Biology subjects

Chandresekaran, B.

Publications and source records attributed to Chandresekaran, B..

8 recordsLinked to original sources

bioRxiv2022

Cortical tracking of continuous speech under bimodal divided attention

Speech processing often occurs amidst competing inputs from other modalities, e.g., listening to the radio while driving. We examined the extent to which dividing attention between auditory and visual modalities (bimodal divided attention) impacts neural processing of natural continuous speech from acoustic to linguistic levels of representation. We recorded electroencephalographic (EEG) responses when human participants performed a challenging primary visual task, imposing low or high cognitive load while listening to audiobook stories as a secondary task. The two dual-task conditions were contrasted with an auditory single-task condition in which participants attended to stories while ignoring visual stimuli. Behaviorally, the high load dual-task condition was associated with lower speech comprehension accuracy relative to the other two conditions. We fitted multivariate temporal response function encoding models to predict EEG responses from acoustic and linguistic speech features at different representation levels, including auditory spectrograms and information-theoretic models of sublexical-, word-form-, and sentence-level representations. Neural tracking of most acoustic and linguistic features remained unchanged with increasing dual-task load, despite unambiguous behavioral and neural evidence of the high load dual-task condition being more demanding. Compared to the auditory single-task condition, dual-task conditions selectively reduced neural tracking of only some acoustic and linguistic features, mainly at latencies >200 ms, while earlier latencies were surprisingly unaffected. These findings indicate that behavioral effects of bimodal divided attention on continuous speech processing occur not due to impaired early sensory representations but likely at later cognitive processing stages. Crossmodal attention-related mechanisms may not be uniform across different speech processing levels.

neuroscience↗

bioRxiv2022

Functional connectivity across the human subcortical auditory system using a Gaussian copula graphical model approach with partial correlations

Abstract/SummaryThe auditory system comprises multiple subcortical brain structures that process and refine incoming acoustic signals along the primary auditory pathway. Due to technical limitations of imaging small structures deep inside the brain, most of our knowledge of the subcortical auditory system is based on research in animal models using invasive methodologies. Advances in ultra-high field functional magnetic resonance imaging (fMRI) acquisition have enabled novel non-invasive investigations of the human auditory subcortex, including fundamental features of auditory representation such as tonotopy and periodotopy. However, functional connectivity across subcortical networks is still underexplored in humans, with ongoing development of related methods. Traditionally, functional connectivity is estimated from fMRI data with full correlation matrices. However, partial correlations reveal the relationship between two regions after removing the effects of all other regions, reflecting more direct connectivity. Partial correlation analysis is particularly promising in the ascending auditory system, where sensory information is passed in an obligatory manner, from nucleus to nucleus up the primary auditory pathway, providing redundant but also increasingly abstract representations of auditory stimuli. While most existing methods for learning conditional dependency structures based on partial correlations assume independently and identically Gaussian distributed data, fMRI data exhibit significant deviations from Gaussianity as well as high temporal autocorrelation. In this paper, we developed an autoregressive matrix-Gaussian copula graphical model (ARMGCGM) approach to estimate the partial correlations and thereby infer the functional connectivity patterns within the auditory system while appropriately accounting for autocorrelations between successive fMRI scans. Our results show strong positive partial correlations between successive structures in the primary auditory pathway on each side (left and right), including between auditory midbrain and thalamus, and between primary and associative auditory cortex. These results are highly stable when splitting the data in halves according to the acquisition schemes and computing partial correlations separately for each half of the data, as well as across cross-validation folds. In contrast, full correlation-based analysis identified a rich network of interconnectivity that was not specific to adjacent nodes along the pathway. Overall, our results demonstrate that unique functional connectivity patterns along the auditory pathway are recoverable using novel connectivity approaches and that our connectivity methods are reliable across multiple acquisitions.

neuroscience↗

bioRxiv2022

Auditory corticostriatal connections in the human brain

Auditory learning depends on sensory, perceptual, decisional, and reward-based processes that are supported by the dorsal striatum. Auditory corticostriatal connections have been well-characterized in animal models including non-human primates, where primary auditory cortex preferentially connects to putamen, and caudate head receives most of its inputs from anterior superior temporal cortex. However, the extent to which human auditory corticostriatal connectivity follows similar organizational principles is challenging to assess due to the density of these striatal structures relative to the resolution of traditional diffusion MRI techniques, as well as dorsal striatums location near multiple major crossing white matter bundles. We leveraged high-quality diffusion-weighted MRI tractography to virtually dissect structural pathways between auditory cortical regions and dorsal striatal regions in a sub-millimeter resolution single-participant dataset. Across most of auditory cortex, putamen connections were more frequent than caudate connections; only anterior-most superior temporal cortex had strong connectivity with caudate, specifically the caudate head. Putamen streamline endpoints were largely along the ventral portion of the structure, ranging from caudal to middle putamen. These results were consistent across analysis and tractography pipelines. In contrast to the auditory findings, visual corticostriatal streamlines did not preferentially reach putamen. We replicate these results in an independent sample of near-millimeter resolution single-session diffusion MRI from the Human Connectome Project. Overall, our results suggest strong structural connectivity between primary and association auditory cortices with putamen but not with any subdivision of caudate. Prioritized connectivity between superior temporal cortex and putamen is highly suggestive of distinct functional roles for striatal subdivisions in auditory perception.

