Background: At present, there is no established biomarker for the diagnosis of depression. Meanwhile, studies show that acoustic features convey emotional information. Therefore, this study explored differences in acoustic characteristics between depressed patients and healthy individuals to investigate whether these characteristics can identify depression.Methods: Participants included 71 patients diagnosed with depression from a regional hospital in Beijing, China, and 62 normal controls from within the greater community. We assessed the clinical symptoms of depression of all participants using the Hamilton Depression Scale (HAMD), Hamilton Anxiety Scale (HAMA), and Patient Health Questionnaire (PHQ-9), and recorded the voice of each participant as they read positive, neutral, and negative texts. OpenSMILE was used to analyze their voice acoustics and extract acoustic characteristics from the recordings.Results: There were significant differences between the depression and control groups in all acoustic characteristics (p < 0.05). Several mel-frequency cepstral coefficients (MFCCs), including MFCC2, MFCC3, MFCC8, and MFCC9, differed significantly between different emotion tasks; MFCC4 and MFCC7 correlated positively with PHQ-9 scores, and correlations were stable in all emotion tasks. The zero-crossing rate in positive emotion correlated positively with HAMA total score and HAMA somatic anxiety score (r = 0.31, r = 0.34, respectively), and MFCC9 of neutral emotion correlated negatively with HAMD anxiety/somatization scores (r = −0.34). Linear regression showed that the MFCC7-negative was predictive on the PHQ-9 score (β = 0.90, p = 0.01) and MFCC9-neutral was predictive on HAMD anxiety/somatization score (β = −0.45, p = 0.049). Logistic regression showed a superior discriminant effect, with a discrimination accuracy of 89.66%.Conclusion: The acoustic expression of emotion among patients with depression differs from that of normal controls. Some acoustic characteristics are related to the severity of depressive symptoms and may be objective biomarkers of depression. A systematic method of assessing vocal acoustic characteristics could provide an accurate and discreet means of screening for depression; this method may be used instead of—or in conjunction with—traditional screening methods, as it is not subject to the limitations associated with self-reported assessments wherein subjects may be inclined to provide socially acceptable responses rather than being truthful.

The default mode network (DMN) is a workspace for convergence of internal and external information. The frontal parietal network (FPN) is indispensable to executive functioning. Yet, how they interplay to support cognitive development remains elusive. Using longitudinal developmental fMRI with an n-back paradigm, we show a heterogeneity of maturational changes in multivoxel activity and network connectivity among DMN and FPN nodes in 528 children and 103 young adults. Compared with adults, children exhibited prominent longitudinal improvement but still inferior behavioral performance, which paired with less pronounced DMN deactivation and weaker FPN activation in children, but stronger DMN coupling with FPN regions. Children’s DMN reached an adult-like level earlier than FPN at both multivoxel activity pattern and intranetwork connectivity levels. Intrinsic DMN-FPN internetwork coupling in children mediated the relationship between age and working memory-related functional coupling of these networks, with posterior cingulate cortex (PCC)-dorsolateral prefrontal cortex (DLPFC) coupling emerging as most prominent pathway. Coupling of PCC-DLPFC may further work together with task-invoked activity in PCC to account for longitudinal improvement in behavioral performance in children. Our findings suggest that the DMN provides a scaffolding effect in support of an immature FPN that is critical for the development of executive functions in children.

The objective identification of depression using physiological data has emerged as a significant research focus within the field of psychiatry. The advancement of wearable physiological measurement devices has opened new avenues for the identification of individuals with depression in everyday-life contexts. Compared to other objective measurement methods, wearables offer the potential for continuous, unobtrusive monitoring, which can capture subtle physiological changes indicative of depressive states. The present study leverages multimodal wristband devices to collect data from fifty-eight participants clinically diagnosed with depression during their normal daytime activities over six hours. Data collected include pulse wave, skin conductance, and triaxial acceleration. For comparison, we also utilized data from fifty-eight matched healthy controls from a publicly available dataset, collected using the same devices over equivalent durations. Our aim was to identify depressive individuals through the analysis of multimodal physiological measurements derived from wearable devices in daily life scenarios. We extracted static features such as the mean, variance, skewness, and kurtosis of physiological indicators like heart rate, skin conductance, and acceleration, as well as autoregressive coefficients of these signals reflecting the temporal dynamics. Utilizing a Random Forest algorithm, we distinguished depressive and non-depressive individuals with varying classification accuracies on data aggregated over 6 h, 2 h, 30 min, and 5 min segments, as 90.0%, 84.7%, 80.1%, and 76.0%, respectively. Our results demonstrate the feasibility of using daily wearable-derived physiological data for depression recognition. The achieved classification accuracies suggest that this approach could be integrated into clinical settings for the early detection and monitoring of depressive symptoms. Future work will explore the potential of these methods for personalized interventions and real-time monitoring, offering a promising avenue for enhancing mental health care through the integration of wearable technology.