Biology subjects

Takerkart, S.

Publications and source records attributed to Takerkart, S..

4 recordsLinked to original sources

bioRxiv2025

Quantifying the effect of forest edge on tropical fauna using explainable ecoacoustics metrics

Tropical forests are biodiversity hotspots but increasingly fragmented by human activities, multiplying forest edges that alter microclimates and species dynamics. While edge effect on plants is well documented, its impact on fauna remains less ex-plored. Ecoacoustics provides a non-invasive and passive approach to monitor faunal communities, yet the complexity of soundscapes and the opacity of machine learning tools often limit ecological interpretability. Here, we show that acoustic diversity in New Caledonian ultramafic forests exhibits a detectable edge effect, with sound-scapes converging towards homogeneity starting 100 m from the forest edge. Using passive acoustic monitoring and 59 ecoacoustic indices, we combined machine learn-ing, Representational Similarity Analysis, and explainable statistical methods from psychophysics. Results reveal a significant correlation between distance to edge and acoustic diversity, particularly pronounced during day-time. Indices such as NDSI, Hf, and SKEWf contributed most to detecting the effect, whereas some other in-dices added noise rather than informative signal. These findings match the botanical threshold described by Blanchard et al. (2023) and highlight both the promise and limitations of indice-based ecoacoustics. By integrating explicability methods, we offer a transparent framework to disentangle complex ecological signals, advancing ecoacoustics as a pertinent tool for studying forest fragmentation and guiding biodi-versity conservation.

ecology↗

bioRxiv2024

Reconstructing Voice Identity from Noninvasive Auditory Cortex Recordings

The cerebral processing of voice information is known to engage, in human as well as non-human primates, "temporal voice areas" (TVAs) that respond preferentially to conspecific vocalizations. However, how voice information is represented by neuronal populations in these areas, particularly speaker identity information, remains poorly understood. Here, we used a deep neural network (DNN) to generate a high-level, small-dimension representational space for voice identity--the voice latent space (VLS)--and examined its linear relation with cerebral activity via encoding, representational similarity, and decoding analyses. We find that the VLS maps onto fMRI measures of cerebral activity in response to tens of thousands of voice stimuli from hundreds of different speaker identities and better accounts for the representational geometry for speaker identity in the TVAs than in A1. Moreover, the VLS allowed TVA-based reconstructions of voice stimuli that preserved essential aspects of speaker identity as assessed by both machine classifiers and human listeners. These results indicate that the DNN-derived VLS provides high-level representations of voice identity information in the TVAs.

neuroscience↗

bioRxiv2020

Inter-subject pattern analysis for multivariate group analysis of functional neuroimaging. A unifying formalization.

Background and objectiveIn medical imaging, population studies have to overcome the differences that exist between individuals to identify invariant image features that can be used for diagnosis purposes. In functional neuroimaging, an appealing solution to identify neural coding principles that hold at the population level is inter-subject pattern analysis, i.e. to learn a predictive model on data from multiple subjects and evaluate its generalization performance on new subjects. Although it has gained popularity in recent years, its widespread adoption is still hampered by the blatant lack of a formal definition in the literature. In this paper, we precisely introduce the first principled formalization of inter-subject pattern analysis targeted at multivariate group analysis of functional neuroimaging. MethodsWe propose to frame inter-subject pattern analysis as a multi-source transductive transfer question, thus grounding it within several well defined machine learning settings and broadening the spectrum of usable algorithms. We describe two sets of inter-subject brain decoding experiments that use several open datasets: a magnetoencephalography study with 16 subjects and a functional magnetic resonance imaging paradigm with 100 subjects. We assess the relevance of our framework by performing model comparisons, where one brain decoding model exploits our formalization while others do not. ResultsThe first set of experiments demonstrates the superiority of a brain decoder that uses subject-by-subject standardization compared to state of the art models that use other standardization schemes, making the case for the interest of the transductive and the multi-source components of our formalization The second set of experiments quantitatively shows that, even after such transformation, it is more difficult for a brain decoder to generalize to new participants rather than to new data from participants available in the training phase, thus highlighting the transfer gap that needs to be overcome. ConclusionThis paper describes the first formalization of inter-subject pattern analysis as a multi-source transductive transfer learning problem. We demonstrate the added value of this formalization using proof-of-concept experiments on several complementary functional neuroimaging datasets. This work should contribute to popularize inter-subject pattern analysis for functional neuroimaging population studies and pave the road for future methodological innovations.

neuroscience↗

bioRxiv2019

Inter-subject pattern analysis: a straightforward and powerful scheme for group-level MVPA.

Multivariate pattern analysis (MVPA) has become vastly popular for analyzing functional neuroimaging data. At the group level, two main strategies are used in the literature. The standard one is hierarchical, combining the outcomes of within-subject decoding results in a second-level analysis. The alternative one, inter-subject pattern analysis, directly works at the group-level by using, e.g, a leave-one-subject-out cross-validation. This study provides a thorough comparison of these two group-level decoding schemes, using both a large number of artificial datasets where the size of the multivariate effect and the amount of inter-individual variability are parametrically controlled, as well as two real fMRI datasets comprising respectively 15 and 39 subjects. We show that these two strategies uncover distinct significant regions with partial overlap, and that inter-subject pattern analysis is able to detect smaller effects and to facilitate the interpretation. The core source code and data are openly available, allowing to fully reproduce most of these results.

neuroscience↗