Mathematical learning disabilities (MLD) affect up to 14% of school-age children, yet the underlying neurocognitive mechanisms remain elusive. We developed drift diffusion model with dynamic performance monitoring (DDM-DPM), an innovative cognitive model that captures both external and internal sources of structural variability in task performance. Combining DDM-DPM with functional brain imaging, we examined symbolic and nonsymbolic quantity discrimination in female and male children with MLD and typically developing children matched on age, gender, and IQ. Children with MLD showed format-dependent alterations in response caution and posterror adjustment, despite similar observed performance measures between groups. The latent cognitive processes during symbolic quantity discrimination predicted broader mathematical abilities better than those during nonsymbolic quantity discrimination. Neuroimaging results revealed that reduced activity in middle frontal gyrus mediated deficits in response caution in symbolic format, while reduced activity in the anterior cingulate cortex mediated deficits in posterror adjustment in symbolic format in children with MLD. These findings provide novel support for a multidimensional deficit view of MLD that extends beyond basic number processing to include metacognitive processes. Our findings also provide novel support for and extend the access deficit model, which posits that individuals with MLD may have relatively intact quantity representations but struggle with numerical representations in symbolic formats. Our study highlights the value of integrating latent cognitive modeling with neuroimaging to reveal subtle mechanisms underlying learning disabilities and identify potential targets for intervention.

Inhibitory control matures progressively from childhood to early adulthood, yet the neural mechanisms driving this development and their relevance to psychiatric risk remain poorly understood. Guided by the Dual Cognitive Control model, we leveraged longitudinal fMRI from two independent cohorts in the US (ABCD, ages 9-12) and Europe (IMAGEN, ages 14-22) to map the spatiotemporal dynamics of reactive and proactive control using novel single-trial modeling and representational similarity analysis. We found both reactive and proactive stopping networks stabilize after mid-adolescence, tracking the developmental patterns of inhibitory control and behavioral stability. By decoding trial-by-trial fluctuations along a speedcaution continuum, we demonstrate that brain-behavior coupling to a proactive "Safe state" tightens progressively with age. Furthermore, network-level representational coherence of this Safe state emerged as a scanner-invariant, trait-like biomarker that robustly predicted inhibitory control, behavioral stability, and transdiagnostic psychopathology across multiple developmental windows, providing a validated neural phenotype for precision psychiatry.

Adolescence is a period of profound social change marked by a developmental shift in attention and motivation from parents toward nonfamilial peers. Autism is a neurodevelopmental condition characterized by lifelong challenges in social communication. However, the neurobiological signatures of adolescent social reorientation in autism are poorly understood. Human voice processing is a primary driver of social learning and communication, but it remains understudied in the autism literature, particularly as it relates to neurodevelopmental change. Here we used functional brain imaging of voice processing in children and adolescents with autism and matched controls (ages 7 to 17) to examine neural responses to mother's voice and nonfamilial voices. We identified divergent age-related patterns in autism across reward, salience, social evaluative, and frontoparietal processing regions consistent with models of an extended voice processing network. Results showed that while typically developing participants exhibited age-related increases in neural activity and connectivity within regions of the voice processing network, individuals with autism showed no age-related increases, and often decreases, in these regions. Adolescents with autism further revealed a reversal of the characteristic developmental pattern: with increasing age, they exhibited decreasing neural engagement with nonfamilial voices and increasing engagement with mother's voice, a pattern that was most pronounced in individuals with more severe social communication challenges. Findings suggest that disrupted organization of the extended voice processing network, including reward, salience, social evaluative, and frontoparietal circuitry, may underlie atypical social neurodevelopment in autism. More broadly, results suggest that individual differences in social communication shape age-related neural patterns supporting social reorientation.

