{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T06:05:33Z","timestamp":1790661933864,"version":"4.1.0"},"reference-count":46,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:00:00Z","timestamp":1790640000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Aging Neurosci."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Amnestic mild cognitive impairment (aMCI) is a heterogeneous transitional cognitive state between normal aging and dementia. A\u03b2 positivity elevates dementia conversion risk but does not equate to prodromal Alzheimer\u2019s disease (AD), and not all A\u03b2-positive aMCI individuals progress to clinical AD dementia. Distinguishing A\u03b2-positive from A\u03b2-negative aMCI facilitates pathological subtyping rather than definitive early AD diagnosis. This cross-sectional study aimed to characterize scalp-level resting-state EEG and structural MRI signatures specific to A\u03b2-positive aMCI, explore their covariate-adjusted correlations with cognitive performance and regional A\u03b2 burden, and develop an internally validated multimodal auxiliary classification model for A\u03b2 stratification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Fifty enrolled aMCI participants (30 A\u03b2-positive, 20 A\u03b2-negative) and 35 cognitively normal controls (NC) completed unified 20-min eyes-closed resting-state scalp EEG, 3.0\u202fT structural MRI,\n                      <jats:sup>18<\/jats:sup>\n                      F-AV45 amyloid PET and\n                      <jats:sup>18<\/jats:sup>\n                      F-FDG cerebral metabolism PET examinations. Artifact-free scalp EEG spectral power and regional \u03b8\/\u03b1 ratio were calculated via Welch spectral analysis; voxel-based morphometry (VBM) was adopted to quantify whole-brain gray matter volume. Covariate-controlled partial correlation corrected for multiple comparisons was applied to evaluate cross-modal associations among cortical A\u03b2 SUVR load, cerebral glucose metabolism and domain-specific cognitive scores. A binary logistic regression classification model combining screened EEG and MRI markers was established, with DeLong test for ROC curve comparison and 1,000-bootstrap internal validation.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      (1) Relative to A\u03b2-negative aMCI and NC groups, the A\u03b2-positive subgroup presented significantly elevated whole-scalp \u03b8 relative power and scalp \u03b8\/\u03b1 ratio, decreased scalp \u03b1 and \u03b2 relative power (all\n                      <jats:italic>q<\/jats:italic>\n                      &amp;lt;\u202f0.05, FDR-corrected), with predominant scalp oscillatory alterations distributed over temporal, parietal and occipital scalp electrode clusters. The temporal scalp \u03b8\/\u03b1 ratio yielded optimal classification performance for A\u03b2 stratification (AUC\u202f=\u202f0.875). (2) The A\u03b2-positive subgroup showed significant gray matter atrophy across 13\u202fAD-vulnerable brain regions including bilateral fusiform gyrus, inferior parietal lobule, parahippocampal gyrus, precuneus and right entorhinal cortex (all\n                      <jats:italic>q<\/jats:italic>\n                      &amp;lt;\u202f0.05), with the maximum effect size identified in the right precuneus (partial\n                      <jats:italic>\u03b7<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      =\u202f0.477, AUC\u202f=\u202f0.778). (3) Within the A\u03b2-positive subgroup, the temporal scalp \u03b8\/\u03b1 ratio was positively correlated with medial temporal A\u03b2 SUVR (\n                      <jats:italic>r<\/jats:italic>\n                      =\u202f0.501, 95% CI [0.146, 0.732],\n                      <jats:italic>q<\/jats:italic>\n                      =\u202f0.030); right precuneus gray matter volume was negatively correlated with multi-region cortical A\u03b2 burden (\n                      <jats:italic>q<\/jats:italic>\n                      &amp;lt;\u202f0.05). Left parahippocampal gyrus volume was positively correlated with MoCA memory subscores (\n                      <jats:italic>r<\/jats:italic>\n                      =\u202f0.479, 95% CI [0.118, 0.709],\n                      <jats:italic>q<\/jats:italic>\n                      &amp;lt;\u202f0.05). No significant associations between scalp EEG metrics and FDG glucose metabolism survived FDR correction. (4) The multimodal logistic regression model integrating temporal scalp \u03b8\/\u03b1 ratio and right precuneus gray matter volume achieved favorable classification efficacy for A\u03b2-positive aMCI differentiation (AUC\u202f=\u202f0.852, 95% CI [0.748, 0.955]), with good calibration and clinical net benefit based solely on internal bootstrap validation.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The temporal scalp \u03b8\/\u03b1 ratio and right precuneus gray matter volume are promising candidate non-invasive auxiliary biomarkers for A\u03b2 pathological stratification in aMCI, rather than validated clinical diagnostic biomarkers. The multimodal model provides supplementary subtyping value within this single-center cohort, while external multicenter validation is mandatory before clinical application. This cross-sectional dataset cannot support prediction of longitudinal progression to AD dementia.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnagi.2026.1896908","type":"journal-article","created":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T05:50:52Z","timestamp":1790661052000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Multimodal neuroimaging and electrophysiological signatures in amnestic mild cognitive impairment with different amyloid-\u03b2 deposition status: correlations with cognitive function and diagnostic value"],"prefix":"10.3389","volume":"18","author":[{"given":"Yang","family":"Liang","sequence":"first","affiliation":[{"name":"Department of Neuroelectrophysiology, Central Hospital of Dalian University of Technology (Dalian Municipal Central Hospital)","place":["Dalian, Liaoning, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peixue","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Neurology, The First Affiliated Hospital of Dalian Medical University","place":["Dalian, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology, Central Hospital of Dalian University of Technology (Dalian Municipal Central Hospital)","place":["Dalian, Liaoning, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunbo","family":"Dong","sequence":"additional","affiliation":[{"name":"Department of Neurology, The First Affiliated Hospital of Dalian Medical University","place":["Dalian, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohong","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Neuroelectrophysiology, Central Hospital of Dalian University of Technology (Dalian Municipal Central Hospital)","place":["Dalian, Liaoning, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,29]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/s0165-0270(00)00324-1","article-title":"A simple algorithm for a digital three-pole Butterworth filter of arbitrary cut-off frequency: application to digital electroencephalography","volume":"104","author":"Alarcon","year":"2000","journal-title":"J. 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