{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T12:56:50Z","timestamp":1790600210184,"version":"4.1.0"},"reference-count":48,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T00:00:00Z","timestamp":1790467200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002848","name":"Agencia Nacional de Investigaci\u00f3n y Desarrollo","doi-asserted-by":"publisher","award":["FONDECYT 3260279"],"award-info":[{"award-number":["FONDECYT 3260279"]}],"id":[{"id":"10.13039\/501100002848","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002848","name":"Agencia Nacional de Investigaci\u00f3n y Desarrollo","doi-asserted-by":"publisher","award":["CIA250006"],"award-info":[{"award-number":["CIA250006"]}],"id":[{"id":"10.13039\/501100002848","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002848","name":"Agencia Nacional de Investigaci\u00f3n y Desarrollo","doi-asserted-by":"publisher","award":["FONDECYT 1231132"],"award-info":[{"award-number":["FONDECYT 1231132"]}],"id":[{"id":"10.13039\/501100002848","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002848","name":"Agencia Nacional de Investigaci\u00f3n y Desarrollo","doi-asserted-by":"publisher","award":["FONDECYT 1241695"],"award-info":[{"award-number":["FONDECYT 1241695"]}],"id":[{"id":"10.13039\/501100002848","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002848","name":"Agencia Nacional de Investigaci\u00f3n y Desarrollo","doi-asserted-by":"publisher","award":["FONDECYT 1260530"],"award-info":[{"award-number":["FONDECYT 1260530"]}],"id":[{"id":"10.13039\/501100002848","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002848","name":"Agencia Nacional de Investigaci\u00f3n y Desarrollo","doi-asserted-by":"publisher","award":["FONDECYT 13240064"],"award-info":[{"award-number":["FONDECYT 13240064"]}],"id":[{"id":"10.13039\/501100002848","id-type":"DOI","asserted-by":"publisher"}]},{"award":["FONDECYT 3260279"],"award-info":[{"award-number":["FONDECYT 3260279"]}],"id":[{"id":"https:\/\/ror.org\/05btm5233","id-type":"ROR","asserted-by":"publisher"}]},{"award":["CIA250006"],"award-info":[{"award-number":["CIA250006"]}],"id":[{"id":"https:\/\/ror.org\/05btm5233","id-type":"ROR","asserted-by":"publisher"}]},{"award":["FONDECYT 1231132"],"award-info":[{"award-number":["FONDECYT 1231132"]}],"id":[{"id":"https:\/\/ror.org\/05btm5233","id-type":"ROR","asserted-by":"publisher"}]},{"award":["FONDECYT 1241695"],"award-info":[{"award-number":["FONDECYT 1241695"]}],"id":[{"id":"https:\/\/ror.org\/05btm5233","id-type":"ROR","asserted-by":"publisher"}]},{"award":["FONDECYT 1260530"],"award-info":[{"award-number":["FONDECYT 1260530"]}],"id":[{"id":"https:\/\/ror.org\/05btm5233","id-type":"ROR","asserted-by":"publisher"}]},{"award":["FONDECYT 13240064"],"award-info":[{"award-number":["FONDECYT 13240064"]}],"id":[{"id":"https:\/\/ror.org\/05btm5233","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Normal aging involves gradual declines in brain function, whereas neurodegenerative disorders show accelerated deterioration. Electroencephalography (EEG) captures alterations in brain oscillatory activity, making it promising for detecting neurodegenerative disorders. However, existing approaches often emphasize classification performance over spectral biomarker identification, limiting clinical translation. Methods: We propose an EEG-based framework integrating explainable machine learning, analyses of inter-subject heterogeneity and temporal stability, and probabilistic modeling to identify reproducible spectral biomarkers of pathological brain aging. Two independent public EEG datasets comprising cognitively normal (CN) individuals and patients with Alzheimer\u2019s disease (AD), Parkinson\u2019s disease (PD), and frontotemporal dementia (FTD) were analyzed. Spectral features from canonical frequency bands were analyzed using SHAP to identify reproducible biomarkers in two datasets. The identified biomarker was subsequently characterized through inter-subject heterogeneity, temporal stability, and empirical distributions to assess disease-related alterations in oscillatory activity. Results: Several spectral