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The level of difficulty of a secondary cognitive task (DT) can affect gait and cortical activity distinctly in individuals with Parkinson's disease (PD). During a simpler ST, individuals with PD may use a compensatory neural mechanism by reallocating neural resources to preserve gait performance; for difficult DT, this compensation may not be the case. However, whether different levels of difficulty of a single-domain DT would distinctively affect gait and cortical activity in individuals with PD compared to neurologically healthy individuals is still unknown. Fourteen individuals with PD and 14 healthy individuals performed walking trials at self-selected speed, under six conditions of walking with an auditory DT and varying levels of difficulty (very easy: VE-SCT, easy: E-SCT, moderate: M-SCT, difficult: D-SCT, and very difficult: VD-SCT). Gait kinematics and cortical activity data were recorded. RM-ANOVAs identified that individuals with PD showed higher DT cost for both step length and step velocity when the cognitive task was D-SCT or VD-SCT, compared to easier tasks (p&#x2009;<&#x2009;0.005). Cortical activity showed a different pattern. During more difficult tasks (M-SCT, D-SCT, VD-SCT), PD individuals had a lower DT cost in delta frequency (frontal and motor areas) and beta frequency (parietal area) compared to the easier tasks (VE-SCT, E-SCT) (p&#x2009;<&#x2009;0.005). These findings suggest that individuals with PD exhibit a distinct pattern of cognitive-motor interaction during dual-task walking, characterized by increased cortical dual-task cost in lower vs. greater gait deterioration in higher task demands. These findings suggest that individuals with PD over-engage cognitive resources while walking with relatively easier DT.

Protecting infants from harm is widely considered a fundamental evolutionary imperative and a cross-cultural universal. While adults exhibit heightened empathic responses to infant pain, the underlying neurocognitive dynamics remain unclear. Using EEG during a pain empathy paradigm, we identified distinct neural responses to infant pain compared to adult pain. Relative to adult pain-neutral condition, infant pain-neutral condition elicited a larger P3 amplitude, suggesting enhanced cognitive empathy. In the oscillatory domain, infant pain (versus infant-neutral) induced enhanced alpha power and greater adaptive modulation of alpha and low beta (15-18 Hz) rhythms. Conversely, adult pain (versus adult-neutral) was associated with stronger suppression of low-alpha (8-10 Hz) activity and reduced adaptive modulation. Furthermore, empathy for infant pain engaged increased posterior-to-anterior information flow, suggesting heightened integration across affective and cognitive networks. These findings collectively suggest that the increased alpha power may reflect rapid threat detection and top-down modulation, while the enhanced adaptive changes signify efficient response optimization during infant pain empathy. Our results are consistent with the model of the parental brain as an evolutionary product that balances conserved subcortical responses with flexible cortical regulation, pointing toward a unique neurophysiological profile supporting the protection of vulnerable offspring.

This study investigates the ongoing electrical activity of local neural networks-referred to as neurodynamics-across 37 anatomically defined brain regions. We analyzed stereotactic intracranial EEG (sEEG) recordings from 106 subjects during wakeful rest, focusing on scale-free (power-law) properties to determine whether distinct brain regions exhibit unique neurodynamic signatures. Results revealed a power-law regime in two frequency ranges (approximately 0.5-4 Hz and 33-80 Hz). Notably, the power-law exponent (slope) in the high-frequency band differed significantly between cortical and subcortical areas (p < 0.01). These findings suggest that local neurodynamics, as reflected in scale-free characteristics, may serve as a functional "fingerprint" for brain region classification. This approach may contribute to functional brain parcellation efforts and offer new insights into the intrinsic organization of neuronal networks as revealed by resting-state activity analysis.

MedIntelliCare is an AI-powered medical assistant designed to enhance diagnostic accuracy, reduce cognitive load on healthcare professionals, and integrate real-time medical data. While current AI-driven medical systems focus on information retrieval and response generation, MedIntelliCare leverages Retrieval-Augmented Generation (RAG) combined with principles from neural computation and decision-making processes. This study explores the system's ability to simulate biologically inspired information processing by integrating brain-like computing, predictive modeling, and multimodal analysis, including EEG and neuroimaging data. By aligning MedIntelliCare with advances in computational neuroscience and intelligent diagnostics, we aim to establish a model that enhances clinical decision support through adaptive information retrieval. The system's future implications include cognitive disorder modeling, brain-computer collaboration, and advanced AI-driven diagnostics inspired by neural processing frameworks. Experimental validation using cosine similarity metrics demonstrates that MedIntelliCare achieves a 73% alignment with expert-generated reports, reinforcing its potential in neuro-inspired medical intelligence.