neuroscience↗

bioRxiv2022

Low amplitude transcutaneous auricular vagus nerve stimulation modulates performance but not pupil size during non-native speech category learning

Sub-threshold transcutaneous auricular vagus nerve stimulation (taVNS) synchronized with behavioral training can selectively enhance non-native speech category learning in adults. Prior work has demonstrated behavioral performance increases when taVNS is paired with easier-to-learn Mandarin tone categories in native English listeners, relative to when taVNS is paired with harder-to-learn Mandarin tone categories or without taVNS. Mechanistically, this temporally-precise plasticity has been attributed to noradrenergic modulation. However, prior work did not specifically utilize methodologies that indexed noradrenergic modulation and, therefore, were unable to explicitly test this hypothesis. Our goals for the current study were to use pupillometry to gain mechanistic insights into taVNS behavioral effects. Participants learned to categorize Mandarin tones while pupillometry was recorded. In a double-blind design, participants were divided into two taVNS groups that, as in the prior study, differed according to whether taVNS was paired with easier-to-learn tones or harder-to-learn tones. We found that taVNS led to faster rates of learning on trials paired with stimulation. Lower amplitude taVNS also led to faster rates of learning compared to higher amplitude taVNS. However, these effects were not group specific, and we did not find evidence of a taVNS correlate in the pupillary response. The results suggest that stimulation amplitude may be a critical determinant of learning outcomes and pupillary modulation. Future studies on subthreshold taVNS need to systematically evaluate the effect of stimulation intensity on behavioral plasticity and potential taVNS biomarkers.

physiology↗

bioRxiv2021

Categorical encoding of voice in human superior temporal cortex

The ability to recognize abstract features of voice during auditory perception is a complex, yet poorly understood, feat of human audition. For the listener, this occurs in near-automatic fasion to seamlessly extract complex cues from a highly variable auditory signal. Voice perception depends on specialized regions of auditory cortex, including superior temporal gyrus (STG) and superior temporal sulcus (STS). However, the nature of voice encoding at the cortical level remains poorly understoood. We leverage intracerebral recordings across human auditory cortex during presentation of voice and non-voice acoustic stimuli to examine voice encoding in auditory cortex, in eight patient-participants undergoing epilepsy surgery evaluation. We show that voice-selectivity increases along the auditory hierarchy from supratemporal plane (STP) to the STG and STS. Results show accurate decoding of vocalizations from human auditory cortical activity even in the complete absence of linguistic content. These findings show an early, less-selective temporal window of neural activity in the STG and STS followed by a sustained, strongly voice-selective window. We then developed encoding models that demonstrate divergence in the encoding of acoustic features along the auditory hierarchy, wherein STG/STS responses were best explained by voice category as opposed to the acoustic features of voice stimuli. This is in contrast to neural activity recorded from STP, in which responses were accounted for by acoustic features. These findings support a model of voice perception that engages categorical encoding mechanisms within STG and STS. Significance StatementVoice perception occurs via specialized networks in higher order auditory cortex, yet how voice features are encoded remains a central unanswered question. With human intracerebral recordings of auditory cortex, we provide evidence for categorical encoding of voice in STG and STS and occurs in the absence of linguistic content. This selectivity strengthens after an initial onset response and cannot be explained by simple acoustic features. Together, these data support the existence of sites within STG and STS that are specialized for voice perception.

neuroscience↗

bioRxiv2021

Linear superposition of responses evoked by individual glottal pulses explain over 80% of the frequency following response to human speech in the macaque monkey

