Search Results (249)

This paper traces the intellectual foundations of neuroarchitecture, the design of environments informed by how the brain processes spatial information, from its origins in 1970s environmental psychology and later connectionist philosophy to its contemporary state. While early computational approaches prioritized speed and efficiency for engineering tasks like pattern recognition, a prescient group of pioneers pursued a different path. They developed biologically plausible neural network models that prioritized neural realism over computational performance. These networks embraced the complex realities of biological brains, incorporating excitatory and inhibitory dynamics, local learning rules, and hierarchical knowledge representation. We examine how the philosophical frameworks developed during this formative period established the theoretical foundation for meaningful interdisciplinary collaboration between neuroscience and design. The field has since expanded significantly through our contemporary understanding of neurodiversity. This broader perspective has the potential to transform neuroarchitecture from a niche research area into a comprehensive approach for creating environments that support cognitive performance and brain health for everyone. Full article

Neuromorphic computing, an interdisciplinary field combining neuroscience and computer science, aims to create efficient, bio-inspired systems. Different from von Neumann architectures, neuromorphic systems integrate memory and processing units to enable parallel, event-driven computation. By simulating the behavior of biological neurons and networks, these systems excel in tasks like pattern recognition, perception, and decision-making. Neuromorphic computing chips, which operate similarly to the human brain, offer significant potential for enhancing the performance and energy efficiency of bio-inspired algorithms. This review introduces a novel five-dimensional comparative framework—process technology, scale, power consumption, neuronal models, and architectural features—that systematically categorizes and contrasts neuromorphic implementations beyond existing surveys. We analyze notable neuromorphic chips, such as BrainScaleS, SpiNNaker, TrueNorth, and Loihi, comparing their scale, power consumption, and computational models. The paper also explores the applications of neuromorphic computing chips in artificial intelligence (AI), robotics, neuroscience, and adaptive control systems, while facing challenges related to hardware limitations, algorithms, and system scalability and integration. Full article

In the realm of wearable technology, achieving robust continuous authentication requires balancing high security with the strict resource constraints of embedded platforms. Conventional machine learning approaches and deep learning-based biometrics often incur high computational costs, making them unsuitable for low-power edge devices. To address this challenge, we propose H-PPG, a lightweight authentication system that integrates photoplethysmography (PPG) and inertial measurement unit (IMU) signals for continuous user verification. Using Hyperdimensional Computing (HDC), a lightweight classification framework inspired by brain-like computing, H-PPG encodes user physiological and motion data into high-dimensional hypervectors that comprehensively represent individual identity, enabling robust, efficient and lightweight authentication. An adaptive learning process is employed to iteratively refine the user’s hypervector, allowing it to progressively capture discriminative information from physiological and behavioral samples. To further enhance identity representation, a dimension regeneration mechanism is introduced to maximize the information capacity of each dimension within the hypervector, ensuring that authentication accuracy is maintained under lightweight conditions. In addition, a user-defined security level scheme and an adaptive update strategy are proposed to ensure sustained authentication performance over prolonged usage. A wrist-worn prototype was developed to evaluate the effectiveness of the proposed approach and extensive experiments involving 15 participants were conducted under real-world conditions. The experimental results demonstrate that H-PPG achieves an average authentication accuracy of 93.5%. Compared to existing methods, H-PPG offers a lightweight and hardware-efficient solution suitable for resource-constrained wearable devices, highlighting its strong potential for integration into future smart wearable ecosystems. Full article

