Biology subjects

Ramanarayanan, V.

Publications and source records attributed to Ramanarayanan, V..

5 recordsLinked to original sources

bioRxiv2026

Modeling framework disentangles cerebellar mechanisms in speech feedback control, revealing trade-offs reshaped by degeneration

A central challenge in systems neuroscience is understanding how computational mechanisms--including those implemented within a single brain region--interact to produce behavior. For example, prior work in the literature attributes many computational functions to the cerebellum, but these functions have been tested in isolation and it remains unclear how they jointly contribute to motor control. Here, we test several established hypotheses of cerebellar function: internal modeling, timing of movement dynamics, sensory-error processing, delay processing, and multimodal integration. We first formalize these functions as mechanistic parameters within a computational model of speech motor control. We then use this formalism to investigate the relative contribution of each function to the abnormal speech corrective response seen in adults with cerebellar degeneration during perturbed auditory feedback. We find the following functions explain most of the behavioral differences: internal modeling, timing of movement dynamics, and multimodal integration. We also show that the key mechanisms have a trade-off relationship, and that cerebellar degeneration modulates those trade-off strengths and boundaries. These results both elaborate the mechanistic function of the cerebellum in speech feedback control and, more broadly, demonstrate the promise of using this paradigm to simultaneously test competing theories of neural function underlying behavior.

neuroscience↗

bioRxiv2026

G quadruplex DNA facilitates a pervasive path to homologous recombination

Homologous recombination (HR) requires efficient homology search and strand invasion, yet how homologous templates are identified within nuclear space remains unclear. Here, we identify G-quadruplex (G4) DNA structures as pervasive effectors of template strand invasion and uncover the G4 helicase DHX36 as a potent suppressor of this process. DHX36 loss stabilizes G4s, enhances HR, and accelerates repair of replication-associated DNA breaks. G4-mediated HR depends on the non-canonical strand invasion factors RAD51AP1, WDR48, and USP1, and requires G4 motifs within the homologous repair template. DHX36 loss partially restores HR and PARP inhibitor resistance in BRCA1-deficient cells, while promoting aberrant recombination and Alternative Lengthening of Telomeres (ALT). Together, our findings establish dynamic G4 regulation as a key determinant of homology search, genome maintenance, and recombination fidelity.

molecular biology↗

bioRxiv2024

Bayesian inference of state feedback control parameters for fo perturbation responses in cerebellar ataxia

Behavioral speech tasks have been widely used to understand the mechanisms of speech motor control in healthy speakers as well as in various clinical populations. However, determining which neural functions differ between healthy speakers and clinical populations based on behavioral data alone is difficult because multiple mechanisms may lead to the same behavioral differences. For example, individuals with cerebellar ataxia (CA) produce abnormally large compensatory responses to pitch perturbations in their auditory feedback, compared to controls, but this pattern could have many explanations. Here, computational modeling techniques were used to address this challenge. Bayesian inference was used to fit a state feedback control (SFC) model of voice fundamental frequency (fo) control to the behavioral pitch perturbation responses of individuals with CA and healthy controls. This fitting process resulted in estimates of posterior likelihood distributions of five model parameters (sensory feedback delays, absolute and relative levels of auditory and somatosensory feedback noise, and controller gain), which were compared between the two groups. Results suggest that the CA group may proportionally weight auditory and somatosensory feedback differently from the control group. Specifically, the CA group showed a greater relative sensitivity to auditory feedback than the control group. There were also large group differences in the controller gain parameter, suggesting increased motor output responses to target errors in the CA group. These modeling results generate hypotheses about how CA may affect the speech motor system, which could help guide future empirical investigations in CA. This study also demonstrates the overall proof-of-principle of using this Bayesian inference approach to understand behavioral speech data in terms of interpretable parameters of speech motor control models. Author summaryCerebellar ataxia is a condition characterized by a loss of coordination in the control of muscle movements, including those required for speech, due to damage in the cerebellar region of the brain. Behavioral speech experiments have been used to understand this disorders impact on speech motor control, but the results can be ambiguous to interpret. In this study, we fit a computational model of the neural speech motor control system to the speech data of individuals with cerebellar ataxia and that of healthy controls to determine what differences in model parameters best explain how the two groups differ in their control of vocal pitch. We found that group differences may be explained by increased sensitivity to auditory feedback prediction errors (differences between the actual sound speakers hear of their own speech as they produce it and the sound they expected to hear) and increased motor response in individuals with cerebellar ataxia. These computational results help us understand how cerebellar ataxia impacts speech motor control, and this general approach can also be applied to study other neurological speech disorders.

neuroscience↗

bioRxiv2021

Investigating the Interplay Between Affective, Phonatory and Motoric Subsystems in Autism Spectrum Disorder Using a Multimodal Dialogue Agent

We explore the utility of an on-demand multimodal conversational platform in extracting speech and facial metrics in children with Autism Spectrum Disorder (ASD). We investigate the extent to which these metrics correlate with objective clinical measures, particularly as they pertain to the interplay be-tween the affective, phonatory and motoric subsystems. 22 participants diagnosed with ASD engaged with a virtual agent in conversational affect production tasks designed to elicit facial and vocal affect. We found significant correlations between vocal pitch and loudness extracted by our platform during these tasks and accuracy in recognition of facial and vocal affect, as-sessed via the Diagnostic Analysis of Nonverbal Accuracy-2 (DANVA-2) neuropsychological task. We also found significant correlations between jaw kinematic metrics extracted using our platform and motor speed of the dominant hand assessed via a standardised neuropsychological finger tapping task. These findings offer preliminary evidence for the usefulness of these audiovisual analytic metrics and could help us better model the interplay between different physiological subsystems in individuals with ASD.

neuroscience↗

bioRxiv2019

The FACTS model of speech motor control: fusing state estimation and task-based control

We present a new computational model of speech motor control: the Feedback-Aware Control of Tasks in Speech or FACTS model. This model is based on a state feedback control architecture, which is widely accepted in non-speech motor domains. The FACTS model employs a hierarchical observer-based architecture, with a distinct higher-level controller of speech tasks and a lower-level controller of speech articulators. The task controller is modeled as a dynamical system governing the creation of desired constrictions in the vocal tract, based on the Task Dynamics model. Critically, both the task and articulatory controllers rely on an internal estimate of the current state of the vocal tract to generate motor commands. This internal state estimate is derived from initial predictions based on efference copy of applied controls. The resulting state estimate is then used to generate predictions of expected auditory and somatosensory feedback, and a comparison between predicted feedback and actual feedback is used to update the internal state prediction. We show that the FACTS model is able to qualitatively replicate many characteristics of the human speech system: the model is robust to noise in both the sensory and motor pathways, is relatively unaffected by a loss of auditory feedback but is more significantly impacted by the loss of somatosensory feedback, and responds appropriately to externally-imposed alterations of auditory and somatosensory feedback. The model also replicates previously hypothesized trade-offs between reliance on auditory and somatosensory feedback in speech motor control and shows for the first time how this relationship may be mediated by acuity in each sensory domain. These results have important implications for our understanding of the speech motor control system in humans.

neuroscience↗