features showed consistent disease-related associations across classification tasks. The \u03b8\/\u03b1 ratio emerged as a representative biomarker, showing reduced temporal stability and broader empirical distributions in most neurodegenerative groups than in CN. Conclusions: Reduced temporal stability of reproducible EEG spectral biomarkers may represent an observable signature of disrupted oscillatory brain organization during pathological aging. The proposed framework characterizes a dimension of EEG spectral biomarkers that static summaries discard, offering an interpretable basis on which future longitudinal studies may build diagnostic and monitoring tools.<\/jats:p>","DOI":"10.3390\/diagnostics16193143","type":"journal-article","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T11:35:59Z","timestamp":1790595359000},"page":"3143","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Interpretable Framework for Identifying EEG Spectral Biomarkers of Pathological Brain Aging"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0869-8215","authenticated-orcid":false,"given":"Yunier","family":"Prieur-Coloma","sequence":"first","affiliation":[{"name":"Advanced Center for Electrical and Electronic Engineering, Universidad T\u00e9cnica Federico Santa Mar\u00eda, Valpara\u00edso 2340000, Chile"},{"name":"ValgrAI-Valencian Graduate School and Research Network for Artificial Intelligence, 46022 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1324-6400","authenticated-orcid":false,"given":"Pavel","family":"Prado","sequence":"additional","affiliation":[{"name":"Facultad de Ciencias de la Rehabilitaci\u00f3n y Calidad de Vida, Universidad San Sebasti\u00e1n, Santiago de Chile 8420524, Chile"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9822-1092","authenticated-orcid":false,"given":"Wael","family":"El-Deredy","sequence":"additional","affiliation":[{"name":"ValgrAI-Valencian Graduate School and Research Network for Artificial Intelligence, 46022 Valencia, Spain"},{"name":"Escuela de Ingenier\u00eda Civil Biom\u00e9dica, Universidad de Valpara\u00edso, Valpara\u00edso 2340000, Chile"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1486-7003","authenticated-orcid":false,"given":"Alejandro","family":"Weinstein","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Universidad T\u00e9cnica Federico Santa Mar\u00eda, Valpara\u00edso 2340000, Chile"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1038\/s42003-024-05927-x","article-title":"Linking structural and functional changes during aging using multilayer brain network analysis","volume":"7","author":"Jauny","year":"2024","journal-title":"Commun. Biol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lee, J., and Kim, H.J. (2022). Normal Aging Induces Changes in the Brain and Neurodegeneration Progress: Review of the Structural, Biochemical, Metabolic, Cellular, and Molecular Changes. Front. Aging Neurosci., 14.","DOI":"10.3389\/fnagi.2022.931536"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hausman, H.K., O\u2019Shea, A., Kraft, J.N., Boutzoukas, E.M., Evangelista, N.D., Van Etten, E.J., Bharadwaj, P.K., Smith, S.G., Porges, E., and Hishaw, G.A. (2020). The Role of Resting-State Network Functional Connectivity in Cognitive Aging. Front. Aging Neurosci., 12.","DOI":"10.3389\/fnagi.2020.00177"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"797","DOI":"10.37349\/emed.2024.00256","article-title":"Functional connectivity and cognitive decline: A review of rs-fMRI, EEG, MEG, and graph theory approaches in aging and dementia","volume":"5","author":"Ranasinghe","year":"2024","journal-title":"Explor. Med."