{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T13:55:45Z","timestamp":1785765345094,"version":"3.56.0"},"reference-count":35,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T00:00:00Z","timestamp":1742256000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Diagnostics"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/diagnostics15060764","type":"journal-article","created":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T11:02:31Z","timestamp":1742295751000},"page":"764","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Electroencephalography-Based Neuroinflammation Diagnosis and Its Role in Learning Disabilities"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8382-8417","authenticated-orcid":false,"given":"G\u00fcnet","family":"Ero\u011flu","sequence":"first","affiliation":[{"name":"Computer Engineering Department, Engineering and Nature Faculty, Bah\u00e7e\u015fehir University, 34349 Istanbul, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Al-Barhamtoshy, H.M., and Motaweh, D.M. (2017, January 21\u201323). Diagnosis of Dyslexia using computational analysis. Proceedings of the IEEE Conference on Informatics, Health & Technology 2017, Riyadh, Saudi Arabia.","DOI":"10.1109\/ICIHT.2017.7899141"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1038\/nm.3160","article-title":"Fetomaternal immune cross-talk and its consequences for maternal and offspring\u2019s health","volume":"19","author":"Arck","year":"2013","journal-title":"Nat. Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1016\/j.coph.2010.04.001","article-title":"Vitamin D: Modulator of the immune system","volume":"10","author":"Baeke","year":"2010","journal-title":"Curr. Opin. Pharmacol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.biopsych.2010.05.028","article-title":"Early life programming and neurodevelopmental disorders","volume":"68","author":"Bale","year":"2010","journal-title":"Biol. Psychiatry"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/S1388-2457(02)00362-0","article-title":"A review of electrophysiology in attention-deficit\/hyperactivity disorder: I. Qualitative and quantitative electroencephalography","volume":"114","author":"Barry","year":"2003","journal-title":"Clin. Neurophysiol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Comer, A.L., Carrier, M., Tremblay, M.\u00c8., and Cruz-Mart\u00edn, A. (2020). The inflamed brain in schizophrenia: The convergence of genetic and environmental risk factors that lead to uncontrolled neuroinflammation. Front. Cell. Neurosci., 14.","DOI":"10.3389\/fncel.2020.00274"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1126\/science.aag3194","article-title":"Maternal immune activation: Implications for neuropsychiatric disorders","volume":"353","author":"Estes","year":"2016","journal-title":"Science"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1038\/nri.2015.5","article-title":"The role of inflammation in depression: From evolutionary imperative to modern treatment target","volume":"16","author":"Miller","year":"2016","journal-title":"Nat. Rev. Immunol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ramirez-Celis, A., and Van de Water, J. (2023). Maternal immune dysregulation and autism spectrum disorder. Neural Engineering Techniques for Autism Spectrum Disorder, Academic Press.","DOI":"10.1016\/B978-0-12-824421-0.00010-2"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1548","DOI":"10.1038\/s41390-024-03454-3","article-title":"Role of perinatal inflammation in neurodevelopmental impairment of small-for-gestational age and extremely preterm infants","volume":"96","author":"Darmstadt","year":"2024","journal-title":"Pediatr. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1176\/appi.ajp.2009.09030361","article-title":"Prenatal infection and schizophrenia: A review of epidemiologic and translational studies","volume":"167","author":"Brown","year":"2010","journal-title":"Am. J. Psychiatry"},{"key":"ref_12","first-page":"2622","article-title":"Vitamin D effects on brain development and function: A review","volume":"23","author":"Eyles","year":"2013","journal-title":"Cereb. Cortex"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2341","DOI":"10.1002\/jbmr.463","article-title":"Vitamin D supplementation during pregnancy: Double-blind, randomized clinical trial of safety and effectiveness","volume":"26","author":"Hollis","year":"2011","journal-title":"J. Bone Miner. Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.clnesp.2024.05.011","article-title":"Vitamins, Minerals and Their Maternal Levels\u2019 Role in Brain Development: An Updated Literature-Review","volume":"63","author":"Hosseini","year":"2024","journal-title":"Clin. Nutr. ESPEN"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"116633","DOI":"10.1016\/j.neuroimage.2020.116633","article-title":"The emergence of dyslexia in the developing brain","volume":"211","author":"Kuhl","year":"2020","journal-title":"Neuroimage"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1038\/nrn2774","article-title":"Abnormal neural power and synchrony in schizophrenia","volume":"11","author":"Uhlhaas","year":"2010","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1002\/ana.20315","article-title":"Neuroglial activation and neuroinflammation in autism","volume":"57","author":"Vargas","year":"2005","journal-title":"Ann. Neurol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Grazioli, S., and Pugin, J. (2018). Mitochondrial damage-associated molecular patterns: From inflammatory signaling to human diseases. Front. Immunol., 9.","DOI":"10.3389\/fimmu.2018.00832"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1002\/j.1556-6676.1986.tb01195.x","article-title":"Wide Range Achievement Test: 1984 revised edition","volume":"64","author":"Reid","year":"1986","journal-title":"J. Couns. Dev."