Social Cognitive and Affective Neuroscience

Interpersonal neural synchrony (INS) in mother-child dyads is often interpreted as a neural marker of relational quality and sensitive caregiving, yet findings on its predictors remain heterogeneous. One possible source of this variability is the diversity of interactional paradigms used in hyperscanning research. This study examined how maternal personality, child temperament, and affective states relate to INS across interaction contexts varying in social interactivity. Thirty-three mother-child dyads (n = 20 female children) participated in a functional near-infrared spectroscopy hyperscanning experiment involving passive video co-exposure, a structured cooperative task, and free interaction. Fronto-temporal activity was recorded simultaneously, and INS was computed using wavelet transform coherence. Above-chance levels of INS emerged in inter-brain region combinations primarily involving the mothers left inferior frontal gyrus (IFG) and the childs right IFG (adjusted ps < 0.030, Cohens d range = 0.14-0.31). Maternal neuroticism was the only significant predictor of INS, with higher levels associated with increased synchrony during passive video co-exposure (adjusted p = 0.012) and free interaction (adjusted p = 0.021), but not during the structured game. These findings indicate that maternal dispositional traits shape INS in a context-dependent manner. Notably, the positive association between neuroticism and INS suggests that heightened neural synchrony may reflect over-attunement in more anxious caregivers, rather than optimal coordination. Excessive synchrony may therefore index tightly coupled, over-monitoring interaction dynamics, consistent with models of affiliative vigilance in anxious parenting. Overall, INS may follow a non-linear pattern in which moderate levels are most adaptive, highlighting its flexible, dynamic, and context-sensitive nature.

Transcendent thinking (TT) is an enduring affective and cognitive process characterized by abstract meaning-making, moral reflection, self-referential integration, and strong emotional engagement. Despite growing interest in its developmental and affective significance, the intrinsic neural dynamics that predict individual differences in disposition to TT remain poorly understood. Most prior work has relied on linear functional connectivity measures, which may be insufficient to capture the nonlinear and multiscale nature of brain dynamics underlying higher-order affective dispositions like TT. Here, we introduce a nonlinear functional brain network (FBN) framework based on multiscale entropy (MSE) to investigate whether intrinsic resting-state nonlinear brain dynamics predict disposition to TT in adolescents. Functional connectivity was defined as inter-regional similarity in MSE profiles derived from resting-state fMRI, yielding weighted networks that capture scale-dependent dynamical correspondence rather than linear synchrony. Graph-theoretical, spectral, and information-theoretic measures were computed and evaluated against signal-level and network-level null models. Predictive performance was assessed using machine-learning models and compared with conventional time series-based FBNs. Global intelligence (IQ) was examined as a control cognitive variable. MSE-based network features, particularly spectral energy and Shannon entropy, showed significant associations with TT and enabled reliable prediction of individual differences, whereas time series-based network measures failed to predict TT. No network measures reliably predicted IQ. Overall, these results indicate that intrinsic nonlinear brain dynamics carry predictive information about affective dispositions, rather than domainspecific or network-localized cognitive abilities such as IQ. This work demonstrates that nonlinear, multiscale network representations of resting-state brain activity provide a principled and predictive framework for modeling individual differences in enduring affective dispositions.

BackgroundDepression is associated with social dysfunction, but the mechanisms linking affective symptoms to disrupted close relationships remain poorly understood. One possibility is that depression alters how people experience rewards shared with close others and how they interpret partners actions. It remains unclear whether neural sensitivity to shared reward predicts social valuation during more complex interactions such as reciprocated trust. MethodsIn this preregistered fMRI study, participants completed a reward-sharing task and a Trust Game with a close friend, a stranger, and a computer. We measured striatal shared reward sensitivity (SRS; friend > computer) and tested whether it related to subsequent investment behavior and brain responses to trust reciprocation. Depressive symptoms and perceived closeness were assessed via self-report. ResultsIn a final sample of n = 123, participants reporting more depressive symptoms invested more in their friend than in the computer. Striatal SRS predicted temporoparietal junction responses to reciprocated trust, but this association depended jointly on social closeness and depression -- with depression reversing the expected pattern among individuals reporting closer relationships. Striatal SRS was also inversely associated with connectivity between the default mode network and cerebellum during reciprocity. ConclusionsThese findings suggest that closeness calibrates the striatal SRS link to regional activity and network-level responses during social exchange, while depression alters how striatal SRS relates to regional activity, potentially disrupting how individuals interpret and respond to close others.

