A novel spatiotemporal graph convolutional network framework for functional connectivity biomarkers identification of Alzheimer’s disease
Authors: Zhang Ying , Xue Le , Zhang Shuoyan , Yang Jiacheng , Zhang Qi , Wang Min , Wang Luyao , Zhang Mingkai , Jiang Jiehui , Li Yunxia ,   for the Alzheimer’s Disease Neuroimaging Initiative, Weiner Michael W. , Aisen Paul , Petersen Ronald , Jack Clifford R. , Jagust William , Trojanowski John Q. , Toga Arthur W. , Beckett Laurel , Green Robert C. , Saykin Andrew J. , Morris John , Shaw Leslie M. , Khachaturian Zaven , Sorensen Greg , Kuller Lew , Raichle Marcus , Paul Steven , Davies Peter , Fillit Howard , Hefti Franz , Holtzman David , Mesulam Marek M. , Potter William , Snyder Peter , Schwartz Adam , Montine Tom , Thomas Ronald G. , Donohue Michael , Walter Sarah , Gessert Devon , Sather Tamie , Jiminez Gus , Harvey Danielle , Bernstein Matthew , Thompson Paul , Schuff Norbert , Borowski Bret , Gunter Jeff , Senjem Matt , Vemuri Prashanthi , Jones David , Kantarci Kejal , Ward Chad , Koeppe Robert A. , Foster Norm , Reiman Eric M. , Chen Kewei , Mathis Chet , Landau Susan , Cairns Nigel J. , Householder Erin , Taylor-Reinwald Lisa , Lee Virginia , Korecka Magdalena , Figurski Michal , Crawford Karen , Neu Scott , Foroud Tatiana M. , Potkin Steven G. , Shen Li , Faber Kelley , Kim Sungeun , Nho Kwangsik , Thal Leon , Buckholtz Neil , Albert Marylyn , Frank Richard , Hsiao John , Kaye Jeffrey , Quinn Joseph , Lind Betty , Carter Raina , Dolen Sara , Schneider Lon S. , Pawluczyk Sonia , Beccera Mauricio , Teodoro Liberty , Spann Bryan M. , Brewer James , Vanderswag Helen , Fleisher Adam , Heidebrink Judith L. , Lord Joanne L. , Mason Sara S. , Albers Colleen S. , Knopman David , Johnson Kris , Doody Rachelle S. , Villanueva-Meyer Javier , Chowdhury Munir , Rountree Susan , Dang Mimi , Stern Yaakov , Honig Lawrence S. , Bell Karen L. , Ances Beau , Carroll Maria , Leon Sue , Mintun Mark A. , Schneider Stacy , Oliver Angela , Marson Daniel , Griffith Randall , Clark David , Geldmacher David , Brockington John , Roberson Erik , Grossman Hillel , Mitsis Effie , de Toledo-Morrell Leyla , Shah Raj C. , Duara Ranjan , Varon Daniel , Greig Maria T. , Roberts Peggy , Onyike Chiadi , D’Agostino Daniel , Kielb Stephanie , Galvin James E. , Cerbone Brittany , Michel Christina A. , Rusinek Henry , de Leon Mony J. , Glodzik Lidia , De Santi Susan , Doraiswamy PMurali , Petrella Jeffrey R. , Wong Terence Z. , Arnold Steven E. , Karlawish Jason H. , Wolk David , Smith Charles D. , Jicha Greg , Hardy Peter , Sinha Partha , Oates Elizabeth , Conrad Gary , Lopez Oscar L. , Oakley MaryAnn , Simpson Donna M. , Porsteinsson Anton P. , Goldstein Bonnie S. , Martin Kim , Makino Kelly M. , Ismail MSaleem , Brand Connie , Mulnard Ruth A. , Thai Gaby , McAdams-Ortiz Catherine , Womack Kyle , Mathews Dana , Quiceno Mary , Diaz-Arrastia Ramon , King Richard , Weiner Myron , Martin-Cook Kristen , DeVous Michael , Levey Allan I. , Lah James J. , Cellar Janet S. , Burns Jeffrey M. , Anderson Heather S. , Swerdlow Russell H. , Apostolova Liana , Tingus Kathleen , Woo Ellen , Silverman Daniel H. S. , Lu Po H. , Bartzokis George , Graff-Radford Neill R. , Parfitt Francine , Kendall Tracy , Johnson Heather , Farlow Martin R. , Hake Ann Marie , Matthews Brandy R. , Herring Scott , Hunt Cynthia , van Dyck Christopher H. , Carson Richard E. , MacAvoy Martha G. , Chertkow Howard , Bergman Howard , Hosein Chris , Hsiung Ging-Yuek Robin , Feldman Howard , Mudge Benita , Assaly Michele , Bernick Charles , Munic Donna , Kertesz Andrew , Rogers John , Trost Dick , Kerwin Diana , Lipowski Kristine , Wu Chuang-Kuo , Johnson Nancy , Sadowsky Carl , Martinez Walter , Villena Teresa , Turner Raymond Scott , Johnson Kathleen , Reynolds Brigid , Sperling Reisa A. , Johnson Keith A. , Marshall Gad , Frey Meghan , Lane Barton , Rosen Allyson , Tinklenberg Jared , Sabbagh Marwan N. , Belden Christine M. , Jacobson Sandra A. , Sirrel Sherye A. , Kowall Neil , Killiany Ronald , Budson Andrew E. , Norbash Alexander , Johnson Patricia Lynn , Allard Joanne , Lerner Alan , Ogrocki Paula , Hudson Leon , Fletcher Evan , Carmichae Owen , Olichney John , DeCarli Charles , Kittur Smita , Borrie Michael , Lee T.-Y. , Bartha Rob , Johnson Sterling , Asthana Sanjay , Carlsson Cynthia M. , Preda Adrian , Nguyen Dana , Tariot Pierre , Reeder Stephanie , Bates Vernice , Capote Horacio , Rainka Michelle , Scharre Douglas W. , Kataki Maria , Adeli Anahita , Zimmerman Earl A. , Celmins Dzintra , Brown Alice D. , Pearlson Godfrey D. , Blank Karen , Anderson Karen , Santulli Robert B. , Kitzmiller Tamar J. , Schwartz Eben S. , Sink Kaycee M. , Williamson Jeff D. , Garg Pradeep , Watkins Franklin , Ott Brian R. , Querfurth Henry , Tremont Geoffrey , Salloway Stephen , Malloy Paul , Correia Stephen , Rosen Howard J. , Miller Bruce L. , Mintzer Jacobo , Spicer Kenneth , Bachman David , Pasternak Stephen , Rachinsky Irina , Drost Dick , Pomara Nunzio , Hernando Raymundo , Sarrael Antero , Schultz Susan K. , Ponto Laura L. Boles , Shim Hyungsub , Smith Karen Elizabeth , Relkin Norman , Chaing Gloria , Raudin Lisa , Smith Amanda , Fargher Kristin , Raj Balebail Ashok , Neylan Thomas , Grafman Jordan , Davis Melissa , Morrison Rosemary , Hayes Jacqueline , Finley Shannon , Friedl Karl , Fleischman Debra , Arfanakis Konstantinos , James Olga , Massoglia Dino , Fruehling JJay , Harding Sandra , Peskind Elaine R. , Petrie Eric C. , Li Gail , Yesavage Jerome A. , Taylor Joy L. , Furst Ansgar J. 
Published: March 2024

