Loading…
Connectome-based predictive modeling shows sex differences in brain-based predictors of memory performance
Alzheimer's disease (AD) takes a more aggressive course in women than men, with higher prevalence and faster progression. Amnestic AD specifically targets the default mode network (DMN), which subserves short-term memory; past research shows relative hyperconnectivity in the posterior DMN in ag...
Saved in:
Published in: | Frontiers in dementia 2023-03, Vol.2, p.1126016 |
---|---|
Main Authors: | , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c2486-ffda1f8528a4956a84883858a85edce004a3b0387cc83d4956db986490ae13fa3 |
---|---|
cites | cdi_FETCH-LOGICAL-c2486-ffda1f8528a4956a84883858a85edce004a3b0387cc83d4956db986490ae13fa3 |
container_end_page | |
container_issue | |
container_start_page | 1126016 |
container_title | Frontiers in dementia |
container_volume | 2 |
creator | Ju, Suyeon Horien, Corey Shen, Xilin Abuwarda, Hamid Trainer, Anne Constable, R Todd Fredericks, Carolyn A |
description | Alzheimer's disease (AD) takes a more aggressive course in women than men, with higher prevalence and faster progression. Amnestic AD specifically targets the default mode network (DMN), which subserves short-term memory; past research shows relative hyperconnectivity in the posterior DMN in aging women. Higher reliance on this network during memory tasks may contribute to women's elevated AD risk. Here, we applied connectome-based predictive modeling (CPM), a robust linear machine-learning approach, to the Lifespan Human Connectome Project-Aging (HCP-A) dataset (
= 579). We sought to characterize sex-based predictors of memory performance in aging, with particular attention to the DMN. Models were evaluated using cross-validation both across the whole group and for each sex separately. Whole-group models predicted short-term memory performance with accuracies ranging from ρ = 0.21-0.45. The best-performing models were derived from an associative memory task-based scan. Sex-specific models revealed significant differences in connectome-based predictors for men and women. DMN activity contributed more to predicted memory scores in women, while within- and between- visual network activity contributed more to predicted memory scores in men. While men showed more segregation of visual networks, women showed more segregation of the DMN. We demonstrate that women and men recruit different circuitry when performing memory tasks, with women relying more on intra-DMN activity and men relying more on visual circuitry. These findings are consistent with the hypothesis that women draw more heavily upon the DMN for recollective memory, potentially contributing to women's elevated risk of AD. |
doi_str_mv | 10.3389/frdem.2023.1126016 |
format | article |
fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_11285565</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3086381979</sourcerecordid><originalsourceid>FETCH-LOGICAL-c2486-ffda1f8528a4956a84883858a85edce004a3b0387cc83d4956db986490ae13fa3</originalsourceid><addsrcrecordid>eNpVkc1OGzEURq2qiKCUF2BRednNBP_MTK5XVRUVqITEhq4tj30NjsZ2aie0vD0TSFFY2bK_813Lh5ALzhZSgrr0xWFcCCbkgnPRM95_ImcCuGyk4urz0X5GzmtdM8aEUrxfwimZScVATAdnZL3KKaHd5ojNYCo6uinogt2GJ6QxOxxDeqD1Mf-ttOI_6oL3WDBZrDQkOhQT0kcwl0qzpxFjLs90g8XnEs0EfCEn3owVzw_rnPy--nm_umlu765_rX7cNla00DfeO8M9dAJMq7reQAsgoQMDHTqLjLVGDkzC0lqQbh9xg4K-Vcwgl97IOfn-1rvZDXGPpG0xo96UEE151tkE_fEmhUf9kJ_09I_QdX03NXw7NJT8Z4d1q2OoFsfRJMy7qiWDXgJXSzVFxVvUllxrQf8-hzO9F6VfRem9KH0QNUFfj1_4jvzXIl8AZf-SeA</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3086381979</pqid></control><display><type>article</type><title>Connectome-based predictive modeling shows sex differences in brain-based predictors of memory performance</title><source>PubMed (Medline)</source><creator>Ju, Suyeon ; Horien, Corey ; Shen, Xilin ; Abuwarda, Hamid ; Trainer, Anne ; Constable, R Todd ; Fredericks, Carolyn A</creator><creatorcontrib>Ju, Suyeon ; Horien, Corey ; Shen, Xilin ; Abuwarda, Hamid ; Trainer, Anne ; Constable, R Todd ; Fredericks, Carolyn A</creatorcontrib><description>Alzheimer's disease (AD) takes a more aggressive course in women than men, with higher prevalence and faster progression. Amnestic AD specifically targets the default mode network (DMN), which subserves short-term memory; past research shows relative hyperconnectivity in the posterior DMN in aging women. Higher reliance on this network during memory tasks may contribute to women's elevated AD risk. Here, we applied connectome-based predictive modeling (CPM), a robust linear machine-learning approach, to the Lifespan Human Connectome Project-Aging (HCP-A) dataset (
