Biology subjects

Bonte, M.

Publications and source records attributed to Bonte, M..

4 recordsLinked to original sources

bioRxiv2025

EEG reveals online monitoring mechanisms of speech production

Speaking involves the orchestration of multiple speech muscles while actively monitoring sensory consequences through auditory and somatosensory feedback. A mistuned sensorimotor mechanism may disrupt the normal integration of motor and auditory brain systems in several developmental and acquired motor speech disorders, including stuttering, speech apraxia, speech sound disorders and dysarthria. Electroencephalography (EEG) provides a non-invasive measure of online neural activity with potential to assess (deficiencies in) sensorimotor integration during speech production. However, the relation between EEG and continuous speechoutput remains poorly characterized. Here, we investigate prediction of auditory speech output using multivariate EEG patterns under three levels of auditory masking. A decoding analysis was employed in combination with a lag-based approach that allowed studying predictions based on instantaneous EEG-speech relations, and their involvement in feedforward and feedback processes. For all masking conditions, we found consistent decoding in instantaneous lags and speech feedback lags, but not in feedforward lags. Furthermore, the level of auditory masking modulated decoding in both the instantaneous and feedback lags. Our results provide insights of neural monitoring during online speech production and offer a window to further study the dysfunction latent in motor speech disorders that may help in optimizing brain-informed therapies for speech fluency.

neuroscience↗

bioRxiv2021

Phonological and temporal regularities lead to differential ERP effects in self- and externally generated speech

Some theories of predictive processing propose reduced sensory and neural responses to anticipated events. Support comes from M/EEG studies, showing reduced auditory N1 and P2 responses to self-compared to externally generated events, or when stimulus properties are more predictable (e.g. prototypical). The current study examined the sensitivity of N1 and P2 responses to statistical regularities of speech. We employed a motor-to-auditory paradigm comparing ERP responses to externally and self-generated pseudowords, varying in phonotactic probability and syllable stress. We expected to see N1 and P2 suppression for self-generated stimuli, with greater suppression effect for more predictable features such as high phonotactic probability and first syllable stress in pseudowords. We observe an interaction between phonotactic probability and condition on the N1 amplitude, with an enhanced effect of phonotactic probability in processing self-generated stimuli. However, the directionality of this effect was reversed compared to what was expected, namely a larger N1 amplitude for high probability items, possibly indicating a perceptual bias toward the more predictable item. We further observed an effect of syllable stress on the P2 amplitude, with greater amplitudes in response to first syllable stress items. The current results suggest that phonotactic probability plays an important role in processing self-generated speech, supporting feedforward models involved in speech production.

neuroscience↗

bioRxiv2019

Machine learning Classification of Dyslexic Children based on EEG Local Network Features

Machine learning can be used to find meaningful patterns characterizing individual differences. Deploying a machine learning classifier fed by local features derived from graph analysis of electroencephalographic (EEG) data, we aimed at designing a neurobiologically-based classifier to differentiate two groups of children, one group with and the other group without dyslexia, in a robust way. We used EEG resting-state data of 29 dyslexics and 15 typical readers in grade 3, and calculated weighted connectivity matrices for multiple frequency bands using the phase lag index (PLI). From the connectivity matrices, we derived weighted connectivity graphs. A number of local network measures were computed from those graphs, and 37 False Discovery Rate (FDR) corrected features were selected as input to a Support Vector Machine (SVM) and a common K Nearest Neighbors (KNN) classifier. Cross validation was employed to assess the machine-learning performance and random shuffling to assure the performance appropriateness of the classifier and avoid features overfitting. The best performance was for the SVM using a polynomial kernel. Children were classified with 95% accuracy based on local network features from different frequency bands. The automatic classification techniques applied to EEG graph measures showed to be both robust and reliable in distinguishing between typical and dyslexic readers.

neuroscience↗

bioRxiv2018

Genome Wide Association Scan identifies new variants associated with a cognitive predictor of dyslexia.

Developmental dyslexia (DD) is one of the most prevalent learning disorders among children and is characterized by deficits in different cognitive skills, including reading, spelling, short term memory and others. To help unravel the genetic basis of these skills, we conducted a Genome Wide Association Study (GWAS), including nine cohorts of reading-impaired and typically developing children of European ancestry, recruited across different countries (N=2,562-3,468).\n\nWe observed a genome-wide significant effect (p<1x10-8) on rapid automatized naming of letters (RANlet) for variants on 18q12.2 within MIR924HG (micro-RNA 924 host gene; p = 4.73x10-9), and a suggestive association on 8q12.3 within NKAIN3 (encoding a cation transporter; p = 2.25 x10-8). RAN represents one of the best universal predictors of reading fluency across orthographies and linkage to RAN has been previously reported within CELF4 (18q12.2), a gene highly expressed in the fetal brain which is co-expressed with NKAIN3 and predicted to be a target of MIR924. These findings suggest new candidate DD susceptibility genes and provide insights into the genetics and neurobiology of dyslexia.

genomics↗