Cognitive Affective & Behavioral Neuroscience最新文献

Justice sensitivity (JS) reflects personal concern and commitment to the principle of justice, showing considerable heterogeneity among the general population. Despite a growing interest in the behavioral characteristics of JS over the past decades, the neurobiological substrates underlying trait JS are not well comprehended. We addressed this issue by employing a machine learning approach to decode the trait JS, encompassing its various orientations, from whole-brain resting-state functional connectivity. We demonstrated that the machine-learning model could decode the individual trait of other-oriented JS but not self-oriented JS from resting-state functional connectivity across multiple neural systems, including functional connectivity between and within parietal lobe and motor cortex as well as their connectivity with other brain systems. Key nodes that contributed to the prediction model included the parietal, motor, temporal, and subcortical regions that have been linked to other-oriented JS. Additionally, the machine learning model can distinctly distinguish between the distinct roles associated with other-oriented JS, including observer, perpetrator, and beneficiary, with key brain regions in the predictive networks exhibiting both similarities and disparities. These findings remained robust using different validation procedures. Collectively, these results support the separation between other-oriented JS and self-oriented JS, while also highlighting the distinct intrinsic neural correlates among the three roles of other-oriented JS: observer, perpetrator, and beneficiary.

Previous electroencephalogram (EEG) studies linked measures of spectral power under rest and fluid intelligence; however, subsequent high-powered studies challenged this relationship. The present study aimed to address previous limitations (low statistical power, lack of preregistration) and investigated the predictability of intelligence measures from resting-state EEG in the CoScience data set (N = 772). Support vector regressions were applied to analyze 8 min of resting-state EEG with eyes open and closed before and after unrelated tasks. The decoding performance between the spectral power of 59 EEG channels within 30 frequency bins and fluid and crystallized intelligence, was evaluated with a tenfold cross-validation. We could not identify any meaningful associations between resting-state EEG spectral power and either fluid or crystallized intelligence-a null finding that is unlikely to be entirely due to a relatively modest restriction of fluid intelligence variance in our student sample. Moreover, we did replicate the previously reported association between state sleepiness and theta power, attesting to the integrity of the CoScience data set. Furthermore, the decomposition of the EEG signal into its periodic and aperiodic components revealed that the aperiodic offset parameter is significantly correlated with state sleepiness, emphasizing the relevance of aperiodic signal components in understanding states of alertness versus sleepiness.

Transcranial direct current stimulation (tDCS) can modulate the brain's temporal dynamics. While tDCS-induced changes in these dynamics have been reported in clinical populations, how tDCS affects intrinsic brain activity in healthy individuals-particularly as reflected in resting-state electroencephalogram (EEG) microstate sequences-remains unclear. This study aims to examine the effects of high-definition tDCS (HD-tDCS) applied to the right dorsolateral prefrontal cortex (dlPFC) on the modulation of EEG microstates in healthy adults. Utilizing a single-blind, sham-controlled design, we measured the impact of HD-tDCS on four primary microstate metrics: coverage, duration, global explained variance (GEV), and occurrence. Twenty-six healthy young adults participated, undergoing both real and sham stimulation sessions separated by a 3-day washout period. Our findings indicate significant modulation of microstates B and D post-stimulation, with increased occurrence, duration, coverage, and GEV of microstate D and decreased occurrence, coverage, and GEV of microstate B. These findings suggest that targeted stimulation of the right dlPFC can effectively modulate brain microstates linked to attention and visual processing. This study highlights the potential of HD-tDCS to influence the visual and dorsal attention networks, thereby contributing to the enhancement of cognitive functions. Future research should broaden the application of HD-tDCS to investigate its effects across different brain regions and their associated cognitive and emotional processes.

Over the past decades, effort-based decision-making paradigms have become popular and effective tools to assess the neurocognitive mechanisms that support motivated behavior in humans and animals. These paradigms are built on a common neuroeconomic framework that conceptualizes motivation as the result of a cost-benefit decision-making process wherein organisms weigh potential rewards against the effort necessary to obtain them. However, specific effort-based choice paradigms often differ substantially in key design aspects, including the effort domain (physical vs. cognitive), the specific task used to manipulate effort, the presence or absence of rewards, punishments or other decision costs (e.g., time, probability), temporal component (i.e., probing effort/reward evaluation before or after effort exertion), and whether task demand is communicated explicitly or whether it has to be inferred through experience. This methodological heterogeneity may explain inconsistencies in the literature regarding the neural substrates underlying effort valuation and how these processes may be affected by a broad range of neuropsychiatric conditions, limiting the explanatory value of single-task studies. In this review, we provide a detailed overview of widely used effort-based decision-making paradigms, discuss associated advantages and challenges, and integrate key findings regarding neural correlates and observed changes in clinical populations. Finally, we offer practical guidance for selecting paradigms best suited to specific research questions.

