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Background <p>The deposition of amyloid-β (Aβ) in the human brain is a hallmark of Alzheimer’s disease and is associated with cognitive decline. Aβ pathology is traditionally assessed at the whole-brain level across neocortical regions using positron emission tomography (PET). However, these measures often show weak associations with future cognitive impairment. A more sensitive pathology metric is needed to quantify early Aβ burden and better predict cognitive decline. Here, we aim to develop a network-based metric of Aβ burden to improve early prediction of cognitive decline in aging populations.</p> Methods <p>We integrated subject-specific brain connectome information with Aβ-PET measures to construct a network-based metric of Aβ burden. Cross-validated predictive modeling was used to evaluate the performance of this metric in predicting longitudinal cognitive decline. Furthermore, we identified a neuropathological signature pattern linked to future cognitive decline, and we validated this pattern in an independent cohort.</p> Results <p>Our results demonstrate that incorporating individualized structural connectome, but not functional connectome, information into Aβ measures enhances predictive performance for prospective cognitive decline. The identified neuropathological signature pattern is reproducible across cohorts.</p> Conclusion <p>These findings advance our understanding of the spatial patterns of Aβ pathology and its relationship to brain networks, highlighting the potential of connectome-informed network-based metrics for Aβ-PET imaging in identifying individuals at higher risk of cognitive decline.</p>

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Integrating individualized connectome with amyloid pathology improves predictive modeling of future cognitive decline