Major depressive disorder (MDD) is accompanied by abnormal reward processing, altered dopamine transmission in the ventral tegmental area-nucleus accumbens-medial prefrontal cortex (VTA-NAc-mPFC) dopaminergic pathway, and disruptions in both neural dynamics and brain energy metabolism. Yet, how these abnormalities converge within a unified framework of neural dynamics and neural energy coding remains unclear. The purpose of this review is to integrate and critically assess computational models of neural dynamics and neural energy coding in MDD, with a particular emphasis on the multiscale modeling approaches developed in our recent work, and to organize these advances into a coherent conceptual framework linking dopamine-related circuit dysfunction to alterations in neural energy consumption. First, we constructed Hodgkin-Huxley (H-H) models for the NAc medium spiny neuron (MSN) to simulate its neurodynamics. Then, using the neural energy model, we explored the energy consumption characteristics of MSNs and found that, in the MDD condition, MSN energy consumption during spiking was lower than in controls, demonstrating the feasibility and sensitivity of this energy-based methodology. To further examine how these mechanisms scale to functional circuits, we constructed a neural network dynamical model for the VTA-NAc-mPFC dopaminergic pathway and applied an augmented neural-energy computation framework to characterize its energy consumption features. Simulations demonstrated that neural energy consumption was substantially lower in the MDD condition, primarily due to decreased mPFC energy expenditure. Distinct energy-coding patterns emerged across neuronal types, and the energy required to encode a single action potential in both MSNs and pyramidal neurons increased under MDD low dopamine situation, indicating reduced energy efficiency. Moreover, the correlation between membrane potential and instantaneous power was moderate (0.6-0.9) rather than tight, and it changed substantially with dopamine levels. This shows that neural energy consumption carries additional neural information that is not reflected directly in membrane potential signals. Together, these findings establish a unified computational framework that links dopamine deficiency, ion-channel-level dysfunction, microcircuit dynamics impairment, and large-scale reductions in neural energy consumption. Our work highlights neural energy coding as a promising mechanistic indicator and potential biomarker for MDD, and provides a generalizable methodology for investigating other neuropsychiatric disorders.

This study examined time-dependent neural correlates and behavioral markers of mental fatigue and fatigability in athletes using single and mixed cognitive task paradigms with electroencephalography (EEG). Forty athletes completed Stroop, Flanker, Arithmetic, or mixed-task paradigms. EEG data were analyzed using power spectral density (PSD) in the theta, alpha, and beta frequency bands and event-related potential (ERP) analysis focusing on P300 amplitude and latency. Behavioral measures included reaction time, error rate, and subjective fatigue assessed using a Visual Analog Scale. The results showed that subjective fatigue increased significantly over time across all task conditions (p&#xa0;<&#xa0;0.001). Behavioral performance differed across tasks, with higher error rates and longer reaction times in the Arithmetic and mixed-task conditions compared to the Stroop and Flanker tasks (p&#xa0;<&#xa0;0.001). PSD analyses revealed significant task-related differences (p&#xa0;<&#xa0;0.05), including higher frontal theta power during the Arithmetic task and lower posterior theta power during the mixed-task condition. Alpha power increased significantly over time in posterior regions (p&#xa0;<&#xa0;0.05). P300 amplitude decreased across time blocks in central, centroparietal, and parietal locations (p&#xa0;<&#xa0;0.001), while P300 latency differed across tasks (p&#xa0;<&#xa0;0.01). In conclusion, increased subjective fatigue and time-dependent changes in posterior EEG activity occurred without behavioral performance decline, supporting a distinction between mental fatigue and fatigability.