Arousal and valence are fundamental dimensions of affective experience signifying levels of activation and pleasantness, respectively. These dimensions play a crucial role in shaping emotional responses and behaviors, with significant implications for psychopathology. Previous machine learning studies had some success decoding these states from brain activation patterns observed during task-based functional magnetic resonance imaging (fMRI), but the results have varied across studies. Moreover, prior studies have often been limited by small sample sizes, weak decoding performance, and non-whole-brain analyses, leaving the neural representations of arousal and valence largely unresolved. Here we successfully decoded arousal and valence from whole-brain task-fMRI data collected from 132 participants during exposure to 300 unique emotional stimuli, including 150 movie clips and 150 text scenarios that reliably induced a wide range of arousal and valence states. Mass univariate general linear models identified block-level activation (emotion stimuli > washout) from all gray matter voxels. Multivariate regression analysis predicted arousal and valence ratings based on these gray matter activations. Patterns in the fMRI data underlying arousal and valence were robust, as they were successfully decoded across both induction modalities using five different linear multivariate regression models. Although significant, decoding from scenarios was less successful than from movies, likely due to their more imaginative nature. In particular, decoding arousal from scenarios only showed low predictive utility. Representations of arousal and valence were widespread throughout the brain, and we reveal cerebellar and brainstem contributions that have largely been absent in past fMRI decoding studies. These findings clarify the distributed neural basis of arousal and valence and provide a foundation for future clinical research on the role of these constructs in affective dysregulation.

Play is a fundamental aspect of childhood and plays a crucial role in the development of creativity, yet its neural mechanisms remain poorly understood. We tested the hypothesis that more frequent play is associated with stronger functional integration among the default mode network (DMN), executive control network (CN), and salience network (SAL), as these cortical networks have been implicated in creativity in adults. In a preregistered study of infants and toddlers (Study 1; N = 143, 10 months-3 years, 67 boys, Baby Connectome Project), parent-reported play and imitation behaviors increased sharply from 1 to 2 years, and were associated with stronger within-DMN connectivity and DMN-CN coupling, controlling for age, sex, and head motion. In middle childhood (Study 2; N = 108, ages 4-11 years, 52 boys), parent-reported play frequency declined with age, as did cross-network coupling involving SAL. However, children who engaged more frequently in play showed higher DMN-SAL and CN-SAL connectivity. Finally, in a quasi-experimental comparison (Study 3; N = 45; ages 4-12 years, 20 boys), children enrolled in a curriculum that includes guided play (Montessori) showed higher DMN-SAL and DMN-CN connectivity than peers in traditional schools, suggesting that pedagogies that center child-led exploration might enable protracted brain network integration. Across these three studies, play was consistently associated with greater integration among DMN, SAL, and CN, a pattern previously linked to creativity in adults. Our findings offer a potential mechanism linking childhood play to later creativity through its role in supporting brain integration during development. Public Significant StatementO_LIPlay is widely believed to nurture childrens creativity, yet the brain mechanisms behind this link are not well understood. C_LIO_LIAcross three studies from infancy to middle childhood, we found that more frequent play was associated with stronger integration among brain networks tied to imagination, attention, and control. C_LIO_LIThese findings suggest that play may help build the neural foundation for later creative thinking. C_LI

