Cortical mean diffusivity detects early age-related changes and associates with cognition and plasma biomarkers
The relationship between age-related cognitive changes and cortical macrostructural properties [i.e. cortical thickness (CTh)] has been extensively studied. However, less is known about the relationship with cortical microstructural characteristics [i.e. cortical mean diffusivity (cMD)] even though...
| Authors: | , , , , , , , , , , , , , , , , |
|---|---|
| Format: | article |
| Status: | Published version |
| Publication Date: | 2026 |
| Country: | España |
| Institution: | Universitat de Lleida (UdL) |
| Repository: | Repositori Obert UdL |
| OAI Identifier: | oai:repositori.udl.cat:10459.1/469799 |
| Online Access: | https://doi.org/10.1093/braincomms/fcaf511 https://hdl.handle.net/10459.1/469799 |
| Access Level: | Open access |
| Keyword: | Cortical mean diffusivity Cortical thickness Plasma biomarkers |
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Cortical mean diffusivity detects early age-related changes and associates with cognition and plasma biomarkers |
| title |
Cortical mean diffusivity detects early age-related changes and associates with cognition and plasma biomarkers |
| spellingShingle |
Cortical mean diffusivity detects early age-related changes and associates with cognition and plasma biomarkers Perera-Cruz, Oriol Cortical mean diffusivity Cortical thickness Plasma biomarkers |
| title_short |
Cortical mean diffusivity detects early age-related changes and associates with cognition and plasma biomarkers |
| title_full |
Cortical mean diffusivity detects early age-related changes and associates with cognition and plasma biomarkers |
| title_fullStr |
Cortical mean diffusivity detects early age-related changes and associates with cognition and plasma biomarkers |
| title_full_unstemmed |
Cortical mean diffusivity detects early age-related changes and associates with cognition and plasma biomarkers |
| title_sort |
Cortical mean diffusivity detects early age-related changes and associates with cognition and plasma biomarkers |
| dc.creator.none.fl_str_mv |
Perera-Cruz, Oriol Solé-Padullés, Cristina Mulet-Pons, Lídia Cabello-Toscano, María Morse, Rachel M. Abellaneda-Pérez, Kilian Perellón-Alfonso, Rubén Cattaneo, Gabriele Solana-Sánchez, Javier Alviarez-Schulze, Vanessa Bargalló, Nuria Fortea, Juan Tormos, Josep M. Pascual-Leone, Alvaro Zetterberg, Henrik Vaqué-Alcázar, Lídia Bartrés-Faz, David |
| author |
Perera-Cruz, Oriol |
| author_facet |
Perera-Cruz, Oriol Solé-Padullés, Cristina Mulet-Pons, Lídia Cabello-Toscano, María Morse, Rachel M. Abellaneda-Pérez, Kilian Perellón-Alfonso, Rubén Cattaneo, Gabriele Solana-Sánchez, Javier Alviarez-Schulze, Vanessa Bargalló, Nuria Fortea, Juan Tormos, Josep M. Pascual-Leone, Alvaro Zetterberg, Henrik Vaqué-Alcázar, Lídia Bartrés-Faz, David |
| author_role |
author |
| author2 |
Solé-Padullés, Cristina Mulet-Pons, Lídia Cabello-Toscano, María Morse, Rachel M. Abellaneda-Pérez, Kilian Perellón-Alfonso, Rubén Cattaneo, Gabriele Solana-Sánchez, Javier Alviarez-Schulze, Vanessa Bargalló, Nuria Fortea, Juan Tormos, Josep M. Pascual-Leone, Alvaro Zetterberg, Henrik Vaqué-Alcázar, Lídia Bartrés-Faz, David |
| author2_role |
author author author author author author author author author author author author author author author author |
| dc.subject.none.fl_str_mv |
Cortical mean diffusivity Cortical thickness Plasma biomarkers |
| topic |
Cortical mean diffusivity Cortical thickness Plasma biomarkers |
| description |
The relationship between age-related cognitive changes and cortical macrostructural properties [i.e. cortical thickness (CTh)] has been extensively studied. However, less is known about the relationship with cortical microstructural characteristics [i.e. cortical mean diffusivity (cMD)] even though these are sensitive to preclinical phases of Alzheimer’s disease. We studied a sample of 964 cognitively healthy adults (age: 40–82 years; 52% females) with available structural and diffusion MRI data. The preclinical Alzheimer’s cognitive composite was used as the cognition measure, and plasma concentrations of neurodegenerative-related (i.e. phosphorylated tau 181 and neurofilament light) and inflammatory (i.e. high-sensitivity C-reactive protein) biomarkers were assessed, together with apolipoprotein ɛ4 status. Neuroimaging data was preprocessed using FreeSurfer and FSL, and a homemade surface-based approach was used to obtain cMD maps. A two-class generalized linear model was used as the main statistical analysis. We identified a significant negative association between both cortical measures (cMD and CTh) and age. cMD associations were more extensive at