Quercetin nanoparticles exhibit a potential therapeutic effect against the neurochemical changes and motor deficits in a rat model of Parkinson's disease.

Verbal communication, by requiring the matching of acoustic and articulatory representations, exemplifies action-perception interactions in humans essential for everyday behavior []. In particular, speech production and perception are intricately interwoven. The perception of speech may rely on the identification of intended vocal tract gestures [], allowing for the prediction of the speech signal, as considered crucial in vocal learning []. Others assume a more moderate role of the motor system, such that it is aiding speech perception particularly in demanding listening situations [,]. During speech production, the motor system engages in operations requiring precise timing [,], with these motor areas possibly being similarly recruited during speech perception [,,]. Specifically, the supplementary motor area (SMA) and (parts of the) inferior frontal gyrus (IFG) are assumed to generate temporal predictions about upcoming sensory events [–]. The SMA and IFG take different roles during speech perception, with the SMA receiving input from subcortical loops serving mostly inhibitory input and the IFG more closely linked to auditory cortex processing in the temporal lobe [,–]. Based on the literature, on the one hand, and on our goal to focus on higher-order speech processing and sequencing [–], on the other, we operationalized speech motor areas as comprising the pars triangularis of the IFG as well as SMA, while not including for these analyses the region of precentral gyrus implicated in speech motor control [,]. A prominent neural oscillatory account of speech perception proposes that slow endogenous brain rhythms in auditory cortex allow for speech segmentation by aligning their neural excitability phase to the speech acoustics (speech tracking; [–]). Additionally, neural oscillations from cortical motor areas may be involved in speech perception to various degrees. Slow and fast neural oscillations are argued to aid temporal predictions from motor areas through coupling with auditory areas [,,,]. The mechanisms of auditory–motor interactions during speech (and auditory) perception, however, are not fully understood, and the involvement of neural oscillations is controversially discussed [–]. A crucial characteristic of neural oscillations is that they reflect endogenous brain rhythms observed in the absence of external stimulation. Here, we put a neural oscillatory framework of auditory–motor interactions to a rigorous test by investigating whether individuals’ peak frequencies of endogenous rhythms of auditory and motor cortical areas (observed during resting-state) and their coupling strength predict auditory cortical tracking during speech comprehension. Oscillatory speech perception models propose that endogenous theta rhythms in auditory cortex synchronize to temporal fluctuations in the speech signal (i.e., the amplitude envelope) and thereby segment it into syllable-sized chunks [–,,]. This brain-to-speech alignment is most pronounced in the theta range (~5 Hz), declining at higher syllabic rates [], as speech comprehension also decreases (for non-speech see: [–]). Given the observation of endogenous theta brain rhythms in auditory cortex [–] and the optimal speech processing in this range, the preferred frequencies of neuronal populations in auditory cortex in the theta range have been proposed to constrain the temporal granularity of perception [,,–]. The hypothesized connection between endogenous theta rhythms and speech processing, a fundamental aspect of oscillatory theories, however, has been rarely investigated directly, i.e., by relating endogenous and functional processing within individuals [,]. Such research may be hindered by the difficulty of quantifying individual endogenous brain rhythms in auditory cortex in the theta range []. While it is relatively straightforward to identify individual peak frequencies in some cases (e.g., posterior alpha peak frequency) because those can be detected in average power spectra, this is typically more challenging in most other frequency bands and areas. We here use spectral-fingerprinting of resting-state brain activity [] to comprehensively identify individual auditory spectral peaks in a completely data-driven way. Specifically, we use time-resolved clustering procedures of normalized data that result in several frequency peaks per individual, which reflect endogenous rhythmic activity.