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Park, J., Ho, R.L.M., Wang, W.E., Chiu, S.Y., Shin, Y.S., and Coombes, S.A. (2025). Age-related changes in neural oscillations vary as a function of brain region and frequency band. Front. Aging Neurosci., 17.","DOI":"10.3389\/fnagi.2025.1488811"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Schulz, M., Mayer, C., Schlemm, E., Frey, B.M., Malherbe, C., Petersen, M., Gallinat, J., K\u00fchn, S., Fiehler, J., and Hanning, U. (2022). Association of Age and Structural Brain Changes with Functional Connectivity and Executive Function in a Middle-Aged to Older Population-Based Cohort. Front. Aging Neurosci., 14.","DOI":"10.3389\/fnagi.2022.782738"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3800","DOI":"10.1021\/acschemneuro.4c00339","article-title":"Alzheimer\u2019s Disease: Exploring the Landscape of Cognitive Decline","volume":"15","author":"Tenchov","year":"2024","journal-title":"ACS Chem. Neurosci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"18008","DOI":"10.1523\/JNEUROSCI.1402-13.2013","article-title":"Changes in Brain Function Occur Years before the Onset of Cognitive Impairment","volume":"33","author":"Goh","year":"2013","journal-title":"J. Neurosci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1038\/s44400-026-00089-5","article-title":"The use of electroencephalography in neurodegenerative disease and its utility in dementia","volume":"2","author":"Guo","year":"2026","journal-title":"npj Dement."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"105659","DOI":"10.1016\/j.ebiom.2025.105659","article-title":"Exploring the neuromagnetic signatures of cognitive decline from mild cognitive impairment to Alzheimer\u2019s disease dementia","volume":"114","author":"Gaubert","year":"2025","journal-title":"eBioMedicine"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"R80","DOI":"10.1016\/j.cub.2018.11.052","article-title":"Electroencephalography","volume":"29","author":"Biasiucci","year":"2019","journal-title":"Curr. Biol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"24365","DOI":"10.1038\/s41598-024-74035-x","article-title":"Differentiating neurodegenerative diseases based on EEG complexity","volume":"14","author":"Mostile","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Aljalal, M., Aldosari, S.A., AlSharabi, K., Abdurraqeeb, A.M., and Alturki, F.A. (2022). Parkinson\u2019s Disease Detection from Resting-State EEG Signals Using Common Spatial Pattern, Entropy, and Machine Learning Techniques. Diagnostics, 12.","DOI":"10.1038\/s41598-022-26644-7"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e076164","DOI":"10.1002\/alz.076164","article-title":"Novel EEG tools to predict early cognitive impairments in Parkinson\u2019s disease","volume":"19","author":"Segura","year":"2023","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Fiscon, G., Weitschek, E., Cialini, A., Felici, G., Bertolazzi, P., De Salvo, S., Bramanti, A., Bramanti, P., and De Cola, M.C. (2018). Combining EEG signal processing with supervised methods for Alzheimer\u2019s patients classification. BMC Med. Inform. Decis. Mak., 18.","DOI":"10.1186\/s12911-018-0613-y"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Miltiadous, A., Tzimourta, K.D., Giannakeas, N., Tsipouras, M.G., Afrantou, T., Ioannidis, P., and Tzallas, A.T. (2021). Alzheimer\u2019s Disease and Frontotemporal Dementia: A Robust Classification Method of EEG Signals and a Comparison of Validation Methods. Diagnostics, 11.","DOI":"10.3390\/diagnostics11081437"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.neurobiolaging.2023.05.010","article-title":"Spectral power ratio as a measure of EEG changes in mild cognitive impairment due to Alzheimer\u2019s disease: A case-control study","volume":"130","author":"Buss","year":"2023","journal-title":"Neurobiol. Aging"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1007\/s00702-025-03065-0","article-title":"Resting-state EEG changes associated with cognitive decline in Parkinson\u2019s disease: A systematic review","volume":"133","author":"Spoa","year":"2026","journal-title":"J. Neural Transm."