BACKGROUND:Understanding which specific behavioral and emotional problems are uniquely associated with achievement and thus potential targets for developing educational interventions. METHODS:Child Behavior Checklist syndrome scales and standardized measures of mathematics and reading achievement were analyzed using structural equation modeling in a community California cohort (N = 252) and the clinically diverse Healthy Brain Network cohort (N = 3,583). RESULTS:Attention and social problems were uniquely associated with lower achievement beyond overall psychopathology levels defined by the covariances among syndrome scores. Attention problems were consistently related to poorer mathematics and reading achievement across clinical status, development, and sex. Social problems showed age- and sex-specific patterns and were associated with lower achievement throughout development for girls but only during adolescence for boys. Models examining achievement-to-psychopathology relationships resulted in poorer fits than psychopathology-to-achievement models in typically developing children and adolescents. However, bidirectional relationships emerged in clinical samples, particularly between attention problems and mathematics achievement and between social problems and reading achievement. CONCLUSIONS:Specific behavioral problems, rather than overall psychopathology, are consistently associated with academic difficulties. Educational screening and interventions should prioritize attention regulation across all developmental stages and implement sex-differentiated social skill support, beginning earlier for girls and during adolescence for boys. These findings replicate across independent samples, demonstrating robust relationships with direct implications for school-based educational and mental health services.

Understanding dynamic mechanisms underlying cognition remains a major challenge in human neuroscience. Here, we develop, validate, and apply Multivariate Dynamical Systems Identification with Amortized Variational Inference (MDSI-AVI), a novel computational framework designed to address critical challenges in capturing asymmetric, context-dependent, whole-brain directed interactions while accounting for regional hemodynamic response variability in fMRI data. MDSI-AVI leverages simulation-based inference through forward and reverse variational inference to address the limitations of conventional variational methods in high-dimensional settings. By averaging over uncertainty in hemodynamic response parameters using forward simulation, MDSI-AVI provides well-calibrated posteriors of directed connectivity that scale efficiently to networks with hundreds of nodes. Applied to Human Connectome Project data (N=728), MDSI-AVI reveals new insights into working memory mechanisms, identifying the dorsal anterior insula as a critical hub influencing activity at the whole-brain level. We demonstrate task-dependent modulation of causal influences, where the salience network drives frontoparietal network activity, which differentially influences the default mode and sensorimotor networks depending on working memory load. These whole-brain causal interactions distinguish task conditions with high accuracy and predict working memory performance. Our framework demonstrates reproducible results across whole-brain parcellations, establishing MDSI-AVI as a robust tool for advancing our understanding of circuit dynamics in cognition and disease.

Amyloid-β (Aβ) accumulation is a continuous process central to pathological aging that begins decades before cognitive impairment emerges. While subthreshold Aβ levels have been linked to future decline in cognitive control, the neural mechanisms connecting this early accumulation to its neurocognitive impact are poorly understood. Brain circuit dynamics, which are essential for cognitive function, may offer a sensitive lens into these initial pathological changes. Here, we tested whether brain state dynamics could serve as sensitive markers for cognitive impairment at an early stage of Aβ burden. Using the Bayesian Switching Dynamic System (BSDS) model, we identified 4 distinct latent brain states from high-temporal-resolution (800 ms) fMRI data acquired from 116 older adults, including 72 cognitively normal (CN) individuals and 44 with mild cognitive impairment (MCI), during an N-back working-memory task. Adopting a dimensional approach, we examined how latent brain state dynamics relate to early amyloid burden, cognitive performance, and clinical symptoms. While Aβ levels failed to differentiate clinical groups or predict clinical symptoms and task performance, the dynamics of latent brain states proved highly sensitive to both early Aβ accumulation and cognition. Canonical correlation analysis revealed a significant relationship between brain state dynamics and early Aβ burden. Furthermore, the temporal properties of brain states were significantly predictive of working memory performance in CN individuals, a relationship that was selectively disrupted in the MCI group. The features of brain dynamics can also successfully predict cognitive impairment. Our findings establish brain state dynamics as sensitive neural markers of initial Aβ accumulation and early cognitive impairment, offering a new framework for developing predictive models to identify individuals at risk for future cognitive decline.