Our thoughts fluctuate dynamically, driven either by external stimuli and tasks (on-task thoughts) or drifting to task-unrelated contents (off-task thoughts or mind wandering). Although research has identified neural markers distinguishing different thought types, the temporal signature (dynamics) of on- and off-task thoughts remains poorly understood. This EEG study investigated different neurodynamical features-autocorrelation window (ACW), Lempel-Ziv complexity (LZC), power-law exponent (PLE), and median frequency (MF)-to differentiate these thoughts in their underlying dynamics during a signal-response task. Off-task thoughts exhibited prolonged ACW, reduced LZC, increased PLE, and smaller MF compared to on-task thoughts, establishing a distinct neurodynamic signature. Through statistical modeling, we identified a hierarchical background-foreground structure among these measures that unfolds along a temporal continuum, transitioning from longer block-level (17-second) to shorter trial-level (3-second) timescale. Notably, the longer background (block-level ACW) and shorter foreground (trial-level ACW and LZC) layers are tightly coupled during the "faster and shorter" on-task thoughts whereas they are more loosely related during "slower and longer" off-task thoughts. These findings, replicated in an independent dataset, demonstrate how the organization of our brain's dynamics, along a temporal continuum of longer background durations to shorter foreground durations, shapes on-task and off-task thoughts thereby yielding their distinct signatures.

Cognitive dysfunction and emotional symptoms frequently co-occur in Parkinson's disease (PD), yet the processes linking these domains remain unclear. One proposed pathway involves differences in emotion regulation (ER) strategy use, which may vary as a function of cognitive functioning. This study examined whether task-based executive functioning (EF) and self-reported functional cognitive impairment (FCI) are associated with anxiety, depression, and mental wellbeing in PD, and whether these relationships are moderated by ER strategies. One hundred and three individuals with PD completed EF tasks, and self-report measures of FCI, ER strategy use, and mental health. Principal component analysis was used to derive task-based EF components. Moderation analyses tested whether self-reported and task-based cognitive functioning interacted with ER strategy use in relation to mental health outcomes. Greater FCI was associated with stronger negative relationships between Seeking Distractions and Ignoring and mental wellbeing, and with higher anxiety and depression when Ignoring was used. Among those with better functional cognition, Withdrawal and Catastrophizing were more strongly related to poorer mental wellbeing, while Putting into Perspective was associated with lower depressive symptoms. Stronger task-based Executive and Inhibitory Control were associated with weaker relationships between Ignoring and anxiety and depression, respectively, whereas greater Acceptance was associated with higher anxiety among those with stronger Executive Control. Cognitive functioning moderates the relationship between ER strategies and mental health outcomes in PD. Functional cognition showed more consistent associations, whereas task-based EF exerted more limited effects. These findings contribute to understanding variability in affective symptoms in PD.

This article proposes a new ontological framework for describing cognitive processes, grounded in intersection theory and an entropy-based model of the space of truths. At the micro-level, we show that neurons and neuronal populations function as thermodynamic systems that reside in regimes of fluctuations, relaxations, and entropic transitions. At the macro-level, these processes manifest as ordered structures-the topologies of truths and their intersections-that jointly shape the cognitive landscape. We introduce the notion of ontological truth as the minimal quantum of reality, as well as the epistemic agent (individual or collective) who performs septation (partitioning), dividing the universal space of truths into known and unknown zones. On this basis, we formulate the entropic trajectory of cognition, which characterizes the evolving balance between the known and the unknown over time. We further show that synchronization of individual agents via the operator of temporal velocity [Formula: see text] yields a collective agent whose entropy is defined as a coherent integration of the entropies of its constituent agents. This construction scales from sensory physiology (receptors as individual agents) to the collective cognition of humanity as a whole. At the concluding level, we introduce the key notion of epistemic bifurcation (splitting)-the multiplicity of individual reconstructions of the same ontological truth-which explains why collective cognition is not a mere sum of private representations but acquires the quality of emergent integration. Taken together, the article demonstrates that cognitive topology is an emergent reflection of thermodynamic dynamics, and that a universal scheme of intersections provides a unified ontological language for processes ranging from the micro-physiology of neurons to macro-level epistemology and the history of civilization. The online version contains supplementary material available at 10.1007/s11571-026-10438-y.

