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Nova · Professor Researcher · re-ranking top 20…
Bharath Chandrasekaran

Bharath Chandrasekaran

· Department Chair; Ralph and Jean Sundin Endowed ProfessorVerified

Northwestern University

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Funding$7.8M1 active
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Research topics

  • Computer Science
  • Machine Learning
  • Artificial Intelligence
  • Audiology
  • Neuroscience
  • Psychology
  • Cognitive psychology
  • Medicine
  • Speech recognition

Selected publications

  • Cortical Tracking of the Speech Envelope in Logopenic Variant Primary Progressive Aphasia

    Frontiers in Human Neuroscience · 2021 · 37 citations

    • Computer Science
    • Psychology
    • Cognitive psychology

    = 10) controls. Despite markedly reduced narrative comprehension relative to controls, individuals with lvPPA had increased cortical tracking of the speech envelope in theta oscillations, which track low-level features (e.g., syllables), but not delta oscillations, which track speech units that unfold across a longer time scale (e.g., words, phrases, prosody). This neural signature was highly correlated across narratives. Results indicate an increased reliance on acoustic cues during speech encoding. This may reflect inefficient encoding of bottom-up speech cues, likely as a consequence of dysfunctional temporoparietal cortex.

  • Bayesian Semiparametric Longitudinal Drift-Diffusion Mixed Models for Tone Learning in Adults

    Journal of the American Statistical Association · 2020 · 15 citations

    • Computer Science
    • Computer Science
    • Machine Learning

    Understanding how adult humans learn nonnative speech categories such as tone information has shed novel insights into the mechanisms underlying experience-dependent brain plasticity. Scientists have traditionally examined these questions using longitudinal learning experiments under a multi-category decision making paradigm. Drift-diffusion processes are popular in such contexts for their ability to mimic underlying neural mechanisms. Motivated by these problems, we develop a novel Bayesian semiparametric inverse Gaussian drift-diffusion mixed model for multi-alternative decision making in longitudinal settings. We design a Markov chain Monte Carlo algorithm for posterior computation. We evaluate the method's empirical performances through synthetic experiments. Applied to our motivating longitudinal tone learning study, the method provides novel insights into how the biologically interpretable model parameters evolve with learning, differ between input-response tone combinations, and differ between well and poorly performing adults. supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

  • Non-invasive peripheral nerve stimulation selectively enhances speech category learning in adults

    npj Science of Learning · 2020 · 60 citations

    Senior authorCorresponding
    • Psychology
    • Audiology
    • Neuroscience

    Adults struggle to learn non-native speech contrasts even after years of exposure. While laboratory-based training approaches yield learning, the optimal training conditions for maximizing speech learning in adulthood are currently unknown. Vagus nerve stimulation has been shown to prime adult sensory-perceptual systems towards plasticity in animal models. Precise temporal pairing with auditory stimuli can enhance auditory cortical representations with a high degree of specificity. Here, we examined whether sub-perceptual threshold transcutaneous vagus nerve stimulation (tVNS), paired with non-native speech sounds, enhances speech category learning in adults. Twenty-four native English-speakers were trained to identify non-native Mandarin tone categories. Across two groups, tVNS was paired with the tone categories that were easier- or harder-to-learn. A control group received no stimulation but followed an identical thresholding procedure as the intervention groups. We found that tVNS robustly enhanced speech category learning and retention of correct stimulus-response associations, but only when stimulation was paired with the easier-to-learn categories. This effect emerged rapidly, generalized to new exemplars, and was qualitatively different from the normal individual variability observed in hundreds of learners who have performed in the same task without stimulation. Electroencephalography recorded before and after training indicated no evidence of tVNS-induced changes in the sensory representation of auditory stimuli. These results suggest that paired-tVNS induces a temporally precise neuromodulatory signal that selectively enhances the perception and memory consolidation of perceptually salient categories.

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