Human interactions span a range of contexts, from cooperation to competition. Negotiation, in particular, is a complex and extended social process in which individuals must reach mutually acceptable decisions despite conflicting incentives. The neural computations that support strategic behavior in such social dilemmas remain insufficiently understood. Here, we combine cognitive computational modeling, electroencephalography (EEG), functional Magnetic Resonance Imaging (fMRI), and fMRI-guided Transcranial Magnetic Stimulation (TMS) to demonstrate that oscillatory activity anchored in the temporoparietal junction (TPJ) causally shifts social learning during strategic bargaining. We found that TPJ metabolic activity and alpha-band oscillations are associated with the use of a feedback-based learning strategy during bargaining. Causal perturbation with rhythmic alpha-frequency TMS selectively modulates this strategy, increasing endogenous alpha oscillations and shifting behavioral learning parameters. Together, these findings reveal a frequency-specific mechanism within the neural substrates of social cognition that implements adaptive social learning, offering insights into potential neuromodulatory targets for ameliorating social dysfunction in neuropsychiatric conditions. Significance StatementStrategic negotiation requires predicting how others will respond to our actions, yet the neural computations supporting this form of social learning have remained elusive. By integrating computational modeling with EEG, fMRI, and frequency-specific TMS, we identify a mechanistic link between alpha-band activity in the temporoparietal junction (TPJ) and feedback-based learning during social exchange. Trial-by-trial estimates of this learning strategy were tracked by TPJ metabolic and oscillatory signals, and rhythmic alpha TMS causally enhanced both the neural signature and the behavioral expression of this strategy. These findings provide causal evidence for a frequency-specific mechanism within the neural systems that supports adaptive social learning. They also highlight the TPJ-alpha system as a promising target for neuromodulatory interventions to improve social functioning in neuropsychiatric conditions. Key FindingsO_LIModel-based behavioral analyses revealed two distinct strategies during social negotiation: a feedback-based learning mechanism (U-strategy) and a reputation-based updating mechanism (A-strategy). C_LIO_LIBoth strategies robustly predicted participants adaptive behavior across samples and conditions, and their modulation accounted for differences in negotiation outcomes. C_LIO_LIEEG analyses revealed frequency-specific alpha and beta power modulation linked to U-strategy computations during partner anticipation, localized to right temporoparietal regions. C_LIO_LIfMRI analyses revealed that trial-by-trial U-strategy estimates selectively modulated BOLD activity within the temporoparietal network associated with mentalizing. C_LIO_LIRhythmic alpha-frequency TMS over individually localized Theory-of-Mind TPJ sites causally altered negotiation behavior, shifting U-learning parameters toward a more conservative strategy. C_LIO_LITMS-EEG analyses demonstrated that alpha-frequency TMS induced time-locked alpha activity in functionally connected frontal sites, consistent with enhanced anticipatory computations. C_LIO_LITogether, these multimodal findings establish a causal, frequency-specific mechanism in the TPJ that implements social value learning during strategic bargaining. C_LI

Development of the brain networks is highly vulnerable to stressful events. Early life stress (ELS) has been linked to multifaceted cognitive and emotional deficits in adulthood. Despite a growing body of evidence showing ELS-induced structural and functional changes in the prefrontal cortex (PFC) and basolateral amygdala (BLA), a circuit crucial for emotional processing, our knowledge of the resulting changes in the network dynamics is incomplete. Here, we investigate how maternal separation (MS) affects prefrontal-amygdala network in terms of neuronal avalanches, spatiotemporal clusters of activity, using simultaneous multielectrode recordings in the medial PFC (mPFC) and the BLA of urethane-anaesthetized juvenile (postnatal day (p) 14 - p15) and young adult (p50 - p 60) rats. Firstly, we show that MS leads to an intensified spread of activity within both regions as reflected in the higher mean branching ratios of the avalanches. Next, we demonstrate that most of the avalanches occur locally in one region, however, a small percentage of avalanches has clusters of activity in both regions simultaneously. We show that in MS animals prefrontal clusters followed by activity in the amygdala tend to be larger compared to controls and each event in the mPFC is followed by smaller number of events in the BLA, pointing towards impaired spread of activity from the mPFC to the BLA. Interestingly, avalanche spread from the BLA to the mPFC remains unaffected by MS. Abovementioned effects manifest only in adulthood and, intriguingly, only in males highlighting prolonged developmental and sex-dependent nature of ELS outcome. Significance statementBrain criticality implies that the brain self-organizers towards critical state, characterized by sustained activity propagation reflected in the unitary branching ratios of neuronal avalanches. Here we show how adverse events during early periods of network maturation, namely ELS, can disrupt developmental trajectories of the critical dynamics in the mPFC-BLA circuit in a sex-specific manner. This study broadens our understanding of the critical dynamics emergence in the prefrontal-limbic network and highlights ELS as a potential criticality control parameter.

