{"doi":"10.1101/2024.10.29.620757","title":"A Surface-based deep learning approach for cortical shape analysis","abstract":"Advances in deep learning hold promise for predicting clinical factors from human brain images. In this study, we applied a spherical harmonics-based convolutional neural network approach (SPHARM-Net) to MRI-derived brain shape metrics to predict age, sex, and Alzheimer's disease (AD) diagnosis. MRI-derived brain features included vertex-wise cortical curvature, convexity, thickness, and surface area. SPHARM-Net performs convolutions using the spherical harmonic transforms, eliminating the need to explicitly define neighborhood size, and achieving rotational equivariance. Sex classification and age regression were carried out in a large sample of healthy adults (UK Biobank; N=32,979), and AD classification performance was tested in a large, publicly available sample (ADNI; N=1,213). SPHARM-Net showed strong performance for sex classification (accuracy=0.91; balanced accuracy= 0.91; AUC=0.97), and age regression (average absolute error=2.97 years; R-squared=0.77; Pearson's coefficient=0.9). AD classification also performed well (accuracy=0.86; balanced accuracy=0.83; AUC=0.9). Our experiments demonstrate promising preliminary performance using the SPHARM-Net for two widely studied benchmarking tasks and for AD classification. Future work will include comparisons of shape-based methods and extending these analysis to more challenging tasks such as mood disorder classification.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2024,"id":505541,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.959,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1244010,"name":"Yuji Zhao","orcid":null,"position":1,"is_corresponding":false},{"id":107810,"name":"Boris A. Gutman","orcid":"0000-0001-5388-897X","position":2,"is_corresponding":false},{"id":107770,"name":"Sophia I. Thomopoulos","orcid":"0000-0002-0046-4070","position":3,"is_corresponding":false},{"id":285284,"name":"Elizabeth Haddad","orcid":"0000-0002-7622-9085","position":4,"is_corresponding":false},{"id":107771,"name":"Alyssa H. Zhu","orcid":"0000-0003-0083-5107","position":5,"is_corresponding":false},{"id":107762,"name":"Neda Jahanshad","orcid":"0000-0003-4401-8950","position":6,"is_corresponding":false},{"id":51712,"name":"Paul M. Thompson","orcid":"0000-0002-4720-8867","position":7,"is_corresponding":false},{"id":107766,"name":"Christopher R. K. Ching","orcid":"0000-0003-2921-3408","position":8,"is_corresponding":false},{"id":1356450,"name":"Yanghee Im","orcid":"0009-0005-1247-1445","position":0,"is_corresponding":true}],"reference_count":12,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:10:47.170978Z","pmid":"39554098","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}