Author

Milene L. Bonte

Maastricht UniversityORCID 0000-0002-0203-8913Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Signature Assays, Organisms & Methods

Click to filter · or open tag

Assay:electronic health records· 3 Organism:human· 3 Approach:statistical model· 3 Research Focus:cell type annotation· 1 Research Focus:GWAS / association mapping· 1 Assay:GWAS summary statistics· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Alessandro GialluisiiD· 1 shared Alexandra K. EmmendorferiD· 1 shared Andrew P. MorrisiD· 1 shared Anniek VaesseniD· 1 shared Anthony P. MonacoiD· 1 shared Arndt Wilcke· 1 shared Beaté St PourcainiD· 1 shared Bernadette M. JansmaiD· 1 shared

Activity by Year

2019-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

1 shared paperGenomics

Alessandro GialluisiORCID

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

EEG reveals online monitoring mechanisms of speech production

João Correia, Jill Kries, Lars Gutschalk Hausfeld, Vincent L. Gracco, Milene L. Bonte

The paper uses EEG to study neural mechanisms of speech production by decoding auditory speech output under different masking conditions, finding consistent decoding in instantaneous and feedback lags.

bioRxiv · Mar 1, 2025 · PreprintNeuroscience

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

Alexandra K. Emmendorfer, Milene L. Bonte, Bernadette M. Jansma, Sonja A. Kotz

The paper examines ERP responses to self- and externally generated speech with varying phonotactic probability and syllable stress, finding unexpected N1 amplitude effects and P2 amplitude effects related to stress.

bioRxiv · May 4, 2021 · PreprintNeuroscience

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

Zahra Rezvani, Marzieh Zare, Gojko Žarić, Milene L. Bonte, Jurgen Tijms, Maurits W. van der Molen, Gorka Fraga González

Local graph measures from resting-state EEG separated dyslexic from typically reading grade-3 children with 95% accuracy using a polynomial-kernel support vector machine.

bioRxiv · Mar 13, 2019 · PreprintNeuroscience

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

Alessandro Gialluisi, Till F. M. Andlauer, Nazanin Mirza‐Schreiber, Kristina Moll, Jessica Becker, Per Hoffmann, Kerstin U. Ludwig, Darina Czamara, Beaté St Pourcain, William M. Brandler, Ferenc Honbolygó, Dénes Tóth, Valéria Csépe, Guillaume Huguet, Andrew P. Morris, , Gerd Schulte‐Körne

The paper conducts a GWAS to identify genetic variants associated with a cognitive predictor of dyslexia, finding significant associations with variants near MIR924HG and NKAIN3.

Translational Psychiatry · Feb 11, 2019Peer-reviewedGenomics