NeuroImage最新文献

This study applied diffusion-prepared pseudo-continuous arterial spin labeling (DP-pCASL) to quantify cerebral blood flow (CBF), arterial transit time (ATT), and blood-brain barrier (BBB) water exchange rate (K_w) before and after focused ultrasound (FUS)-mediated blood-brain barrier opening (BBBO) in the dorsal striatum of four non-human primates. Six baseline and seven BBBO sessions were performed. DP-pCASL was acquired approximately 45 min after FUS sonication combined with intravenous microbubbles, and contrast-enhanced T1-weighted imaging was subsequently used to confirm the BBBO region. Whole-brain analyses revealed no significant changes in CBF or ATT following BBBO (permutation p > 0.05). Region-of-interest analysis within the sonicated caudate demonstrated a significant localized decrease in K_w, with median (IQR) values of 45.0 (40.6 - 55.6) min⁻¹ at the BBBO site versus 61.6 (58.3 - 70.4) min⁻¹ in the contralateral control region (p < 0.05), confirming spatially specific suppression of transendothelial water flux. In contrast, whole-brain K_w increased significantly following BBBO, with median (IQR) values of 49.8 (46.3 - 55.9) min⁻¹ in non-BBBO sessions versus 59.4 (56.6 - 66.3) min⁻¹ in BBBO sessions (p < 0.01), indicating a diffuse enhancement of water exchange across the brain. These findings establish DP-pCASL-derived K_w as a sensitive, non-contrast biomarker for both local and global BBB permeability changes induced by focused ultrasound, supporting its potential for longitudinal monitoring in preclinical and clinical neurotherapeutic applications.

Understanding the mechanisms of motor imagery, the mental simulation of movement without execution, is key for the development of neurotechnologies, including understanding inter-individual variability in motor imagery performance. For instance, for detecting covert motor intent in noncommunicative patients or refining motor commands through brain-computer interfaces. While motor imagery engages motor-related brain regions, its precise mechanisms remain unclear, particularly in relation to cardiac dynamics. Evidence suggests heart-rate variability features have potential to enhance tasks' classifications, yet the brain-heart relationship is not well understood. In this study, we examined motor imagery learning using a task involving right-hand grasping imagery. We found that motor imagery is correlated with a task-dependent modulation of cardiac sympathetic activity and its relation with directed functional connectivity from the supplementary motor area to premotor and primary motor cortices. Additionally, cerebellar-supplementary motor area segregation, in relation to cardiac parasympathetic activity, indexed longitudinal motor learning. These results suggest that dynamic reconfiguration of brain-heart interactions contributes to sensorimotor function and learning-related physiology during motor imagery, supporting the brain-heart axis as a functional component of motor imagery rather than a passive correlate.

Auditory areas on the superior temporal plane and lateral convexity are key initial stages of speech processing in the human cortex, representing acoustic and phonetic attributes in a temporally precise manner. More complex representations in auditory-related cortex along the ventral and dorsal processing streams and prefrontal cortex are associated with perception and action. In this study, we used intracranial electroencephalography (iEEG) to clarify where and how activity leading to perceptually driven behavioral events emerges. Participants were patients undergoing iEEG monitoring for medically intractable epilepsy. Stimuli were monosyllabic words, and participants pressed a button in response to a semantic target category. Significant high gamma activity after stimulus onset and immediately prior to motor response defined stimulus- and behavior-related activity patterns, respectively. The stimulus-related pattern was more common than behavior-related throughout the cortical auditory hierarchy as well as sensorimotor cortex. Behavior-related activity was sparsely represented, with the highest prevalence in the prefrontal cortex and a more limited representation in anterior temporal and parieto-occipital cortex. Hemispheric asymmetries included a higher prevalence of stimulus-related activity in the right sensorimotor cortex and a higher prevalence of the behavior-related pattern in the left prefrontal cortex. Faster behavioral responses were associated with greater stimulus-locked high gamma power in non-core auditory, prefrontal, and premotor cortex. Results reveal the cortical distribution of sensory stimulus-driven responses and activity time-locked to behavior and provide insights into neural substrates of speech perception.

The right dorsolateral prefrontal cortex (rDLPFC) plays a crucial role in fairness-related decision-making, yet it remains unclear whether its role reflects a fairness-promoting mechanism or a broader control process that regulates competing motivations. Here, we combined noninvasive brain stimulation with a third-party ultimatum game to examine how modulating rDLPFC excitability influences fairness decisions in which fairness-related considerations and concern for others differ in relative salience. Ninety healthy female university students (mean age 20.92 ± 2.35) decided whether to accept or reject offers on behalf of university students or charity recipients across three inequality conditions (advantageous, disadvantageous, and equal). Behavioral analyses revealed context-dependent effects of rDLPFC stimulation. Compared with the sham condition, anodal stimulation increased acceptance of advantageous inequality offers for university students (β = 1.27, P = 0.002) but decreased acceptance for charity recipients (β = -1.77, P = 0.003), with no significant effects in disadvantageous or equality conditions. Subjective emotion and fairness ratings followed similar patterns, and mediation analyses indicated that emotion partially mediated the influence of stimulation on choice. These findings support a domain-general account, indicating that the rDLPFC flexibly regulates dominant motivational tendencies depending on social context. Overall, the study demonstrates the adaptive role of the rDLPFC in integrating fairness norms with competing social motives during decision-making.