earlier ages (<50 years), while CTh associations were greater at older ages (>50 years). cMD was positively correlated with cognition and with both neurodegenerative-related biomarkers in prefrontal regions, while the association was negative and more widespread for the inflammatory biomarker. CTh was positively correlated with cognition in more restricted areas than cMD and only negatively correlated with neurofilament light. Also, cMD presented lower levels in apolipoprotein ɛ4 carriers compared to non-carriers, while no results were found for CTh. Correlating cMD with CTh resulted in a regional pattern of negative and positive correlations, differencing somatosensory and associative areas, respectively. Altogether, we show that cMD can capture microstructural cortical changes occurring across adulthood into older age before CTh alterations. Indeed, it seems more sensitive to age-related cognitive decline and pathological and inflammatory processes related to risk profiles, showing an opposite trend to CTh in relation to neurodegenerative biomarker levels. Furthermore, our results suggest a pattern relating the two cortical metrics perhaps reflecting a cortical organization pattern. |
| publishDate |
2026 |
| dc.date.none.fl_str_mv |
2026 |
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info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
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article |
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publishedVersion |
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https://doi.org/10.1093/braincomms/fcaf511 https://hdl.handle.net/10459.1/469799 |
| url |
https://doi.org/10.1093/braincomms/fcaf511 https://hdl.handle.net/10459.1/469799 |
| dc.language.none.fl_str_mv |
Inglés |
| language_invalid_str_mv |
Inglés |
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info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-137234OB-I00 info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-095181-B-C21 Reproducció del document publicat a https://doi.org/10.1093/braincomms/fcaf511 Brain Communications, 2026, vol. 8, núm. 1, 2026, fcaf511 info:eu-repo/grantAgreement/EC/H2020/860197 |
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cc-by (c) Oriol Perera-Cruz et al., 2026 Attribution 4.0 International info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ |
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cc-by (c) Oriol Perera-Cruz et al., 2026 Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ |
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openAccess |
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Oxford University Press |
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Oxford University Press |
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reponame:Repositori Obert UdL instname:Universitat de Lleida (UdL) |
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Universitat de Lleida (UdL) |
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1869416282032439297 |
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Cortical mean diffusivity detects early age-related changes and associates with cognition and plasma biomarkersPerera-Cruz, OriolSolé-Padullés, CristinaMulet-Pons, LídiaCabello-Toscano, MaríaMorse, Rachel M.Abellaneda-Pérez, KilianPerellón-Alfonso, RubénCattaneo, GabrieleSolana-Sánchez, JavierAlviarez-Schulze, VanessaBargalló, NuriaFortea, JuanTormos, Josep M.Pascual-Leone, AlvaroZetterberg, HenrikVaqué-Alcázar, LídiaBartrés-Faz, DavidCortical mean diffusivityCortical thicknessPlasma biomarkersThe relationship between age-related cognitive changes and cortical macrostructural properties [i.e. cortical thickness (CTh)] has been extensively studied. However, less is known about the relationship with cortical microstructural characteristics [i.e. cortical mean diffusivity (cMD)] even though these are sensitive to preclinical phases of Alzheimer’s disease. We studied a sample of 964 cognitively healthy adults (age: 40–82 years; 52% females) with available structural and diffusion MRI data. The preclinical Alzheimer’s cognitive composite was used as the cognition measure, and plasma concentrations of neurodegenerative-related (i.e. phosphorylated tau 181 and neurofilament light) and inflammatory (i.e. high-sensitivity C-reactive protein) biomarkers were assessed, together with apolipoprotein ɛ4 status. Neuroimaging data was preprocessed using FreeSurfer and FSL, and a homemade surface-based approach was used to obtain cMD maps. A two-class generalized linear model was used as the main statistical analysis. We identified a significant negative association between both cortical measures (cMD and CTh) and age. cMD associations