Biology subjects

Ginzburg, J.

Publications and source records attributed to Ginzburg, J..

5 recordsLinked to original sources

bioRxiv2025

Mu suppression reveals auditory-motor predictions after short motor training in non-musicians

Auditory-motor coupling is a bidirectional neural mechanism that supports speech and music, with evidence of motor system activation during passive listening to both spoken language and learned melodies. Such activation is anticipatory, occurs in non-musicians, and can be elicited at the single-note level. These findings support the idea that motor activity guides auditory perception by relaying predictive timing information. However, the neural processes underlying this activity are not fully understood. EEG studies in musicians have linked it to mu-band suppression, but the temporal scale and the generalizability to the broader population remain unclear. We recruited 25 non-musicians who learned to play a simple melody on a piano-like keyboard. Before and after training, participants passively listened to the trained melody and control melodies. Offline, EEG data from the motor training were used to create a time-frequency mask with which to identify mu suppression occurring during passive listening. Significant mu suppression emerged before each note only during post-training exposure to the practiced melody. Results suggest that mu suppression occurs at the single-note level following short motor training and is not dependent on prior musical experience. Our findings support the notion that motor activity aids perception by anticipating the unfolding of learned auditory-motor sequences.

neuroscience↗

bioRxiv2025

Neural coding of spectrotemporal modulations in the auditory cortex supports speech and music categorization

Auditory processing is typically described as hierarchical, culminating in neural representation of abstract categories. However, it remains unclear whether category-selective responses in auditory cortex require representational mechanisms beyond the coding of acoustic features, or whether the acoustic representations already available in the auditory cortex are sufficient to account for categorization. Here, we test whether cortical coding of spectrotemporal modulation (STM) features is sufficient to support speech-music categorization by combining human intracranial recordings with continuous behavioral judgments of a naturalistic soundtrack in which speech and music occur both separately and simultaneously. We show that temporal and spectral modulation patterns largely characterize speech and music, respectively, and that cortical auditory regions robustly track these features over time, with distinct oscillatory frequency bands preferentially encoding temporal and spectral modulations. Critically, cortical representations of STMs predicted perceptual categorical judgments gathered in an independent sample. Finally, speech- and music-related STM representations showed stronger tracking of category-specific acoustical features in left versus right cortical auditory regions, respectively. These findings indicate that the efficient neural coding of acoustical features provides a sufficient basis for the categorical distinction between speech and music, and that the temporal and spectral components of this representation are implemented through distinct oscillatory mechanisms.

neuroscience↗

bioRxiv2025

Optimizing short-channel regression in fNIRS: an empirical evaluation in a naturalistic multimodal paradigm

SignificanceFunctional Near-Infrared Spectroscopy (fNIRS) is increasingly favored for its portability and suitability for ecological paradigms, yet methodological standardization remains a challenge regarding the optimal use of short-separation channels (SC) to remove systemic physiological noise. AimWe aim to evaluate and compare methods of SC regression as implemented in the most widely used fNIRS analysis toolboxes, with the goal of reaching a consensus on best practices for incorporating SC into generalized linear model (GLM)-based analyses. Specifically, we compared ten SC regression strategies addressing SC selectivity, dimensionality reduction strategies, and SC availability. Approach16 healthy adults passively listened and watched ecological auditory, visual, and audiovisual stimuli while occipital and bilateral auditory cortices were recorded. ResultsOxygenated hemoglobin signals (HbO) processed without SC regression produced uninterpretable results in the context of the present study. Non-selective SC regression methods that pooled all available SC signals consistently outperformed anatomically or functionally restricted approaches. Orthogonalization further enhanced performance by reducing redundancy and capturing shared systemic variance, improving detection of stimulus-specific cortical responses and contrast sensitivity. Deoxygenated hemoglobin signals (HbR), while less sensitive to systemic artifacts than HbO, benefited most, similarly to HbO, from the pooled, orthogonalized SC signal approach. ConclusionOverall, our findings highlight the essential role of SC regression in recovering physiologically meaningful signals in fNIRS and recommend including all available SC channels within the GLM, coupled with orthogonalization techniques, as a generalizable best practice for denoising across hardware configurations.

neuroscience↗

bioRxiv2023

Role of the prefrontal cortex in musical and verbal short-term memory: A functional near-infrared spectroscopy study

Auditory short-term memory (STM) is a key process in auditory cognition, with evidence for partly distinct networks subtending musical and verbal STM. The delayed matching-to-sample task (DMST) paradigm has been found suitable for comparing musical and verbal STM and for manipulating memory load. In this study, musical and verbal DMSTs were investigated with measures of activity in frontal areas with functional near-infrared spectroscopy (fNIRS): Experiment 1 compared musical and verbal DMSTs with a low-level perception task (that does not entail encoding, retention, or retrieval of information), to identify frontal regions involved in memory processes. Experiment 2 manipulated memory load for musical and verbal materials to uncover frontal brain regions showing parametric changes in activity with load and their potential differences between musical and verbal materials. A FIR model was used to deconvolute fNIRS signals across successive trials without making assumptions with respect to the shape of the hemodynamic response in a DMST. Results revealed the involvement of the dorso-lateral prefrontal cortex (dlPFC) and inferior frontal gyri (IFG), but not of the superior frontal gyri (SFG) in both experiments, in keeping with previously reported neuroimaging data (including fMRI). Experiment 2 demonstrated a parametric variation of activity with memory load in bilateral IFGs during the maintenance period, with opposite directions for musical and verbal materials. Activity in the IFGs increased with memory load for verbal sound sequences, in keeping with previous results with n-back tasks. The decreased activity with memory load observed with musical sequences is discussed in relation to previous research on auditory STM rehearsal strategies. This study highlights fNIRS as a promising tool for investigating musical and verbal STM not only for typical populations, but also for populations with developmental language disorders associated with functional alterations in auditory STM.

neuroscience↗

bioRxiv2023

Spectro-temporal acoustical markers differentiate speech from song across cultures

Humans produce two forms of cognitively complex vocalizations: speech and song. It is debated whether these differ based primarily on culturally specific, learned features, or if acoustical features can reliably distinguish them. We study the spectro-temporal modulation patterns of vocalizations produced by 369 people living in 21 urban, rural, and small-scale societies across six continents. Specific ranges of spectral and temporal modulations, overlapping within categories and across societies, significantly differentiate speech from song. Machine-learning classification shows that this effect is cross-culturally robust, vocalizations being reliably classified solely from their spectro-temporal features across all 21 societies. Listeners unfamiliar with the cultures classify these vocalizations using similar spectro-temporal cues as the machine learning algorithm. Finally, spectro-temporal features are better able to discriminate song from speech than a broad range of other acoustical variables, suggesting that spectro-temporal modulation--a key feature of auditory neuronal tuning--accounts for a fundamental difference between these categories. Two-Sentence SummaryWhat distinguishes singing from speaking? The authors show that consistent acoustical spectro-temporal features are sufficient to distinguish speech and song reliably across different societies throughout the world.

neuroscience↗