Understanding the neurophysiological mechanisms underlying driving behavior in young drivers is essential for improving cognitive-aware driver assistance and vehicle-human interaction systems. This study systematically examines EEG dynamics and functional brain network reconfigurations across both manual and video-based car-following observation, providing a neurophysiological framework for differentiating driving modes among young adult drivers. EEG characteristics were analyzed under three car-following strategies-aggressive, conservative, and personalized-implemented within a simulated driving environment, to capture the variability of cognitive engagement during distinct control demands. Key findings reveal that power spectral density (PSD) in the &#x3b8;, &#x3b2;, and &#x3b3; bands, combined with brain functional connectivity (BFN) measures, effectively characterizes workload-related modulation and attentional resources across driving conditions. A novel computational framework integrating Time-Frequency Common Mutual Information (TFCMI) features with a Parallel Compact Convolutional Neural Network (PCNet) achieved an average classification accuracy of 85.26%, surpassing traditional single-modality approaches. Neurotopographic results further indicate context-dependent functional specialization: frontal regions showed stronger activation and connectivity during manual control, while occipital regions exhibited enhanced synchronization during video-based car-following observation tasks. Collectively, these findings advance the understanding of driving-related cognitive processes in young drivers and provide neuroergonomic insights for designing adaptive human-machine interfaces in future intelligent transportation systems. The online version contains supplementary material available at 10.1007/s11571-026-10442-2.

Beyond traditional, dyadic human-robot interaction, embedding robots into multi-human teams, such as search and rescue (SAR), requires an understanding of fundamental aspects of team composition and dynamics. While considerable work has examined how robot agents influence both taskwork and teamwork, few studies have focused on identifying which factor best explains differences in team outputs. This research investigates the neurodynamics of taskwork and teamwork as SAR teams transition between multi-human (mH) and multi-human-robot (mHR) configurations. Electroencephalogram (EEG) has been a key tool in human teamwork research because of its sensitivity to changes in cognitive states such as mental workload, sustained attention, and engagement. Specifically, EEG power spectral density (PSD), particularly frontal theta activity (4-7 Hz), has been used to assess variations in mental workload and social cognition associated with task performance. EEG hyperscanning, which evaluates interbrain synchrony between two or more individuals, using metrics such as weighted phase lag index (wPLI), has been widely employed to study teamwork among humans. In this study, PSD and EEG hyperscanning were used to analyze taskwork and teamwork in 22 teams comprising a highly engaged SAR team member (mission commander), a less-involved member (safety officer), and a navigator as they searched for victims in a virtual emergency environment. The navigator was either a trained researcher posing as a participant or a virtual robot, with the robot's performance manipulated using the Wizard of Oz technique. Results for taskwork show that the social-cognitive abilities of mission commanders, but not those of safety officers, are adversely impacted by a robot navigator compared with a human navigator, despite the perceived workload remaining stable. Although team trust outcomes were similar, neural synchrony across occipital, parietal, and temporal regions increased in mHR teams relative to mH teams, indicating different neurodynamical patterns of teamwork. The study findings provide evidence that both taskwork and teamwork are fundamentally altered in mHR teams, regardless of the effectiveness of robotic capabilities and functions, compared with mH teams. Therefore, beyond dyadic interactions, multi-human robot teaming must be viewed as a fundamentally distinct team construct rather than simply an extension of human-human teaming.

Perceptual decision-making involves distinct sub-processes, including sensory encoding, decision formation, and motor execution. Studying the specific contributions of cortical areas to these components could deepen our understanding of decision-making mechanisms and inform therapeutic approaches for cognitive impairment. Single-pulse transcranial magnetic stimulation (spTMS) enables the functional investigation of cortical involvement during task performance, revealing the participation of specific regions in cognitive processes. When combined with the drift diffusion model (DDM), spTMS can precisely characterize effects on different decision sub-processes. In this study, 30 healthy participants performed a perceptual decision-making task requiring right-hand finger responses to complex visual stimuli. We delivered spTMS to sensorimotor cortex regions at two time points during task performance (200 ms and 800 ms post-stimulus onset). Results demonstrated region-specific modulation patterns: stimulation of the premotor dorsal caudal cortex (PMdc) selectively reduced reaction time (RT) by decreasing non-decision time (NDT), indicating its role in motor preparation. In contrast, primary motor cortex (M1) and primary somatosensory cortex (S1) stimulation produced opposing effects - decreased NDT coupled with increased evidence accumulation time (EAT) - resulting in no net RT change. These findings highlight how spTMS combined with DDM can dissect distinct cortical contributions to decision-making sub-processes.

