Biology subjects

Sadagopan, S.

Publications and source records attributed to Sadagopan, S..

9 recordsLinked to original sources

bioRxiv2026

The exocyst is an insulin-sensitive regulator of amyloid precursor protein trafficking and amyloid-beta generation in neurons

Intracellular trafficking of amyloid precursor protein (APP) critically influences amyloidogenic processing, yet the mechanisms regulating this pathway remain incompletely defined. The exocyst is a highly conserved, insulin-responsive, eight-protein Rab effector complex that directs intracellular transport vesicle targeting and docking. We identified APP in a proteomics screen of neuronal cell surface proteins altered after chemical inhibition of exocyst activity. In SH-SY5Y cells expressing a mutant APP that enhances amyloidogenic processing, RNAi-mediated silencing of exocyst subunits significantly decreased sAPP and A{beta} secretion, leading to significant intracellular APP accumulation. We found high-resolution co-localization of APP with exocyst subunits in soma and neurites of differentiated human SH-SY5Y neurons and mouse primary hippocampal neurons, and live-cell TIRF microscopy identified highly coordinated movement between fluorescently-tagged exocyst and APP proteins. These interactions were confirmed in these cells and in mouse brain histological sections by proximity ligation assays (PLAs) demonstrating close (<40nm) APP-EXOC5 association. To examine if exocyst activity in neurons is regulated by insulin, as it is in adipocytes and muscle, we generated a SH-SY5Y cell line with pHluorin-tagged GLUT4. Inhibition of the exocyst prevented exocytosis of GLUT4 to the plasma membrane in response to insulin. Additionally, using PLAs in mouse primary hippocampal neurons and SH-SY5Y neurons, we found that GLUT4-EXOC5 associations were increased by insulin signaling, but APP-EXOC5 associations were markedly reduced, indicating insulin-dependent retargeting of the exocyst complex away from APP+ vesicles towards GLUT+ vesicles. All together, these data identify the exocyst as a novel insulin-regulated mediator of neuronal APP trafficking and A{beta} secretion. In BriefWe show that the insulin-responsive exocyst regulates amyloidogenic processing of APP in neurons and that insulin signaling shifts the exocyst away from APP trafficking to promote the translocation of GLUT4-containing vesicles to the plasma membrane of neurons.

neuroscience↗

bioRxiv2026

Multiparametric Classification of Pure-tone Responses Distinguishes Neurons in Inferior Colliculus Subdivisions

The inferior colliculus (IC) integrates ascending auditory and descending multimodal inputs within distinct subdivisions, the central nucleus (CNIC) and cortex (CtxIC). Despite differences in connectivity, auditory responses in these subdivisions are similar, complicating localization during in-vivo recordings. Here, we tested whether recordings can be assigned to CNIC or CtxIC using only response properties in awake and anesthetized mice. We constructed frequency response areas (FRAs) from pure tone responses and extracted tuning and firing metrics. Individual FRA features could not reliably localize recordings. In contrast, a random forest classifier combining FRA-derived features accurately localized recordings to CNIC or CtxIC across states. These findings demonstrate that while IC subdivisions differ only subtly along individual response parameters, appropriate multiparametric approaches can enable robust classification. More broadly, our results illustrate how biologically meaningful distinctions may be revealed by combining weakly informative features, an approach that can be applied across diverse brain regions and modalities.

neuroscience↗

bioRxiv2022

Adaptive mechanisms facilitate robust performance in noise and in reverberation in an auditory categorization model

For robust vocalization perception, the auditory system must generalize over variability in vocalization production as well as variability arising from the listening environment (e.g., noise and reverberation). We previously demonstrated that a hierarchical model generalized over production variability by detecting sparse intermediate-complexity features that are maximally informative about vocalization category from a dense spectrotemporal input representation. Here, we explore three biologically feasible model extensions to generalize over environmental variability: (1) training in degraded conditions, (2) adaptation to sound statistics in the spectrotemporal stage and (3) sensitivity adjustment at the feature detection stage. All mechanisms improved vocalization categorization performance, but improvement trends varied across degradation type and vocalization type. One or both adaptive mechanisms were required for model performance to approach the behavioral performance of guinea pigs on a vocalization categorization task. These results highlight the contributions of adaptive mechanisms at multiple auditory processing stages to achieve robust auditory categorization.

neuroscience↗

bioRxiv2022

Vocalization categorization behavior explained by a feature-based auditory categorization model

Vocal animals produce multiple categories of calls with high between- and within-subject variability, over which listeners must generalize to accomplish call categorization. The behavioral strategies and neural mechanisms that support this ability to generalize are largely unexplored. We previously proposed a theoretical model that accomplished call categorization by detecting features of intermediate complexity that best contrasted each call category from all other categories. We further demonstrated that some neural responses in the primary auditory cortex were consistent with such a model. Here, we asked whether a feature-based model could predict call categorization behavior. We trained both the model and guinea pigs on call categorization tasks using natural calls. We then tested categorization by the model and guinea pigs using temporally and spectrally altered calls. Both the model and guinea pigs were surprisingly resilient to temporal manipulations, but sensitive to moderate frequency shifts. Critically, model performance quantitatively matched guinea pig behavior to a remarkable degree. By adopting different model training strategies and examining features that contributed to solving specific tasks, we could gain insight into possible strategies used by animals to categorize calls. Our results validate a model that uses the detection of intermediate-complexity contrastive features to accomplish call categorization.