= 579). We sought to characterize sex-based predictors of memory performance in aging, with particular attention to the DMN. Models were evaluated using cross-validation both across the whole group and for each sex separately. Whole-group models predicted short-term memory performance with accuracies ranging from ρ = 0.21-0.45. The best-performing models were derived from an associative memory task-based scan. Sex-specific models revealed significant differences in connectome-based predictors for men and women. DMN activity contributed more to predicted memory scores in women, while within- and between- visual network activity contributed more to predicted memory scores in men. While men showed more segregation of visual networks, women showed more segregation of the DMN. We demonstrate that women and men recruit different circuitry when performing memory tasks, with women relying more on intra-DMN activity and men relying more on visual circuitry. These findings are consistent with the hypothesis that women draw more heavily upon the DMN for recollective memory, potentially contributing to women's elevated risk of AD.</description><identifier>ISSN: 2813-3919</identifier><identifier>EISSN: 2813-3919</identifier><identifier>DOI: 10.3389/frdem.2023.1126016</identifier><identifier>PMID: 39082002</identifier><language>eng</language><publisher>Switzerland: Frontiers Media S.A</publisher><subject>Dementia</subject><ispartof>Frontiers in dementia, 2023-03, Vol.2, p.1126016</ispartof><rights>Copyright © 2023 Ju, Horien, Shen, Abuwarda, Trainer, Constable and Fredericks.</rights><rights>Copyright © 2023 Ju, Horien, Shen, Abuwarda, Trainer, Constable and Fredericks. 2023 Ju, Horien, Shen, Abuwarda, Trainer, Constable and Fredericks</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2486-ffda1f8528a4956a84883858a85edce004a3b0387cc83d4956db986490ae13fa3</citedby><cites>FETCH-LOGICAL-c2486-ffda1f8528a4956a84883858a85edce004a3b0387cc83d4956db986490ae13fa3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11285565/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11285565/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39082002$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ju, Suyeon</creatorcontrib><creatorcontrib>Horien, Corey</creatorcontrib><creatorcontrib>Shen, Xilin</creatorcontrib><creatorcontrib>Abuwarda, Hamid</creatorcontrib><creatorcontrib>Trainer, Anne</creatorcontrib><creatorcontrib>Constable, R Todd</creatorcontrib><creatorcontrib>Fredericks, Carolyn A</creatorcontrib><title>Connectome-based predictive modeling shows sex differences in brain-based predictors of memory performance</title><title>Frontiers in dementia</title><addtitle>Front Dement</addtitle><description>Alzheimer's disease (AD) takes a more aggressive course in women than men, with higher prevalence and faster progression. Amnestic AD specifically targets the default mode network (DMN), which subserves short-term memory; past research shows relative hyperconnectivity in the posterior DMN in aging women. Higher reliance on this network during memory tasks may contribute to women's elevated AD risk. Here, we applied connectome-based predictive modeling (CPM), a robust linear machine-learning approach, to the Lifespan Human Connectome Project-Aging (HCP-A) dataset (
= 579). We sought to characterize sex-based predictors of memory performance in aging, with particular attention to the DMN. Models were evaluated using cross-validation both across the whole group and for each sex separately. Whole-group models predicted short-term memory performance with accuracies ranging from ρ = 0.21-0.45. The best-performing models were derived from an associative memory task-based scan. Sex-specific models revealed significant differences in connectome-based predictors for men and women. DMN activity contributed more to predicted memory scores in women, while within- and between- visual network activity contributed more to predicted memory scores in men. While men showed more segregation of visual networks, women showed more segregation of the DMN. We demonstrate that women and men recruit different circuitry when performing memory tasks, with women relying more on intra-DMN activity and men relying more on visual circuitry. These findings are consistent with the hypothesis that women draw more heavily upon the DMN for recollective memory, potentially contributing to women's elevated risk of AD.