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Rutledge, K.J., and Edeh, O. (2023). Epigenetics in Neurodevelopmental and Neurodegenerative Disorders. Applied Epigenetics for Mental Health Professionals, American Psychiatric Association Publishing.","DOI":"10.1176\/appi.books.9781615379828"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"36879","DOI":"10.1109\/ACCESS.2021.3062709","article-title":"Advance machine learning methods for dyslexia biomarker detection: A review of implementation details and challenges","volume":"9","author":"Usman","year":"2021","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"26R","DOI":"10.1203\/PDR.0b013e318212c196","article-title":"Schizophrenia and autism: Both shared and disorder-specific pathogenesis via perinatal inflammation?","volume":"69","author":"Meyer","year":"2011","journal-title":"Pediatr. Res."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Jankovi\u0107, M.M. (2022, January 25\u201326). Biomarker-based approaches for dyslexia screening: A review. Proceedings of the 2022 IEEE Zooming Innovation in Consumer Technologies Conference (ZINC), Novi Sad, Serbia.","DOI":"10.1109\/ZINC55034.2022.9840532"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Bosch-Bayard, J., Peluso, V., Galan, L., Valdes Sosa, P., and Chiarenza, G.A. (2018). Clinical and electrophysiological differences between subjects with dysphonetic dyslexia and non-specific reading delay. Brain Sci., 8.","DOI":"10.3390\/brainsci8090172"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/S0165-0173(98)00056-3","article-title":"EEG alpha and theta power reflect cognitive and memory performance: A review and analysis","volume":"29","author":"Klimesch","year":"1999","journal-title":"Brain Res. Rev."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"345","DOI":"10.31887\/DCNS.2012.14.4\/gbuzsaki","article-title":"Brain rhythms and neural syntax: Implications for efficient coding of cognitive content and neuropsychiatric disease","volume":"14","author":"Watson","year":"2012","journal-title":"Dialogues Clin. Neurosci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.1016\/j.clinph.2010.05.032","article-title":"EEG analysis in attention-deficit\/hyperactivity disorder: A comparative study","volume":"122","author":"Clarke","year":"2011","journal-title":"Clin. Neurophysiol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1146\/annurev-clinpsy-050718-095530","article-title":"Epigenetics, development, and psychopathology","volume":"16","author":"Meaney","year":"2020","journal-title":"Annu. Rev. Clin. Psychol."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Karim, I., Abdul, W., and Kamaruddin, N. (2013, January 26\u201327). Classification of dyslexic and normal children during resting condition using KDE and MLP. Proceedings of the 2013 5th International Conference on Information and Communication Technology for the Muslim World (ICT4M), Rabat, Morocco.","DOI":"10.1109\/ICT4M.2013.6518886"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Benfatto, M., \u00d6qvist Seimyr, G., Ygge, J., Pansell, T., Rydberg, A., and Jacobson, C. (2016). Screening for dyslexia using eye tracking during reading. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0165508"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2995","DOI":"10.3233\/BME-141120","article-title":"Automatic classification of dyslexic children by applying machine learning to fMRI images","volume":"24","author":"Chimeno","year":"2014","journal-title":"Bio-Med. Mater. Eng."},{"key":"ref_32","first-page":"97","article-title":"Machine learning and Dyslexia: Diagnostic and classification system (DCS) for kids with learning disabilities","volume":"7","author":"Khan","year":"2018","journal-title":"Int. J. Eng. Technol."},{"key":"ref_33","unstructured":"Spoon, K., Crandall, D., and Siek, K. (2019, January 15). Towards Detecting Dyslexia in children\u2019s handwriting using neural networks. Proceedings of the International Conference on Machine Learning AI for Social Good Workshop, Long Beach, CA, USA."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1080\/21622965.2021.1908897","article-title":"A mobile app that uses neurofeedback and multi-sensory learning methods improves reading abilities in dyslexia: A pilot study","volume":"11","author":"Teber","year":"2022","journal-title":"Appl. Neuropsychol. Child"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1080\/13682820600806680","article-title":"Subtyping of children with developmental dyslexia via bootstrap aggregated clustering and the gap statistic: Comparison with the double-deficit hypothesis","volume":"42","author":"King","year":"2007","journal-title":"Int. J. Lang. Commun. Disord."}],"updated-by":[{"DOI":"10.3390\/diagnostics15141772","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T00:00:00Z","timestamp":1742256000000}}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/6\/764\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T14:49:34Z","timestamp":1754232574000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/6\/764"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,18]]},"references-count":35,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["diagnostics15060764"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics15060764","relation":{"correction":[{"id-type":"doi","id":"10.3390\/diagnostics15141772","asserted-by":"object"}]},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,18]]}}}