Background/Objectives: Hybrid EEG-fNIRS emotion recognition aims to accurately identify an individual’s emotional state by analyzing neurophysiological signals and constitutes an important research direction in affective brain–computer interfaces and human–computer interaction. In recent years, EEG-fNIRS emotion recognition has advanced from handcrafted feature extraction and shallow fusion to deep learning and graph-based modeling. However, most existing methods rely on predefined fixed frequency-band partitioning and second-order graph structures that only support pairwise connections, making it difficult to accommodate inter-subject frequency variability and to characterize high-order brain network relationships such as multi-channel synergistic activation within a frequency band and cross-frequency coupling. Methods: To address these issues, this paper proposes an EEG-fNIRS emotion recognition framework based on a Spatial-Frequency Hypergraph Neural Network (SF-HGNN). First, a Dynamic Frequency Band Decomposition module is designed to achieve adaptive optimization of the EEG and fNIRS frequency bands; second, a Multi-scale Temporal Convolution module extracts temporal features at different time scales; third, a Spatial-Frequency Adaptive Hypergraph Convolution module is constructed to model intra-band cross-channel spatial synergy and channel-wise cross-frequency coupling; and finally, a Cross-Modal Attention Fusion mechanism achieves high-order interaction between the complementary information of the two modalities. The proposed method was validated on the public ENTER dataset comprising 50 participants and four emotion categories (sadness, happiness, fear, and calm). Results: Experimental results show that SF-HGNN achieves accuracies of 82.94% and 68.85% in subject-dependent and subject-independent experiments, respectively; ablation studies and visualization analyses further verify the effectiveness of each module and the interpretability of the model. Conclusions: Future work will focus on validation with larger-scale data and improving cross-subject domain generalization.
Objective: To evaluate the sustained real-world effectiveness and safety of catechol-O-methyltransferase (COMT) inhibitors added to levodopa in patients with Parkinson’s disease (PD) presenting early motor fluctuations. Methods: REONPARK is an ongoing, multicenter, prospective observational study conducted in Spain. Adult patients with PD and end-of-dose motor fluctuations for <2 years initiating a COMT inhibitor were included. Outcomes assessed at 3, 6, and 12 months included Clinician and Patient Global Impression of Change (CGI-C, PGI-C), Movement Disorder Society–Unified Parkinson’s Disease Rating Scale (MDS-UPDRS), Wearing-off Questionnaire (WOQ-19), Non-Motor Symptoms Scale (NMSS), Parkinson’s Disease Questionnaire-8 (PDQ-8), and safety. This article presents the 3-, 6-, and 12-month planned interim analysis of the study. Results: A total of 143 patients were included (99.3% treated with opicapone), of whom 138, 131, and 89 patients completed the 3, 6, and 12 months of follow-up, respectively. Levodopa doses remained stable in most patients (73% at 12 months). Significant and sustained improvements were observed in MDS-UPDRS Parts III and IV scores, as well as the total MDS-UPDRS score over 12 months, with greater likelihood of Part IV improvement in patients receiving <4.5 mg/kg/day of levodopa. OFF time decreased by 1.6–2.0 h from baseline. ON-time quality was maintained over time with 97.7% of patients reporting no or minimal functional impact from dyskinesia, while 47.7% of patients had no impact from motor fluctuations and 36.4% of patients no longer experienced OFF periods at 12 months. PDQ-8 scores remained stable and no overall benefit on total non-motor symptoms burden was demonstrated, despite improvements in selected domains among patients with a higher baseline non-motor symptoms burden. WOQ-19 scores improved significantly. Conclusions: In PD patients with early motor fluctuations, opicapone initiation was associated with sustained improvements in motor outcomes and reduced OFF time, and maintained the ON-time quality over 12 months, with stable levodopa dosing.
Background: Future-oriented cognition involves several neurocognitive processes putatively supported by the dorsolateral prefrontal cortex (DLPFC) and beta-band oscillations. This study aimed to corroborate the role of beta oscillations in the DLPFC during three future cognition tasks: mental time travel (MTT), delay discounting (DD), and time-based prospective memory (PM). Methods: Thirty-three participants (22–29 years) completed the three tasks under two conditions—active and sham stimulation—administered in a counterbalanced within-subjects design, one week apart. Beta-frequency transcranial alternating current stimulation (tACS; 22 Hz, 1.5 mA) was applied bilaterally over the DLPFC (F3–F4). Results: Beta-tACS was associated with dissociable effects across domains. In MTT, stimulation selectively improved accuracy for ordinality judgements anchored to past and present reference points (2014, 2024), without affecting future projections (2034) or reaction times. In DD, beta-tACS did not modify the steepness of temporal discounting in a statistically significant way. In PM, stimulation enhanced both dichotomous accuracy and continuous temporal precision, with no changes in clock-checking behavior. Conclusions: These findings provide preliminary evidence that suggests a possible role of DLPFC beta oscillations in supporting the maintenance and utilization of more concrete temporal representations across distinct temporal domains, resulting in anchoring in more available representations (MTT) and focus on temporal information (PM), challenging unitary accounts of prefrontal beta function and highlighting task-dependent neuromodulation effects within the future temporal domain.
Background/Objectives: Transcranial magnetic stimulation (TMS) initiates a cascade from intracranial electric-field exposure through neural recruitment and plasticity to distributed network responses. Neuronavigation improves the geometric reproducibility of delivery but does not guarantee equivalent cortical exposure or target engagement. This narrative review integrates these levels within an operational framework for precision TMS. Methods: Six domain-specific PubMed searches covering 1 January 1985 to 31 July 2026 were supplemented by Google Scholar and citation tracking. A documented rerun on 17 August 2026 yielded 6430 records (5617 unique after cross-query deduplication). Evidence was synthesized narratively; no quantitative synthesis or formal risk-of-bias assessment was performed. Results: Neuronavigation improves geometric precision by stabilizing target definition and coil pose, whereas individualized electric-field models estimate intracranial exposure. Neither establishes biological precision, which also depends on neuronal orientation, brain state, circuit architecture, medication, and behavior. Motor-system measures are not validated as universal biomarkers for nonmotor cortex, and no single validated biomarker captures TMS-induced plasticity. Convergent, controlled multimodal evidence may strengthen inference about target engagement; adaptive and closed-loop approaches remain experimental. Conclusions: Geometric delivery, modeled exposure, biological engagement, and durable functional or clinical benefit require separate validation. Spatial accuracy alone does not establish clinical value.
