The human brain is an intricate and dynamic network of interconnected regions (Zhang and Zhu, 2020). As individuals age, even in the absence of neurological disorders, structural and functional transformations occur, affecting various sensory systems, including auditory speech perception. Resting-state research, which examines the brain’s intrinsic activity in the absence of external stimuli, has become an essential tool for investigating how aging influences auditory processing and brain network connectivity (Chen et al., 2022). Functional connectivity, which measures the temporal synchronization between brain regions, provides critical insights into interregional interactions and neural coordination (Friston, 2011). The brain’s network architecture comprises functionally linked regions that support complex cognitive and perceptual functions, such as auditory speech recognition (Paranawithana et al., 2024). Graph theory metrics have been widely applied to examine age-related alterations in the topological organization of brain networks (Luo et al., 2020). However, the precise mechanisms underlying age-related auditory function decline remain insufficiently understood, and research on resting-state auditory processing remains relatively limited.

Functional near-infrared spectroscopy (fNIRS) has emerged as a powerful neuroimaging modality for examining resting-state auditory function and brain development (Paranawithana et al., 2024; Liu et al., 2022). Studies using resting-state connectivity analyses have revealed that, in older adults, the functional connectivity strength of key brain networks involved in auditory processing—including the default mode network (DMN), the fronto-parietal network (FPN), and the dorsal attention network (DAN)—declines significantly with age. These findings indicate that aging is associated with coherence between brain regions essential for auditory perception (Avelar-Pereira et al., 2017). Additionally, connectivity within large-scale networks, such as the DMN, FPN, DAN, and salience network (SN), progressively weakens with age (Khalilian et al., 2024). The auditory network undergoes age-related dedifferentiation, marked by reduced activity and weakened functional connectivity. These changes extend beyond the auditory network itself, affecting its interactions with broader brain systems, such as DMN and SN (Belden et al., 2023). However, evidence regarding age-related functional connectivity alterations remains inconsistent. Some studies have reported reduced connectivity between DMN and DAN, as well as disruptions among components of the dorsal attention, ventral attention, and sensorimotor networks in older adults compared with younger adults (Spreng et al., 2016; Betzel et al., 2014). Other studies have observed preserved or even compensatory increases in connectivity within certain networks in aging populations (Sala-Llonch et al., 2015). These discrepancies underscore the complexity and variability of aging-related effects on brain network organization. Aging disrupts the efficiency, integration, and stability of structural brain networks, leading to widespread alterations in global and local functional connectivity, network segregation, and modularity (Wang et al., 2024a). Despite these insights, research using fNIRS to investigate age-related changes in resting-state cortical network connectivity remains limited, highlighting the need for further exploration in this domain.

Auditory speech perception relies on a complex interaction between bottom-up mechanisms, such as sound signal processing, and top-down mechanisms, including contextual information. Notably, younger and older adults adopt distinct strategies for speech recognition in noisy environments. While younger adults prioritize auditory cues, older adults tend to depend more on contextual semantics to compensate for age-related declines in auditory perception (Moberly et al., 2023). Although fNIRS provides valuable insights into functional connectivity by capturing brain oxygenation changes, it primarily reflects averaged activity over longer time scales, offering a relatively static representation of brain function. In contrast, electroencephalography (EEG) microstates provide a dynamic perspective, revealing the rapid transitions between brain networks during the resting state. These microstates—derived through data-driven clustering of EEG maps across all participants—represent distinct patterns of functional activity within brief time windows, capturing transient network activations and enabling the assessment of rapid cortical reorganization. EEG microstates are typically clustered into four canonical classes (A–D) and correspond to large-scale functional brain networks, with microstate A linked to the auditory network, microstate B to the visual network, microstate C to the salience network, and microstate D to the attentional control network. Older adults exhibit prolonged microstate durations and reduced transition frequencies, suggesting a decline in the flexibility and adaptability of their brain networks (Tarailis et al., 2024). Microstate A is associated with auditory function, particularly within temporal cortical regions. However, its precise role in auditory processing remains inconclusive. Some studies associate microstate A with language processing and auditory fatigue (Korn et al., 2021; Tomescu et al., 2022), whereas others have reported no significant correlation between microstate A and auditory tasks (D’Croz-Baron et al., 2021; Milz et al., 2016). Overall, the relationship between microstate dynamics and auditory processing is intricate and remains underexplored, particularly in the context of aging.

Advancements in neuroimaging have led to the increasing application of multimodal integration techniques, providing researchers with a more comprehensive understanding of brain function. In detail, the combination of EEG and fNIRS offers a complementary perspective, simultaneously capturing both static functional connectivity and dynamic brain activity (Chiarelli et al., 2021; Wang et al., 2025). This study utilizes a simultaneous EEG-fNIRS approach to examine age-related alterations in the resting-state auditory network. Functional connectivity and network topology alterations in auditory-related brain regions are examined using fNIRS, with particular focus on graph-theoretical metrics that capture small-world properties and information processing efficiency, including the clustering coefficient, characteristic path length, small-worldness, and global/local efficiency. In parallel, EEG microstates provide insights into dynamic brain activity during rest. Furthermore, the study explores how different microstate types and their parameters contribute to age-related declines in auditory processing abilities. Additionally, auditory processing in real-world noisy environments is assessed using Mandarin speech recognition tasks, examining how age-related shifts in brain connectivity and microstate dynamics impact performance. Given the complexity of age-related neural changes and the limited research integrating EEG and fNIRS in this context, the present study adopts an exploratory framework. Our goal was to leverage multimodal neuroimaging techniques to comprehensively characterize age-sensitive neural features—both static (i.e., fNIRS-based functional connectivity and network topology) and dynamic (i.e., EEG microstates)—that may underlie individual differences in speech-in-noise perception. By examining these complementary dimensions, this exploratory study aims to generate candidate neurophysiological markers of aging-related auditory decline, thereby informing future mechanistic studies and guiding the development of targeted interventions.