Brain–computer interface-based (BCI) training induces neural plasticity and promotes motor recovery in stroke patients by pairing movement intentions with congruent electrical stimulation of the affected limb, eliciting somatosensory afferent feedback. However, this training can potentially be refined further to enhance rehabilitation outcomes. It is not known how specific the afferent feedback needs to be with respect to the efferent activity from the brain. This study investigated how corticospinal excitability, a marker of neural plasticity, was modulated by four types of BCI-like interventions that varied in the specificity of afferent feedback relative to the efferent activity. Fifteen able-bodied participants performed four interventions: (1) wrist extensions paired with radial nerve peripheral electrical stimulation (PES) (matching feedback), (2) wrist extensions paired with ulnar nerve PES (non-matching feedback), (3) wrist extensions paired with sham radial nerve PES (no feedback), and (4) palmar grasps paired with radial nerve PES (partially matching feedback). Each intervention consisted of 100 pairings between visually cued movements and PES. The PES was triggered based on the peak of maximal negativity of the movement-related cortical potential associated with the visually cued movement. Before, immediately after, and 30 min after the intervention, transcranial magnetic stimulation-elicited motor-evoked potentials were recorded to assess corticospinal excitability. Only wrist extensions paired with radial nerve PES significantly increased the corticospinal excitability with 57 ± 49% and 65 ± 52% immediately and 30 min after the intervention, respectively, compared to the pre-intervention measurement. In conclusion, maximizing the induction of neural plasticity with an associative BCI requires that the afferent feedback be precisely matched to the efferent brain activity. Full article

Traumatic brain injury (TBI) is a highly heterogeneous disease and achieving an accurate diagnosis remains a significant challenge. Biomarkers play a crucial role in minimizing the reliance on invasive techniques like computed tomography, which also have significant economic costs. Human samples were obtained from prospective cohort studies. Mice were subjected to an experimental model of traumatic brain injury. Biomarker levels, gene expression, and blood–brain barrier integrity were analyzed using ELISA, qRT-PCR, and Evans Blue assay; data were statistically evaluated using parametric or non-parametric tests as appropriate. This study focuses on evaluating the role of matricellular protein thrombospondin-1 (TSP-1) and the tight junction proteins occludin and ZO-1 as potential biomarkers of TBI. We showed that lower serum TSP-1 levels correlated with poor patient outcomes at 6 months compared to those patients with a good outcome. Additionally, the disruption of the blood–brain barrier (BBB) and subsequent release of tight junction proteins allowed us to identify occludin as a potential biomarker for prognosis in a cohort of TBI patients and as a diagnosis biomarker in a subgroup of patients with mild TBI, but its discriminative power as a diagnosis biomarker appears modest, as reflected by an AUC of 0.693. On the other hand, ZO-1 exhibited increased levels but limited diagnostic utility. These findings highlight the critical role of TSP-1 in maintaining BBB integrity and regulating the inflammatory response after a TBI, supported by the worsened condition observed in TSP-1-deficient animals. These results demonstrate the potential of TSP-1 and occludin as valuable biomarkers for secondary injury and disease progression in patients with mild to moderate/severe TBI. Full article

Diffuse optical imaging (DOI) uses scattered light to non-invasively sense and image highly diffuse media, including biological tissues such as the breast and brain. Despite its clinical potential, widespread adoption remains limited because physical constraints, limited available datasets, and conventional reconstruction algorithms struggle with the strongly nonlinear, ill-posed inverse problem posed by multiple photon scattering. We introduce Diffuse optical Imaging using Genetic Programming (DI-GP), a physics-guided and fully interpretable genetic programming framework for DOI. Grounded in the diffusion equation, DI-GP evolves closed-form symbolic mappings that enable fast and accurate 2-D reconstructions in strongly scattering media. Unlike deep neural networks, Genetic Programming (GP) naturally produces symbolic expressions, explicit rules, and transparent computational pipelines—an increasingly important capability as regulatory and high-stakes domains (e.g., FDA/EMA, medical imaging regulation) demand explainable and auditable AI systems, and where training data are often scarce. DI-GP delivers substantially faster inference and improved qualitative and quantitative reconstruction performance compared to analytical baselines. We validate the approach in both simulations and tabletop experiments, recovering targets without prior knowledge of shape or location at depths exceeding ~25 transport mean-free paths. Additional experiments demonstrate centimeter-scale imaging in tissue-like media, highlighting the promise of DI-GP for non-invasive deep-tissue imaging and its potential as a foundation for practical DOI systems. Full article

