Biology subjects

Pavao, R.

Publications and source records attributed to Pavao, R..

6 recordsLinked to original sources

bioRxiv2024

Sufficiency of ITD for (biased) human auditory azimuthal perception

Experiments on human auditory perception have shown that interaural time difference (ITD) is sufficient to generate spatial percepts, even though stimuli containing only the ITD cue are perceived as being emitted from inside the head instead of from external locations at specific azimuths. These experiments are thus interpreted as "lateralization" instead of "localization" tasks. In fact, lateralized spatial perception has been quantified using tasks in which participants have to report their estimates by selecting a putative location inside the head, or matching the perceived position to sounds with a given interaural level difference. Therefore, these estimates are made with respect to internal frames of reference, but it is unclear whether these percepts have any significance for the more ecological problem of locating an external sound source. In order to investigate the link between internalized spatial percepts and sound localization, we designed a new task in which subjects are instructed to report externalized azimuthal location for sounds containing only ITD cues. Despite the mismatch between an internalized percept having to be reported as emanating from an external location, subjects were able to estimate azimuths consistently. Furthermore, normalized estimates were indistinguishable from those obtained using traditional lateralization tasks. Our results revealed a direct relationship between perceived azimuths and ITD, which deviates from that obtained from acoustical analysis of binaural recordings, revealing estimation biases. Intriguingly, these results indicate that externalized percepts are not required for the generation of azimuthal percepts.

neuroscience↗

bioRxiv2022

Low noise HRTFs and delay line corrections are detrimental to the prediction of ITD discrimination thresholds from environmental statistics

Interaural Time Difference (ITD) is the main cue for azimuthal auditory perception in humans. ITDs at each frequency contribute differently to azimuth discrimination, which can be quantified by their azimuthal Fisher Information. Consistently, human ITD discrimination thresholds are predicted by the azimuthal information. However, this prediction is poor for frequencies below 500 Hz. Such poor prediction could be ascribed to the strategy of quantifying azimuthal information using HRTFs obtained in unnaturalistic anechoic chambers or by using a direct method which does not incorporate the delay lines proposed by the Jeffress-Colburn model. In the present study, we obtained ITD discrimination thresholds from extensive sampling across frequency and ITD, and applied multiple strategies for quantifying azimuthal information. These strategies employed HRTFs obtained in realistic and anechoic chambers, with and without considering delay lines. We found that ITD discriminability thresholds across the complete range of frequencies are better predicted by azimuthal information conveyed by ITD cues when (1) we use naturalistic high-noise HRTFs, and (2) ITD delay compensation is not applied. Our results support that auditory perception is shaped by natural environments, which include high reverberation in low frequencies. Moreover, we also suggest that delay lines are not a crucial feature for determining ITD discrimination thresholds in the human auditory system. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=186 SRC="FIGDIR/small/507313v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@1540172org.highwire.dtl.DTLVardef@2aea34org.highwire.dtl.DTLVardef@175ff15org.highwire.dtl.DTLVardef@1bdf2d1_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2020

ACE2 interaction networks in COVID-19: a physiological framework for prediction of outcome in patients with cardiovascular risk factors

