Dynamic functional network connectivity associated with musical emotions evoked by different tempi

Music tempo modulates emotional states as revealed through EEG insights

Music can effectively influence human emotions, with different melodies and rhythms eliciting varying emotional responses. Among these, tempo is one of the most important parameters affecting emotions. This study explores the impact of music tempo on emotional states and the associated brain functional networks. A total of 26 participants without any history of neurological or psychiatric disorders and music training took part in the experiment, using classical piano music clips at different tempi (56, 106, 156 bpm) as stimuli. The study was conducted using emotional scales and electroencephalogram (EEG) analysis. The results showed that the valence level of emotions significantly increased with music tempo, while the arousal level exhibited a "V" shape relationship. EEG analysis revealed significant changes in brainwave signals across different frequency bands under different tempi. For instance, slow tempo induced higher Theta and Alpha power in the frontal region, while fast tempo increased Beta and Gamma band power. Moreover, fast tempo enhanced the average connectivity strength in the frontal, temporal, and occipital regions, and increased phase synchrony value (PLV) between the frontal and parietal regions. However, slow tempo improves PLV between the occipital and parietal regions. The findings of this study elucidate the effects of music tempo on the brain functional networks related to emotion regulation, providing a theoretical basis for music-assisted diagnosis and treatment of mood disorders. Furthermore, these results suggest potential applications in emotion robotics, emotion-based human-computer interaction, and emotion-based intelligent control.

Therapeutic Effect of Music Therapy on Patients with End-stage Cancer: A Retrospective Study

OBJECTIVE

The aim of this study was to retrospectively analyze the effect of music therapy on patients with end-stage cancer in hospice care.

METHODS

This retrospective cohort study included 195 patients with end-stage cancer from January 2021 to December 2023. The conventional group comprised patients who received routine hospice care, whereas the combination group comprised those who received routine hospice care and music therapy. The immune indicators, anxiety and depression scores, quality of life scores, and sleep quality scores of both groups were compared before and after management.

RESULTS

Before management, no significant differences were observed in the immune indicators, anxiety and depression scores, quality of life scores, and sleep quality scores between both groups (P > 0.05). However, after management, the immune indicators lymphocytes CD3+ and CD4+ were significantly higher in the combination group than in the conventional group (P < 0.05); in contrast, anxiety and depression and the Pittsburgh Sleep Quality Index scores were lower in the combination group than in the conventional group (P < 0.05). Lastly, the World Health Organization Quality of Life Brief Version scores were significantly higher in all domains in the combination group than in those in the conventional group; furthermore, the degree of decline in the physical, psychological, and social relationship domain scores was smaller in the combination group than in the conventional group (P < 0.05).

CONCLUSION

For patients with end-stage cancer, music therapy can improve their immune status, quality of life, and sleep and ameliorate their anxiety and depression.

Identification of suicidality in patients with major depressive disorder via dynamic functional network connectivity signatures and machine learning

Major depressive disorder (MDD) is a severe brain disease associated with a significant risk of suicide. Identification of suicidality is sometimes life-saving for MDD patients. We aimed to explore the use of dynamic functional network connectivity (dFNC) for suicidality detection in MDD patients. A total of 173 MDD patients, including 48 without suicide risk (NS), 74 with suicide ideation (SI), and 51 having attempted suicide (SA), participated in the present study. Thirty-eight healthy controls were also recruited for comparison. A sliding window approach was used to derive the dFNC, and the K-means clustering method was used to cluster the windowed dFNC. A linear support vector machine was used for classification, and leave-one-out cross-validation was performed for validation. Other machine learning methods were also used for comparison. MDD patients had widespread hypoconnectivity in both the strongly connected states (states 2 and 5) and the weakly connected state (state 4), while the dysfunctional connectivity within the weakly connected state (state 4) was mainly driven by suicidal attempts. Furthermore, dFNC matrices, especially the weakly connected state, could be used to distinguish MDD from healthy controls (area under curve [AUC] = 82), and even to identify suicidality in MDD patients (AUC = 78 for NS vs. SI, AUC = 88 for NS vs. SA, and AUC = 74 for SA vs. SI), with vision-related and default-related inter-network connectivity serving as important features. Thus, the dFNC abnormalities observed in this study might further improve our understanding of the neural substrates of suicidality in MDD patients.