Background: Glioblastoma (GBM) is among the most aggressive and morphologically heterogeneous brain tumors. Beyond static imaging biomarkers, its structural organization can be viewed as a nonlinear dynamical system. Characterizing morphodynamic attractors within such a system may reveal latent stability patterns of morphological change and potential indicators of morphodynamic organization. Methods: We analyzed 494 subjects from the multi-institutional BraTS 2020 dataset using a fully automated computational pipeline. Each multimodal MRI volume was encoded into a 16-dimensional latent space using a 3D convolutional autoencoder. Synthetic morphological trajectories, generated through bidirectional growth–shrinkage transformations of tumor masks, enabled training of a contraction-regularized Neural Ordinary Differential Equation (Neural ODE) to model continuous-time latent morphodynamics. Morphological complexity was quantified using fractal dimension (DF), and local dynamical stability was measured via a Lyapunov-like exponent (λ). Robustness analyses assessed the stability of DF–λ regimes under multi-scale perturbations, synthetic-order reversal (directionality; sign-aware comparison) and stochastic noise, including cross-generator generalization against a time-shuffled negative control. Results: The DF–λ morphodynamic phase map revealed three characteristic regimes: (1) stable morphodynamics (λ < 0), associated with compact, smoother boundaries; (2) metastable dynamics (λ ≈ 0), reflecting weakly stable or transitional behavior; and (3) unstable or chaotic dynamics (λ > 0), associated with divergent latent trajectories. Latent-space flow fields exhibited contraction-induced attractor-like basins and smoothly diverging directions. Kernel-density estimation of DF–λ distributions revealed a prominent population cluster within the metastable regime, characterized by moderate-to-high geometric irregularity (DF ≈ 1.85–2.00) and near-neutral dynamical stability (λ ≈ −0.02 to +0.01). Exploratory clinical overlays showed that fractal dimension exhibited a modest negative association with survival, whereas λ did not correlate with clinical outcome, suggesting that the two descriptors capture complementary and clinically distinct aspects of tumor morphology. Conclusions: Glioblastoma morphology can be represented as a continuous dynamical process within a learned latent manifold. Combining Neural ODE–based dynamics, fractal morphometry, and Lyapunov stability provides a principled framework for dynamic radiomics, offering interpretable morphodynamic descriptors that bridge fractal geometry, nonlinear dynamics, and deep learning. Because BraTS is cross-sectional and the synthetic step index does not represent biological time, any clinical interpretation is hypothesis-generating; validation in longitudinal and covariate-rich cohorts is required before prognostic or treatment-monitoring use. The resulting DF–λ morphodynamic map provides a hypothesis-generating morphodynamic representation that should be evaluated in covariate-rich and longitudinal cohorts before any prognostic or treatment-monitoring use. Full article

Background and Clinical Significance: Tumefactive demyelinating lesions (TDLs) are large demyelinating lesions that mimic intracranial tumors, posing a diagnostic challenge in both clinical presentation and conventional imaging. Distinguishing TDLs from central nervous system tumors can be challenging due to their similar imaging appearances. Specific magnetic resonance imaging (MRI) features such as open-ring contrast enhancement, mild mass effect, lack of cortical involvement, and rapid responsiveness to corticosteroids favor a demyelinating etiology of the lesion. This report presents a case of a tumefactive demyelination lesion showing a T2/fluid-attenuated inversion recovery (FLAIR) mismatch sign suggestive of a low-grade astrocytoma, focusing on imaging findings, therapeutic response, and diagnostic considerations. Case Description: A 63-year-old woman presented with headache, progressive speech impairment, and difficulty swallowing. MRI revealed a large lesion in the left frontal lobe with a T2/FLAIR mismatch sign, which initially suggested a low-grade astrocytoma. Additionally, the lesion was hypodense on noncontrast computed tomography (CT), did not show open-ring enhancement, and only had mild mass effect with perifocal edema. Given these conflicting imaging findings, a biopsy was considered; however, the patient declined the procedure and agreed to a follow-up. Corticosteroid therapy was initiated to reduce swelling, resulting in a significant reduction in the lesion within two weeks. A follow-up MRI confirmed near-complete regression of the lesion after two months. Conclusions: While a T2/FLAIR mismatch sign correlates with isocitrate dehydrogenase (IDH)-mutant 1p/19q non-codeleted astrocytoma, the dynamic radiological and clinical response to corticosteroids was more indicative of demyelination. This case highlights the importance of considering TDLs in the differential diagnosis of tumor-like brain lesions to avoid unnecessary invasive interventions like biopsy or surgical removal. Full article