While generative artificial intelligence (GenAI) has advanced personalized interactive learning, the cognitive and neural mechanisms underlying learners' informal reasoning improvement remain underexplored. Thus, we conducted sliding-window correlation and k-means clustering to capture learners' dynamic functional connectivity (dFC) states, time-varying correlations among brain regions, and examined their correlations with the informal reasoning improvement. 78 participants completed a learning task under either a traditional search engine-supported interactive learning mode (TSE group) or a human-GenAI interactive learning mode (GenAI group). Functional near-infrared spectroscopy (fNIRS) was employed to measure cortical hemodynamic responses from the medial and dorsolateral prefrontal cortex and the right temporo-parietal regions. The results showed that the two groups demonstrated no significant difference in informal reasoning improvement. Moreover, both groups presented a series of dynamic dFC states throughout the learning process, and the properties of these dFC states were similar across groups. Nevertheless, the neural correlates underlying informal reasoning improvement differed across groups. In the GenAI group, State 1, associated with goal-directed sense-making processes, showed a significant positive correlation with informal reasoning improvement. In contrast, in the TSE group, State 3, associated with the retrieval and extraction of task-relevant information, was significantly positively correlated with informal reasoning improvement. These findings deepen our understanding of brain dynamics in learning and uncover shared and distinct neural mechanisms that characterize the GenAI-supported and traditional search engine-supported interactive learning modes.

Transcranial Magnetic Stimulation with simultaneous Electroencephalogram (TMS-EEG) allows for the assessment of neurophysiological properties of cortical neurons. However, TMS-evoked EEG potentials (TEPs) can be affected by components unrelated to TMS direct neuronal activation. Accurate, automatic tools are therefore needed to establish the quality of TEPs. We defined innovative comparisons, including effects of both baseline and post-TMS responses, while employing a sequence-to-sequence machine learning model to objectively ascertain active TMS vs. sham stimulation responses. Two independent TMS-EEG datasets including TMS and several sham stimulation conditions were obtained from the left motor area of 33 healthy individuals (total: 27,590 trials across datasets). A Bi-directional Long Short-Term Memory (BiLSTM) machine learning network was used to label each time point of the EEG signals as pertaining to TMS or sham conditions. For TMS conditions, post-stimulus vs. baseline/pre-stimulus EEG comparisons yielded moderate (60 %-75 %) single-trial accuracy and high-accuracy (>75 %) for 20 trials across datasets. For sham conditions, post- vs. baseline/pre-stimulus EEG comparisons yielded lower accuracy rates than for TMS conditions, except for unmasked auditory stimulation. Baseline/pre-stimulus TMS vs. baseline/pre-stimulus sham EEG comparisons showed chance-level accuracy. Conversely, post-stimulus TMS vs. post-stimulus sham EEG comparisons had moderate (single trial) to high (20 trial) accuracy, except for TMS with and without the click noise masking. Consistently across datasets, TEPs after active TMS are discernible from various sham stimulations after few trials and at the single-subject level using a BiLSTM ML model. This approach offers objective criteria to support TEP authenticity, which may help address ongoing discussions about TEP characteristics in TMS-EEG studies.

The human brain depends on dynamic interactions among modular networks, where connector and provincial hubs facilitate efficient information integration. Most previous studies have relied on single metrics or qualitative labels to identify hubs, overlooking multi-metric integration and the quantitative contributions of nodes. Here, we introduce the Multi-Indicator Entropy Hub Score (MIEHS), which integrates six graph-theoretical metrics to quantify hub properties. Validated on benchmark and simulated networks as well as resting-state fMRI data from the Midnight Scan Club dataset, MIEHS reliably identifies hubs. High-scoring connector hubs were localized in the attention network, whereas high-scoring provincial hubs were concentrated in the default mode network. Gradient mapping further revealed that connector hubs bridge unimodal and transmodal regions, supporting information transfer from primary sensory areas to higher-order cognitive regions, while provincial hubs primarily sustain intra-network communication. Null model analyses highlighted the stability of hubs within the default mode and limbic networks. Although hubs are widely studied, they have not yet been established as robust clinical biomarkers. Using Partial Least Squares analysis in the UCLA dataset (HC = 110, ADHD = 37, BD = 40, SCHZ = 37), we observed significant associations between hub alterations in the DMN, SMN, limbic, DAN, and control networks and measures of cognitive flexibility, abstract reasoning, and verbal expression. Together, these findings demonstrate that MIEHS provides a robust and versatile framework for mapping brain network organization and characterizing functional reconfiguration.

