{"doi":"10.1002/alz.70061","title":"Connectome‐based predictive modeling of brain pathology and cognition in autosomal dominant Alzheimer's disease","abstract":"<jats:title>Abstract</jats:title>\n                  <jats:sec>\n                    <jats:title>INTRODUCTION</jats:title>\n                    <jats:p>\n                      Autosomal dominant Alzheimer's disease (ADAD) through genetic mutations can result in near complete expression of the disease. Tracking AD pathology development in an ADAD cohort of Presenilin‐1 (\n                      <jats:italic>PSEN1)</jats:italic>\n                      E280A carriers’ mutation has allowed us to observe incipient tau tangles accumulation as early as 6 years prior to symptom onset.\n                    </jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS</jats:title>\n                    <jats:p>\n                      Resting‐state functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) scans were acquired in a group of\n                      <jats:italic>PSEN1</jats:italic>\n                      carriers (\n                      <jats:italic>n</jats:italic>\n                       = 32) and non‐carrier family members (\n                      <jats:italic>n</jats:italic>\n                       = 35). We applied connectome‐based predictive modeling (CPM) to examine the relationship between the participant's functional connectome and their respective tau/amyloid‐β levels and cognitive scores (word list recall).\n                    </jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS</jats:title>\n                    <jats:p>CPM models strongly predicted tau concentrations and cognitive scores within the carrier group. The connectivity patterns between the temporal cortex, default mode network, and other memory networks were the most informative of tau burden.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>DISCUSSION</jats:title>\n                    <jats:p>These results indicate that resting‐state functional magnetic resonance imaging (fMRI) methods can complement PET methods in early detection and monitoring of disease progression in ADAD.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Highlights</jats:title>\n                    <jats:p>\n                      <jats:list list-type=\"bullet\">\n                        <jats:list-item>\n                          <jats:p>Connectivity‐based predictive modeling of tau and amyloid‐β in ADAD carriers.</jats:p>\n                        </jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Strong predictions for tau deposition; weaker predictions for amyloid‐β.</jats:p>\n                        </jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Cognitive scores for memory and mental state are predicted strongly.</jats:p>\n                        </jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Connectivity between IPL, DAN, DMN, temporal cortex most predictive.</jats:p>\n                        </jats:list-item>\n                      </jats:list>\n                    </jats:p>\n                  </jats:sec>","journal":"Alzheimer's &amp; Dementia","year":2025,"id":615637,"datarank":0.31191623125197543,"base_score":2.0794415416798357,"endowment":2.0794415416798357,"self_citation_contribution":0.31191623125197543,"citation_network_contribution":0.0,"self_endowment_contribution":0.31191623125197543,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":7,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1586930,"name":"Joshua Fox‐Fuller","orcid":null,"position":1,"is_corresponding":false},{"id":1219703,"name":"Vincent Malotaux","orcid":"0000-0003-0737-9220","position":2,"is_corresponding":false},{"id":255197,"name":"Ana Baena","orcid":"0000-0002-5622-4519","position":3,"is_corresponding":false},{"id":1586933,"name":"Nikole Bonillas Felix","orcid":null,"position":4,"is_corresponding":false},{"id":1586934,"name":"Sergio Alvarez","orcid":null,"position":5,"is_corresponding":false},{"id":1205038,"name":"David Aguillon","orcid":null,"position":6,"is_corresponding":false},{"id":226616,"name":"Francisco Lopera","orcid":"0000-0003-3986-1484","position":7,"is_corresponding":false},{"id":1586938,"name":"David C. Somers","orcid":null,"position":8,"is_corresponding":false},{"id":226624,"name":"Yakeel T. Quiroz","orcid":"0000-0001-9714-8244","position":9,"is_corresponding":false},{"id":653158,"name":"Vaibhav Tripathi","orcid":"0000-0001-7520-4188","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Connectome‐based predictive modeling of brain pathology and cognition in autosomal dominant Alzheimer's disease","abstract":"<jats:title>Abstract</jats:title>\n                  <jats:sec>\n                    <jats:title>INTRODUCTION</jats:title>\n                    <jats:p>\n                      Autosomal dominant Alzheimer's disease (ADAD) through genetic mutations can result in near complete expression of the disease. Tracking AD pathology development in an ADAD cohort of Presenilin‐1 (\n                      <jats:italic>PSEN1)</jats:italic>\n                      E280A carriers’ mutation has allowed us to observe incipient tau tangles accumulation as early as 6 years prior to symptom onset.