Background/Objectives: Alzheimer’s disease (AD) is a neurodegenerative disorder with a high incidence but limited agents. Herein, PZ-2891 was discovered as a novel anti-AD candidate. Both in vivo and in vitro pharmacodynamic (PD) studies and pharmacokinetic (PK) properties were investigated and illustrated in this research. Methods: A computer-generated random number table was used to divide mice into various groups randomly. Injecting Aβ into the mice hippocampus to mimic AD-like pathologies, neurobehavioral tests, including the Morris maze, Y maze, open field test (OFT) and novel object recognition (NOR), were operated to evaluate the cognitive improvement in PZ-2891. D-galactose (D-gal), okadaic acid (OA) and lipopolysaccharide (LPS) were employed to trigger neural injuries in vitro. A reliable analytic method was developed to profile PZ-2891’s PK properties in SD rats through a triple quadrupole liquid chromatography–mass spectrometry (LC–MS/MS) instrument. Results: PZ-2891 markedly alleviated cognitive impairment in the Aβ-induced model mice. It also protected nerve cells from oxidative stress and inflammatory injuries and significantly reduced AD-typical pathological biomarkers. The PK results showed that PZ-2891 was exposed rapidly in both plasma and brain, with a brain-to-blood ratio of around 0.59, C_max of around 454.50 ± 151.35 ng/mL, T_max of around 0.49 ± 0.15 h and oral bioavailability of around 19.74 ± 6.78%. Conclusions: These findings suggest that PZ-2891, an agonist of PANK2, is a novel and potential candidate agent for AD with excellent efficacy and PK properties. Full article

Background and Clinical Significance: Adults suffering from cerebellar metastases are often at high risk for rapid deterioration of their neurological status because the posterior fossa has limited compliance and the location of these metastases are close to the brain stem and important cerebrospinal fluid (CSF) pathways. In this paper, we present a longitudinal, patient-centered report on the history of an elderly individual who suffered from cognitive comorbidities and experienced a sudden loss of function in her cerebellum. Our goal in reporting this case is to provide a comparison between the patient’s pre-operative and post-operative neurological examinations; the imaging studies she had before and after surgery; the surgical techniques utilized during her operation; and the outcome of her post-operative course in a way that will be helpful to other patients who have experienced a similar situation. Case Presentation: We report the case of an 80-year-old woman who initially presented with progressive ipsilateral limb-trunk ataxia, impaired smooth pursuit eye movement, and rebound nystagmus, but preserved pyramidal and sensory functions. Her quantitative bedside assessments included some of the components of the Scale for the Assessment and Rating of Ataxia (SARA), and a National Institute of Health Stroke Scale (NIHSS) score of 3. These findings indicated dysfunction of the left neocerebellar hemisphere and possible dentate nucleus involvement. The patient’s magnetic resonance imaging (MRI) results demonstrated an expansive mass with surrounding vasogenic edema and marked compression and narrowing of the exits of the fourth ventricle which placed the patient’s CSF pathways at significant risk of occlusion, while the aqueduct and inlets were patent. She then underwent a left lateral suboccipital craniectomy with controlled arachnoidal CSF release, preservation of venous drainage routes, subpial corticotomy oriented along the lines of the folia, stepwise internal debulking, and careful protection of the cerebellar peduncles and dentate nucleus. Dural reconstruction utilized a watertight pericranial graft to restore the cisternal compartments. Her post-operative intensive care unit (ICU) management emphasized optimal venous outflow, normoventilation, and early mobilization. Histopathology confirmed the presence of metastatic carcinoma, and staging suggested that the most likely source of the primary tumor was the lungs. Immediately post-operation, computed tomography (CT) imaging revealed a smooth resection cavity with open foramina of Magendie and Luschka, intact contours of the brain stem, and no evidence of bleeding or hydrocephalus. The patient’s neurological deficits, including dysmetria, scanning dysarthria, and ataxic gait, improved gradually during the first 48 h post-operatively. Upon discharge, the patient demonstrated an improvement in her limb-kinetic subscore on the International Cooperative Ataxia Rating Scale (ICARS) and demonstrated independent ambulation. At two weeks post-operation, CT imaging revealed decreasing edema and stable cavity size, and the patient’s modified Rankin scale had improved from 3 upon admission to 1. There were no episodes of CSF leakage, wound complications, or new cranial nerve deficits. A transient post-operative psychotic episode that was likely secondary to her underlying Alzheimer’s disease was managed successfully with short-course pharmacotherapy. Conclusions: The current case study demonstrates the value of anatomy-based microsurgical planning, preservation of venous and CSF pathways, and targeted peri-operative management to facilitate rapid recovery of function in older adults who suffer from cerebellar metastasis and cognitive comorbidities. The case also demonstrates the importance of early multidisciplinary collaboration to allow for timely initiation of both adjuvant stereotactic radiosurgery and molecularly informed systemic therapy. Full article

