{"doi":"10.17615/qxdm-qb76","title":"Connectome-scale assessments of structural and functional connectivity in MCI: Structural and Functional Connectivity in MCI","abstract":"Mild cognitive impairment (MCI) has received increasing attention not only because of its potential as a precursor for Alzheimer's disease (AD), but also as a predictor of conversion to other neurodegenerative diseases. Although MCI has been defined clinically, accurate and efficient diagnosis is still challenging. While neuroimaging techniques hold promise, compared to commonly-used biomarkers including amyloid plaques, tau protein levels and brain tissue atrophy, neuroimaging biomarkers are less well validated. In the present paper, we propose a connectomes-scale assessment of structural and functional connectivity in MCI via two independent multimodal DTI/fMRI datasets. We first used DTI-derived structural profiles to explore and tailor the most common and consistent landmarks, then applied them in a whole-brain functional connectivity analysis. The next step fused the results from two independent datasets together and resulted in a set of functional connectomes with the most differentiation power, hence named as “connectome signatures”. Our results indicate that these “connectome signatures” have significantly high MCI-vs-controls classification accuracy, at more than 95%. Interestingly, through functional meta-analysis, we found that the majority of “connectome signatures” are mainly derived from the interactions among different functional networks, e.g., cognition-perception and cognition-action domains, rather than from within a single network. Our work provides support for using functional “connectome signatures” as neuroimaging biomarkers of MCI.","journal":"UNC Libraries","year":2021,"id":230881,"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":0.9521,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":837206,"name":"Douglas P. Terry","orcid":"0000-0003-1707-3225","position":1,"is_corresponding":false},{"id":548771,"name":"Kaiming Li","orcid":"0000-0001-9383-3017","position":2,"is_corresponding":false},{"id":270449,"name":"Dinggang Shen","orcid":"0000-0002-7934-5698","position":3,"is_corresponding":false},{"id":63598,"name":"Tianming Liu","orcid":"0000-0002-8132-9048","position":4,"is_corresponding":false},{"id":540990,"name":"Dajiang Zhu","orcid":"0000-0002-6940-3911","position":5,"is_corresponding":false},{"id":837820,"name":"A. Nicholas Puente","orcid":null,"position":6,"is_corresponding":false},{"id":837821,"name":"Luc Miller","orcid":null,"position":7,"is_corresponding":false},{"id":540985,"name":"Lihong Wang","orcid":"0009-0002-7085-301X","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-18T23:55:09.211909Z","pmid":null,"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":[]}