Background: Nonnegative matrix factorization (NMF) has been successfully used for electroencephalography (EEG) spectral analysis. Since NMF was proposed in the 1990s, many adaptive algorithms have been developed. However, the performance of their use in EEG data analysis has not been fully compared. Here, we provide a comparison of four NMF algorithms in terms of accuracy of estimation, stability (repeatability of the results) and time complexity of algorithms with simulated data. In the practical application of NMF algorithms, stability plays an important role, which was an emphasis in the comparison. A Hierarchical clustering algorithm was implemented to evaluate the stability of NMF algorithms.Results: In simulation-based comprehensive analysis of fit, stability, accuracy of estimation and time complexity, hierarchical alternating least squares (HALS) low-rank NMF algorithm (lraNMF_HALS) outperformed the other three NMF algorithms. In the application of lraNMF_HALS for real resting-state EEG data analysis, stable and interpretable features were extracted.Conclusion: Based on the results of assessment, our recommendation is to use lraNMF_HALS, providing the most accurate and robust estimation.

Panic disorder (PD) involves recurrent panic attacks and anticipatory anxiety, with interoceptive dysfunction potentially contributing to its pathophysiology. This study analyzed differences in heartbeat evoked potential (HEP) and spectral power between 19 PD patients and 21 HCs during eyes-closed (EC) and eyes-open (EO) conditions to assess interoceptive processing and cortical arousal patterns. Key findings showed: (1) Compared to healthy controls, panic disorder patients exhibited higher interoceptive attention scores (BPQ-VSF: t = -4.944, p < .001; noticing: t = -1.156, p = .255), greater interaction between interoception and emotion (not-worrying: t = 4.487, p < .001; emotional awareness: Z = 2.009, p = .004), greater difficulty in regulating attention to maladaptive interoceptive signals (not distracting: t = 2.504, p = .017), and lower sense of safety and trust in the body (trusting: t = -2.858, p = .004); (2) PD patients exhibited reduced HEP amplitudes in left parieto-occipital regions during EC compared to HCs; (3) Spectral analysis revealed increased beta-band power in PD patients during EC (t = -0.804, p < .05), further elevated during EO (t = -0.553, p < .001) - a pattern absent in HCs. These results demonstrate impaired interoceptive regulation (diminished HEP modulation) and cortical hyperarousal (elevated beta oscillations) in PD. The study provides novel electrophysiological evidence for PD's neuropathology, identifying interoception-arousal dysregulation as a potential treatment target.

Previous researches demonstrate that major depression disorder (MDD) is associated with widespread network dysconnectivity, and the dynamics of functional connectivity networks are important to delineate the neural mechanisms of MDD. Cortical electroencephalography (EEG) oscillations act as coordinators to connect different brain regions, and various assemblies of oscillations can form different networks to support different cognitive tasks. Studies have demonstrated that the dysconnectivity of EEG oscillatory networks is related with MDD. In this study, we investigated the oscillatory hyperconnectivity and hypoconnectivity networks in MDD under a naturalistic and continuous stimuli condition of music listening. With the assumption that the healthy group and the MDD group share similar brain topology from the same stimuli and also retain individual brain topology for group differences, we applied the coupled nonnegative tensor decomposition algorithm on two adjacency tensors with the dimension of time × frequency × connectivity × subject, and imposed double-coupled constraints on spatial and spectral modes. The music-induced oscillatory networks were identified by a correlation analysis approach based on the permutation test between extracted temporal factors and musical features. We obtained three hyperconnectivity networks from the individual features of MDD and three hypoconnectivity networks from common features. The results demonstrated that the dysfunction of oscillation-modulated networks could affect the involvement in music perception for MDD patients. Those oscillatory dysconnectivity networks may provide promising references to reveal the pathoconnectomics of MDD and potential biomarkers for the diagnosis of MDD.