{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T07:37:50Z","timestamp":1774597070577,"version":"3.50.1"},"reference-count":31,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T00:00:00Z","timestamp":1774569600000},"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. Comput. Neurosci."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Group-aware learning has recently emerged as a promising paradigm for neuroimaging-based disease diagnosis, as population-level interactions can provide complementary information beyond individual imaging features. However, most existing approaches rely on explicitly constructed graphs, which introduce non-trivial design choices, scalability limitations, and sensitivity to graph topology. By incorporating the design philosophy of participatory interaction, we propose IP-Mamba, a scalable and memory-efficient framework tailored for neuroimaging cohorts that models implicit population interactions without the computational burden of explicit graph construction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>IP-Mamba treats a mini-batch of subjects as an unordered set and employs a bidirectional Mamba-based sequence modeling mechanism to capture latent inter-subject dependencies. To address the inherent order sensitivity of sequence models, we introduce a Shuffle Consistency Strategy, which promotes permutation equivariance under random permutations of subject order, thereby aligning the model behavior with the clinically-relevant, set-based nature of population data. This design enables efficient implicit hypergraph modeling while maintaining linear computational complexity with respect to the population size. We evaluate IP-Mamba on the OASIS-1 dataset, focusing on the binary classification of Alzheimer\u2019s disease (Normal Controls vs. Abnormal) as an early clinical screening task. To address severe class imbalance and ensure diagnostic stability, we implement a Contextual Population Support Set inference mechanism coupled with a robust hybrid SVM decision layer.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Experimental results demonstrate that IP-Mamba achieves a balanced accuracy of 87.84% and maintains a high sensitivity (Recall) of 89% for the minority disease class. Compared to conventional 3D CNNs and Transformer-based baselines, IP-Mamba provides highly competitive diagnostic robustness while maintaining a highly efficient linear\n                      <jats:italic>O<\/jats:italic>\n                      (\n                      <jats:italic>N<\/jats:italic>\n                      ) memory scaling without the quadratic computational bottlenecks typical of graph-based attention networks.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Comprehensive ablation studies further confirm the necessity of bidirectional modeling and shuffle consistency regularization. Overall, IP-Mamba offers a principled, memory-efficient alternative to explicit graph-based methods, providing a scalable solution for population-aware neuroimaging analysis under imbalanced clinical settings.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fncom.2026.1780552","type":"journal-article","created":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T06:41:40Z","timestamp":1774593700000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Designing implicit population learners: a permutation-equivariant state space approach for brain disease diagnosis"],"prefix":"10.3389","volume":"20","author":[{"given":"Chuan","family":"Yang","sequence":"first","affiliation":[{"name":"Sanjiang University","place":["Nanjing, China"]},{"name":"City University of Macau","place":["Macau, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,3,27]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"103411","DOI":"10.1016\/j.jbi.2020.103411","article-title":"Deep ensemble learning for Alzheimer\u2019s disease classification.","volume":"105","author":"An","year":"2020","journal-title":"J. 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