The emergence of SARS-CoV-2 and its rapid global spread underscores the urgent need for novel therapeutic strategies. This study investigates the antiviral potential of Allium sativum (garlic) extracts against SARS-CoV-2, focusing on disruption of the spike protein’s receptor-binding domain (RBD) interaction with angiotensin-converting enzyme 2 (ACE2), a critical step in viral entry. Two garlic cultivars (Tigre and Fermín) were processed via oven-drying or freeze-drying, followed by maceration with CH₂Cl₂/MeOH (1:1) and fractionation with liquid–liquid partition. ELISA immunoassays revealed that freeze-dried Tigre (TL) extracts had the highest inhibitory activity (42.16% at 0.1 µg/mL), with its aqueous fraction achieving 57.26% inhibition at 0.01 µg/mL. Chemical profiling via GC-MS found sulfur and other types of compounds. Molecular docking identified three garlic TL-derived aqueous fraction compounds with strong binding affinities (ΔG = −7.5 to −6.9 kcal/mol) to the RBD-ACE2 interface. Furthermore, ADME in silico analysis highlighted one of them (L17) as the main candidate, having high gastrointestinal absorption, blood–brain barrier permeability, and compliance with drug-likeness criteria. These findings underscore garlic-derived compounds as promising inhibitors of SARS-CoV-2 entry, calling for further preclinical validation. The study integrates experimental and computational approaches to advance natural product-based antiviral discovery, emphasizing the need for standardized formulations to address therapeutic variability across viral variants. Full article

Event-Related Potentials (ERPs) are low-amplitude neural responses elicited by sensory or cognitive stimuli, widely exploited as biomarkers in the early diagnosis of neurodevelopmental and neurodegenerative disorders such as autism spectrum disorder and Alzheimer’s disease, and as control signals in brain–computer interface (BCI) systems for severely disabled individuals. However, their extremely low signal-to-noise ratio (SNR) necessitates robust denoising, especially in real-time BCI applications where low latency, minimal computational overhead, and single-channel operation are critical constraints. While advanced offline methods like Independent Component Analysis (ICA) and wavelet-based thresholding offer effective denoising in multichannel settings, they are ill-suited for embedded, causal, and resource-constrained environments. To address this gap, we propose a Partial-Update Variable Step-Size LMS (PU-VSS-LMS) algorithm that complementarily combines dynamic step-size adaptation with a magnitude-driven partial-update strategy. Evaluated on synthetic ERP-like signals embedded in realistic EEG noise (SNR = 6 dB and 0 dB), PU-VSS-LMS achieves lower mean squared error (MSE: 0.0780 vs. 0.0850 at 6 dB) and higher output SNR (8.10 dB vs. 7.80 dB) than standard VSS-LMS, while outperforming ICA in waveform preservation and noise suppression. Importantly, it reduces computational load by 75% (updating only 4 of 16 coefficients), enabling faster execution without sacrificing accuracy. These results establish PU-VSS-LMS as a highly efficient and effective solution for real-time ERP denoising in embedded, single-channel biomedical systems. Full article