Autism diagnosis lacks objective context-specific neurobiological markers, as traditional structural and resting-state neuroimaging fails to capture the dynamic social-cognitive processing differences that define the condition. We developed DualPathNet, an interpretable dual-stream deep learning network that simultaneously captures stable trait-like patterns and transient event-specific neural responses during naturalistic movie viewing. Across 555 children (274 ASD, 281 controls), our framework achieved > 70% accuracy using only 2-3 minutes of emotionally challenging stimuli, substantially outperforming 63% resting-state scans. Explainable AI with DualPathNet revealed that autism-related neural signatures were selectively expressed during high-demand social-emotional moments requiring empathy and emotion regulation, rather than uniformly expressed across all contexts. Critically, temporally specific neural responses during emotionally salient events predicted core autism symptoms including repetitive behaviors and social deficits. Our neuro-AI approach demonstrates that autism involves dynamic, context-dependent neural vulnerabilities rather than static disruptions, providing interpretable biomarkers for precision diagnosis and targeted intervention.

The default mode network (DMN) plays a fundamental role in internally focused cognition, and its disruption is implicated in numerous brain disorders. While neuroimaging has revealed DMN suppression by salient stimuli, the cellular mechanisms orchestrating this process remain unknown. Using whole-brain computational modeling informed by neuronal biophysics and retrograde tracer-derived directional mouse brain connectomics, we demonstrate that stimulation of the insula node of the salience network suppresses DMN activity, whereas cingulate cortex stimulation produces antagonistic effects, enhancing retrosplenial cortex activity. Prelimbic cortex stimulation showed intermediate patterns, partially replicating insula-mediated suppression while failing to suppress cingulate regions, suggesting its role as a functional bridge between networks. Systematic brain-wide analysis confirmed the insula's unique pattern of simulated DMN suppression. Comprehensive parameter space exploration demonstrated that DMN emergence as a functionally segregated network is robust across wide ranges of excitatory-inhibitory balance regimes and cholinergic modulation. However, outside these boundaries, DMN integrity breaks down through three distinct failure modes: loss of responsiveness, reversal of suppression to enhancement, and network fragmentation. The retrosplenial cortex emerged as a particularly vulnerable regulatory hub whose excitatory-inhibitory disruption reversed normal suppression patterns across the DMN, while prelimbic cortex demonstrated remarkable robustness. Brain-wide analysis also identified a functionally segregated frontal network displaying antagonistic dynamics with the DMN. Our findings provide mechanistic insights into DMN robustness and vulnerability, establishing a framework that links cellular excitatory-inhibitory balance to large-scale network dynamics. This model could explain how region-specific disruptions can produce the heterogeneous patterns of DMN dysfunction observed across brain disorders. Significance Statement:To respond to important external events, the brain must suppress internal thought processes implicating the default mode network. This suppression fails in psychiatric conditions, that also involve imbalances between excitatory and inhibitory neurons. However, the connection between cellular imbalances and default-mode-network dysfunction has remained unclear. We used brain-wide computer simulations incorporating neuronal properties to understand how imbalance at the cellular scale disrupts network function. Our simulations reveal the precise excitatory-inhibitory balance needed for normal suppression. Additionally, we identified distinct failure modes and discovered that certain brain hubs are more vulnerable than others to disruption. Our findings reveal how cellular alterations scale up to cause network-level dysfunction, potentially guiding treatments targeting specific brain regions in individual patients.

Nonergodicity and Simpson's paradox present significant, yet underappreciated challenges in cognitive neuroscience. Leveraging brain imaging and behavioral data from over 4000 individuals and a Bayesian computational model of cognitive dynamics, we investigated brain-behavior relationships underlying cognitive control at both between-subjects and within-subjects levels. Strikingly, brain-behavior associations reversed across levels of analysis, revealing pervasive nonergodicity. Within-subjects analysis uncovered dissociated neural representations of reactive and proactive control and revealed that individuals who adaptively versus maladaptively regulated cognitive control exhibited distinct brain-behavior associations. Our findings demonstrate that between-subjects analyses can fundamentally mischaracterize within-individuals mechanisms, as group-level patterns not only disagreed with individual-level patterns but often reversed them. This work highlights the necessity of distinguishing between-subjects and within-subjects inferences in neuroscience, with implications for understanding cognitive mechanisms and designing personalized interventions.