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

A complex feature-based representation of vocalizations emerges in the superficial layers of primary auditory cortex

Early in auditory processing, neural responses faithfully reflect acoustic input. At higher stages of auditory processing, however, neurons become selective for particular call types, eventually leading to specialized regions of cortex that preferentially process calls at the highest auditory processing stages. We previously proposed that an intermediate step in how non-selective responses are transformed into call-selective responses is the detection of informative call features. But how neural selectivity for informative call features emerges from non-selective inputs, whether feature selectivity gradually emerges over the processing hierarchy, and how stimulus information is represented in non-selective and feature-selective populations remain open questions. In this study, using unanesthetized guinea pigs, a highly vocal and social rodent, as an animal model, we characterized the neural representation of calls in three auditory processing stages - the thalamus (vMGB), and thalamorecipient (L4) and superficial layers (L2/3) of primary auditory cortex (A1). We found that neurons in vMGB and A1 L4 did not exhibit call-selective responses and responded throughout the call durations. However, A1 L2/3 neurons showed high call-selectivity with about a third of neurons responding to only one or two call types. These A1 L2/3 neurons only responded to restricted portions of calls suggesting that they were highly selective for call features. Receptive fields of these A1 L2/3 neurons showed complex spectrotemporal structures that could underlie their high call feature selectivity. Information theoretic analysis revealed that in A1 L4 stimulus information was distributed over the population and was spread out over the call durations. In contrast, in A1 L2/3, individual neurons showed brief bursts of high stimulus-specific information, and conveyed high levels of information per spike. These data demonstrate that a transformation in the neural representation of calls occurs between A1 L4 and A1 L2/3, leading to the emergence of a feature-based representation of calls in A1 L2/3. Our data thus suggest that observed cortical specializations for call processing emerge in A1, and set the stage for further mechanistic studies.

neuroscience↗

bioRxiv2020

Pupillometry as a reliable metric of auditory detection and discrimination across diverse stimulus paradigms in animal models

Estimates of detection and discrimination thresholds are often used to explore broad perceptual similarities between human subjects and animal models. Pupillometry shows great promise as a non-invasive, easily-deployable method of comparing human and animal thresholds. Using pupillometry, previous studies in animal models have obtained threshold estimates to simple stimuli such as pure tones, but have not explored whether similar pupil responses can be evoked by complex stimuli, what other stimulus contingencies might affect stimulus-evoked pupil responses, and if pupil responses can be modulated by experience or short-term training. In this study, we used an auditory oddball paradigm to estimate detection and discrimination thresholds across a wide range of stimuli in guinea pigs. We demonstrate that pupillometry yields reliable detection and discrimination thresholds across a range of simple (tones) and complex (conspecific vocalizations) stimuli; that pupil responses can be robustly evoked using different stimulus contingencies (low-level acoustic changes, or higher level categorical changes); and that pupil responses are modulated by short-term training. These results lay the foundation for using pupillometry as a high-throughput method of estimating thresholds in large experimental cohorts, and unveil the full potential of using pupillometry to explore broad similarities between humans and animal models.

animal behavior and cognition↗

bioRxiv2018

Optimal features for auditory categorization

Humans and vocal animals use vocalizations (human speech or animal calls) to communicate with members of their species. A necessary function of auditory perception is to generalize across the high variability inherent in the production of these sounds and classify them into perceptually distinct categories ( words or call types). Here, we demonstrate using an information-theoretic approach that production-invariant classification of calls can be achieved by detecting mid-level acoustic features. Starting from randomly chosen marmoset call features, we used a greedy search algorithm to determine the most informative and least redundant set of features necessary for call classification. Call classification at >95% accuracy could be accomplished using only 10 - 20 features per call type. Most importantly, predictions of the tuning properties of putative neurons selective for such features accurately matched some previously observed responses of superficial layer neurons in primary auditory cortex. Such a feature-based approach succeeded in categorizing calls of other species such as guinea pigs and macaque monkeys, and could also solve other complex classification tasks such as caller identification. Our results suggest that high-level neural representations of sounds are based on task-dependent features optimized for specific computational goals.

neuroscience↗