Difficulty with reversal learning (RL)-appropriately shifting behavior following outcome contingency changes-may represent a shared or distinct mechanism across eating disorder (ED) diagnoses. We tested whether RL-related neural correlates differ among adults without and with EDs (anorexia nervosa, restricting subtype [AN-R], AN binge-eating/purging subtype [AN-BP], and bulimia nervosa [BN]) and whether these correlates correspond to ED-symptom severity and frequency. We hypothesized that individuals with EDs would demonstrate differential neural activation during RL relative to individuals without, that activation would differentiate AN-BP and BN versus AN-R, and that activation would predict ED-severity metrics. Medically stable participants with AN-R (n = 22), AN-BP (n = 20), and BN (n = 29) comprised the ED group (N = 71), contrasted with non-ED controls (N = 27). Participants (91% female; M_age = 25.9; 80% white, 14.5% Asian) completed clinical interviews and, in a separate session, a probabilistic RL task during functional magnetic resonance imaging. We examined differences in neural activation during RL in the ventral striatum and ventrolateral prefrontal cortex (vlPFC) between the ED and non-ED groups and between diagnostic groups, and conducted exploratory whole-brain analyses. Relations between neural activation and ED symptoms were examined. Lower right vlPFC and ventral striatum activation during RL characterized EDs. No between-ED diagnosis differences emerged. Lower right vlPFC activation predicted more frequent binge eating and purging but not global ED psychopathology. Individuals with EDs may experience difficulty recruiting certain RL-related brain regions, which may relate to difficulty changing ED behaviors. Future directions include investigation of how RL-associated neural networks maintain ED symptoms and influence treatment outcomes.

Background: Identifying neural markers of clinical symptom fluctuations is prerequisite to developing more precise brain-targeted treatments in psychiatry. We have recently shown that working memory (WM) in healthy adults is dependent on the rise and fall interplay between alpha (8-12 Hz)/beta (15-29 Hz) and high-frequency activity (HFA; 55-80 Hz) bursts within frontoparietal regions, and that deviations in these patterns lead to WM performance errors. However, it is not known whether such bursting deviations correlate to clinically relevant WM-related symptoms or clinical status in individuals with WM deficits.

Methods: We utilized trial-by-trial burst characterization and source localization approaches collected during a Sternberg WM paradigm within our magnetic resonance imaging/electroencephalography dataset of 27 adolescents with attention deficit hyperactivity disorder (ADHD) who completed the EEG procedures regularly during their participation in intermittent theta burst stimulation research studies, including an intermittent theta burst stimulation clinical trial (for a total of n = 180 participant/sessions). Source localizing electroencephalography data to each participant's structural MRI, the rate and volume of alpha, beta, and HFA bursts were examined within the dorsolateral prefrontal cortex and posterior parietal cortex.

Results: Alpha/beta bursting decreased during stimuli encoding and increased during the delay, while HFA bursting was elevated during encoding and decreased during the delay. Deviations in bursting patterns were associated with WM errors and clinical symptoms.

Conclusions: Dysfunctional alpha/beta and HFA burst dynamics within the frontoparietal region underlie both intra-individual WM performance and WM symptom fluctuations in adolescents with ADHD. Such burst dynamics reflect a novel target and biomarker for WM-related treatment development.

Rewards are essential for adaptive decision-making and well-being. While people can obtain both pro-self rewards (e.g., receiving personal bonuses) and prosocial rewards (e.g., acquiring funds for charity) in daily life, the hedonic value associated with these rewards may demonstrate habituation through repeated exposure. However, the neural mechanisms underlying reward habituation and their relationship with real-world emotional dynamics remain less understood. This study combined fMRI and the experience sampling method (ESM) to investigate neural habituation to pro-self and prosocial rewards, and the association between such habituation and real-world emotional inertia. Sixty-one participants (aged 18-25, 46 females) were scanned while performing a monetary gambling task, and their emotional states were assessed over the following 2 weeks. Analyses indicated differences in neural habituation between pro-self and prosocial rewards in the left dorsolateral prefrontal cortex (DLPFC) and right inferior temporal gyrus, although this difference should be interpreted with caution owing to the relatively weak prosocial responses. Moreover, individuals with weaker neural habituation in the left DLPFC to prosocial rewards tended to exhibit lower inertia of negative emotions in real-world contexts. These findings suggest that prosocial rewards may exhibit neural habituation patterns that differ from those of pro-self rewards and that sustained DLPFC activity in response to prosocial rewards may contribute to lower negative emotional inertia, pointing to a potential neurobehavioral pathway linking reward processing to daily emotional well-being.

