The precuneus is a heterogeneous posteromedial hub, but whether theta-burst stimulation (TBS) modulates its subregions differentially remains unclear. Here, we examined subregion-specific effects of intermittent TBS (iTBS) and continuous TBS (cTBS) applied to the right precuneus using structural MRI, resting-state functional MRI, and spectral dynamic causal modelling in 33 healthy adults scanned at baseline, post-iTBS, and post-cTBS. Three right precuneus subregions were defined as dorsal-anterior, dorsal-posterior, and ventral. Cortical thickness differed significantly across subregions, with greater thickness in the dorsal-anterior than in the dorsal-posterior and ventral precuneus. Whole-brain static functional connectivity showed significant main effects of condition and subregion, but no condition × subregion interaction. By contrast, dynamic functional connectivity revealed a significant condition × subregion interaction in the right orbital inferior frontal gyrus. Under iTBS, the ventral precuneus showed greater dynamic connectivity variability than the dorsal-anterior subregion, whereas under cTBS both dorsal subregions showed greater variability than the ventral subregion. Spectral dynamic causal modelling further demonstrated subregion-specific modulation of effective connectivity, with the most consistent between-region effects involving pathways to the left superior temporal gyrus and additional changes in self-connections. Baseline cortical thickness did not predict stimulation-induced changes in static or dynamic connectivity. These findings indicate baseline structural heterogeneity within the right precuneus and suggest that TBS related functional network reorganization differs across connectivity measures. Subregion-specific effects were most clearly expressed in dynamic and directed connectivity rather than uniformly across all connectivity measures. The results highlight the importance of subregion informed approaches for understanding and targeting precuneus centered networks.
Early differentiation of mild cognitive impairment (MCI) from Alzheimer’s disease (AD) remains difficult because diagnosis integrates cognitive, imaging, laboratory, and neurological evidence. EEG-based models may provide complementary information, but fixed descriptors can miss signal-specific structure. SOFEx was developed as an adaptive and interpretable EEG feature-engineering framework. The analysis used 1,905 EEG segments from 451 recordings of 168 participants (59 AD, seven MCI, and 102 controls). SOFEx combined self-organized rose pattern extraction, fold-wise feature selection, channel-wise classification, and iterative majority voting. Validation used leave-one-subject-out (LOSO), leave-one-record-out (LORO), and participant-grouped 10-fold cross-validation (CV). Five classifiers were compared under LOSO, and a support vector machine (SVM) was retained. LOSO, used as the primary evaluation of generalization to unseen participants, achieved 84.09
High-altitude hypoxia has been examined with conventional electroencephalography (EEG), event-related potentials, and neuroimaging, but its association with millisecond-scale resting-state EEG microstate dynamics and peripheral physiology remains unclear. We studied healthy adults including 124 low-altitude (LA) participants (50 men, 74 women) and 174 high-altitude (HA) participants (42 men, 132 women; age 28.05 ± 8.59 years; age unavailable for LA). Coverage, duration, global explained variance (GEV), occurrence, and transition probabilities were extracted for four canonical microstates. LA–HA differences were tested using unadjusted linear models; within-HA associations among the high-altitude acclimatization index (AAI), serum albumin, and Microstate A-related indicators were examined using covariate-adjusted regressions and exploratory bootstrapped mediation analyses. Compared with LA participants, HA participants showed increased Microstate A duration and GEV, decreased occurrence, and unchanged coverage, together with reduced Microstate B-related indicators, enhanced Microstate C-related indicators, and increased A→B and A→C transitions. Within HA participants, AAI was negatively associated with serum albumin, and exploratory mediation analyses indicated indirect associations between AAI and Coverage A, Occurrence A, GEV A, and Microstate A-originating transitions through serum albumin. These findings suggest that chronic high-altitude exposure is associated with resting-state EEG microstate reorganization in healthy adults and that acclimatization-related individual differences may be linked to Microstate A dynamics through serum albumin-related peripheral status.
Resting-state EEG studies in Parkinson’s disease (PD) have largely emphasized static power or connectivity, whereas fixed-sequence OFF-to-ON session differences may also involve the temporal organization of network states. Paired source-space evidence under such fixed acquisition protocols remains limited. We analyzed 64-channel eyes-closed resting-state EEG in 39 patients with PD contributing paired OFF/ON recordings for the prespecified 90 s primary analysis; 24 also contributed paired 178 s recordings for duration robustness. After individual MRI-based source reconstruction, low-beta (13–20 Hz) dynamic amplitude-envelope connectivity was computed with 2 s windows. Primary endpoints were switching rate (SR) and normalized Shannon entropy (H); fractional occupancy (FO_path) and mean dwell time (MDT_path) of an internally defined OFF-dominant state were prespecified secondary conditional endpoints and are interpreted only as conditional summaries. PD-only linear mixed-effects models quantified the fixed-sequence OFF-to-ON session contrast; medication state and session order were necessarily collinear. In the 90 s analysis, the later ON session showed higher SR (β = 3.3224, Holm p = 0.00406) and H (β = 0.06781, Holm p = 0.000749) than the earlier OFF session. FO_path (β = −0.09167, Holm p = 1.08 × 10⁻⁵) and MDT_path (β = −1.05171, Holm p = 0.00524) were lower in the later ON session. The 178 s reanalysis preserved the same directions, although SR and especially MDT_path were more parameter-sensitive across sensitivity analyses. The fixed OFF-then-ON protocol was associated with reproducible within-subject differences in low-beta state dynamics, with the strongest evidence arising from SR and H. Because medication state was perfectly collinear with session order, these findings cannot distinguish medication-specific effects from order-, time-, vigilance-, fatigue-, or adaptation-related influences. FO_path and MDT_path remain conditional summaries tied to an internally defined OFF-dominant state, and MDT was less stable in the 178 s and OFF-only sensitivity analyses.
Epilepsy is one of the most heterogeneous and unpredictable neurological disorders, characterized by complex brain dynamics, significant interindividual variability, and a therapeutic response that is often difficult to predict. Despite major advances in neuroimaging, electrophysiology, and antiepileptic treatments, clinical decision-making still faces major challenges, including precisely localizing the epileptogenic zone, predicting seizures, identifying drug-resistant patients, and optimizing personalized therapeutic strategies. Over the past two decades, computational models have gradually emerged as promising tools for addressing these challenges. Early approaches relied primarily on mechanistic biophysical models aimed at reproducing the neural dynamics and mechanisms of hyperexcitability involved in epileptogenesis. More recently, the rise of artificial intelligence and deep learning has facilitated the development of predictive models capable of leveraging large-scale multimodal data. Finally, the emergence of digital brain twins and adaptive closed-loop systems now paves the way for personalized computational medicine integrating simulation, prediction, and real-time therapeutic intervention. This review provides a critical analysis of the major international initiatives developed between 2004 and 2026, including the EPICLUSTER/EBRAINS, EPINOSTICS, ENIGMA-Epilepsy, NeuroPace RNS, SeizeIT, EpiBioS4Rx, and NeuroVista projects, as well as several translational platforms based on artificial intelligence and biophysical modeling. We evaluate the actual contribution of these models to clinical decision-making in epilepsy, their translational contributions, their methodological limitations, and the challenges associated with their integration into hospital settings. We argue that the future of computational epileptology does not rest on the opposition between artificial intelligence and mechanistic models, but on their convergence within explainable, personalized, and clinically interpretable hybrid architectures.