Electroencephalography (EEG) provides excellent temporal resolution for brain activity analysis but limited spatial resolution at the sensors, making source unmixing essential. Our objective is to enable accurate brain activity analysis from EEG by providing a fast, calibration-free alternative to independent component analysis (ICA) that preserves ICA-like component interpretability for real-time and large-scale use. We introduce a convolutional neural network (CNN) that estimates ICA-like component activations and scalp topographies directly from short, preprocessed EEG epochs, enabling real-time and large-scale analysis. EEG data were acquired from 44 participants during a 40-min lecture on image processing and preprocessed using standard EEGLAB procedures. The CNN was trained to estimate ICA-like components and evaluated against ICA using waveform morphology, spectral characteristics, and scalp topographies. We term the approach “adaptive” because, at test time, it is calibration-free and remains robust to user/session variability, device/montage perturbations, and within-session drift via per-epoch normalization and automated channel quality masking. No online weight updates are performed; robustness arises from these inference-time mechanisms and multi-subject training. The proposed method achieved an average F1-score of 94.9%, precision of 92.9%, recall of 97.2%, and overall accuracy of 93.2%. Moreover, mean processing time per subject was reduced from 332.73 s with ICA to 4.86 s using the CNN, a ~68× improvement. While our primary endpoint is ICA-like decomposition fidelity (waveform, spectral, and scalp-map agreement), the clean/artifact classification metrics are reported only as a downstream utility check confirming that the CNN-ICA outputs remain practically useful for routine quality control. These results show that CNN-based EEG decomposition provides a practical and accurate alternative to ICA, delivering substantial computational gains while preserving signal fidelity and making ICA-like decomposition feasible for real-time and large-scale brain activity analysis in clinical, educational, and research contexts. Full article

Background/Objectives: Vector-borne diseases like malaria remain a major global health concern, worsened by insecticide resistance in mosquito populations. Quinoline-based compounds have been extensively studied for their pharmacological effects, including antimalarial and larvicidal properties. Modifying quinoline structures with hydrazone groups may enhance their biological activity and physicochemical properties. This study reports the synthesis, structural characterization, and larvicidal testing of a new series of aryl hydrazones (6a–i) derived from 8-trifluoromethyl quinoline. Methods: Compounds 6a–i were prepared via condensation reactions and characterized using ¹H NMR, ¹⁹F-NMR, ¹³C NMR, and HRMS techniques. Their larvicidal activity was tested against Anopheles arabiensis. Single-crystal X-ray diffraction (XRD) was performed on compound 6d to determine its three-dimensional structure. Hirshfeld surface analysis, fingerprint plots, and interaction energy calculations (HF/3-21G) were used to examine intermolecular interactions. Quantum chemical parameters were computed using density functional theory (DFT). Molecular docking studies were performed for the synthesized compounds 6a–i against the target acetylcholinesterase from the malaria vector (6ARY). In silico ADMET properties were also calculated to evaluate the drug-likeness of all the tested compounds. Results: Compound 6a showed the highest larvicidal activity, causing significant mortality in Anopheles arabiensis larvae. Single-crystal XRD analysis of 6d revealed a monoclinic crystal system with space group P2₁/c, stabilized by N–H···N intermolecular hydrogen bonds. Hirshfeld analysis identified H···H (22.0%) and C···H (12.1%) interactions as key contributors to molecular packing. Density functional theory results indicated a favorable HOMO–LUMO energy gap, supporting molecular stability and good electronic distribution. The most active compounds, 6a and 6d, also showed strong binding interactions with the target protein 6ARY and satisfactory ADMET properties. The BOILED-Egg model is a powerful tool for predicting both blood–brain barrier (BBB) and gastrointestinal permeation by calculating the lipophilicity and polarity of the reported compounds 6a–i. Conclusions: The synthesized arylhydrazone derivatives demonstrated promising larvicidal activity. Combined crystallographic and computational studies support their structural stability and suitability for further development as eco-friendly bioactive agents in malaria vector control. Full article

