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<p>We previously proposed an MRI-based machine learning model to describe the mesoscopic architecture of the human brain to aid in classifying subjects as having non-AD related pathology (nADrp) or AD related pathology (ADrp), including mild cognitive impairment (MCI) and Alzheimer’s disease (AD). The method, developed on data from patients scanned at 1.5T showed high performance, but did not generalise well to scans obtained from 3T MRI. In the current work we overcome the problem and extend the approach to patients scanned longitudinally, and at different field strengths. Retrospective T1-MRI data from 1592 subjects scanned at&#xa0;3T were included to develop the machine learning models. Three additional longitudinal datasets (n = 211) at different magnetic field strengths—1.5 and 3T—were adopted to evaluate the models. Radiomic features were extracted from each brain region. A logistic regression method with least absolute shrinkage and selection operator (LASSO) model selection was employed to classify nADrp from ADrp (classifier 1) or MCI from AD (classifier 2). Classifier 1 that discriminates nADrp from ADrp achieves high performance, with area under the curve (AUC) of the receiver operating characteristics (ROC) of 0.84 in the independent hold-out cross-sectional dataset. High performance was also seen in external testing datasets for classifier 1 (AUC of 0.70 to 0.96). Classifier 2 that discriminates MCI from AD achieves AUC of 0.79 in the independent hold-out dataset and moderate to good performance in the external testing datasets (AUC of 0.56 to 0.93). The new data analysis methods, trained on 3T data, demonstrate potential for aiding AD early detection and disease progression on both 3T and 1.5T scanners.</p>

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A computational model to describe multi-regional brain architecture during neurodegeneration in Alzheimer’s disease