The neurochemical mechanisms underlying individual differences in children's mathematical and reading abilities remain largely unknown. Here we investigate how neurotransmitter systems relate to brain structural organization supporting these abilities in two independent cohorts of children (N = 991). We mapped brain-wide structural phenotypes associated with academic performance onto a comprehensive PET atlas of 19 neurotransmitter receptors and transporters. Across both domains and cohorts, NMDA glutamatergic receptor distribution showed the most consistent associations with brain structural organization supporting academic abilities (replication Bayes factors >9e4 for mathematics; >4 for reading). NMDA receptor density corresponded with multiple functional networks for mathematical abilities but showed more spatially focused associations within visual networks for reading, suggesting both shared and domain-specific neurochemical mechanisms. Dopaminergic, cholinergic, serotonergic, and GABAergic systems showed weaker, non-replicable associations. These findings bridge molecular neurochemistry and macro-scale brain architecture supporting academic skills, identifying glutamatergic signaling as a candidate target for interventions addressing learning disabilities.

Learning disabilities affect a substantial proportion of children worldwide, with far-reaching consequences for their academic, professional, and personal lives. Here we develop digital twins—biologically plausible personalized deep neural networks (pDNNs)—to investigate the neurophysiological mechanisms underlying learning disabilities in children. Our pDNN reproduces behavioral and neural activity patterns observed in affected children, including lower performance accuracy, slower learning rates, neural hyperexcitability, and reduced neural differentiation of numerical problems. Crucially, pDNN models reveal aberrancies in the geometry of manifold structure, providing a comprehensive view of how neural excitability influences both learning performance and the internal structure of neural representations. Our findings not only advance knowledge of the neurophysiological underpinnings of learning differences but also open avenues for targeted, personalized strategies designed to bridge cognitive gaps in affected children. This work reveals the power of digital twins integrating artificial intelligence and neuroscience to uncover mechanisms underlying neurodevelopmental disorders.

Emotions coordinate our behavior and physiological states during survival-salient events and pleasurable interactions. Even though we are often consciously aware of our current emotional state, such as anger or happiness, the mechanisms giving ...Emotions are often felt in the body, and somatosensory feedback has been proposed to trigger conscious emotional experiences. Here we reveal maps of bodily sensations associated with different emotions using a unique topographical self-report method. In ...

Previous studies exploring category-sensitive representations of numbers and letters have predominantly focused on individual brain regions. This study expands upon this research through computationally rigorous whole-brain neural decoding using Elastic Net (ND-EN), facilitating the analysis of neural patterns across the entire brain with greater precision. To establish the robustness and generalizability of our results, we also conducted innovative probabilistic meta-analyses of the extant functional neuroimaging literature. The investigation comprised both an active task, requiring participants to distinguish between numbers and letters, and a passive task where they simply viewed these symbols. ND-EN revealed that, during the active task, a distributed network-including the ventral temporal-occipital cortex, intraparietal sulcus, middle frontal gyrus, and insula-actively differentiated between numbers and letters. This distinction was not evident in the passive task, indicating that the task engagement level plays a crucial role in such neural differentiation. Further, regional neural representational similarity analyses within the ventral temporal-occipital cortex revealed similar activation patterns for numbers and letters, indicating a lack of differentiation in regions previously linked to these visual symbols. Thus, our findings indicate that category-sensitive representations of numbers and letters are not confined to isolated regions but involve a broader network of brain areas, and are modulated by task demands. Supporting these empirical findings, probabilistic meta-analyses conducted with NeuroLang and the Neurosynth database reinforced our observations. Together, the convergence of evidence from multivariate neural pattern analysis and meta-analysis advances our understanding of how numbers and letters are represented in the human brain.

