional modulation, seen in the significantly poorer neural segregation of the attended vs. the ignored speech streams. These results shed light on a neurobiological mechanism for speech-in-noise comprehension and have implications for neurofeedback in hearing devices.

In dynamic environments, flexible cognitive control adaptively adjusts processing through proactive mechanisms deployed in advance and reactive mechanisms engaged upon conflict. Previous studies have primarily focused on identifying neural networks supporting specific control components, while less is known about how multiple components interact over time to support adaptive control. To characterize these temporal dynamics, we combined EEG recordings with a face-word Stroop paradigm under changing conflict environment. A hierarchical Bayesian model was used to estimate trial-wise learning rate, predicted conflict level, and prediction error, providing computational indices of cognitive control flexibility. Neural correlation analysis indicated that these variables correlated with Theta, Alpha, and Beta oscillations in distinct brain regions. Granger causality analyses revealed connectivity patterns among these regions that varied across different task phase. Furthermore, connections reflecting updates to predicted conflict level prior to stimulus onset indexed individual strength in proactive control, while connections reflecting learning rate updates after stimulus onset indexed reactive control. These findings highlight how oscillatory dynamics coordinate multiple control components and provide new insight into how proactive and reactive control emerge as distinct modes within this interconnected neural architecture of flexible cognitive control.

The relationship between graduate students and their advisors is regarded as the core relational bond within the educational ecosystem, and serves as a crucial factor influencing the mental health of graduate students. Communication between graduate students and advisors not only facilitates the intergenerational transfer of knowledge but also embodies dynamic interpersonal emotion regulation. As a cornerstone of relational harmony, individual mental health, and collective well-being, interpersonal emotion regulation aligns with Social Baseline Theory, which posits that emotional and behavioral regulation operate more smoothly and require fewer psychological resources when individuals are surrounded by familiar and predictable others. This study recruited 62 teacher-student dyads to examine the interaction between graduate students and teachers and explored the impact of teacher-student closeness on the effectiveness of graduate students' interpersonal emotion regulation and underlying neural mechanisms. Higher levels of teacher-student closeness were associated with stronger interpersonal emotion regulation in graduate students when using both cognitive reappraisal and expressive suppression strategies (F(1, 60)=4.28, p=0.04<0.05, η_p²=0.07). Hyper-scanning revealed that when the teacher-student closeness was high, the interpersonal brain synchronization in the right dorsolateral prefrontal cortex was significantly enhanced (F(1, 39)=7.22, p=0.01<0.05, η_p²=0.16). Moreover, it positively predicted the effectiveness of interpersonal emotion regulation (R²=0.18, Beta=0.43, t=2.12, p=0.048). These findings provide support for both the behavioral and neural underpinnings for the interpersonal emotion regulation mechanisms in teacher-student interactions, thereby offering theoretical and practical insights for building mental health support systems for graduate students.

Interpersonal motor interactions are central to social life, yet it remains unclear how social cues relevant to detecting engagement are encoded in the brain. Recent evidence suggests that regions traditionally associated with mentalizing, such as the dorsomedial prefrontal cortex (dmPFC) and temporo-parietal junction (TPJ), co-activate with nodes of the Action Observation Network (AON) during motor engagement with others, pointing to a synergistic role in the processing of action features during interaction. Using fMRI and Representational Similarity Analysis (RSA), we examined brain responses to reach-to-grasp actions varying in Goal (passing vs. placing), Perspective (2nd vs. 3rd person), and Gaze visibility, creating a gradient of perceived engagement. Our results show TPJ-AON convergent representational geometry during action observation, with temporo-parietal and premotor regions, but not dmPFC, showing correlated dynamics of pattern modulation. Model-based analyses showed a graded organisation within the right temporo-parietal cortex, with perspective encoded in the rTPJ, goal-related information in superior temporal regions, and perceived engagement uniquely encoded in the rIPL. Moreover, the rTPJ and left premotor cortex shared representational geometry for action direction, linking mentalizing and sensorimotor systems in the encoding of first-person-relevant cues. In contrast, the dmPFC showed an isolated representational geometry and did not encode action features or perceived engagement, consistent with its recruitment during richer interactive contexts. Together, these findings support a distributed, sensorimotor account of engagement encoding and reveal new functional links among key social cognition areas.

Amblyopia is a neurodevelopmental disorder characterized by reduced visual acuity due to abnormal visual experience during critical periods. In adulthood, the diminished plasticity of the primary visual cortex (V1) presents a major barrier to effective treatment. Here, we investigate whether baicalin, a flavonoid derived from Scutellaria baicalensis, can restore ocular dominance plasticity (ODP) and promote functional recovery in a mouse model of adult amblyopia. Using intrinsic signal optical imaging and electrophysiological recording, we demonstrate that 10 mg/kg baicalin treatment reactivates ODP in adult mice, whereas 5mg/kg or Scutellaria water extract fails to do so. Furthermore, baicalin combined with reverse suturing in adult amblyopic mice restored both ocular dominance distribution and visual acuity to normal levels. Baicalin treatment reduced the expression of two major GABA synthetic enzymes (glutamate decarboxylase, GAD65/67) and perineuronal nets in V1, while administration of the GABA_A receptor agonist muscimol during the baicalin treatment blocked the rescued ODP. These findings suggested that a reduction in cortical inhibition might underlie the restoration of visual plasticity in adults. Our results suggest that baicalin may serve as a potential therapy for adult amblyopia.

Human neuroimaging studies indicate that the early visual cortex (EVC), including the primary visual cortex (V1), is involved in haptic exploration of objects, even when visual information is not available. However, it remains unknown whether the features of haptically explored objects, like size, are represented in the EVC. Here, we investigated whether we can use the activity pattern in the EVC and other task-relevant brain regions to decode stimulus size during haptic exploration, and whether this effect is due to visual imagery. Twenty-five right-handed participants haptically explored or imagined the size of three rings (small, medium, large) in a slow-event-related fMRI study. Participants were blindfolded during the training and fMRI sessions. Using multivariate pattern analysis, we found that V1 and the occipital pole (OP) showed accurate decoding of stimulus size during haptic exploration, but not imagery trials. This suggests that the activity patterns observed in the haptic condition cannot be explained by visual imagery. Frontal and parietal regions, as well as the multisensory lateral occipital tactile-visual area (LOtv), showed accurate size decoding during both haptic and imagery conditions, suggesting a flexible representation of stimulus size that adapts to task demands. In addition, stimulus size could be decoded across tasks in the anterior and posterior intraparietal sulcus (aIPS, pIPS), and dorsal premotor cortex (dPM). Psychophysiological interaction analysis indicated that V1 and OP showed stronger functional connectivity with ventral and dorsal visual stream areas during the haptic as compared to the imagery task. Overall, stimulus size information is similarly represented in frontal and parietal cortices across haptic exploration and imagery, but not in early visual areas, demonstrating that only regions specialized for haptic exploration and imagery support generalized size representations.