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.clinph.2023.03.363","article-title":"Spectral features of resting-state EEG in Parkinson\u2019s Disease: A multicenter study using functional data analysis","volume":"151","author":"Ospina","year":"2023","journal-title":"Clin. Neurophysiol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"110608","DOI":"10.1016\/j.compbiomed.2025.110608","article-title":"The role of data partitioning on the performance of EEG-based deep learning models in supervised cross-subject analysis: A preliminary study","volume":"196","author":"Zanola","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"56053","DOI":"10.1109\/ACCESS.2025.3554137","article-title":"EEG Multi-Mode Oscillatory Brain State Allocation Using Switching Spectral Gaussian Processes","volume":"13","author":"Torres","year":"2025","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1038\/s41514-025-00243-y","article-title":"Evaluating EEG complexity and spectral signatures in Alzheimer\u2019s disease and frontotemporal dementia: Evidence for rostrocaudal asymmetry","volume":"11","author":"Ghassemkhani","year":"2025","journal-title":"npj Aging"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e70835","DOI":"10.1002\/alz.70835","article-title":"Tracking electrophysiological signatures of Alzheimer\u2019s disease: A systematic review of multimodal studies","volume":"21","author":"Sohrabpour","year":"2025","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"102538","DOI":"10.1016\/j.pneurobio.2023.102538","article-title":"Adverse and compensatory neurophysiological slowing in Parkinson\u2019s disease","volume":"231","author":"Wiesman","year":"2023","journal-title":"Prog. Neurobiol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1038\/s41392-023-01486-5","article-title":"Role of neuroinflammation in neurodegeneration development","volume":"8","author":"Zhang","year":"2023","journal-title":"Signal Transduct. Target. Ther."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1186\/s40708-025-00260-3","article-title":"Advancing Alzheimer\u2019s disease detection: A novel convolutional neural network based framework leveraging EEG data and segment length analysis","volume":"12","author":"Tawhid","year":"2025","journal-title":"Brain Inform."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"046042","DOI":"10.1088\/1741-2552\/adf6e6","article-title":"xEEGNet: Towards explainable AI in EEG dementia classification","volume":"22","author":"Zanola","year":"2025","journal-title":"J. Neural Eng."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Senkaya, Y., Kurnaz, C., and Ozbilgin, F. (2025). Enhancing Alzheimer\u2019s Diagnosis with Machine Learning on EEG: A Spectral Feature-Based Comparative Analysis. Diagnostics, 15.","DOI":"10.3390\/diagnostics15172190"},{"key":"ref_29","unstructured":"Stoica, P., and Moses, R.L. (2005). Spectral Analysis of Signals, Pearson Prentice Hall."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Buzs\u00e1ki, G. (2006). Rhythms of the Brain, Oxford University Press.","DOI":"10.1093\/acprof:oso\/9780195301069.001.0001"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"15950","DOI":"10.1038\/s41598-025-00292-z","article-title":"EEG-based neurodegenerative disease diagnosis: Comparative analysis of conventional methods and deep learning models","volume":"15","author":"Nayana","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"110248","DOI":"10.1016\/j.compbiomed.2025.110248","article-title":"Exploring the diagnostic potential of EEG theta power and interhemispheric correlation of temporal lobe activities in Alzheimer\u2019s Disease through random forest analysis","volume":"192","author":"Lee","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Koul, A., Becchio, C., and Cavallo, A. (2018). Cross-Validation Approaches for Replicability in Psychology. Front. Psychol., 9.","DOI":"10.3389\/fpsyg.2018.01117"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"016048","DOI":"10.1088\/1741-2552\/acb96e","article-title":"An attention-based deep learning approach for the classification of subjective cognitive decline and mild cognitive impairment using resting-state EEG","volume":"20","author":"Sibilano","year":"2023","journal-title":"J. Neural Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"22886","DOI":"10.1038\/s41598-026-52330-z","article-title":"SHAP analysis of an improved EEG-based mental workload classification framework: Utilizing data augmentation and explainable AI","volume":"16","author":"Chaturvedi","year":"2026","journal-title":"Sci. Rep."