Biology subjects

Huth, A.

Publications and source records attributed to Huth, A..

7 recordsLinked to original sources

bioRxiv2026

The Premotor Language Area Encodes a Full Acoustic-to-semantic Speech Hierarchy

Classic neurobiological models of human speech and language have emphasized the dominant role of temporal lobe in speech perception, while premotor regions including the ventral premotor cortex (PMv) are situated at the level of articulatory processing. However, accumulating evidence from neuroimaging, clinical, and computational studies suggests that premotor cortex may contribute to speech processing beyond articulation. The precise extent and functional organization of these speech-related representations, however, remain unclear. In this study, we combined naturalistic speech perception with computational encoding models to characterize the organization of speech representations within PMv. We functionally localized a cortical region that encompasses previously described premotor speech areas, which we term the premotor language area (PML). Using acoustic, phonemic, semantic, and deep neural speech representations, we found that PML contains representations spanning the full speech-processing hierarchy, from low-level acoustic features to high-level semantic information. These representations are arranged along a smooth posterior-anterior gradient, with increasingly abstract speech representations emerging toward anterior PML. Moreover, this organizational gradient mirrors the canonical speech processing hierarchy in the temporal auditory regions. These findings challenge the traditional view of premotor cortex as primarily an acoustic-articulatory unit, and instead identify PML as a hierarchically organized speech-processing region that parallels the temporal auditory cortex. This provides a new framework for understanding the role of premotor cortex in speech perception.

neuroscience↗

bioRxiv2026

Repetitive anatomical patterns for thalamocortical projections of higher-order thalamic nuclei

Cortico-thalamo-cortical circuits entail extensive trans-thalamic connectivity between cortical areas, yet their structural organization and function remain poorly understood. Here, the thalamocortical projections of several higher-order thalamic nuclei were characterized by retrograde tracing from two cortical areas, the primary somatosensory (S1) and motor (M1) cortices. Cholera toxin B conjugated with different fluorophores allowed for simultaneous detection of projection neurons targeting S1 and M1. A cell detection pipeline based on neural networks was developed to allow semi-automated analysis of large thalamic imaging volumes to quantitatively infer the spatial distribution of projection neurons in the posterior complex (PO) and the adjacent ethmoid nucleus (Eth), nucleus centrolateralis (CL), nucleus paracentralis (PCN), and the nucleus parafascicularis (PF). The arrangement of neurons projecting to both, primary somatosensory and motor cortices, occurs at different connection strengths and was topographically organized in all nuclei studied. Co-injections into both cortical areas revealed projection neurons with axons branching into both S1 and M1 cortices. Our work introduces a pipeline for semi-automated quantitative analysis of thalamic projection patterns that could be useful for connectivity analyses in general. This approach revealed repetitive anatomical patterns in different thalamic nuclei with regard to projection strength, spatial organization and fraction of projection neurons targeting two cortical areas simultaneously.

neuroscience↗

bioRxiv2026

Loss of competitive strength in European conifer species under climate change

Climate change is expected to alter species assemblages by affecting the outcome of competition between species. Investigating processes of competition remains challenging particularly in tree communities, as they unfold over extensive spatio-temporal scales. Here, we developed a deep-learning approach to leverage a novel database of 135 million simulated local-scale tree responses to climate across continental Europe to investigate changes in the competitiveness of nine major tree species under different scenarios of climate change. Specifically, we trained a Deep Neural Network on local process model projections to investigate climate change effects on indicators of competitive strength and species dominance. We found decreasing competitive strength for all investigated evergreen coniferous species across their distribution, while major deciduous broadleaved species such as Quercus robur and Fagus sylvatica increased in competitiveness. Changes in tree species competition with climate differed locally, but most investigated species lost competitive strength at their warm range edges. As a consequence of these changes, up to 19% of Europes forests could experience a change in the dominant tree species until the end of the 21st century. Our results suggest a profound climate-induced reassembly of Europes forests and identify areas that may require specific attention in forest policy and management.

ecology↗

bioRxiv2024

Geometric and demographic effects explain contrasting fragmentation-biodiversity relationships across scales