The frequency-following response (FFR) is a scalp-recorded electrophysiological potential that closely follows the periodicity of complex sounds such as speech. It has been suggested that FFRs reflect the linear superposition of responses that are triggered by the glottal pulse in each cycle of the fundamental frequency (F0 responses) and sequentially propagate through auditory processing stages in brainstem, midbrain, and cortex. However, this conceptualization of the FFR is debated, and it remains unclear if and how well a simple linear superposition can capture the spectro-temporal complexity of FFRs that are generated within the highly recurrent and non-linear auditory system. To address this question, we used a deconvolution approach to compute the hypothetical F0 responses that best explain the FFRs in rhesus monkeys to human speech and click trains with time-varying pitch patterns. The linear superposition of F0 responses explained well over 90% of the variance of click train steady state FFRs and well over 80% of mandarin tone steady state FFRs. The F0 responses could be measured with high signal-to-noise ratio and featured several spectro-temporally and topographically distinct components that likely reflect the activation of brainstem (<5ms; 200-1000 Hz), midbrain (5-15 ms; 100-250 Hz) and cortex (15-35 ms; ~90 Hz). In summary, our results in the monkey support the notion that FFRs arise as the superposition of F0 responses by showing for the first time that they can capture the bulk of the variance and spectro-temporal complexity of FFRs to human speech with time-varying pitch. These findings identify F0 responses as a potential diagnostic tool that may be useful to reliably link altered FFRs in speech and language disorders to altered F0 responses and thus to specific latencies, frequency bands and ultimately processing stages.

neuroscience↗

bioRxiv2021

Deconstructing the Cortical Sources of Frequency Following Responses to Speech: A Cross-species Approach

Time-varying pitch is a vital cue for human speech perception. Neural processing of time-varying pitch has been extensively assayed using scalp-recorded frequency-following responses (FFRs), an electrophysiological signal thought to reflect integrated phase-locked neural ensemble activity from subcortical auditory areas. Emerging evidence increasingly points to a putative contribution of auditory cortical ensembles to the scalp-recorded FFRs. However, the properties of cortical FFRs and precise characterization of laminar sources are still unclear. Here we used direct human intracortical recordings as well as extra- and intracranial recordings from macaques and guinea pigs to characterize the properties of cortical sources of FFRs to time-varying pitch patterns. We found robust FFRs in the auditory cortex across all species. We leveraged representational similarity analysis as a translational bridge to characterize similarities between the human and animal models. Laminar recordings in animal models showed FFRs emerging primarily from the thalamorecepient layers of the auditory cortex. FFRs arising from these cortical sources significantly contributed to the scalp-recorded FFRs via volume conduction. Our research paves the way for a wide array of studies to investigate the role of cortical FFRs in auditory perception and plasticity. Significance StatementFrequency following responses (FFRs) to speech are scalp-recorded neural signals that inform the fidelity of sound encoding in the auditory system. FFRs, long believed to arise from brainstem and midbrain, have shaped our understanding of sub-cortical auditory processing and plasticity. Non-invasive studies have shown cortical contributions to the FFRs, however, this is still actively debated. Here we employed direct cortical recordings to trace the cortical contribution to the FFRs and characterize the properties of these cortical FFRs. With extra-cranial and intra-cranial recordings within the same subjects we show that cortical FFRs indeed contribute to the scalp-recorded FFRs, and their response properties differ from the sub-cortical FFRs. The findings provide strong evidence to revisit and reframe the FFR driven theories and models of sub-cortical auditory processing and plasticity with careful characterization of cortical and sub-cortical components in the scalp-recorded FFRs.

neuroscience↗

bioRxiv2021

Separate neural dynamics underlying the acquisition of different auditory category structures

Current models of auditory category learning argue for a rigid specialization of hierarchically organized regions that are fine-tuned to extracting and mapping acoustic dimensions to categories. We test a competing hypothesis: the neural dynamics of emerging auditory representations are driven by category structures and learning strategies. We designed a category learning experiment where two groups of learners learned novel auditory categories with identical dimensions but differing category structures: rule-based (RB) and information-integration (II) based categories. Despite similar learning accuracies, strategies and cortico-striatal systems processing feedback differed across structures. Emergent neural representations of category information within an auditory frontotemporal pathway exclusively for the II learning task. In contrast, the RB task yielded neural representations within distributed regions involved in cognitive control that emerged at different time-points of learning. Our results demonstrate that learners neural systems are flexible and show distinct spatiotemporal patterns that are not dimension-specific but reflect underlying category structures. SignificanceWhether it is an alarm signifying danger or the characteristics of background noise, humans are capable of rapid auditory learning. Extant models posit that novel auditory representations emerge in the superior temporal gyrus, a region specialized for extracting behaviorally relevant auditory dimensions and transformed onto decisions via the dorsal auditory stream. Using a computational cognitive neuroscience approach, we offer an alternative viewpoint: emergent auditory representations are highly flexible, showing distinct spatial and temporal trajectories that reflect different category structures.

bioinformatics↗