</description><subject>Dementia</subject><issn>2813-3919</issn><issn>2813-3919</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNpVkc1OGzEURq2qiKCUF2BRednNBP_MTK5XVRUVqITEhq4tj30NjsZ2aie0vD0TSFFY2bK_813Lh5ALzhZSgrr0xWFcCCbkgnPRM95_ImcCuGyk4urz0X5GzmtdM8aEUrxfwimZScVATAdnZL3KKaHd5ojNYCo6uinogt2GJ6QxOxxDeqD1Mf-ttOI_6oL3WDBZrDQkOhQT0kcwl0qzpxFjLs90g8XnEs0EfCEn3owVzw_rnPy--nm_umlu765_rX7cNla00DfeO8M9dAJMq7reQAsgoQMDHTqLjLVGDkzC0lqQbh9xg4K-Vcwgl97IOfn-1rvZDXGPpG0xo96UEE151tkE_fEmhUf9kJ_09I_QdX03NXw7NJT8Z4d1q2OoFsfRJMy7qiWDXgJXSzVFxVvUllxrQf8-hzO9F6VfRem9KH0QNUFfj1_4jvzXIl8AZf-SeA</recordid><startdate>20230320</startdate><enddate>20230320</enddate><creator>Ju, Suyeon</creator><creator>Horien, Corey</creator><creator>Shen, Xilin</creator><creator>Abuwarda, Hamid</creator><creator>Trainer, Anne</creator><creator>Constable, R Todd</creator><creator>Fredericks, Carolyn A</creator><general>Frontiers Media S.A</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20230320</creationdate><title>Connectome-based predictive modeling shows sex differences in brain-based predictors of memory performance</title><author>Ju, Suyeon ; Horien, Corey ; Shen, Xilin ; Abuwarda, Hamid ; Trainer, Anne ; Constable, R Todd ; Fredericks, Carolyn A</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2486-ffda1f8528a4956a84883858a85edce004a3b0387cc83d4956db986490ae13fa3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Dementia</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ju, Suyeon</creatorcontrib><creatorcontrib>Horien, Corey</creatorcontrib><creatorcontrib>Shen, Xilin</creatorcontrib><creatorcontrib>Abuwarda, Hamid</creatorcontrib><creatorcontrib>Trainer, Anne</creatorcontrib><creatorcontrib>Constable, R Todd</creatorcontrib><creatorcontrib>Fredericks, Carolyn A</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Frontiers in dementia</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ju, Suyeon</au><au>Horien, Corey</au><au>Shen, Xilin</au><au>Abuwarda, Hamid</au><au>Trainer, Anne</au><au>Constable, R Todd</au><au>Fredericks, Carolyn A</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Connectome-based predictive modeling shows sex differences in brain-based predictors of memory performance</atitle><jtitle>Frontiers in dementia</jtitle><addtitle>Front Dement</addtitle><date>2023-03-20</date><risdate>2023</risdate><volume>2</volume><spage>1126016</spage><pages>1126016-</pages><issn>2813-3919</issn><eissn>2813-3919</eissn><abstract>Alzheimer's disease (AD) takes a more aggressive course in women than men, with higher prevalence and faster progression. Amnestic AD specifically targets the default mode network (DMN), which subserves short-term memory; past research shows relative hyperconnectivity in the posterior DMN in aging women. Higher reliance on this network during memory tasks may contribute to women's elevated AD risk. Here, we applied connectome-based predictive modeling (CPM), a robust linear machine-learning approach, to the Lifespan Human Connectome Project-Aging (HCP-A) dataset (
= 579). We sought to characterize sex-based predictors of memory performance in aging, with particular attention to the DMN. Models were evaluated using cross-validation both across the whole group and for each sex separately. Whole-group models predicted short-term memory performance with accuracies ranging from ρ = 0.21-0.45. The best-performing models were derived from an associative memory task-based scan. Sex-specific models revealed significant differences in connectome-based predictors for men and women. DMN activity contributed more to predicted memory scores in women, while within- and between- visual network activity contributed more to predicted memory scores in men. While men showed more segregation of visual networks, women showed more segregation of the DMN. We demonstrate that women and men recruit different circuitry when performing memory tasks, with women relying more on intra-DMN activity and men relying more on visual circuitry. These findings are consistent with the hypothesis that women draw more heavily upon the DMN for recollective memory, potentially contributing to women's elevated risk of AD.</abstract><cop>Switzerland</cop><pub>Frontiers Media S.A</pub><pmid>39082002</pmid><doi>10.3389/frdem.2023.1126016</doi><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2813-3919 |
ispartof | Frontiers in dementia, 2023-03, Vol.2, p.1126016 |
issn | 2813-3919 2813-3919 |
language | eng |
recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_11285565 |
source | PubMed (Medline) |
subjects | Dementia |
title | Connectome-based predictive modeling shows sex differences in brain-based predictors of memory performance |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-01T06%3A21%3A54IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Connectome-based%20predictive%20modeling%20shows%20sex%20differences%20in%20brain-based%20predictors%20of%20memory%20performance&rft.jtitle=Frontiers%20in%20dementia&rft.au=Ju,%20Suyeon&rft.date=2023-03-20&rft.volume=2&rft.spage=1126016&rft.pages=1126016-&rft.issn=2813-3919&rft.eissn=2813-3919&rft_id=info:doi/10.3389/frdem.2023.1126016&rft_dat=%3Cproquest_pubme%3E3086381979%3C/proquest_pubme%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c2486-ffda1f8528a4956a84883858a85edce004a3b0387cc83d4956db986490ae13fa3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=3086381979&rft_id=info:pmid/39082002&rfr_iscdi=true |