Search bioRxiv⌕ Search

bioRxiv · 10.1101/2020.12.16.423049

Auditory statistics development does not rely on vision, but the processing of sound local features is hampered by late-onset sight loss

Abstract

The human auditory system relies on both detailed and summarized representations to recognize different sounds. As local features can exceed the storage capacity, average statistics are computed over time to generate more compact representations at the expense of temporal details availability. This study aimed to identify whether these fundamental sound analyses develop and function exclusively under the influence of the auditory system or interact with other modalities, such as vision. We employed a validated computational synthesis approach allowing to control directly statistical properties embedded in sounds. To address whether the two modes of auditory representation (local features processing and statistical averaging) are influenced by the availability of visual input in different phases of development, we tested samples of sighted controls (SC), congenitally blind (CB), and late-onset (> 10 years of age) blind (LB) individuals in two separate experiments which uncovered auditory statistics computations from behavioral performances. In experiment 1, performance relied on the availability of local features at specific time points; in experiment 2, performance benefited from computing average statistics over longer durations. As expected, when sound duration increased, detailed representation gave way to summary statistics in SC. In both experiments, the sample of CB individuals displayed a remarkably similar performance revealing that both local and global auditory processes are not altered by blindness since birth. Conversely, LB individuals performed poorly compared to the other groups when relying on local features, with no impact on statistical averaging. The dampening in the performance was not associated with the onset and duration of visual deprivation. Results provide clear evidence that vision is not necessary for the development of the auditory computations tested here. Remarkably, a functional interplay between acoustic details processing and vision emerges at later developmental phases. Findings are consistent with a model in which the efficiency of local auditory processing is vulnerable in case sight becomes unavailable. Ultimately results are in favor of a shared computational framework for auditory and visual processing of local features, which emerges in late development.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Berto, M., Pietrini, P., Ricciardi, E., Bottari, D.. 2020-12-16. Auditory statistics development does not rely on vision, but the processing of sound local features is hampered by late-onset sight loss. https://doi.org/10.1101/2020.12.16.423049

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

The Unreasonable Effectiveness of Cell Types in Describing Neuronal Physiological Features

Single-cell RNA sequencing (scRNA-seq) captures detailed gene expression profiles at scale, while patch-clamp recordings measure intrinsic neuronal electrophysiological properties. Modeling the relations between these two modalities remains a challenge. Here, we compare how well electrophysiological features can be predicted by traditional transcriptomic cell type classification, representations derived from a foundational model (scGPT) pretrained on large-scale scRNA-seq datasets, ion channel-coding genes, and highly variable genes. Using paired transcriptomic and electrophysiological patch-sequencing data from 495 human neurons from neurosurgical tissue, we find that cluster-level cell type representations consistently outperform highly variable gene selection, ion channel gene selection, and context-enriched scGPT embeddings. Notably, performance varies across model architectures and initializations, and the best results are obtained by combining the outputs of separate cell type and scGPT-based models. Together, these findings suggest that traditional discrete cellular classification is highly effective in predicting physiological features. For maximum performance it can be complemented by pretrained transformer models.

neuroscience↗

A nonlinear inhibition pathway underlying cortical responses to tuned holographic optogenetic perturbations

Optogenetics enables causal manipulation of cortical activity. Perturbation responses can be counterintuitive due to network interactions, making theory essential for predicting them. Existing approaches often rely on linear approximations, which fail for many biologically relevant perturbations. Here we develop a nonlinear theory of responses to holographic perturbations in cell-type-specific recurrent networks with structured connectivity. We fit a nonlinear model to mouse V1 data, which shows cotuned-ensemble suppression: perturbing spatially clustered neurons with similar preferred orientations yields markedly stronger short-range suppression than perturbing untuned ensembles. We show that cotuned-ensemble suppression arises from a feature-tuned, nonlinear inhibition pathway implicating somatostatin-positive (SST) interneurons. The theory predicts that cotuned ensembles suppress parvalbumin-positive (PV) neurons but facilitate SST neurons, and links the degree of cotuned-ensemble suppression or facilitation to the variance of the SST response. This framework identifies mechanisms by which nonlinear inhibition sculpts cortical dynamics and establishes a predictive basis for targeted optogenetic interventions.

neuroscience↗

Proteomic signatures of APOE ε4 across human tissues and cell types in Alzheimers disease

The apolipoprotein E {varepsilon}4 (APOE {varepsilon}4) allele is the strongest genetic risk factor for late-onset Alzheimers disease (AD). However, the underlying molecular mechanisms remain unclear. This study included 1691 participants from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP), 1226 participants from the Accelerating Medicines Partnership - Alzheimers Disease (AMP-AD) Diverse Cohorts Study, and 735 participants from the Alzheimers Disease Neuroimaging Initiative (ADNI). To characterise APOE {varepsilon}4 molecular effects, we analysed proteomic data from plasma, cerebrospinal fluid (CSF), and induced pluripotent stem cell (iPSC)-derived astrocytes and neurons, as well as transcriptomic and proteomic data from multiple brain regions. The association of APOE {varepsilon}4 with AD neuropathology was also examined. APOE {varepsilon}4 carriers shared a plasma proteomic signature enriched for immune processes, irrespective of AD diagnosis. A machine learning classifier trained on this signature discriminated APOE {varepsilon}4 carriers from non-carriers in an independent cohort using CSF proteomics. APOE {varepsilon}4 carriage was associated with higher Braak stages and Consortium to Establish a Registry for Alzheimers Disease (CERAD) score. However, only limited APOE {varepsilon}4-associated transcriptomic and proteomic changes were observed in bulk brain tissue, with poor cross-layer concordance. Proteomic analyses of iPSC-derived astrocytes and neurons further revealed cell-type-specific APOE {varepsilon}4-associated changes. APOE {varepsilon}4 is associated with a consistent proteomic signature across plasma and CSF. Its molecular effects in the brain differ across cell types, brain regions and molecular layers. These findings support the need for cell-type-resolved multi-omic studies to elucidate how APOE {varepsilon}4 confers AD risk.

neuroscience↗