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A contrastive deep learning framework was used to identify A\n                      <jats:sup>+<\/jats:sup>\n                      \u2010specific network dimensions and predict individual A\u03b2 and p\u2010tau levels.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>\n                      A\n                      <jats:sup>+<\/jats:sup>\n                      \u2010specific signatures were localized to the right superior temporal and anterior cingulate cortices and linked to attention and memory functions, with transcriptomic enrichment implicating synaptic dysfunction and glial activity. In contrast, signature dimensions shared between A\n                      <jats:sup>+<\/jats:sup>\n                      and A\n                      <jats:sup>\u2212<\/jats:sup>\n                      individuals involved language\u2010related regions and aging\u2010associated molecular pathways.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSION<\/jats:title>\n                    <jats:p>These findings suggest that contrastive graph learning may help separate amyloid\u2010associated functional network variation from broader background biological variability, providing insight into the heterogeneity of AD\u2010related biomarkers and cognitive dysfunction.<\/jats:p>\n                  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