Social cognition develops through a complex interplay between neural maturation and environmental factors, yet the neurobehavioral mechanisms underlying this process remain unclear. Using a naturalistic fMRI paradigm, we investigated the effects of age and parental caregiving on social brain development and Theory of Mind (ToM) in 34 mother-child dyads. The functional maturity of social brain networks was positively associated with age, while mother-child neural synchronization during movie viewing was related to dyadic relationship quality. Crucially, parenting and child factors interactively shaped social cognition outcomes, mediated by ToM abilities. Our findings demonstrate the dynamic interplay of neurocognitive development and interpersonal synchrony in early childhood social cognition, and provide novel evidence for neurodevelopmental plasticity and reciprocal determinism. This integrative approach, bridging brain, behavior, and parenting environment, advances our understanding of the complex mechanisms shaping social cognition. The insights gained can inform personalized interventions promoting social competence, emphasizing the critical importance of nurturing parental relationships in facilitating healthy social development.

This article provides an overview of the insular cortex's multifaceted role in the human brain. We discuss its structural and functional architecture, and pivotal functions in cognitive control, behavioral regulation, and emotional processing. The review delves into the insula's integral involvement in interoceptive awareness, emphasizing its role in bridging internal physiological states with external stimuli to facilitate adaptive behaviors. We examine how the insula's subdivisions contribute to diverse cognitive and affective processes. The insula emerges as a crucial hub for the dynamic regulation of cognitive and emotional states and the interplay between the mind, body, and environment.

The insular cortex serves as a critical hub for human cognition, but how its anatomically distinct subregions coordinate diverse cognitive, emotional, and social functions remains unclear. Using the Human Connectome Project’s multi-task fMRI dataset (N = 524), we investigated how insular subregions dynamically engage during seven different cognitive tasks spanning executive function, social cognition, emotion, language, and motor control. Our findings reveal five key principles of human insular organization. First, insular subregions maintain distinct functional signatures that enable reliable differentiation based on activation and connectivity patterns across cognitive domains. Second, these subregions dynamically reconfigure their network interactions in response to specific task demands while preserving their core functional architecture. Third, clear functional specialization exists along the insula’s dorsal-ventral axis: the dorsal anterior insula selectively responds to cognitive control demands through interactions with frontoparietal networks, while the ventral anterior insula preferentially processes emotional and social information via connections with limbic and default mode networks. Fourth, we observed counterintuitive connectivity patterns during demanding cognitive tasks, with the dorsal anterior insula decreasing connectivity to frontoparietal networks while increasing connectivity to default mode networks—suggesting a complex information routing mechanism rather than simple co-activation of task-relevant networks. Fifth, while a basic tripartite model captures core functional distinctions, finer-grained parcellations revealed additional cognitive-affective domain-specific advantages that are obscured by simpler parcellation approaches. Our results illuminate how the insula’s organization supports its diverse functional roles through selective engagement of distinct neural networks, providing a novel framework for understanding both normal cognitive function and clinical disorders involving insular dysfunction.

This study investigates the neural underpinnings of cognitive control deficits in attention-deficit/hyperactivity disorder (ADHD), focusing on trial-level variability of neural coding. Using fMRI, we apply a computational approach to single-trial neural decoding on a cued stop-signal task, probing proactive and reactive control within the dual control model. Reactive control involves suppressing an automatic response when interference is detected, and proactive control involves implementing preparatory strategies based on prior information. In contrast to typically developing children (TD), children with ADHD show disrupted neural coding during both proactive and reactive control, characterized by increased temporal variability and diminished spatial stability in neural responses in salience and frontal-parietal network regions. This variability correlates with fluctuating task performance and ADHD symptoms. Additionally, children with ADHD exhibit more heterogeneous neural response patterns across individuals compared to TD children. Our findings underscore the significance of modeling trial-wise neural variability in understanding cognitive control deficits in ADHD.

