TY - JOUR
T1 - Phonological ability and neural congruency
T2 - Phonological loop or more?
AU - Christoforou, Christoforos
AU - Theodorou, Maria
AU - Fella, Argyro
AU - Papadopoulos, Timothy C.
N1 - Publisher Copyright:
© 2023 International Federation of Clinical Neurophysiology
PY - 2023/12
Y1 - 2023/12
N2 - Objective: We explored neural components in Electroencephalography (EEG) signals during a phonological processing task to assess (a) the neural origins of Baddeley's working-memory components contributing to phonological processing, (b) the unitary structure of phonological processing and (c) the neural differences between children with dyslexia (DYS) and controls (CAC). Methods: EEG data were collected from sixty children (half with dyslexia) while performing the initial- and final- phoneme elision task. We explored a novel machine-learning-based approach to identify the neural components in EEG elicited in response to the two conditions and capture differences between DYS and CAC. Results: Our method identifies two sets of phoneme-related neural congruency components capturing neural activations distinguishing DYS and CAC across conditions. Conclusions: Neural congruency components capture the underlying neural mechanisms that drive the relationship between phonological deficits and dyslexia and provide insights into the phonological loop and visual-sketchpad dimensions in Baddeley's model at the neural level. They also confirm the unitary structure of phonological awareness with EEG data. Significance: Our findings provide novel insights into the neural origins of the phonological processing differences in children with dyslexia, the unitary structure of phonological awareness, and further verify Baddeley's model as a theoretical framework for phonological processing and dyslexia.
AB - Objective: We explored neural components in Electroencephalography (EEG) signals during a phonological processing task to assess (a) the neural origins of Baddeley's working-memory components contributing to phonological processing, (b) the unitary structure of phonological processing and (c) the neural differences between children with dyslexia (DYS) and controls (CAC). Methods: EEG data were collected from sixty children (half with dyslexia) while performing the initial- and final- phoneme elision task. We explored a novel machine-learning-based approach to identify the neural components in EEG elicited in response to the two conditions and capture differences between DYS and CAC. Results: Our method identifies two sets of phoneme-related neural congruency components capturing neural activations distinguishing DYS and CAC across conditions. Conclusions: Neural congruency components capture the underlying neural mechanisms that drive the relationship between phonological deficits and dyslexia and provide insights into the phonological loop and visual-sketchpad dimensions in Baddeley's model at the neural level. They also confirm the unitary structure of phonological awareness with EEG data. Significance: Our findings provide novel insights into the neural origins of the phonological processing differences in children with dyslexia, the unitary structure of phonological awareness, and further verify Baddeley's model as a theoretical framework for phonological processing and dyslexia.
KW - Baddeley's working memory model
KW - EEG
KW - Machine Learning
KW - Neural congruency
KW - Phoneme Elision
KW - Phonological awareness
KW - Phonological Awareness Unitary structure
UR - http://www.scopus.com/inward/record.url?scp=85178305942&partnerID=8YFLogxK
U2 - 10.1016/j.clinph.2023.10.015
DO - 10.1016/j.clinph.2023.10.015
M3 - Article
C2 - 37988851
AN - SCOPUS:85178305942
SN - 1388-2457
VL - 156
SP - 228
EP - 241
JO - Clinical Neurophysiology
JF - Clinical Neurophysiology
ER -