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Huitema, B. (2011). The Analysis of Covariance and Alternatives: Statistical Methods for Experiments, Quasi-Experiments, and Single-Case Studies, John Wiley & Sons.","DOI":"10.1002\/9781118067475"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1137\/070710111","article-title":"Power-Law Distributions in Empirical Data","volume":"51","author":"Clauset","year":"2009","journal-title":"SIAM Rev."},{"key":"ref_38","unstructured":"Prieur Coloma, Y., Prado, P., El-Deredy, W., and Weinstein, A. (2026). EEG Spectro-Temporal Variability Analysis in Normal Aging and Neurodegenerative Disorders, Frontiers Media S.A."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1038\/s41597-023-02806-8","article-title":"The BrainLat project, a multimodal neuroimaging dataset of neurodegeneration from underrepresented backgrounds","volume":"10","author":"Prado","year":"2023","journal-title":"Sci. Data"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1477","DOI":"10.1109\/TNSRE.2024.3379891","article-title":"A Multi-Modal Classification Method for Early Diagnosis of Mild Cognitive Impairment and Alzheimer\u2019s Disease Using Three Paradigms with Various Task Difficulties","volume":"32","author":"Chen","year":"2024","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1609","DOI":"10.1109\/TETCI.2024.3353610","article-title":"Exploring Rhythms and Channels-Based EEG Biomarkers for Early Detection of Alzheimer\u2019s Disease","volume":"8","author":"Siuly","year":"2024","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Guo, Z., Jiao, R., He, H., Jiang, N., and He, J. (2025). Abnormal resting-state EEG neural oscillations and functional connectivity in mild cognitive impairment. Front. Aging Neurosci., 17.","DOI":"10.3389\/fnagi.2025.1640966"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Fr\u00f6hlich, S., Kutz, D.F., M\u00fcller, K., and Voelcker-Rehage, C. (2021). Characteristics of Resting State EEG Power in 80+-Year-Olds of Different Cognitive Status. Front. Aging Neurosci., 13.","DOI":"10.3389\/fnagi.2021.675689"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Ma, Y., Bland, J.K.S., and Fujinami, T. (2024). Classification of Alzheimer\u2019s Disease and Frontotemporal Dementia Using Electroencephalography to Quantify Communication between Electrode Pairs. Diagnostics, 14.","DOI":"10.3390\/diagnostics14192189"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1007\/s11571-026-10506-3","article-title":"Aperiodic EEG spectral features as consistent indicators of Parkinson\u2019s disease: A machine learning approach utilizing fitting oscillations and one-over-F decomposition","volume":"20","author":"Chi","year":"2026","journal-title":"Cogn. Neuro."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Tao, H., and Fujisawa, K. (2026). Energy Homeostasis Disruption in Neurological Disorders: Mitochondrial Dysfunction, High-Energy Phosphate Transfer, and Extracellular ATP-Dependent Purinergic Dysregulation. Int. J. Mol. Sci., 27.","DOI":"10.3390\/ijms27136066"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1162\/imag_a_00001","article-title":"Developmental changes in individual alpha frequency: Recording EEG data during public engagement events","volume":"1","author":"Turner","year":"2023","journal-title":"Imaging Neurosci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"121685","DOI":"10.1016\/j.neuroimage.2026.121685","article-title":"The individual alpha bandwidth: A trait-like neural marker associated with sensory processing and executive function","volume":"325","author":"Jiang","year":"2026","journal-title":"NeuroImage"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/19\/3143\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T11:57:19Z","timestamp":1790596639000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/19\/3143"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,27]]},"references-count":48,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2026,10]]}},"alternative-id":["diagnostics16193143"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16193143","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,27]]}}}