Biology subjects

Guenther, F. H.

Publications and source records attributed to Guenther, F. H..

5 recordsLinked to original sources

bioRxiv2026

Machine learning surrogate forward models for biomechanical laryngeal control

Accurate modeling of laryngeal motor control is key to understanding typical and disordered voice production. However, traditional biomechanical plant models based on ordinary differential equations (ODEs) often involve high computational costs and numerical instabilities, limiting their use in real-time closed-loop control frameworks. This study evaluates feature-driven machine learning (ML) regressors, specifically Random Forest (RF), Multilayer Perceptron Neural Networks (NN), and Polynomial Regression (PR), as surrogate forward models mapping laryngeal motor inputs to fundamental frequency and sound pressure level. Training data were generated with two biomechanical vocal fold models: the extended body-cover and the triangular body-cover. Results demonstrate that ML surrogates reduce execution times from seconds to milliseconds (e.g., 2 ms for PR), enabling stable real-time tracking via inverse Jacobian control. While RF provides the highest accuracy, NN and PR offer smoother control signals and smaller memory footprints. A practical performance threshold was identified near N = 1,000 training samples, below which accuracy degraded substantially when models were trained from scratch. These findings support ML surrogates as efficient and adaptable alternatives to direct numerical simulation, providing a foundation for future subject-specific modeling through transfer learning in data-limited clinical scenarios.

bioengineering↗

bioRxiv2025

Independent Basal Ganglia Neural Populations Encode Speech Production and Ambient-Noise Levels

The function of the basal ganglia (BG), a set of nuclei deep in the brain, is still under debate despite their clinical relevance. Lines of evidence from non-human primate and human experiments suggest that one key causal contribution of the adult primate BG is invigoration of movement according to environmental and energetic constraints. Previous studies have come to this conclusion using primarily limb motor control tasks. We hypothesized BG are also important for invigorating the respiratory, vocal, and articulatory motor systems during speech production. To test this, we used a rare clinical opportunity to record local field potentials-broadband gamma activity (LFP-BGA) and single unit (SU) activity from human basal ganglia during deep brain stimulation implant surgeries. Participants repeated sentences aloud in the operating room. We modulated environmental and energetic constraints of speech production by inducing the Lombard Effect, wherein people reliably increase the intensity of their voice in the presence of background noise. Participants increased voice intensity, pitch, vowel space size, and respiratory depth in the presence of ambient noise. [~]40% of LFP and SU sites were modulated by speech production. Comparing globus pallidus (GP) LFP-BGA, herein reported for the first time during speech, to subthalamic nucleus (STN) LFP-BGA revealed GP activated after STN. STN was more coincident with the speech preparatory period and GP more coincident with speech onset, suggesting differential roles in speech production. We found only weak evidence that STN or GP activity encodes the level of produced speech intensity. Instead, we found that LFP-BGA and SU-FR robustly track the ambient background noise levels. Linear decoding from SU-firing rate revealed we could infer the background noise levels with >75% accuracy in any given time point in the trial, including baseline. Importantly, the ambient-noise-modulated sites were an independent population from speech-modulated sites. We conclude that human BG encode the energetic constraints of communication. We theorize that the code is leveraged in downstream BG-cortical loop nodes to invigorate speech oro-motor gestures and co-speech manual limb gestures, both of which respond to ambient noise levels to maintain high-fidelity communication.

neuroscience↗

bioRxiv2020

Neural substrates of verbal repetition deficits in primary progressive aphasia

In this cross-sectional study, we examined the relationship between cortical thickness and performance on several verbal repetition tasks in a cohort of patients with primary progressive aphasia (PPA) in order to test predictions generated by theoretical accounts of phonological working memory (PWM) that predict phonological content buffers in left posterior inferior frontal sulcus (pIFS) and supramarginal gyrus (SMG). Cortical surfaces were reconstructed from magnetic resonance imaging scans from 42 participants diagnosed with PPA. Cortical thickness was measured in a set of anatomical regions spanning the entire cerebral cortex. Correlation analyses were performed between cortical thickness and average score across three PWM related tasks: the Repetition subtest from the Western Aphasia Battery, a forward digit span task, and a backward digit span task. Significant correlations were found between average PWM score across tasks and cortical thickness in left SMG and left pIFS, in support of prior theoretical accounts of PWM. Exploratory whole-brain correlation analyses performed for each of the three behavioral tasks individually revealed a distinct set of positively-correlated regions for each task. Comparison of cortical thickness measures from different PPA subtypes to cortical thickness in age-matched controls further revealed unique patterns of atrophy in the different PPA subtypes.

neuroscience↗

bioRxiv2020

The neural circuitry underlying the "rhythm effect" in stuttering

PurposeStuttering is characterized by intermittent speech disfluencies which are dramatically reduced when speakers synchronize their speech with a steady beat. The goal of this study was to characterize the neural underpinnings of this phenomenon using functional magnetic resonance imaging. MethodData were collected from 17 adults who stutter and 17 adults who do not stutter while they read sentences aloud either in a normal, self-paced fashion or paced by the beat of a series of isochronous tones ("rhythmic"). Task activation and task-based functional connectivity analyses were carried out to compare neural responses between speaking conditions and groups. ResultsAdults who stutter produced fewer disfluent trials in the rhythmic condition than in the normal condition. While adults who do not stutter had greater activation in the rhythmic condition compared to the normal condition in regions associated with speech planning, auditory feedback control, and timing perception, adults who stutter did not have any significant changes. However, adults who stutter demonstrated increased functional connectivity between bilateral inferior cerebellum and bilateral orbitofrontal cortex as well as increased connectivity among cerebellar regions during rhythmic speech as compared to normal speech. ConclusionModulation of connectivity in the cerebellum and prefrontal cortex during rhythmic speech suggests that this fluency-inducing technique activates a compensatory timing system in the cerebellum and potentially modulates top-down motor control and attentional systems. These findings corroborate previous work associating the cerebellum with fluency in adults who stutter and indicate that the cerebellum may be targeted to enhance future therapeutic interventions.

neuroscience↗

bioRxiv2019

Reliability of single-subject neural activation patterns across speech production tasks

Speech neuroimaging research targeting individual speakers could help elucidate differences that may be crucial to understanding speech disorders. However, this research necessitates reliable brain activation across multiple speech production sessions. In the present study, we evaluated the reliability of speech-related brain activity measured by functional magnetic resonance imaging data from twenty neuro-typical subjects who participated in two experiments involving reading aloud simple speech stimuli. Using traditional methods like the Dice and intraclass correlation coefficients, we found that most individuals displayed moderate to high reliability. We also found that a novel machine-learning subject classifier could identify these individuals by their speech activation patterns with 97% accuracy from among a dataset of seventy-five subjects. These results suggest that single-subject speech research would yield valid results and that investigations into the reliability of speech activation in people with speech disorders are warranted.

neuroscience↗