NeuroImage最新文献

The brain constantly makes predictions about upcoming input, and prediction errors (PEs) have been shown to promote encoding of the unexpected information. So far, previous experimental designs have left it unclear if PEs that may be evoked by the first exposure to a coherent novel stimulus, based on individual knowledge, experiences, and beliefs, can affect subsequent memory processes. In the current study, we aimed to test the neural and mnemonic consequences of these initial PEs and how they influence such outcomes together with later induced, experimental PEs. To this end, participants (N = 42) listened to naturalistic dialogues, which induced an initial PE, while undergoing fMRI scanning. Later, the dialogues were modified to induce a second, experimental PE, and memory for the original and modified versions was assessed using a recognition test. The results showed that initial PEs, like experimentally induced PEs, shifted the balance from top-down predictions to bottom-up processing, as reflected in reduced predictive reinstatement and stronger activation in the auditory cortex upon re-exposure. Moreover, semantic components of both initial and experimental PEs enhanced learning, while IFG activation biased memory towards the currently activated representation rather than the novel input. Taken together, these findings provide first evidence for the existence and relevance of initial PEs that are evoked during the encoding of coherent episodes not obviously violating world knowledge based on individual experiences and beliefs, indicating that they should be taken into consideration in paradigms investigating episodic PEs.

Collective human behavior plays a crucial role in the development of culture. However, whether and how different forms of collective behavior contain different social dynamics remains a cross-disciplinary debate regarding the mentalization during joint action in psychology as well as the sociality of music in ethnomusicology. This study delves into the comparison between congruent and incongruent joint actions from an interpersonal neural standpoint within the context of a joint musical performance. Simultaneously recording the neural activities of fifty pairs of string players during performance, we identified distinct regions within the mentalizing network, specifically the prefrontal cortex (PFC) and the left temporoparietal junction (TPJ), that support congruent (unison) and incongruent (melody-accompaniment) musical performances, respectively. During incongruent performances, higher levels of interpersonal neural coupling (INC) were observed in the left TPJ, an area responsible for adjusting the differences between self and others. In contrast, during congruent performances, higher INC was seen in the PFC, an area associated with monitoring and predicting the actions of others. Quantitative and qualitative data showed converging evidence that incongruent performances were more demanding, requiring more attention to the partner and precise coordination of intonation and rhythm. Moreover, the melody player led the accompanist in terms of INC during incongruent performances, which also revealed greater consensus in ratings between players and the audience. Our study highlighted the social significance of incongruent joint actions.

Large-scale brain networks are well-established in resting-state research and are increasingly being used in task-based functional magnetic resonance imaging (fMRI) studies. However, the mechanisms by which brain networks dynamically reorganize across the various stages of decision-making remain unclear. Here, we investigated the neural basis of decision-making by integrating voxel-based morphometry and fMRI within a modified "Wheel of Fortune" gambling task. Stage-specific brain activation was characterized using the Yeo-7 network atlas to delineate large-scale network dynamics across task stages. We found that: (1) Reaction time (RTs) were significantly longer during choose conditions compared to follow conditions; (2) Gray matter volume correlated with individual variability in RT and predicted RT during choose conditions using multivariate pattern analysis with a Kernel Ridge Regression model, effects absent during follow conditions; (3) A negative correlation was observed between RT and activation in the right superior temporal gyrus and left mid-cingulate cortex; (4) Choice stage involved more extensive network engagement than the result and rating stages, with the rating stage showing the lowest overall activation. Network-specific fractional contributions revealed dominant engagement of the ventral attention network, default mode network, and somato-motor network during the choice stage; the frontoparietal network (FPN), dorsal attention network (DAN), and visual network during the result stage; and the DAN and FPN during the rating stage. These findings provide structural and functional explanations for individual differences in decision speed within a gambling paradigm, revealing the distinct and dynamic roles of brain networks across decision stages and offering mechanistic insights into the neural architecture of this process.