(1) BackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (coronavirus disease 2019; COVID-19) is associated with adverse outcomes in patients with cardiovascular disease (CVD). The aim of the study was to characterize the interaction between SARS-CoV-2 and Angiotensin-Converting Enzyme 2 (ACE2) functional networks with a focus on CVD.; (2) MethodsUsing the network medicine approach and publicly available datasets, we investigated ACE2 tissue expression and described ACE2 interaction networks which could be affected by SARS-CoV-2 infection in the heart, lungs and nervous system. We compared them with changes in ACE-2 networks following SARS-CoV-2 infection by analyzing public data of stem cell-derived cardiomyocytes (hiPSC-CMs). This analysis was performed using the NERI algorithm, which integrates protein-protein interaction with co-expression networks. We also performed miRNA-target predictions to identify which ones regulate ACE2-related networks and could play a role in the COVID19 outcome. Finally, we performed enrichment analysis for identifying the main COVID-19 risk groups. (3) ResultsWe found similar ACE2 expression confidence levels in respiratory and cardiovascular systems, supporting that heart tissue is a potential target of SARS-CoV-2. Analysis of ACE2 interaction networks in infected hiPSC-CMs identified multiple hub genes with corrupted signalling which can be responsible for cardiovascular symptoms. The most affected genes were EGFR, FN1, TP53, HSP90AA1, and APP, while the most affected interactions were associated with MAST2 and CALM1. Enrichment analysis revealed multiple diseases associated with the interaction networks of ACE2, especially cancerous diseases, obesity, hypertensive disease, Alzheimers disease, non-insulin-dependent diabetes mellitus, and congestive heart failure. Among affected ACE2-network components connected with SARS-Cov-2 interactome, we identified AGT, CAT, DPP4, CCL2, TFRC and CAV1, associated with cardiovascular risk factors. We described for the first time miRNAs which were common regulators of ACE2 networks and virus-related proteins in all analyzed datasets. The top miRNAs were miR-27a-3p, miR-26b-5p, miR-10b-5p, miR-302c-5p, hsa-miR-587, hsa-miR-1305, hsa-miR-200b-3p, hsa-miR-124-3p, and hsa-miR-16-5p.; (4) ConclusionOur study provides a complete mechanistic framework for investigating the ACE2 network which was validated by expression data. This framework predicted risk groups, including the established ones, thus providing reliable novel information regarding the complexity of signalling pathways affected by SARS-CoV-2. It also identified miR which could be used in personalized diagnosis in COVID-19.

genomics↗

bioRxiv2018

Reactivation of time cell sequences in the hippocampus

The temporal order of an experience is a fundamental property of episodic memories, yet the mechanism for the consolidation of temporal sequences in long-term memory is still unknown. A potential mechanism for memory consolidation depends on the reactivation of neuronal sequences in the hippocampus. Despite abundant evidence of sequence reactivation in the formation of spatial memory, the reactivation of hippocampal neuronal sequences carrying non-spatial information has been much less explored. In this work, we recorded the activity of time cell sequences while rats performed multiple 15-s treadmill runnings during the intertrial intervals of a spatial alternation memory task. We observed forward and reverse reactivations of time cell sequences often occurring during sharp-wave ripple events following reward consumption. Surprisingly, the reactivation events specifically engaged cells coding temporal information. The reactivation of time cell sequences may thus reflect the organization of temporal order required for episodic memory formation.

neuroscience↗

bioRxiv2018

Anticipated ITD statistics built-in human sound localization

A neural code adapted to the statistical structure of sensory cues may optimize perception. We investigated whether interaural time difference (ITD) statistics inherent in natural acoustic scenes are parameters determining spatial discriminability. The natural ITD rate of change across azimuth (ITDrc) and ITD variability over time (ITDv) were combined in a Fisher information statistic for assessing the amount of azimuthal information conveyed by this sensory cue. We hypothesized that natural ITD statistics drive the neural code for ITD and thus influence spatial perception. Human spatial discriminability and spatial novelty detection of sounds with invariant statistics correlated with natural ITD statistics. The lack of natural statistics in the stimuli suggests that this correlation results from natural statistics driving the neural code. Additionally, the density distribution of ITD tuning matching natural statistics is consistent with classic models of ITD coding and can explain the ITD tuning distribution observed in the mammalian brainstem. Impact statementHuman brain has incorporated natural statistics of spatial cues to the neural code supporting perception of sound location. Major subject areaNeuroscience; Research organismHuman;

neuroscience↗

bioRxiv2017

On information metrics for spatial coding

The hippocampal formation is involved in navigation, and its neuronal activity exhibits a variety of spatial correlates (e.g., place cells, grid cells). The quantification of the information encoded by spikes has been standard procedure to identify which cells have spatial correlates. For place cells, most of the established metrics derive from Shannons mutual information (Shannon, 1948), and convey information rate in bits/sec or bits/spike (Skaggs et al., 1993; Skaggs et al., 1996). Despite their widespread use, the performance of these metrics in relation to the original mutual information metric has never been investigated. In this work, using simulated and real data, we find that the current information metrics correlate less with the accuracy of spatial decoding than the original mutual information metric. We also find that the top informative cells may differ among metrics, and show a surrogate-based normalization that yields comparable spatial information estimates. Since different information metrics may identify different neuronal populations, we discuss current and alternative definitions of spatially informative cells, which affect the metric choice.

neuroscience↗