Cortical oscillations across sleep-wake cycles are essential for coordinating functional brain dynamics. High-density electroencephalography (HDEEG) combined with electrical source imaging (ESI) provides a noninvasive approach to map cortical dynamics; however, its ability to capture spatial ongoing oscillations across different vigilance states remains uncertain. Here, we directly compared HDEEG source imaging by comparing it to a normative intracranial EEG (iEEG) atlas from 110 epilepsy patients with electrodes in healthy brain regions (https://mni-open-ieegatlas.research.mcgill.ca/). Wavelet-based Maximum Entropy on the Mean (wMEM) was applied to localize oscillatory patterns using overnight HDEEG recordings from 35 healthy adults (16 females, mean age 31.1±6.3 years). Virtual iEEG (ViEEG) signals were estimated by applying an iEEG forward model to wMEM sources to examine oscillatory patterns across 5 frequency bands, 38 regions, and 4 vigilance states. We found that HDEEG source imaging exhibited comparable spectral patterns of iEEG in low frequencies but overestimated oscillatory activities at high frequencies. Lateral cortical regions exhibited more accurate source estimation than medial regions (p<0.05). After removing the aperiodic components, the spectral alignment between ViEEG and iEEG significantly improved except for N3 sleep (p<0.05). Oscillatory peak patterns in ViEEG reflect state-dependent dynamics that are broadly consistent with iEEG peaks (p<0.05). HDEEG-derived ViEEG and magnetoencephalography-derived ViEEG approximated iEEG spectral features, showing complementary correspondence. These findings reveal that vigilance states significantly shape cortical oscillations by altering their spectral and spatial profiles. Our results establish high-density EEG as a powerful tool for large-scale, noninvasive investigations of human sleep neurophysiology and brain network dynamics.

Magnitude, such as time, space and number, constitutes a foundational construction of perceptual and cognitive experience. A Theory of Magnitude proposes that these dimensions are processed by a shared magnitude system. However, it remains unclear how this system supports the manipulation of multiple continuous magnitude information, such as arithmetic operation across domains and whether it operates as a fully shared or partially specialized subsystem. Using functional magnetic resonance imaging, we investigated magnitude arithmetic in numerical, spatial and temporal domains. Conjunction analyses were conducted at each arithmetic stage (first operand, second operand, and probe) to identify overlapping activations across magnitude dimensions. To explore domain-specific differences, voxel-wise factorial ANOVA and task-dependent functional connectivity analyses were applied. Conjunction analyses revealed several overlapping neural substrates at each stage, including key components of the magnitude system. Voxel-wise factorial ANOVA demonstrated that numerical and spatial arithmetic showed greater activation in the bilateral superior occipital gyrus and left middle occipital gyrus, whereas temporal arithmetic elicited stronger activation in left precentral gyrus. Functional connectivity analyses revealed that temporal arithmetic showed greater coupling with bilateral precentral gyrus and cerebellum, whereas numerical and spatial arithmetic elicited stronger coupling with left inferior occipital gyrus, by using bilateral IPS as seed regions. In summary, arithmetic across different magnitude domains relies on a shared magnitude network, with numerical and spatial operations depending on visuospatial mechanisms while temporal operations depend on sensorimotor mechanisms. This dissociation indicates that static and dynamic magnitudes are supported by distinct subsystems, providing evidence for a partially shared magnitude system.

Stress significantly contributes to cardiovascular diseases such as Takotsubo syndrome (TTS), which mimics an acute coronary syndrome without coronary obstruction. TTS is triggered by surgery, trauma, and emergency treatments in patients, and is reproduced in animal models by a catecholamine surge that impacts cardiac sympathetic innervation. The action of catecholamines on energy metabolism is well documented in the heart, less so in the brain. We investigated the effects of acute catecholaminergic stress on regional cerebral glucose metabolism and interregional metabolic organization in a TTS rat model using FDG-PET and quantitative two-tissue compartment modeling. Adult female rats received a single intraperitoneal injection of isoprenaline (ISO) (50 mg/kg). Dynamic FDG-PET imaging was performed at baseline, 2 hours (acute phase), and 7 days (recovery phase) post-injection. Kinetic parameters, namely glucose inflow (K1) and glucose phosphorylation (k3), were quantified in 58 brain regions. Interregional metabolic coordination, defined as statistically significant linear correlations between regional kinetic parameters, was assessed across functional brain areas. During the acute phase, the catecholaminergic surge induced widespread reductions in glucose inflow and regional decreases in phosphorylation, particularly in the limbic and sensorimotor areas. During the recovery phase, most regions remained below baseline. Metabolic coordination increased for glucose inflow in both phases but declined for phosphorylation, especially during recovery, indicating a disruption of metabolic synchronization. Persistent changes in brain metabolism imply that mid-to-long-term changes in regional cerebral metabolism may contribute to long-term TTS consequences.