Human-dog interaction is widely used to alleviate stress, yet the accompanying cortical and autonomic dynamics during acute stress and recovery remain incompletely characterized. In this study, 70 adult dog owners completed a standardized stress protocol while prefrontal cortex activity was continuously monitored with functional near-infrared spectroscopy (fNIRS), alongside subjective stress and salivary cortisol measures. Participants then underwent a recovery phase involving interaction with a companion dog, manipulating contact type (direct in person vs. indirect video conferencing), and familiarity (own vs. unfamiliar dog). Stress responses were quantified through heart rate (HR), heart rate variability (HRV), low- and high-frequency spectral power (LF, HF, and LF/HF), and prefrontal functional connectivity (FC) based on maximum cross-correlation coefficients between fNIRS channels. As expected, HR, HRV-derived indices, and FC increased from baseline to the stress phase, confirming robust engagement of autonomic and prefrontal networks. During the recovery phase, all dog interaction conditions demonstrated reductions in HR, LF/HF ratio, and FC toward or below baseline, consistent with physiological and neural stress recovery; direct interaction was associated with particularly pronounced parasympathetic enhancement and a drop in FC that fell significantly below baseline in some cases. Across groups, HRV, LF/HF, and FC were the most consistent predictors of subjective stress ratings, whereas associations with cortisol were limited. These findings suggest that human-dog interaction promotes coordinated autonomic and prefrontal recovery from acute stress, and that fNIRS-derived metrics might provide a marker of stress modulation that can distinguish high-cognitive load and low cognitive demand states beyond traditional stress indices.

Despite substantial progress in understanding how visual features of food are processed in the brain, it remains unclear how subjective and nutritional properties, such as perceived palatability, caloric content, and health value, are reflected in neural representational structure. Using functional MRI and representational similarity analysis (RSA), we examined how visual, subjective, and nutritional food properties are encoded in ventral visual cortex. Univariate analyses revealed reliable activation differences between high- and low-calorie foods in lateral occipitotemporal cortex (LOTC) and fusiform gyrus. RSA further revealed a functional dissociation within the ventral stream: LOTC showed systematic correspondence with both visual and subjective dimensions, whereas fusiform cortex exhibited a selective association with perceived caloric content, with both effects persisting after controlling for visual similarity. These results suggest that food-related dimensions not fully captured by the tested visual models are reflected within visual representational spaces, and that LOTC and fusiform cortex show dissociable representational profiles with respect to subjective and perceived nutritional food dimensions.