The hypothalamus is a key homeostatic regulatory region which contains nuclei and subregions known to mediate a range of body functions. Numerous studies have revealed that the hypothalamus is critical in coordinating sexual dimorphism in neuroendocrine and behavioural phenotypes and displays sex-related structural differences. The hypothalamus is critical for the body's stress response and cortisol release, and females are twice as likely as males to develop many diseases related to hypothalamic-pituitary-adrenal axis dysfunction. Given this, it is important understand the sex plays in hypothalamic structure and function. In this study, we used ultra-high field functional magnetic resonance imaging to determine sex-related differences in regional hypothalamic resting state connectivity in 217 control participants: 123 females, 94 males. We found robust sex-related difference in the anatomy and function of the left supraoptic and anterior hypothalamic regions. Both hypothalamic regions displayed greater regional volumes in males compared with females. In addition, both regions displayed negative connectivity strengths in females and positive connectivity strengths in males with numerous brain regions, most significantly with association cortical areas such as the dorsolateral and medial prefrontal and cingulate cortices. These results reveal that discrete regions of the hypothalamus display sex-related differences in structure and function, as assessed resting functional connectivity differences with various brain regions. These differences are critical for our understanding of the role of the hypothalamus in fundamental physiological processes and may underpin sex-specific vulnerabilities to neurological and psychiatric disorders.

Interpersonal synchrony-moving and thinking in time with someone else-may be a key engine of children's learning. We studied 5-6-year-olds and their caregivers as they played an imitation-based "Mirror Game" and a goal-directed "Labyrinth Game," recording full-body motion and dual-EEG. To ensure effects reflected real interaction, we compared each dyad to many randomly recombined "pseudo pairs." Dyads aligned both their movements and their brain activity, with the most consistent neural coupling in the gamma range-a rhythm linked to attention and real-time coordination. Alignment shifted with task demands and was associated with more efficient performance, yet it did not track children's general motor ability (MABC-2), suggesting that synchrony is an emergent property of interaction rather than a simple proxy for motor maturity. By jointly measuring behavior and brain in naturalistic tasks, this work points to synchrony as a measurable mechanism-and potential target-for boosting engagement and motor learning in early childhood.

We assessed the coma outcome prediction using a deep learning analysis applied to resting EEG on the first and second day after cardiac arrest (CA), and its complementarity to clinical prognosis. We recorded 62-channel resting-state EEG in comatose patients across three Swiss hospitals during the first (N=165) and second (N=100) day of coma. Patient outcome was classified as favorable if the best Cerebral Performance Category was 1-2. A convolutional neural network provided a predicted probability for favorable outcome for each patient and recording day. Predictive performance was additionally evaluated on an external 19-channel dataset collected outside Switzerland (N=60). The deep learning prediction was compared to EEG-based clinical markers, brainstem reflexes and motor responses. On the first day, for 62 channels, sensitivity and specificity for favorable outcome were 0.98±0.01 and 0.88±0.05 when maximizing both metrics. A sensitivity of 0.98±0.01 and a specificity of 0.64±0.14 was achieved when maximizing the sensitivity for favorable outcome and a sensitivity of 0.41±0.11 and a specificity of 0.99±0.01 when maximizing unfavorable outcome specificity. On the first day, using 19 channels, we obtained marginally lower values for sensitivity at 0.95±0.02 and specificity at 0.84±0.05 for favorable outcome. On the external dataset, sensitivity and specificity for favorable outcome were 0.83 and 0.92. The second day was less predictive with 0.63±0.04 sensitivity and 0.80±0.09 specificity for favorable outcome. The outcome prediction was consistent with clinical markers, except brainstem reflexes. On the first day of coma, a deep learning analysis of resting-state EEG provides accurate outcome prediction, complementing clinical markers.

Neuroticism, a stable personality trait marked by heightened negative affect and emotional volatility, is a well-established transdiagnostic risk factor for internalizing psychopathology. While early research emphasized amygdala hyperreactivity as a core neural correlate, emerging evidence suggests that neuroticism may be more accurately characterized by dysfunctional connectivity between the amygdala and broader regulatory networks involved in emotion processing and cognitive control. In this cross-sectional fMRI study, 115 healthy adults completed a classification task involving negative emotional facial expressions. Neuroticism was assessed using a latent factor score derived from five validated self-report instruments. Brain activity and psychophysiological interaction analyses were conducted using both region-of-interest and whole-brain approaches. Associations between neural measures and neuroticism were tested using robust regression, controlling for age and sex. No evidence was found for an association between neuroticism and regional brain activity. However, higher neuroticism was associated with increased task-dependent functional connectivity between the amygdala and both the hippocampus and dorsolateral prefrontal cortex. Whole-brain analyses further revealed associations between neuroticism and amygdala coupling with regions implicated in emotion regulation and salience processing, including the anterior insula and dorsal cingulate cortex. These findings support the conceptualization of neuroticism as a network-level phenomenon, characterized by dysregulated interactions within fronto-limbic and salience circuits, rather than by localized changes in brain activity. Specifically, increased amygdala-hippocampal and amygdala-prefrontal connectivity may underlie the persistence and regulation difficulties of negative emotions that characterize the neurotic phenotype.