were more extensive at earlier ages (<50 years), while CTh associations were greater at older ages (>50 years). cMD was positively correlated with cognition and with both neurodegenerative-related biomarkers in prefrontal regions, while the association was negative and more widespread for the inflammatory biomarker. CTh was positively correlated with cognition in more restricted areas than cMD and only negatively correlated with neurofilament light. Also, cMD presented lower levels in apolipoprotein ɛ4 carriers compared to non-carriers, while no results were found for CTh. Correlating cMD with CTh resulted in a regional pattern of negative and positive correlations, differencing somatosensory and associative areas, respectively. Altogether, we show that cMD can capture microstructural cortical changes occurring across adulthood into older age before CTh alterations. Indeed, it seems more sensitive to age-related cognitive decline and pathological and inflammatory processes related to risk profiles, showing an opposite trend to CTh in relation to neurodegenerative biomarker levels. Furthermore, our results suggest a pattern relating the two cortical metrics perhaps reflecting a cortical organization pattern.his project has received funding from Institut Guttmann and Fundació Abertis and has also been partially supported by an Institució Catalana de Recerca i Estudis Avançats (ICREA) Academia 2024 grant award to D.B.-F. and a grant from the Spanish Ministry of Science, Innovation and Universities and the European Regional Development Fund (with reference PID2022-137234OB-I00). R.P.-A. was supported by a fellowship from ‘la Caixa’ Foundation (ID 100010434, grant no. LCF/BQ/DI19/11730050). A.P.-L. was partly supported by the National Institutes of Health (R01AG076708, R01AG059089), Jack Satter Foundation and the BrightFocus Foundation. H.Z. is a Wallenberg scholar and a distinguished professor at the Swedish Research Council supported by grants from the Swedish Research Council (#2023-00356, #2022-01018 and #2019-02397); the European Union’s Horizon Europe research and innovation programme under grant agreement no. 101053962; Swedish State Support for Clinical Research (#ALFGBG-71320); the Alzheimer Drug Discovery Foundation (ADDF), USA (#201809-2016862); the AD Strategic Fund from the Alzheimer’s Association (#ADSF-21-831376-C, #ADSF-21-831381-C, #ADSF-21- 831377-C and #ADSF-24-1284328-C), the European Partnership on Metrology, co-financed from the European Union’s Horizon Europe Research and Innovation Programme and by the Participating States (NEuroBioStand, #22HLT07); the Bluefield Project, Cure Alzheimer’s Fund; the Olav Thon Foundation; the Erling-Persson Family Foundation, Familjen Rönströms Stiftelse, Familjen Beiglers Stiftelse, Stiftelsen för Gamla Tjänarinnor, and Hjärnfonden (#FO2022-0270); the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 860197 (MIRIADE); the EU Joint Programme— Neurodegenerative Disease Research (JPND2021-00694); the National Institute for Health and Care Research University College London Hospitals Biomedical Research Centre; the UK Dementia Research Institute at UCL (UKDRI-1003); and an anonymous donor. L.V.-A. was supported by Instituto de Salud Carlos III through the Sara Borrell Postdoctoral Fellowship (CD23/00235). L.M.-P. was supported by a fellowship associated with the Spanish Ministry of Science, Innovation and Universities and the European Regional Development (RTI2018-095181-B-C21 grant; grant number PRE2019-089449). The project that gave rise to these results received the support of a fellowship from the ‘la Caixa’ Foundation (ID 100010434). The fellowship code is ‘LCF/BQ/ DI23/11990053’. O.P.-C. was supported by a fellowship associated with the Spanish Ministry of Science, Innovation and Universities and the European Regional Development (grant number PREP2022-001046 associated with the project PID2022-137234OB-I00).Oxford University Press2026info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://doi.org/10.1093/braincomms/fcaf511https://hdl.handle.net/10459.1/469799reponame:Repositori Obert UdL instname:Universitat de Lleida (UdL)Inglésinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-137234OB-I00info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-095181-B-C21Reproducció del document publicat a https://doi.org/10.1093/braincomms/fcaf511Brain Communications, 2026, vol. 8, núm. 1, 2026, fcaf511info:eu-repo/grantAgreement/EC/H2020/860197cc-by (c) Oriol Perera-Cruz et al., 2026Attribution 4.0 Internationalinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/oai:repositori.udl.cat:10459.1/4697992026-06-24T12:42:17Z |
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15,81155 |