Fear learning and extinction unfold as time-dependent processes. Herein, we examined how fear learning dynamically reorganizes brain activity immediately after learning, and whether such reorganization can be modulated with TMS application during extinction learning to prospectively predict extinction-memory expression. Eighty-seven healthy adults completed a three-day Pavlovian threat-learning protocol with resting-state fMRI acquired before and after conditioning (Day 1), dorsolateral prefrontal cortex (DLPFC) transcranial magnetic stimulation (TMS) applied during extinction learning (Day 2), and fMRI during extinction recall and renewal (Day 3). Using coactivation pattern analysis with a hidden Markov model within a 24-nodes threat-circuit parcellation, we identified a fear-learning-induced brain state characterized by global threat-circuit coactivation with heightened engagement and transition uncertainty post conditioning, and a progressive increase in engagement across post-conditioning. Critically, conditioning-induced functional connectivity reorganization within this state predicted individual differences in extinction recall- and renewal-related brain activation under TMS-modulated extinction (cross-validated; recall r = 0.47, p = 0.001; renewal r = 0.37, p = 0.01; permutation-tested), but not under natural extinction. Similar associations were observed between neural features and behavioral expression. These findings demonstrate that fear learning reshapes spontaneous brain-state dynamics and that such learning-induced reorganization serves as an interpretable biomarker for neuromodulation-linked extinction-memory expression.

Fluctuations in attentional states, such as mind-wandering (MW), are associated with critical variability in task performance. While fMRI studies highlight the opposing roles of task-positive (e.g., dorsal attention network) and task-negative (e.g., default mode network) systems, the electrophysiological mechanisms underlying these dynamics remain poorly understood. Using intracranial electrocorticography in humans performing a sustained attention task, we identified global oscillatory dynamics linked to attentional shifts. MW was characterized by (i) reduced theta ({theta}) and alpha ({square}) power, (ii) decreased aperiodic signal components, indicating a shift toward cortical inhibition, (iii) enhanced phase synchronization across networks, and (iv) strengthened {theta} phase-behavior correlations ({rho}). These features support a non-network-specific framework in which low-frequency {theta} dynamics--captured by both {theta} power and {rho}--are associated with attentional fluctuations, while aperiodic offset relates to attentional state indirectly through its association with {rho} (Structural Equation Modeling: power[-&gt;]state {beta} = -0.118, p = 0.002; {rho}[-&gt;]state {beta} = 0.246, p < 0.001; offset[-&gt;]{rho} {beta} = -0.222, p < 0.001). Our study provides a unified neurophysiological framework for understanding how spontaneous neural activity can drive attentional fluctuations and performance variability, with implications for research on attention, learning, and neuropsychiatric disorders.

Human action selection under reinforcement is thought to rely on two distinct strategies: model-free and model-based reinforcement learning. While behavior in sequential decision-making tasks often reflects a mixture of both, the neural basis of individual differences in their expression remains unclear. To investigate this, we conducted a large-scale fMRI study with 179 participants performing a variant of the two-step task. Using both cluster-defined subgroups and computational parameter estimates, we found that the ventromedial prefrontal cortex encodes model-based and model-free value signals differently depending on individual strategy use. Model-based value signals were strongly linked to the degree of model-based behavioral reliance, whereas model-free signals appeared regardless of model-free behavioral influence. Leveraging the large sample, we found individuals lacking both model-based behavior and model-based neural signals exhibited impaired state prediction errors, suggesting a difficulty in building or updating their internal model of the environment. These findings indicate that model-free signals are ubiquitous across individuals, even in those not behaviorally relying on model-free strategies, while model-based representations appear only in those individuals utilizing such a strategy at the behavioral level, the absence of which may depend in part on underlying difficulties in forming accurate model-based predictions.

Humanity has always admired and created artwork, but the neurocognitive mechanisms behind artistic experience are still elusive. Professional artists and their intimate relationship with their artworks provide a unique opportunity to study the nature of art experience due to their expertise in both art making and art appreciation. During two fMRI tasks, professional artists (N=20) made aesthetic judgments on their own and other artists paintings (aesthetic appreciation task); they also mentally reconstructed the moments when they conceived their artworks or, as a control condition, when they visited now-familiar places for the first time (reconstruction by imagery task). During art appreciation of their own (as compared to other artists) paintings, participants showed stronger recruitment of bilateral posterior parietal cortices, the left lateral occipitotemporal cortex, and the dorso-central sector of the right insula, that is, action-related brain regions also involved in encoding the emotional components of movements. The reconstruction of their own artistic creation (as compared to episodic memory retrieval) involved the left fronto-parietal network associated with motor cognition. Altogether, these results suggest that the mental representations of the actions involved in creating art are integral to the overall artistic experience of painters, supporting an embodied view of the artists experience of art.