Background: Theta-band cordance is a quantitative EEG (qEEG) metric that integrates absolute and relative spectral power and correlates with regional cerebral perfusion. Although widely applied in psychiatric and neurophysiological research, its longitudinal trajectory in healthy adults remains largely unknown. This study aimed to characterize multi-year changes in theta cordance across cortical regions, determine which areas show stability versus decline, and evaluate whether individuals maintain a trait-like cordance profile over time. Methods: Nineteen cognitively healthy, medication-free adults underwent resting-state EEG recordings at two time points, separated by an average of 6.4 years (range: 1.9–14.8). Theta cordance (4–8 Hz) was computed at 19 scalp electrodes using the Leuchter algorithm and aggregated into eight lobar regions (left/right frontal, temporal, parietal, occipital). Paired-samples t-tests assessed longitudinal changes. Inter-regional Pearson correlations examined evolving connectivity patterns. Canonical correlation analysis (CCA), validated via LOOCV and bootstrap confidence intervals, evaluated multivariate stability between baseline and follow-up cordance profiles. Results: Theta cordance remained normally distributed at both time points. Significant longitudinal decreases emerged in the right temporal (t(18) = 5.34, p < 0.001, d = 1.23) and right frontal (t(18) = 2.65, p = 0.016, d = 0.61) regions, while other lobes showed no significant change. Midline Cz demonstrated a robust increase over time (p < 0.001). CCA revealed a strong cross-time association (Rc = 0.999, p = 0.029), indicating preservation of a stable, frontally anchored cordance profile despite regional right-hemisphere decline. Inter-regional correlation matrices showed both preserved posterior synchrony and emerging inverse anterior–posterior and cross-hemispheric relationships, suggesting age-related reorganization of cortical connectivity. Conclusions: Theta cordance exhibits a mixed pattern of trait-like stability and region-specific aging effects. A dominant, stable fronto-central profile persists across years, yet the right frontal and right temporal cortices show significant decline, consistent with lateralized vulnerability in normative aging. Evolving inter-regional correlation patterns further indicate network-level reorganization. Longitudinal cordance assessment may provide a noninvasive marker of functional brain aging and help differentiate normal aging trajectories from early pathological change. This longitudinal quantitative EEG (qEEG) study examined theta-band cordance dynamics across cortical regions in healthy adults over an average follow-up of 6.4 years (range: 1.9–14.8). Resting-state EEGs were recorded at two time points from 19 participants and analyzed using Leuchter’s cordance algorithm across 19 scalp electrodes. Regional cordance values were computed for frontal, temporal, parietal, and occipital lobes. Paired-samples t-tests revealed significant longitudinal decreases in theta cordance in the right frontal (p = 0.016, d = 0.61) and right temporal lobes (p < 0.001, d = 1.23), while other regions remained stable. Inter-regional Pearson correlations showed strong bilateral synchrony in posterior regions and emergent inverse anterior–posterior relationships over time. Canonical correlation analysis revealed a robust multivariate association (Rc = 0.999, p = 0.029) between baseline and follow-up patterns. Partial correlations (controlling for follow-up interval) identified region-specific trait stability, highest in left occipital and right frontal cortices. These findings suggest that theta cordance reflects both longitudinally stable neural traits and regionally specific aging effects in cortical physiology. Full article