  • 期刊论文

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  • 发表日期：

    2026年6月14日

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  • Xingfeng Li,
  • Andrea G. Rockall,
  • Paul Edison,
  • Michael W. 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,
  • Rachel Nosheny,
  • Melanie J. Miller,
  • Catherine Conti,
  • 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,
  • Karen Crawford,
  • Scott Neu,
  • Naomi Saito,
  • Hannatu Amaza,
  • Matt Glittenberg,
  • Isabella Hoang,
  • Kaori Kubo Germano,
  • Joe Strong,
  • Joel Felmlee,
  • Nick C. Fox,
  • Paul Thompson,
  • Charles DeCarli,
  • Arvin Forghanian-Arani,
  • Bret Borowski,
  • Calvin Reyes,
  • Chad Ward,
  • Robert A. Koeppe,
  • Gil Rabinovici,
  • Victor Villemagne,
  • Brian LoPresti,
  • John C. Morris,
  • Erin Franklin,
  • Haley Bernhardt,
  • Nigel J. Cairns,
  • Lisa Taylor-Reinwald,
  • Virginia M. Y. Lee,
  • Magdalena Korecka,
  • Magdalena Brylska,
  • Yang Wan,
  • J. Q. Trojanowski,
  • Tatiana M. Foroud,
  • Shannon L. Risacher,
  • Hannah Craft,
  • Liana G. Apostolova,
  • Kaci Lacy,
  • Rima Kaddurah-Daouk,
  • Li Shen,
  • David Soleimani-Meigooni,
  • Renaud La Joie,
  • Konstantinos Chiotis,
  • Charles Windon,
  • Julien Lagarde,
  • Jason Karlawish,
  • Claire Erickson,
  • Joshua Grill,
  • Emily Largent,
  • Kristin Harkins,
  • Leon Thal,
  • Zaven Khachaturian,
  • 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,
  • Lisa Silbert,
  • Jeffrey Kaye,
  • Sylvia White,
  • Aimee Pierce,
  • Amy Thomas,
  • Tera Clay,
  • Daniel Schwartz,
  • Gillian Devereux,
  • Janet Taylor,
  • Jennifer Ryan,
  • Mike Nguyen,
  • Yanan Shang,
  • Lon S. Schneider,
  • Cynthia Munoz,
  • Carlota Conant,
  • Katherin Martin,
  • Kristin O’Leary,
  • Sonia Pawluczyk,
  • Elizabeth Trejo,
  • Karen Dagerman,
  • Mauricio Becerra,
  • Julia Boudreau,
  • James Brewer,
  • Antonio Gama,
  • Jennifer Frascino,
  • Judith Heidebrink,
  • Ronald Petersen,
  • Bradley Boeve,
  • David Jones,
  • David Knopman,
  • Hugo Botha,
  • Jonathan Graff-Radford,
  • Val Lowe,
  • Sudha Seshadri,
  • William Hu,
  • Jacobo Mintzer,
  • Christopher Fowler,
  • Stephanie R. Rainey-Smith,
  • Sabine Bird,
  • Julia Bomke,
  • Pierrick Bourgeat,
  • Belinda M. Brown,
  • Samantha C. Burnham,
  • Ashley I. Bush,
  • Carolyn Chadunow,
  • Steven Collins,
  • James Doecke,
  • Vincent Doré,
  • Kathryn A. Ellis,
  • Lis Evered,
  • Amir Fazlollahi,
  • Jurgen Fripp,
  • Samantha L. Gardener,
  • Simon Gibson,
  • Robert Grenfell,
  • Elise Harrison,
  • Richard Head,
  • Liang Jin,
  • Adrian Kamer,
  • Fiona Lamb,
  • Nicola T. Lautenschlager,
  • Simon M. Laws,
  • Qiao-Xin Li,
  • Lucy Lim,
  • Yen Ying Lim,
  • Andrea Louey,
  • S. Lance Macaulay,
  • Lucy Mackintosh,
  • Ralph N. Martins,
  • Paul Maruff,
  • Colin L. Masters,
  • Simon McBride,
  • Lidija Milicic,
  • Madeline Peretti,
  • Kelly Pertile,
  • Tenielle Porter,
  • Morgan Radler,
  • Alan Rembach,
  • Joanne Robertson,
  • Mark Rodrigues,
  • Christopher C. Rowe,
  • Rebecca Rumble,
  • Olivier Salvado,
  • Greg Savage,
  • Brendan Silbert,
  • Magdalene Soh,
  • Hamid R. Sohrabi,
  • Kevin Taddei,
  • Tania Taddei,
  • Christine Thai,
  • Brett Trounson,
  • Regan Tyrrell,
  • Michael Vacher,
  • Shiji Varghese,
  • Victor L. Villemagne,
  • Michael Weinborn,
  • Michael Woodward,
  • Ying Xia,
  • David Ames,
  • Eric O. Aboagye

摘要

We previously proposed an MRI-based machine learning model to describe the mesoscopic architecture of the human brain to aid in classifying subjects as having non-AD related pathology (nADrp) or AD related pathology (ADrp), including mild cognitive impairment (MCI) and Alzheimer’s disease (AD). The method, developed on data from patients scanned at 1.5T showed high performance, but did not generalise well to scans obtained from 3T MRI. In the current work we overcome the problem and extend the approach to patients scanned longitudinally, and at different field strengths. Retrospective T1-MRI data from 1592 subjects scanned at 3T were included to develop the machine learning models. Three additional longitudinal datasets (n = 211) at different magnetic field strengths—1.5 and 3T—were adopted to evaluate the models. Radiomic features were extracted from each brain region. A logistic regression method with least absolute shrinkage and selection operator (LASSO) model selection was employed to classify nADrp from ADrp (classifier 1) or MCI from AD (classifier 2). Classifier 1 that discriminates nADrp from ADrp achieves high performance, with area under the curve (AUC) of the receiver operating characteristics (ROC) of 0.84 in the independent hold-out cross-sectional dataset. High performance was also seen in external testing datasets for classifier 1 (AUC of 0.70 to 0.96). Classifier 2 that discriminates MCI from AD achieves AUC of 0.79 in the independent hold-out dataset and moderate to good performance in the external testing datasets (AUC of 0.56 to 0.93). The new data analysis methods, trained on 3T data, demonstrate potential for aiding AD early detection and disease progression on both 3T and 1.5T scanners.