Subcortical neural response to click and speech stimuli in infants and young children with congenital cytomegalovirus: a preliminary study.

Excessive social media use (SMU) can induce psychiatric symptoms, including anxiety [,], depression [,], and insomnia [], as well as cognitive disturbances such as impaired attention, cognitive control, and memory function [-]. Noninvasive neuromodulation techniques such as transcranial magnetic stimulation (TMS) and transcranial electrical stimulation (tES) show promise in treating social media addiction but require precise targeting of specific brain regions and the decoding of neural activity to observe treatment-responsive brain dynamics []. For instance, during TMS treatment for nicotine or alcohol addiction, functional magnetic resonance imaging (fMRI) monitoring of prefrontal-striatal circuitry responsiveness has been shown to enhance therapeutic efficacy [,]. Consequently, comprehensive investigations are needed to decode the neural signatures of SMU, thereby facilitating the development of targeted neuromodulation. Previous fMRI studies have shown that SMU is associated with altered functional connectivity (FC) in the right inferior frontal gyrus [], a key region implicated in cognitive control processes that appears particularly vulnerable in adolescents exhibiting problematic smartphone use. Additionally, converging evidence points to functional alterations in the brain associated with SMU in both the ventral attention network [,] and the default mode network (DMN) [,]. However, the predominance of correlational designs means that causal relationships between SMU and neurobiological changes cannot be established. Consequently, the current evidence base remains insufficient to guide the development of precisely targeted neuromodulation. Critically, most existing studies rely on temporally averaged fMRI signals, which inherently lack the millisecond-scale temporal resolution required for real-time optimization of TMS or tES protocols []. While dynamic fMRI analysis has emerged as a valuable tool for characterizing variability and stability in brain activity [], the intrinsic hemodynamic response delay fundamentally limits its ability to capture rapid dynamics in neural activity. In contrast, electroencephalography (EEG) microstate analysis provides millisecond-level temporal precision, offering unique insights into the temporal organization of resting-state networks [,]. Based on our previous work on EEG microstates [,] and evidence demonstrating FC plasticity due to SMU [], including abnormal connectivity among the DMN, visual network (VN), and bilateral frontoparietal network [], integrating EEG temporal resolution with fMRI spatial resolution may help further reveal brain alterations associated with SMU.