Dietary patterns worldwide have shifted toward increased consumption of ultraprocessed foods (UPFs), which has been linked to higher disease burden. One mechanism proposed to impact both their consumption and contribution to metabolic disease is altered post-ingestive metabolic response in comparison to nutritionally similar foods. Here, we recruited 57 healthy-weight 18-45-year-old adults to examine the effects of food processing on postprandial metabolism and brain response. Despite nutritional matching, UPF meals evoked a greater insulinemic and energetic response with attenuated carbohydrate oxidation relative to non-UPF meals. Next, between-condition differences in peak carbohydrate oxidation were associated with mesolimbic and superior temporal gyrus activation in response to food cues. Finally, although food value did not differ between conditions, brain responses correlated with food valuation were positive for non-UPF but negative for UPF in visual cortex and striatum. These findings demonstrate that food processing influences post-ingestive metabolism in a way that could help explain long term health effects and differences in food reward through mechanisms beyond calories and macronutrient composition alone.

Eating behaviors and their associated cognitions exist along a biopsychosocial continuum, yet their structural organization remains largely unmapped in non-Western contexts. Adopting a dimensional network perspective, this study characterizes the architecture of non-clinical eating behaviors in India--a region defined by a unique interplay of cultural, structural, and psychological influences. We utilized Mixed Graphical Models (MGMs) to estimate a weighted network of 35 variables from a geographically diverse Indian cohort (N=1,508). Our analysis reveals that the Indian eating behavior landscape is a highly optimized, small-world system (S=54.64) defined by a dual-layered hierarchy of influence. We found that structural and cultural variables--notably HomeTypes and Religion--serve as the primary local anchors (highest Expected Influence), driving the state of their immediate modules. Conversely, systemic integration across the entire network is maintained by a "socio-economic and regulatory bridge" comprising Employment, Education, and Self-Esteem. These nodes exhibited the highest betweenness centrality, functioning as the critical "highways" that link disparate socio-economic, psychological, and behavioral modules. Notably, while Shape and Weight Concern were highly predictable, they functioned as local cluster nodes rather than global integrators--directly challenging the body-image-centric models dominant in Western literature. These results demonstrate that in the Global South, structural social determinants form the primary scaffold of the biopsychosocial system. Our findings provide a data-driven blueprint for systemic, culturally attuned public health interventions that prioritize structural stability alongside individual regulatory resilience. Significance StatementWhile eating behaviors are traditionally conceptualized as individual psychological phenomena, this study reveals that in the Global South, they are fundamentally anchored by systemic social determinants. Using network science to map the biopsychosocial landscape of a large Indian cohort, we demonstrate a specific hierarchy of influence: while cultural and living conditions (e.g., religion and home type) act as local anchors for behavior, socio-economic factors (employment and education) and core psychological traits (self-esteem) function as the primary structural bridges that integrate the entire system. This architecture provides an empirical corrective to Western-centric models that prioritize body image as the central driver of eating pathology. Our findings suggest that in developing economies, public health strategies may be most effective when they target these "upstream" structural integrators, reframing eating behavior as a systemic expression of social, economic, and cultural stability.

Sustained attention is an important neurobiological process. Difficulties with attention play a key role in neurodevelopmental disorders, such as attention-deficit/hyperactivity disorder (ADHD) and autism. Here, we identified functional connections consistently associated with sustained attention across datasets, participant populations, and fMRI scan types. We interrogated five transdiagnostic, previously published connectome-based models predicting attention and autistic phenotypes. All models were related to sustained attention, including in samples comprising participants with autism. We found that model similarity was associated with participant characteristics, including age and clinical diagnosis, and predicted behavioral measure. As expected, models predicting attention phenotypes shared more similar features with each other than models predicting autism symptoms. Furthermore, predictive features overlapped more between datasets that included participants of similar ages (i.e., youth vs. adult) and diagnostic status (autism diagnosis vs. no diagnosis). This suggests that functional connectivity patterns predicting individual differences in behavior are phenotype-specific and may vary as a function of age and clinical diagnosis.