Mathematical cognition engages a distributed brain network, but the causal dynamics of information flow within it, particularly how memory circuits interact with other brain regions across development, remain unknown. We examined causal dynamic interactions in typically developing children and adolescents/young adults (AYA) using fMRI during three tasks involving mental arithmetic and symbolic and non-symbolic number comparison. Using multivariate dynamic state-space identification modeling, we found that causal dynamic interactions differed between children and AYA across all three tasks, especially during arithmetic processing. The left medial temporal lobe (MTL) served as a causal signaling hub in AYA across all three tasks, but not in children. The left angular gyrus (AG) maintained consistent hub-like properties during arithmetic task across development. Compared to AYA, children exhibited heightened causal interactions in both the MTL and AG. Moreover, network hub properties of these regions correlated with individual’s mathematical achievement specifically during arithmetic processing. Together, we found that the MTL transitioned from heightened, context-dependent, interactions in childhood to a stable causal hub in adulthood, while the AG maintained as a hub during arithmetic processing across development. This dissociation between memory systems, coupled with their task-specific relationship to mathematical abilities, provides novel insights into how brain networks mature to support mathematical cognition.

Children exhibit remarkable variability in their mathematical problem-solving abilities, yet the cognitive, metacognitive and affective mechanisms underlying these individual differences remain poorly understood. We developed a novel Bayesian model of arithmetic problem-solving (BMAPS) to uncover the latent processes governing children's arithmetic strategy choice and efficiency. BMAPS inferred cognitive parameters related to strategy execution and metacognitive parameters related to strategy selection, revealing key mechanisms of adaptive problem solving. BMAPS parameters collectively explained individual differences in problem-solving performance, predicted longitudinal gains in arithmetic fluency and mathematical reasoning, and mediated the effects of anxiety and attitudes on performance. Clustering analyses using BMAPS parameters revealed distinct profiles of strategy use, metacognitive efficiency, and developmental change. By quantifying the fine-grained dynamics of strategy selection and execution and their relation to affective factors and academic outcomes, BMAPS provides new insights into the cognitive and metacognitive underpinnings of children's mathematical learning. This work advances powerful computational methods for uncovering latent mechanisms of complex cognition in children.

Deep neural networks are increasingly crucial for analysing dynamic functional brain imaging data, offering unprecedented accuracy in distinguishing brain activity patterns across health and disease. However, they often function as black boxes obscuring the neurobiological features driving classifications between groups. This study systematically investigates explainable AI (xAI) methods to address this challenge, employing two complementary simulation approaches: recurrent neural networks for controlled parameter exploration, and The Virtual Brain for biophysically realistic whole-brain modelling. These simulations generate fMRI datasets with known regional alterations in excitation/inhibition (E/I) balance, mimicking mechanisms implicated in psychiatric and neurological disorders. Our comprehensive validation demonstrates that Integrated Gradients and DeepLift successfully identify ground-truth affected regions across challenging conditions, including high noise (-10dB SNR), low prevalence (1% of regions), and subtle E/I alterations. This performance remains robust across three different attribution methods and baseline choices, establishing the reliability of xAI for functional neuroimaging analysis. Critically, successful cross-species validation using both human (68-region) and mouse (426-region) connectomes demonstrates the approach's ability to detect mechanistic alterations across different scales of brain organization. Application to the multisite ABIDE resting-state fMRI dataset (N=834) reveals that regions within the default mode network, particularly the posterior cingulate cortex and precuneus, most clearly differentiated children with autism from neurotypical controls. The convergence between these empirical findings and our biophysical simulations of E/I imbalance provides computational support for mechanistic theories of E/I imbalance in autism while demonstrating how xAI can bridge cellular-level mechanisms with clinical biomarkers. This work establishes a framework for reliable interpretation of deep neural network models in functional neuroimaging, with implications for understanding brain disorders and developing targeted brain stimulation strategies. ### Competing Interest Statement The authors have declared no competing interest.