Search

Summary Regularized generalized canonical correlation analysis (RGCCA) is a general multiblock data analysis framework that encompasses several important multivariate analysis methods such as principal component analysis, partial least squares regression, and several versions of generalized canonical correlation analysis. In this article, we extend RGCCA to the case where at least one block has a tensor structure. This method is called multiway generalized canonical correlation analysis (MGCCA). Convergence properties of the MGCCA algorithm are studied, and computation of higher-level components are discussed. The usefulness of MGCCA is shown on simulation and on the analysis of a cognitive study in human infants using electroencephalography (EEG).

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.

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.

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.

International audience; Creating invariant representations from an everchanging 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 the visual domain, an efficient neural solution to overcome variability consists in factorizing the input into a reduced set of orthogonal components. Here, we asked whether a similar decomposition strategy is used in early speech perception. Using a 256-channel electroencephalographic system, we recorded the neural responses of 3-mo-old infants to 120 natural consonant–vowel syllables with varying acoustic and phonetic profiles. Using multivariate pattern analyses, we show that syllables are factorized into distinct and orthogonal neural codes for consonants and vowels. Concerning consonants, we further demonstrate the existence of two stages of processing. A first phase is characterized by orthogonal and context-invariant neural codes for the dimensions of manner and place of articulation. Within the second stage, manner and place codes are integrated to recover the identity of the phoneme. We conclude that, despite the paucity of articulatory motor plans and speech production skills, pre-babbling infants are already equipped with a structured combinatorial code for speech analysis, which might account for the rapid pace of language acquisition during the first year.

Cerebellar plasticity is a critical mechanism for optimal feedback control. While Purkinje cell activity of the oculomotor vermis predicts eye movement speed and direction, more lateral areas of the cerebellum may play a role in more complex tasks, including decision-making. It is still under question how this motor-cognitive functional dichotomy between medial and lateral areas of the cerebellum plays a role in optimal feedback control. Here we show that elite athletes subjected to a trajectory prediction, go/no-go task manifest superior subsecond trajectory prediction accompanied by optimal eye movements and changes in cognitive load dynamics. Moreover, while interacting with the cerebral cortex, both the medial and lateral cerebellar networks are prominently activated during the fast feedback stage of the task, regardless of whether or not a motor response was required for the correct response. Our results show that cortico-cerebellar interactions are widespread during dynamic feedback and that experience can result in superior task-specific decision skills.

International audience; Infant electroencephalography (EEG) presents several challenges compared with adult data: recordings are typically short and heavily contaminated by motion artifacts, and the signal changes throughout development. Traditional data preprocessing pipelines, developed mainly for event-related potential analyses, require manual steps. However, larger datasets make this strategy infeasible. Moreover, new analytical approaches may have different preprocessing requirements. We propose an Automated Pipeline for Infants Continuous EEG (APICE). APICE is fully automated, flexible, and modular. The use of multiple algorithms and adaptive thresholds for artifact detection makes it suitable across age groups and testing procedures. Furthermore, the preprocessing is performed on continuous data, enabling better data recovery and flexibility (i.e., the same preprocessing is usable for different analyzes). Here we describe APICE and validate its performance in terms of data quality and data recovery using two very different infant datasets. Specifically, (1) we show how APICE performs when varying its artifacts rejection sensitivity; (2) we test the effect of different data cleaning methods such as the correction of transient artifacts, Independent Component Analysis, and Denoising Source Separation; and (3) we compare APICE with other available pipelines. APICE uses EEGLAB and compatible custom functions. It is freely available at https://github.com/neurokidslab/eeg_preprocessing, together with example scripts.