\n                    </jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS</jats:title>\n                    <jats:p>\n                      Resting‐state functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) scans were acquired in a group of\n                      <jats:italic>PSEN1</jats:italic>\n                      carriers (\n                      <jats:italic>n</jats:italic>\n                       = 32) and non‐carrier family members (\n                      <jats:italic>n</jats:italic>\n                       = 35). We applied connectome‐based predictive modeling (CPM) to examine the relationship between the participant's functional connectome and their respective tau/amyloid‐β levels and cognitive scores (word list recall).\n                    </jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS</jats:title>\n                    <jats:p>CPM models strongly predicted tau concentrations and cognitive scores within the carrier group. The connectivity patterns between the temporal cortex, default mode network, and other memory networks were the most informative of tau burden.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>DISCUSSION</jats:title>\n                    <jats:p>These results indicate that resting‐state functional magnetic resonance imaging (fMRI) methods can complement PET methods in early detection and monitoring of disease progression in ADAD.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Highlights</jats:title>\n                    <jats:p>\n                      <jats:list list-type=\"bullet\">\n                        <jats:list-item>\n                          <jats:p>Connectivity‐based predictive modeling of tau and amyloid‐β in ADAD carriers.</jats:p>\n                        </jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Strong predictions for tau deposition; weaker predictions for amyloid‐β.</jats:p>\n                        </jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Cognitive scores for memory and mental state are predicted strongly.</jats:p>\n                        </jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Connectivity between IPL, DAN, DMN, temporal cortex most predictive.</jats:p>\n                        </jats:list-item>\n                      </jats:list>\n                    </jats:p>\n                  </jats:sec>","is_dataset_classified":null,"base_score":2.0794415416798357,"endowment":2.0794415416798357,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"40110659","pmcid":"PMC11923559","openalex_id":"https://openalex.org/W4408671574","authors":[],"funders":[{"funder_name":"National Institute on Aging","grant_id":"R01AG054671","title":null},{"funder_name":"National Institute on Aging","grant_id":"RF1AG077627","title":null},{"funder_name":"National Institutes of Health","grant_id":"1RF1AG077627-01","title":"Nerve growth factor (NGF) metabolic dysfunction as a marker of cognitive decline in autosomal dominant Alzheimer's disease"},{"funder_name":"National Institutes of Health","grant_id":"5R01AG054671-03","title":"Relationship between tau pathology and cognitive impairment in autosomal dominant Alzheimer's disease"}],"total_grants":4,"fwci":5.2037,"citation_percentile":0.95692994,"influential_citations":0,"citation_trend":[{"year":2025,"count":3},{"year":2026,"count":4}],"oa_status":"hybrid","license":"cc-by","oa_locations":[{"url":"https://doi.org/10.1002/alz.70061","host_type":"journal"},{"url":"https://doi.org/10.1002/alz.70061","host_type":"publisher"},{"url":"https://alz-journals.onlinelibrary.wiley.com/doi/pdf/10.1002/alz.70061","host_type":"publisher"},{"url":"https://pubmed.ncbi.nlm.nih.gov/40110659","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11923559","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC11923559","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC11923559?pdf=render","host_type":"Europe_PMC"},{"url":"https://doi.org/10.1101/2024.09.01.24312913","host_type":""},{"url":"https://pubmed.ncbi.nlm.nih.gov/39281738","host_type":""},{"url":"http://dx.doi.org/10.1002/alz.70061","host_type":""},{"url":"http://dx.doi.org/10.1101/2024.09.01.24312913","host_type":""}],"fields_of_study":["Functional Brain Connectivity Studies","Dementia and Cognitive Impairment Research","Alzheimer's disease research and treatments","03 medical and health sciences","0302 clinical medicine","Humans","Alzheimer Disease","Connectome","Male","Female","Magnetic Resonance Imaging","Positron-Emission Tomography","Brain","Presenilin-1","Middle Aged","Cognition","tau Proteins","Amyloid beta-Peptides","Neuropsychological Tests","Adult","Aged","Mutation"],"mesh_terms":["Adult","Aged","Alzheimer Disease","Brain","Cognition","Female","Humans","Magnetic Resonance Imaging","Male","Middle Aged","Mutation","Neuropsychological Tests","Amyloid beta-Peptides","tau Proteins","Positron-Emission Tomography","Presenilin-1","Connectome"],"keywords":["PSEN1","Neuroscience","Connectome","Resting state fMRI","Functional magnetic resonance imaging","Presenilin","Cognition","Human Connectome Project","Alzheimer's disease","Psychology","Magnetic resonance imaging","Neuroimaging","Default mode network","Disease","Medicine","Pathology","Functional connectivity","Predictive modeling","CPM","Rsfmri","Adad","Male","Adult","Amyloid beta-Peptides","Brain","tau Proteins","Middle Aged","Neuropsychological Tests","Article","Alzheimer Disease","Positron-Emission Tomography","Mutation","Presenilin-1","Humans","Female","Research Article","Aged"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-02T20:38:33.144715Z","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":[]}