Background: Several studies have demonstrated that potent sociocognitive factors, particularly perceived race, strongly influence empathy leading to altered social interactions, especially in clinical settings. This systematic review aimed to clarify how racial bias influences brain responses to others' pain.

Methods: For this review, 17 eligible studies included based a systematic search of PubMed, Scopus, Embase, and Web of Science for this review. All included studies utilized event-related potentials to investigate the neural correlates of empathy for pain in visual empathy judgment tasks.

Results: Early event-related potential (ERP) components, such as P1 and N170, reflect initial processing biases in racial pain judgment. The P2 component highlights automatic emotional biases, evidenced by enhanced neural responses to same-race suffering. Finally, the flexible P3 component indicates higher-level cognitive appraisal and controlled processing, influenced by individual beliefs and social context. These ERP findings are consistent with neurocognitive models that involve brain networks involved in salience recognition (e.g., anterior cingulate cortex, insula) and top-down regulation (e.g., dorsolateral prefrontal cortex) and are aligned with theoretical frameworks, such as predictive coding theory and dual-process theories.

Conclusions: Reviewed ERP evidence provides a precise neurocognitive time course of how racial bias shapes empathic pain processing. Distinct ERP components reveal a progression from rapid perception (e.g., P1, N170) to automatic affective bias (P2) and later cognitive appraisal (P3). These findings underscore empathy as a dynamic and flexible process, amenable to targeted interventions. This neurophysiological insight establishes a promising foundation for developing strategies to motivate prosocial behavior, challenge discrimination, and advance equity.

Positive cues provide information about target features, facilitating processing of the upcoming input. The role of "negative cues," which signal distractor features, is less clear. Their use seems to incur a certain mental effort, and consequently, their effectiveness could depend on whether the benefit of reducing search effort outweighs the effort required to use them. Crucially, when search effort is predictable, participants can rely on expected effort in deciding whether to use a negative cue. This subjective effort/benefit evaluation may explain the inconsistent effects sometimes observed with negative cues. However, the influence of expected effort has not yet been examined in this context. Varying effort expectation may provide a way to test this prediction. To this end, we manipulated effort expectation by embedding a small number of easy trials within blocks of mainly difficult trials and vice versa. We assumed that expected effort can boost cue utilization, especially when a difficult search was expected. The impact of positive and negative cues was compared to neutral cues under identical expectations. We found that benefits of positive cues increased when search was more effortful. Negative cueing effects, however, showed no reliable benefits, even when higher effort was expected. Interestingly, search performance was better when the expected effort matched actual task difficulty, though this pattern was more robust in difficult compared to easy trials. This asymmetry reveals an interaction between effort expectation and task difficulty. We discuss the neural underpinnings of how these factors might influence visual search.

Though largely ignored in the past, aperiodic (i.e., irregular, non-oscillatory) activity is emerging as an important element of the electroencephalography (EEG) signal. Certain characteristics of broadband aperiodic activity have been shown to reflect dynamic neurophysiological states that are sensitive to cognitive task demands and predictive of individual differences in cognitive capacity. In the current study, we test whether aperiodic neural activity can be used to index cognitive effort during speech comprehension in older listeners. Older adults with varying hearing acuity (N = 48) listened to sentences in quiet and in background noise while EEG was recorded. Consistent with listening effort, aperiodic neural activity was sensitive to increased acoustic challenge such that the aperiodic slope flattened and offset was reduced with increasing noise. Moreover, noise-induced changes in aperiodic activity were greater for older adults with poorer hearing acuity. In an age-comparative analysis using additional data from young normal hearing adults (N = 35), age-related reductions in aperiodic slope and offset were observed, replicating past work, while sensitivity to background noise was observed in both age groups. These findings highlight the importance of considering aperiodic EEG activity in assessing cognitive listening effort specifically, and in challenging cognitive tasks more generally.