Traumatic experiences can disrupt one's sense of safety, self-efficacy, and relationships. Prolonged stress may lead to anxiety, depression, and diminished agency. The embodied, subjective manifestations of trauma call for personalized therapeutic approaches that address symptoms and foster resilience. Group Creative Arts Therapies (CATs) offer relational aesthetic interventions that promote resilience and trauma recovery. Incorporating body-based methods, movement, materials and visual expression, CATs support interoceptive awareness, multisensory integration, embodiment, and emotional-cognitive processing. This article presents a review and conceptual framework of group CAT interventions during wartime, focusing on challenges related to body awareness, self-efficacy, and autobiographical memory. It examines how creative aesthetic approaches help process trauma and strengthen resilience. Drawing on predictive processing accounts of brain function, the article explores the neuropsychological impact of trauma and how creative group work may modulate related brain mechanisms. Creative techniques can foster bodily anchored self-awareness, self-efficacy and processes of traumatic memory reconsolidation. Aesthetic experiences are associated with changes in brain activation and connectivity through processes of embodiment, externalization, and meaning making. On an intrapersonal level, converging evidence highlights the role of sensory and sensorimotor processing, along with the dynamic interplay between Default Mode, Executive Control, and Salience networks, as conceptualized in the Triple Network Model. On an interpersonal level, the literature points to the dynamics of brain and body synchronization, as emerging phenomena during shared creative engagement. These neurodynamics provide a coherent framework for understanding how creative arts-based psychotherapeutic group work can support trauma processing and the cultivation of resilience.

Brain-computer interface (BCI) establishes a bidirectional pathway between the brain and external devices. Its applications fall into two main categories: utilizing the brain as a controller (e.g., for prosthetics) or as a modulation target (e.g., for cognitive regulation). Progress in BCI is constrained by two core bottlenecks: in brain control, limited understanding of neural coding mechanisms restricts improvements in the accuracy and robustness of encoding/decoding algorithms; in brain regulation, one-size-fits-all regulatory strategies struggle to address significant individual variability, resulting in heterogeneous therapeutic responses. Inspired by neuroscience advances, this perspective proposes a new biological brain - digital twin brain based BCI (BDBCI) paradigm. Here, the biological brain acts as an empirical anchor and ultimate validation platform, while a high-fidelity digital twin brain (DTB) serves as a theoretical inference engine and virtual testbed. Specifically, experimental induction is applied to the biological brain to distill preliminary conclusions, such as brain-behavior mappings and brain-stimulation causal relationships, which are then used to construct and calibrate the DTB model. Subsequently, on the DTB platform, large-scale model deduction is conducted to validate and deepen these preliminary insights mechanistically, thereby optimizing control/regulation parameters or informing the parameter ranges for the next round of experimental induction and model deduction. Through this BDBCI paradigm, we aim to advance BCI research from empirical trial-and-error toward a new era of model-driven, predictable, and explainable precision science.

Fast-spiking basket cells (FSBCs) govern hippocampal oscillations through their rapid and sustained firing patterns, which drive rhythmic inhibition onto postsynaptic neurons, thereby enforcing population synchrony in the gamma and other frequency bands that support cognitive processes. Despite the established role of FSBCs in hippocampal oscillations, the precise mechanisms by which their dendrites influence membrane potential responses across different frequency bands remain unclear. In this study, we simulate oscillation-like input protocols to explore how dendrites modulate the spectral responses of the membrane potentials of FSBCs. Our results show that FSBCs exhibit both slow and fast oscillatory components, which are shaped by their action potentials. Input synchrony is essential for determining both the fast-band response frequency and its coupling with the slow frequency, while the neuron's intrinsic firing dynamics maintain the stability of the fast-band peak frequency across theta-range inputs. Although dendritic Na[Formula: see text]/A-type K[Formula: see text] channel blockade and cp-AMPA enhancement both increase fast-band frequency, they differentially affect phase-amplitude coupling, with blockade reducing and cp-AMPA enhancement increasing it, highlighting the role of intrinsic dendritic conductances and cp-AMPA inputs in promoting coupling. Furthermore, we show that the spatial distribution of synaptic inputs along dendrites affects the response frequencies, with distinct frequencies observed at different dendritic locations according to their electrotonic distance. These findings provide insights into how the intrinsic properties of FSBCs influence their response to oscillatory inputs. The online version contains supplementary material available at 10.1007/s11571-026-10418-2.

