Background Attention-deficit/hyperactivity disorder (ADHD) has traditionally been conceptualized categorically, with efforts to identify disorder-specific neurobiological endophenotypes. However, dimensional models suggest that brain-behavior organization may follow developmental axes that cut across diagnostic boundaries. We tested whether neural dynamics and cortical excitability differentiate those with ADHD diagnoses from typically developing (TD) peers, and whether brain-behavior covariance aligns with diagnostic or developmental dimensions. Methods We studied 84 participants aged 8-17 years (51 ADHD, 33 TD). High-density electrophysiological (hdEEG) measures included task-free source-resolved data used to derive mean global brain fluidity (variance of dynamic functional connectivity) and region-specific cortical excitability. Behavioural measures included self- and parent-report questionnaires, cognitive control (CC) tasks, and neuropsychological tests. Partial least squares (PLS) assessed multivariate brain-behavior associations including age, followed by clustering based on latent component scores. Results Group differences emerged in parent-report questionnaires and CC tasks, but not in neuropsychological measures. ADHD individuals showed higher mean global brain fluidity and increased cortical excitability. The excitability-fluidity relationship was network-dependent: higher excitability predicted higher fluidity in task-positive networks and lower fluidity in default-mode and salience networks, with no group effects. PLS identified a latent dimension linking neural metrics with age, verbal fluency, inhibitory control, and positive affect, but it did not distinguish ADHD from TD. Clustering revealed two neurodevelopmental profiles spanning both groups. Conclusions While ADHD is associated with mean-level differences in neural dynamics, brain-behaviour organization follows a developmental neurocognitive-affective axis that transcends the diagnostic boundary. These findings support a dimensional framework for understanding neurobiological variation in neurodevelopmental conditions. ### Competing Interest Statement The authors have declared no competing interest.

**Abstract:** The sudden emergence of creative insights remains a poorly understood phenomenon within neuroscience. This paper presents a novel framework for quantifying the neural correlates of insight generation by combining high-density electroencephalography (hdEEG) with Bayesian hierarchical modeling focused on prefrontal cortex (PFC) activity. Our method, termed "Dynamic Insight Mapping" (DIM), leverages recent advances in time-frequency analysis and Bayesian inference to identify transient neural signatures predictive of subjective insight reports, ultimately offering a pathway to understand and potentially enhance creative cognition.

**Abstract:** Current methods for assessing consciousness in comatose patients rely on broad EEG analyses and lack the precision to differentiate subtle shifts in brain activity indicative of residual awareness. This research introduces a novel framework, “Dynamic Cerebellar Microcircuit Profiling” (DCMP), leveraging high-density EEG (hdEEG) to quantify cerebellar microcircuit dynamics and correlate these patterns with quantitative measures of consciousness levels. DCMP utilizes adaptive Kalman filtering to track the temporal evolution of cerebellar neural populations and applies advanced machine learning techniques to classify patients into distinct states of consciousness.

**Abstract:** Chronic pain, a prevalent and debilitating condition, often resists conventional treatment due to its complex neurobiological underpinnings and individual variability. This paper proposes a closed-loop, personalized approach to chronic pain management leveraging deep learning to predict and modulate cortical oscillatory activity associated with pain perception. Our framework, termed “Chronosync,” utilizes high-density electroencephalography (hdEEG) data and a recurrent neural network (RNN) architecture to dynamically adapt transcranial alternating current stimulation (tACS) parameters, aiming to restore disrupted neuronal synchronization and alleviate pain symptoms.

**Abstract:** This research proposes Adaptive Affective Resonance Mapping (AARM), a novel Brain-Computer Interface (BCI) system enabling paralyzed individuals to synthesize and express nuanced emotions in real-time. Unlike existing systems focused on basic emotional states, AARM leverages high-density electroencephalography (hdEEG) to decode subtle affective dynamics and translates them into dynamically-controlled visual and auditory cues. Utilizing a resonance-based mapping algorithm coupled with reinforcement learning, AARM personalizes the translation between neural activity and emotional expression, yielding more accurate and expressive communication opportunities for severely impaired individuals.

**Abstract:** Current neuroimaging techniques often lack the spatial and temporal resolution required to comprehensively map the complex attentional modulation pathways within the human visual cortex. This work proposes a novel framework leveraging high-density electroencephalography (hdEEG) data coupled with a hierarchical Bayesian inference model to reconstruct neural activity patterns with unprecedented precision. Integrating dynamic causal modeling and recurrent neural network (RNN) architectures within a Bayesian framework allows robust estimation of effective connectivity and neural dynamics, yielding a detailed understanding of attentional influence across hierarchical cortical areas.

**Abstract:** This paper introduces a novel framework for predicting relapse risk and tailoring therapeutic interventions in patients with Substance Use Disorder (SUD) by leveraging real-time monitoring of brain’s reward circuit activity. The system integrates high-density electroencephalography (hdEEG) data with behavioral observations and clinical records, employing Adaptive Kalman Filtering (AKF) to track dynamic changes in reward network functionality and Reinforcement Learning (RL) to personalize behavioral interventions promoting abstinence.

**Abstract:** High-density electroencephalography (hdEEG) offers unprecedented spatial resolution, yet is severely hampered by the presence of physiological artifacts such as blinks, cardiac activity, and muscle movements. Manual artifact rejection is time-consuming and subjective, limiting the real-time applicability of hdEEG. This research presents a novel automated artifact rejection pipeline—Multi-Modal Data Fusion and Hierarchical Signal Decomposition (MMFD-HSD)—that enhances detection accuracy and reduces latency compared to existing methods.

Patients with epilepsy are characterized by a dysregulation of excitation-inhibition balance (E/I).

Patients with epilepsy are characterized by a dysregulation of excitation-inhibition balance (E/I).

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