Abstract
Background
Functional connectivity (FC) biomarkers play a crucial role in the early diagnosis and mechanistic study of Alzheimer’s disease (AD). However, the identification of effective FC biomarkers remains challenging. In this study, we introduce a novel approach, the spatiotemporal graph convolutional network (ST-GCN) combined with the gradient-based class activation mapping (Grad-CAM) model (STGC-GCAM), to effectively identify FC biomarkers for AD.

Methods
This multi-center cross-racial retrospective study involved 2,272 participants, including 1,105 cognitively normal (CN) subjects, 790 mild cognitive impairment (MCI) individuals, and 377 AD patients. All participants underwent functional magnetic resonance imaging (fMRI) and T1-weighted MRI scans. In this study, firstly, we optimized the STGC-GCAM model to enhance classification accuracy. Secondly, we identified novel AD-associated biomarkers using the optimized model. Thirdly, we validated the imaging biomarkers using Kaplan–Meier analysis. Lastly, we performed correlation analysis and causal mediation analysis to confirm the physiological significance of the identified biomarkers.

Results
The STGC-GCAM model demonstrated great classification performance (The average area under the curve (AUC) values for different categories were: CN vs MCI = 0.98, CN vs AD = 0.95, MCI vs AD = 0.96, stable MCI vs progressive MCI = 0.79). Notably, the model identified specific brain regions, including the sensorimotor network (SMN), visual network (VN), and default mode network (DMN), as key differentiators between patients and CN individuals. These brain regions exhibited significant associations with the severity of cognitive impairment (p < 0.05). Moreover, the topological features of important brain regions demonstrated excellent predictive capability for the conversion from MCI to AD (Hazard ratio = 3.885, p < 0.001). Additionally, our findings revealed that the topological features of these brain regions mediated the impact of amyloid beta (Aβ) deposition (bootstrapped average causal mediation effect: β = -0.01 [-0.025, 0.00], p < 0.001) and brain glucose metabolism (bootstrapped average causal mediation effect: β = -0.02 [-0.04, -0.001], p < 0.001) on cognitive status.

Conclusions
This study presents the STGC-GCAM framework, which identifies FC biomarkers using a large multi-site fMRI dataset.