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月5日

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  • Hengda He,
  • Qolamreza R. Razlighi,
  • Yunglin Gazes,
  • Christian Habeck,
  • Yaakov Stern,
  • Michael Weiner,
  • Paul Aisen,
  • Ronald Petersen,
  • Clifford R. Jack Jr.,
  • William Jagust,
  • Susan Landau,
  • Monica Rivera-Mindt,
  • Ozioma Okonkwo,
  • Leslie M. Shaw,
  • Edward B. Lee,
  • Arthur W. Toga,
  • Laurel Beckett,
  • Danielle Harvey,
  • Robert C. Green,
  • Andrew J. Saykin,
  • Kwangsik Nho,
  • Richard J. Perrin,
  • Duygu Tosun,
  • Pallavi Sachdev,
  • Erin Drake,
  • Tom Montine,
  • Cat Conti,
  • Rachel Nosheny,
  • Diana Truran Sacrey,
  • Juliet Fockler,
  • Melanie J. Miller,
  • Winnie Kwang,
  • Chengshi Jin,
  • Adam Diaz,
  • Miriam Ashford,
  • Derek Flenniken,
  • Adrienne Kormos,
  • Michael Rafii,
  • Rema Raman,
  • Gustavo Jimenez,
  • Michael Donohue,
  • Jennifer Salazar,
  • Andrea Fidell,
  • Virginia Boatwright,
  • Justin Robison,
  • Caileigh Zimmerman,
  • Yuliana Cabrera,
  • Sarah Walter,
  • Taylor Clanton,
  • Elizabeth Shaffer,
  • Caitlin Webb,
  • Lindsey Hergesheimer,
  • Stephanie Smith,
  • Sheila Ogwang,
  • Olusegun Adegoke,
  • Payam Mahboubi,
  • Jeremy Pizzola,
  • Cecily Jenkins,
  • Naomi Saito,
  • Kedir Adem Hussen,
  • Hannatu Amaza,
  • Mai Seng Thao,
  • Shaniya Parkins,
  • Omobolanle Ayo,
  • Matt Glittenberg,
  • Isabella Hoang,
  • Kaori Kubo Germano,
  • Joe Strong,
  • Trinity Weisensel,
  • Fabiola Magana,
  • Lisa Thomas,
  • Vanessa Guzman,
  • Adeyinka Ajayi,
  • Joseph Di Benedetto,
  • Sandra Talavera,
  • Joel Felmlee,
  • Nick C. Fox,
  • Paul Thompson,
  • Charles DeCarli,
  • Arvin Forghanian-Arani,
  • Bret Borowski,
  • Calvin Reyes,
  • Caitie Hedberg,
  • Chad Ward,
  • Christopher Schwarz,
  • Denise Reyes,
  • Jeff Gunter,
  • John Moore-Weiss,
  • Kejal Kantarci,
  • Leonard Matoush,
  • Matthew Senjem,
  • Prashanthi Vemuri,
  • Robert Reid,
  • Ian Malone,
  • Sophia I. Thomopoulos,
  • Talia M. Nir,
  • Neda Jahanshad,
  • Alexander Knaack,
  • Evan Fletcher,
  • Stephanie Rossi Chen,
  • Mark Choe,
  • Karen Crawford,
  • Paul A. Yushkevich,
  • Sandhitsu Das,
  • Robert A. Koeppe,
  • Gil Rabinovici,
  • Victor Villemagne,
  • Brian LoPresti,
  • John Morris,
  • Erin Franklin,
  • Haley Bernhardt,
  • Nigel J. Cairns,
  • Lisa Taylor-Reinwald,
  • Virginia M. Y. Lee,
  • Magdalena Korecka,
  • Magdalena Brylska,
  • Yang Wan,
  • J. Q. Trojanowski,
  • Scott Neu,
  • Tatiana M. Foroud,
  • Taeho Jo,
  • Shannon L. Risacher,
  • Hannah Craft,
  • Liana G. Apostolova,
  • Kelly Nudelman,
  • Kelley Faber,
  • Zoë Potter,
  • Kaci Lacy,
  • Rima Kaddurah-Daouk,
  • Li Shen,
  • David Soleimani-Meigooni,
  • Renaud La Joie,
  • Konstantinos Chiotis,
  • Maison Abu Raya,
  • Agathe Vrillon,
  • Charles Windon,
  • Julien Lagarde,
  • Zoe Lin,
  • Aidyn Rose Hills,
  • Jason Karlawish,
  • Claire Erickson,
  • Joshua Grill,
  • Emily Largent,
  • Kristin Harkins,
  • Leon Thal,
  • Zaven Kachaturian,
  • Richard Frank,
  • Peter J. Snyder,
  • Neil Buckholtz,
  • John K. Hsiao,
  • Laurie Ryan,
  • Susan Molchan,
  • Maria Carrillo,
  • William Potter,
  • Lisa Barnes,
  • Marie Bernard,
  • Hector González,
  • Carole Ho,
  • Jonathan Jackson,
  • Eliezer Masliah,
  • Donna Masterman,
  • Nina Silverberg

摘要

Background

The deposition of amyloid-β (Aβ) in the human brain is a hallmark of Alzheimer’s disease and is associated with cognitive decline. Aβ pathology is traditionally assessed at the whole-brain level across neocortical regions using positron emission tomography (PET). However, these measures often show weak associations with future cognitive impairment. A more sensitive pathology metric is needed to quantify early Aβ burden and better predict cognitive decline. Here, we aim to develop a network-based metric of Aβ burden to improve early prediction of cognitive decline in aging populations.

Methods

We integrated subject-specific brain connectome information with Aβ-PET measures to construct a network-based metric of Aβ burden. Cross-validated predictive modeling was used to evaluate the performance of this metric in predicting longitudinal cognitive decline. Furthermore, we identified a neuropathological signature pattern linked to future cognitive decline, and we validated this pattern in an independent cohort.

Results

Our results demonstrate that incorporating individualized structural connectome, but not functional connectome, information into Aβ measures enhances predictive performance for prospective cognitive decline. The identified neuropathological signature pattern is reproducible across cohorts.

Conclusion

These findings advance our understanding of the spatial patterns of Aβ pathology and its relationship to brain networks, highlighting the potential of connectome-informed network-based metrics for Aβ-PET imaging in identifying individuals at higher risk of cognitive decline.