Metaphors have long played multiple roles in conceptualizing the mind and brain, guiding the development and refinement of theoretical models and empirical questions. Early analogies&#xa0;(comparing the brain to hydraulic systems, telephone exchanges, factories, or libraries) offered shortcuts to understanding aspects of cognition, memory, and brain dynamics. From theoretical frameworks, metaphors like the mind as a computer evolved into central scientific metaphors, shaping core theoretical frameworks, inspiring predictions, and informing research methodologies. As such, metaphors play a key role in guiding scientific inquiries. Building on that premise, we propose music as a scientific metaphor for understanding multiple brain dynamics and cognitive functions. Unlike metaphors focusing on static components or linear flows, music emphasizes continuous adaptation, context-dependence, and cultural embedding, and presents a model for simultaneous engagement with multiple layers of meaning. Integrating analytical techniques from music theory and experiential insights from performance and listening, we can deepen our understanding of mind and brain dynamics and provide fresh epistemological pathways for interdisciplinary research. Music has a hierarchical structure, temporal complexity, and capacity to integrate multiple processes that parallel key features of the brain's architecture and cognitive functions. Drawing from research on neural oscillations, plasticity, predictive coding, and emotional processing, we illustrate how the musical paradigm can capture the rich entanglement of mind and brain, from large-scale brain dynamics and developmental trajectories to the emergence of consciousness and the interplay of affective states.

We introduce a brain-constrained neurocomputational model designed to simulate higher cognitive functions of the human brain, implemented using NEST, a widely used open-source simulator optimised for high-performance spiking neural network simulations. Previously implemented in the custom-built C-based Felix simulation library, transitioning the model to NEST enhances accessibility, reproducibility, and computational efficiency. At the cellular level, the model comprises spiking excitatory neurons and local inhibitory neurons, whereas at the network level, it replicates the structural and functional organisation of 12 cortical regions spanning frontal, temporal, and occipital cortices, along with their associated inter-area connectivity. Additionally, global inhibition mechanisms and neuronal noise are integrated. Learning in the model follows biologically plausible Hebbian plasticity principles, incorporating both long-term potentiation and long-term depression. To validate the NEST implementation, we replicated previous simulation findings obtained with the Felix-based model. The new implementation successfully reproduced the same topographical distribution of cell assemblies following associative learning of object and action words within action and perception systems, replicating a range of previous neuroimaging results. Although the NEST model produced larger cell assemblies than Felix, the overall topographical patterns remained similar, indicating preservation of fundamental network characteristics. Moreover, the transition to NEST significantly enhanced computational efficiency, reducing simulation runtime nearly sixfold compared to Felix. This improvement in computational speed is crucial for expanding the model to include additional cortical regions, such as extending to the right hemisphere, which necessitates increased computational resources. The online version contains supplementary material available at 10.1007/s11571-026-10415-5.

Alzheimer's disease (AD) pathology begins years before symptoms appear, and dynamic flexibility of the medial temporal lobe (MTL) may serve as an early functional biomarker. Using data from 656 older adults in the Rutgers Aging and Brain Health Alliance study, we evaluated whether cognitive, genetic, biochemical, and demographic predictors could estimate MTL dynamic flexibility, despite substantial missingness (1,866 missing values; 25.86%). Only 42 participants (6.40%) had complete data; therefore, we compared case deletion with five imputation strategies (MICE, GAIN, MissForest, MIWAE, ReMasker) and eight regression models, assessing prediction accuracy using repeated 5-fold cross-validation. Complete-case analysis yielded limited performance (average [Formula: see text], [Formula: see text]). After imputation, all methods improved accuracy, with MissForest paired with Bagging Trees or Random Forest achieving the lowest prediction error ([Formula: see text]). The greatest improvement in concordance occurred when GAIN was combined with Bagging Trees/Random Forest ([Formula: see text]), representing a &#xa0;57% gain over the best complete-case model. A Scheirer-Ray-Hare ANOVA confirmed significant differences across imputation strategies ([Formula: see text]). Runtime analyses showed GAIN and MissForest to be both accurate and computationally efficient, while deep generative imputers were slower. These findings demonstrate that robust imputation is essential for maximizing data utility and predictive reliability in high-missingness neuroimaging studies and highlight the potential of ensemble tree models combined with advanced imputation techniques for estimating MTL dynamic flexibility in aging populations.