RT Journal Article
T1 A Bayesian framework for phase-amplitude cross-frequency coupling inference: Application to reading disability detection
A1 Castillo-Barnes, Diego
A1 Ortiz-García, Andrés
A1 Figueiredo, Patrícia
A1 Gallego-Molina, Nicolás J.
K1 Estadística bayesiana
K1 Electroencefalografía
K1 Procesado de señales
AB Reading difficulties are often associated with altered brain connectivity, but detecting these differences reliably is challenging. We present a Bayesian phase-amplitude coupling (PAC) framework to measure cross-frequency brain interactions, addressing the limitations of traditional PAC methods in EEG. Unlike standard PAC approaches that may miss complex directional interactions between brain rhythms, our Bayesian model incorporates prior knowledge of significant coupling at each electrode to guide its estimations, yielding a robust measure of neural synchronization both within and across brain regions. We applied this model to EEG recordings from 48 children (15 with reading difficulties, 33 controls) during auditory steady-state stimulation at 4.8, 16, and 40 Hz. The Bayesian approach revealed clear cross-frequency coupling patterns: significant theta–gamma coupling was found in both groups, especially in occipital–parietal regions involved in phonological processing and attention. Importantly, the reading difficulties group showed stronger and more widespread frontoparietal coupling at 16 Hz than the controls, including a prominent connection from electrode CP6 to FC6-suggesting a possible compensatory mechanism or disrupted pathway. No significant coupling was detected at 40 Hz, though near-significant trends hint at a subtle role for gamma oscillations. Finally, using PAC features from our model, a simple classifier distinguished children with and without reading difficulties with balanced accuracies around 75–80 % (significantly above chance), demonstrating the method’s practical efficacy. These results highlight that the Bayesian PAC framework not only uncovers meaningful brain connectivity patterns in noisy EEG data but also serves as a promising tool for identifying biomarkers of reading disabilities and potentially other cognitive conditions.
PB Elsevier
SN 0957-4174
YR 2025
FD 2025-06-12
LK https://hdl.handle.net/10630/39036
UL https://hdl.handle.net/10630/39036
LA eng
NO Castillo-Barnes, D., Ortiz, A., Figueiredo, P., & Gallego-Molina, N. J. (2025). A Bayesian framework for phase-amplitude cross-frequency coupling inference: Application to reading disability detection. Expert Systems with Applications, 291, 128510.
NO Funding for open access charge: Universidad de Málaga / CBUA
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 12 abr 2026