A growing body of evidence highlights the significant role of internal rhythmic signals from the heart, lungs, and stomach in shaping perception. These physiological cycles influence sensory processing across various tasks, ranging from basic detection to complex functions such as emotion recognition and decision-making. The first section of this review discusses the physiological underpinnings of each organ-brain axis and synthesizes research illustrating how cardiac, respiratory, and gastric rhythms impact perceptual experience. Altogether, these findings highlight the influence of interoceptive rhythms on moment-to moment perception. Although effects may vary based on specific task demands, a key trend emerges: perception may fluctuate as signal processing resources are dynamically required between external sensory demands and internal bodily cycles. Acknowledging limitations of existing research, the second section indicates strategies to enhance the ecological validity and generalizability of interoception-perception studies. Our review of participant demographics reveals a pressing need for greater diversity. Also, we propose incorporating multimodal physiological recordings, wearable technologies, and active paradigms that better reflect real-world behaviors. Finally, we examine emerging theoretical models integrated into ecologically relevant research designs. By proposing a novel model of Perception-Interoception interactions, as well as bridging traditional laboratory methods with naturalistic settings, our novel ecological framework may advance the understanding of how interoceptive signals shape embodied perception in daily life.

Humans comprehend speech in noisy environments more effectively when they can see the talker's facial movements. While the benefits of audiovisual (AV) speech are well established, the specific visual features that support this enhancement and its underlying neural mechanisms remain unclear. Here, we examine how simplified facial signals that preserve structural and dynamic information affect AV speech-in-noise comprehension as well as neural speech tracking. In a behavioural experiment, participants viewed natural or progressively simplified facial videos while listening to short sentences in background noise. Visual stimuli included natural facial recordings, coarse facial outlines, and a simple geometric analogue of visual speech-a disk whose radius oscillated with the speech envelope. In an EEG experiment, we assessed how the progressively simplified visual signals influenced cortical tracking of the speech envelope during continuous AV speech. Behaviourally, we found that comprehension improved with increasing visual detail, while the disk provided no AV benefit, underscoring the importance of dynamic facial cues. For the EEG experiment, only the most natural visual signals enhanced delta-band (1-4 Hz) temporal response functions (TRFs) relative to audio-only stimulation, peaking around 180 ms. This neural enhancement correlated with behavioural benefit across participants. Theta-band effects were weaker and less consistent, suggesting a more limited role in AV integration. Together, these findings highlight the importance of facial detail in AV speech perception, with natural visual input driving stronger delta-band tracking and potentially reflecting alignment of auditory processing with word-level visual cues.

Constrained Spherical Deconvolution (CSD) is widely used to estimate the white matter fiber orientation distribution (FOD) from diffusion MRI data. Its angular resolution depends on the maximum spherical harmonic order (l_max): low l_max yields smooth but poorly resolved FODs, while high l_max, as in Super-CSD, enables resolving fiber crossings with small inter-fiber angles but increases sensitivity to noise. In this proof-of-concept study, we introduce Spatially Regularized Super-Resolved CSD (SR²-CSD), a novel method that regularizes Super-CSD using a spatial FOD prior estimated via a self-calibrated total variation denoiser. We evaluated SR²-CSD against CSD and Super-CSD across four datasets: (i) the HARDI-2013 challenge numerical phantom, assessing angular and peak number errors across multiple signal-to-noise ratio (SNR) levels and CSD variants (single-/multi-shell, single-/multi-tissue); (ii) the Sherbrooke in vivo dataset, evaluating spatial coherence of FODs; (iii) a six-subject test-retest dataset acquired with both full (96 gradient directions) and subsampled (45 directions) protocols, assessing reproducibility; and (iv) the DiSCo phantom, evaluating tractography accuracy under varying SNR levels and multiple noise repetitions. Across all evaluations, SR²-CSD consistently reduced angular and peak number errors, improved spatial coherence, enhanced test-retest reproducibility, and yielded connectivity matrices more strongly correlated with ground-truth. Most improvements were statistically significant under multiple-comparison correction. These results demonstrate that incorporating spatial priors into CSD is feasible, mitigates estimation instability, and improves FOD reconstruction accuracy.