Biology subjects

Gennari, G.

Publications and source records attributed to Gennari, G..

4 recordsLinked to original sources

bioRxiv2022

Probabilistic forward replay of anticipated stimulus sequences in human primary visual cortex and hippocampus

The ability to recognize and predict future spatiotemporal sequences is vital for perception. It has been proposed that the brain makes intelligent guesses about future inputs by forward replaying these events. However, it is unknown whether and how this mechanism incorporates the probabilistic structure that is inherent to naturalistic environments. Here we tested forward replay in human V1 and hippocampus using a probabilistic cueing paradigm. Participants were exposed to two visual moving dot sequences (A and B) that shared the same starting point. Each stimulus sequence was paired with either a high or a low tone that predicted which sequence would follow with 80% cue validity (probabilistic context) or 50% cue validity (random context). We found that after exposure, the auditory cue together with the starting point triggered simultaneous forward replay of both the likely (A) and the less likely (B) stimulus sequence. Crucially, forward replay preserved the probabilistic relationship of the environment, such that the likely sequence was associated with greater anticipatory V1 activity compared to the less likely stimulus sequence. Analogous to V1, forward replay in hippocampus was also found to preserve the probabilistic cue-sequence relationship. Further, the anterior hippocampus was found to represent the predicted stimulus sequence, irrespective of the input, while the posterior hippocampus revealed a prediction error-like signal that was only observed when predictions were violated. These findings show how mnemonic and sensory areas coordinate predictive representations in probabilistic contexts to improve perceptual processing.

neuroscience↗

bioRxiv2021

Automated Pipeline for Infants Continuous EEG (APICE): a flexible pipeline for developmental studies

Infant electroencephalography (EEG) presents several challenges compared with adult data. Recordings are typically short. Motion artifacts heavily contaminate the data. The EEG neural signal and the artifacts change throughout development. Traditional data preprocessing pipelines have been developed mainly for event-related potentials analyses, and they required manual steps, or use fixed thresholds for rejecting epochs. However, larger datasets make the use of manual steps infeasible, and new analytical approaches may have different preprocessing requirements. Here we propose an Automated Pipeline for Infants Continuous EEG (APICE). APICE is fully automated, flexible, and modular. Artifacts are detected using multiple algorithms and adaptive thresholds, making it suitable to different age groups and testing procedures without redefining parameters. Artifacts detection and correction of transient artifacts is performed on continuous data, allowing for better data recovery and providing flexibility (i.e., the same preprocessing is usable for different analyses). Here we describe APICE and validate it using two infant datasets of different ages tested in different experimental paradigms. We also tested the combination of APICE with common data cleaning methods such as Independent Component Analysis and Denoising Source Separation. APICE uses EEGLAB and compatible custom functions. It is freely available at https://github.com/neurokidslab/eeg_preprocessing, together with example scripts.

neuroscience↗

bioRxiv2021

Orthogonal neural codes for phonetic features in the infant brain

Creating invariant representations from an ever-changing speech signal is a major challenge for the human brain. Such an ability is particularly crucial for preverbal infants who must discover the phonological, lexical and syntactic regularities of an extremely inconsistent signal in order to acquire language. Within visual perception, an efficient neural solution to overcome signal variability consists in factorizing the input into orthogonal and relevant low-dimensional components. In this study we asked whether a similar neural strategy grounded on phonetic features is recruited in speech perception. Using a 256-channel electroencephalographic system, we recorded the neural responses of 3-month-old infants to 120 natural consonant-vowel syllables with varying acoustic and phonetic profiles. To characterize the specificity and granularity of the elicited representations, we employed a hierarchical generalization approach based on multivariate pattern analyses. We identified two stages of processing. At first, the features of manner and place of articulation were decodable as stable and independent dimensions of neural responsivity. Subsequently, phonetic features were integrated into phoneme-identity (i.e. consonant) neural codes. The latter remained distinct from the representation of the vowel, accounting for the different weights attributed to consonants and vowels in lexical and syntactic computations. This study reveals that, despite the paucity of articulatory motor plans and productive skills, the preverbal brain is already equipped with a structured phonetic space which provides a combinatorial code for speech analysis. The early availability of a stable and orthogonal neural code for phonetic features might account for the rapid pace of language acquisition during the first year. SIGNIFICANCE STATEMENTFor adults to comprehend spoken language, and for infants to acquire their native tongue, it is fundamental to perceive speech as a sequence of stable and invariant segments despite its extreme acoustic variability. We show that the brain can achieve such a critical task thanks to a factorized representational system which breaks down the speech input into minimal and orthogonal components: the phonetic features. These elementary representations are robust to signal variability and are flexibly recombined into phoneme-identity percepts in a secondary processing phase. In contradiction with previous accounts questioning the availability of authentic phonetic representations in early infancy, we show that this neural strategy is implemented from the very first stages of language development.

neuroscience↗

bioRxiv2019

Spatio-Temporal Dynamics of Landscape Use by the Bumblebee Bombus atratus (Hymenoptera: Apidae) and its relationship with pollen provisioning.

Understanding how bees use resources at a landscape scale is essential for developing meaningful management plans that sustain populations and the pollination services they provide. Bumblebees are important pollinators for many wild and cultivated plants, and have experienced steep population declines worldwide. Bee foraging behavior can be influenced by resource availability and the bees lifecycle stage. To better understand these relationships, we studied the habitat selection of Bombus atratus by tracking 17 queen bumblebees with radio telemetry in blueberry fields in Entre Rios province, Argentina. To evaluate land use and floral resources used by bumblebees, we tracked bees before and after nest establishment and estimated home ranges using minimum convex polygons and kernel density methods. We also classify the pollen of their body to determine which botanical resources they use from the floral species available. We characterized land use for each bee as the relative proportion of GPS points inside of each land use. Bumblebees differed markedly in their movement behavior in relation to nest establishment. They moved over larger areas and mostly within blueberry fields before to nest establishment, in contrast to after establishing the nest that they preferred the edges near forest plantations and changed the nutritional resources by wild floral species. Our study is the first to track queen bumblebee movements in an agricultural setting and relate movement change across time and space with pollen resource availability. This study provides insight into the way bumblebee queens use different habitat elements at crucial periods in their lifecycle, showing the importance of mass flowering crops like blueberry in the first stages of queens lifecycle and how diversified landscapes help support bee populations as their needs changes during different phases of their lifecycle.

ecology↗