Electroencephalography-Based Neuroinflammation Diagnosis and Its Role in Learning Disabilities.

Abstract

Background/Objectives: Learning disabilities (LDs) are complex neurodevelopmental conditions influenced by genetic, epigenetic, and environmental factors. Recent research suggests that maternal autoimmune conditions, perinatal stress, and vitamin D deficiency may contribute to neuroinflammation, which, in turn, can disrupt brain development. Chronic neuroinflammation, driven by activated microglia and astrocytes, has been associated with synaptic dysfunction and cognitive impairment, potentially impacting learning and memory processes. This study aims to explore the relationship between neuroinflammation and LDs, emphasizing the role of electroencephalography (EEG) biomarkers in early diagnosis and intervention. Methods: A systematic analysis was conducted to examine the prevalence, core symptoms, and typical age of diagnosis of LDs. EEG biomarkers, particularly theta, gamma, and alpha power, were assessed as indicators of neuroinflammatory states. Additionally, artificial neural networks (ANNs) were employed to classify EEG patterns associated with LDs, evaluating their diagnostic accuracy. Results: Findings indicate that EEG biomarkers can serve as potential indicators of neuroinflammatory patterns in children with LDs. ANNs demonstrated high classification accuracy in distinguishing EEG signatures related to LDs, highlighting their potential as a diagnostic tool. Conclusions: EEG-based biomarkers, combined with machine learning approaches, offer a non-invasive and precise method for detecting neuroinflammatory patterns associated with LDs. This integrative approach advances precision medicine by enabling early diagnosis and targeted interventions for neurodevelopmental disorders. Further research is required to validate these findings and establish standardized diagnostic protocols.