There is consensus that habitat loss is a major driver of biodiversity loss, while the effects of fragmentation, given a constant total habitat amount, are still debated. Here, we use a process-based metacommunity model to show how strongly scale- and context-dependent fragmentation-biodiversity relationships can emerge from the interplay of two types of fragmentation effects - geometric and demographic. Geometric effects arise from the spatial distribution of species and landscape modification, whereas demographic effects reflect long-term changes in species demographic rates following landscape modification. We introduce a novel approach to partitioning these two types of effects and assess how key ecological processes and factors, such as dispersal, habitat heterogeneity, and edge effects, influence geometric, demographic, and net fragmentation effects across spatial scales. We conclude that the framework of geometric and demographic effects can reconcile previous apparently conflicting results and hopefully unlock and advance the debate on biodiversity change in modified landscapes.

ecology↗

bioRxiv2023

Latitudinal scaling of aggregation with abundance and its consequences for coexistence in species rich forests

The search for simple principles underlying the complex spatial structure and dynamics of plant communities is a long-standing challenge in ecology1-6. In particular, the relationship between the spatial distribution of plants and species coexistence is challenging to resolve in species-rich communities7-9. Analysing the spatial patterns of tree species in 21 large forest plots, we find that rare species tend to be more spatially aggregated than common species, and a latitudinal gradient in the strength of this negative correlations that increases from tropical to temperate forests. Our analysis suggests that latitudinal gradients in animal seed dispersal10 and mycorrhizal associations11,12,13 may jointly generate this intriguing pattern. To assess the consequences of negative aggregation-abundance correlations for species coexistence, we present here a framework to incorporate the observed spatial patterns into population models8 along with an analytical solution for the local extinction risk14 of species invading from low abundances in dependence of spatial structure, demographic parameters, and immigration. For example, the stabilizing effect of the observed spatial patterns reduced the local extinction risk of species when rare almost by a factor of two. Our approach opens up new avenues for integrating observed spatial patterns into mathematical theory, and our findings demonstrate that spatial patterns, such as species aggregation and segregation, can contribute substantially to coexistence in species-rich communities. This underscores the need to understand the interactions between multiple ecological processes and spatial patterns in greater detail.

ecology↗

bioRxiv2018

Antigen-specific T-cell receptor signatures of cytomegalovirus infection

Cytomegalovirus (CMV) is a prevalent human pathogen. The virus cannot be eliminated from the body, but is kept in check by CMV-specific T cells. Patients with an insufficient T-cell response, such as transplant recipients, are at high risk of developing CMV disease. However, the CMV-specific T-cell repertoire is complex, and is not yet clear which T cells protect best against virus reactivation and disease. Here we present a highly resolved characterization of CMV-specific CD8+ T cells based on enrichment by specific peptide stimulation and mRNA sequencing of their T-cell receptor {beta} chains (TCR{beta}). Our analysis included recently identified T-cell epitopes restricted through HLA-C, whose presentation is resistant to viral immunomodulation, and well-studied HLA-B-restricted epitopes. In 8 healthy virus carriers, we identified a total of 1052 CMV-specific TCR{beta} chains. HLA-C-restricted, CMV-specific TCR{beta} clonotypes the ex vivo T-cell response, and contributed the highest-frequency clonotype of the entire repertoire in 2 of 8 donors. We analyzed sharing and similarity of CMV-specific TCR{beta} sequences and identified 63 public or related sequences belonging to 17 public TCR{beta} families. In our cohort and in an independent cohort of 352 donors, the cumulative frequency of these public TCR{beta} family members was a highly discriminatory indicator of carrying both CMV infection and the relevant HLA type. Based on these findings, we propose CMV-specific TCR{beta} signatures as a biomarker for an antiviral T-cell response to identify patients in need of treatment and to guide future development of immunotherapy.

immunology↗

bioRxiv2018

Incorporating Context into Language Encoding Models for fMRI

Language encoding models help explain language processing in the human brain by learning functions that predict brain responses from the language stimuli that elicited them. Current word embedding-based approaches treat each stimulus word independently and thus ignore the influence of context on language understanding. In this work, we instead build encoding models using rich contextual representations derived from an LSTM language model. Our models show a significant improvement in encoding performance relative to state-of-the-art embeddings in nearly every brain area. By varying the amount of context used in the models and providing the models with distorted context, we show that this improvement is due to a combination of better word embeddings learned by the LSTM language model and contextual information. We are also able to use our models to map context sensitivity across the cortex. These results suggest that LSTM language models learn high-level representations that are related to representations in the human brain.

neuroscience↗