How does the human brain represent the meaning of abstract symbols? Some theories postulate the existence of semantic spaces where concepts occupy positions that reflect their conceptual relationships. In the number domain, psychological evidence suggests that integers are represented along a mental number line which, with education, integrates higher-level number concepts such as fractions. To test this hypothesis, we recorded whole-brain 7T fMRI responses to integer and fraction symbols during a magnitude comparison task. Consistent with predictions, we found both behavioral and neural numerical distance effects. Activation vectors in intraparietal, inferior temporal, prefrontal, hippocampal, and parahippocampal cortices formed a two-dimensional semantic space organized by numerical magnitude and domain (fractions versus integers). Gaussian fits revealed a topographic map of numerical preferences in the anterior inferior parietal cortex, common to both domains. Our results suggest that, in educated adults, a joint semantic map integrates fractions and integers and supports symbolic magnitude representation and comparison.

Self-transcendence, the reorientation of experience away from the self toward others, nature, or broader meaning, is a fundamental dimension of human psychology, yet its causal neural architecture remains poorly understood. Here we applied lesion network mapping to 88 neurosurgical patients with pre- and post-operative assessments of trait self-transcendence to identify the distributed brain network whose disruption alters this capacity. The resulting network showed significant spatial correspondence with the default mode network and, at a finer parcellation level, with frontoparietal control subnetworks. Leave-one-out analyses identified posterior midline regions as the most stable correlates of increased self-transcendence following brain lesions. Independent validation against fMRI meta-analyses of self-referential processing, compassion, and ketamine administration, alongside a neuromodulation target previously shown to modulate the sense of self, converged on a consistent model. These findings provide causal evidence for a network architecture in which posterior midline hubs constrain, and brainstem and anterior midline regions facilitate, self-transcendent experience.

Craving is a hallmark feature of substance use disorders (SUDs) and a major risk factor for relapse, yet reliable biomarkers that enable individual-level prediction remain scarce. Here, we applied connectome-based predictive modeling (CPM) to resting-state functional magnetic resonance imaging (fMRI) data in a transdiagnostic sample of individuals with cannabis, opioid, or tobacco use disorder (n = 78). Using CPM, we identified a distributed functional brain network that reliably predicted self-reported craving. Computational lesion analyses revealed key contributions from the right medial orbitofrontal cortex, right dorsal posterior cingulate cortex, and left lateral medial frontal gyrus. Importantly, the craving network generalized across two independent datasets. In alcohol-dependent patients (n = 41), the identified craving network, along with its positive and negative subnetworks, predicted distinct cognitive and motivational components of craving. In a second external dataset of smokers (n = 28), the craving network predicted both nicotine craving after abstinence as well as intra-individual changes in craving between sated and craving states. Together, these findings provide evidence for a robust, transdiagnostic craving signature in SUDs. Future work should assess the networks predictive utility for longitudinal outcomes such as relapse risk and treatment response.

Decisions based on evidence accumulated over time require rules governing when to end the accumulation process and commit to a choice. These rules control inherent trade-offs between decision speed and accuracy, which require careful balance to maximize quantities that depend on both like reward rate. We previously showed that, to maximize reward rate, normative decision rules adapt to changing task conditions (Barendregt et al., 2022). Here we used a novel task to examine whether and how people use adaptive rules for individual decisions under a variety of conditions, including changes in decision outcomes across trials and changes in evidence quality both across and within trials. We found that the participants tended to use rules that adjusted, at least partially, to predictable changes in task conditions to improve reward rate, consistent with a rationally bounded implementation of normative principles. These findings help inform our understanding of the extent and limits of flexible decision formation in the brain.