{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/neuropath-a-neural-pathway-transformer-for","title":"NeuroPath: A Neural Pathway Transformer for Joining the Dots of Human Connectomes","arxiv_id":"2409.17510","date":"2024-09-26","proceeding":null,"authors":["Ziquan Wei","Tingting Dan","Jiaqi Ding","Guorong Wu"],"abstract":"Although modern imaging technologies allow us to study connectivity between two distinct brain regions in-vivo, an in-depth understanding of how anatomical structure supports brain function and how spontaneous functional fluctuations emerge remarkable cognition is still elusive. Meanwhile, tremendous efforts have been made in the realm of machine learning to establish the nonlinear mapping between neuroimaging data and phenotypic traits. However, the absence of neuroscience insight in the current approaches poses significant challenges in understanding cognitive behavior from transient neural activities. To address this challenge, we put the spotlight on the coupling mechanism of structural connectivity (SC) and functional connectivity (FC) by formulating such network neuroscience question into an expressive graph representation learning problem for high-order topology. Specifically, we introduce the concept of topological detour to characterize how a ubiquitous instance of FC (direct link) is supported by neural pathways (detour) physically wired by SC, which forms a cyclic loop interacted by brain structure and function. In the clich\\'e of machine learning, the multi-hop detour pathway underlying SC-FC coupling allows us to devise a novel multi-head self-attention mechanism within Transformer to capture multi-modal feature representation from paired graphs of SC and FC. Taken together, we propose a biological-inspired deep model, coined as NeuroPath, to find putative connectomic feature representations from the unprecedented amount of neuroimages, which can be plugged into various downstream applications such as task recognition and disease diagnosis. We have evaluated NeuroPath on large-scale public datasets including HCP and UK Biobank under supervised and zero-shot learning, where the state-of-the-art performance by our NeuroPath indicates great potential in network neuroscience.","url_abs":"https://arxiv.org/abs/2409.17510v3","url_pdf":"https://arxiv.org/pdf/2409.17510v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"neuropath-a-neural-pathway-transformer-for","repo_url":"https://github.com/Chrisa142857/neuro_detour","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"2-task-classification","task_name":"2-task Classification"},{"task_slug":null,"task_name":"4-task Classification"},{"task_slug":null,"task_name":"Alzheimer's Detection"},{"task_slug":null,"task_name":"Functional Connectivity"},{"task_slug":"graph-classification","task_name":"Graph Classification"},{"task_slug":"graph-representation-learning","task_name":"Graph Representation Learning"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"zero-shot-learning","task_name":"Zero-Shot Learning"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/graph-classification-on-adni","task":"Graph Classification","dataset":"ADNI","model":"NeuroPath","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"85.56"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-hcp-aging","task":"Graph Classification","dataset":"HCP Aging","model":"NeuroPath","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"98.23"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-oasis","task":"Graph Classification","dataset":"OASIS","model":"NeuroPath","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"90.01"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-uk-biobank-brain-mri","task":"Graph Classification","dataset":"UK Biobank Brain MRI","model":"NeuroPath","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"99.59"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2409.17510","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.17510"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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