Emily A Boeke1, Avram J Holmes2, Elizabeth A Phelps3. 1. Department of Psychology, New York University, New York, New York. 2. Department of Psychology, Yale University, New Haven, Connecticut; Department of Psychiatry, Yale University, New Haven, Connecticut. 3. Department of Psychology, Harvard University, Cambridge Massachusetts. Electronic address: phelps@fas.harvard.edu.
Abstract
BACKGROUND: The field of psychiatry has long sought biomarkers that can objectively diagnose patients, predict treatment response, or identify individuals at risk of illness onset. However, reliable psychiatric biomarkers have yet to emerge. The recent application of machine learning techniques to develop neuroimaging-based biomarkers has yielded promising preliminary results. However, much of the work in this domain has not met best practice standards from the field of machine learning. This is especially true for studies of anxiety, creating uncertainty about the potential for anxiety biomarker development. METHODS: We applied machine learning tools to predict trait anxiety from neuroimaging measurements in humans. Using publicly available data from the Brain Genomics Superstruct Project, we compared a suite of neuroimaging-based machine learning models predicting anxiety within a discovery sample (n = 531, 307 women) via k-fold cross-validation, and we tested the final model (a stacked model incorporating region-to-region functional connectivity, amygdala seed-to-voxel connectivity, and volumetric and cortical thickness data) in a held-out, unseen test sample (n = 348, 209 women). RESULTS: Though the best model was able to predict anxiety within the discovery sample (cross-validated R2 of .06, permutation test p < .001), the generalization test within the holdout sample failed (R2 of -.04, permutation test p > .05). CONCLUSIONS: In this study, we did not find evidence of a generalizable anxiety biomarker. However, we encourage other researchers to investigate this topic, utilizing large samples and proper methodology, to clarify the potential of neuroimaging-based anxiety biomarkers.
BACKGROUND: The field of psychiatry has long sought biomarkers that can objectively diagnose patients, predict treatment response, or identify individuals at risk of illness onset. However, reliable psychiatric biomarkers have yet to emerge. The recent application of machine learning techniques to develop neuroimaging-based biomarkers has yielded promising preliminary results. However, much of the work in this domain has not met best practice standards from the field of machine learning. This is especially true for studies of anxiety, creating uncertainty about the potential for anxiety biomarker development. METHODS: We applied machine learning tools to predict trait anxiety from neuroimaging measurements in humans. Using publicly available data from the Brain Genomics Superstruct Project, we compared a suite of neuroimaging-based machine learning models predicting anxiety within a discovery sample (n = 531, 307 women) via k-fold cross-validation, and we tested the final model (a stacked model incorporating region-to-region functional connectivity, amygdala seed-to-voxel connectivity, and volumetric and cortical thickness data) in a held-out, unseen test sample (n = 348, 209 women). RESULTS: Though the best model was able to predict anxiety within the discovery sample (cross-validated R2 of .06, permutation test p < .001), the generalization test within the holdout sample failed (R2 of -.04, permutation test p > .05). CONCLUSIONS: In this study, we did not find evidence of a generalizable anxiety biomarker. However, we encourage other researchers to investigate this topic, utilizing large samples and proper methodology, to clarify the potential of neuroimaging-based anxiety biomarkers.
Authors: Franziskus Liem; Gaël Varoquaux; Jana Kynast; Frauke Beyer; Shahrzad Kharabian Masouleh; Julia M Huntenburg; Leonie Lampe; Mehdi Rahim; Alexandre Abraham; R Cameron Craddock; Steffi Riedel-Heller; Tobias Luck; Markus Loeffler; Matthias L Schroeter; Anja Veronica Witte; Arno Villringer; Daniel S Margulies Journal: Neuroimage Date: 2016-11-23 Impact factor: 6.556
Authors: D Rangaprakash; Gopikrishna Deshpande; Thomas A Daniel; Adam M Goodman; Jennifer L Robinson; Nouha Salibi; Jeffrey S Katz; Thomas S Denney; Michael N Dretsch Journal: Hum Brain Mapp Date: 2017-03-15 Impact factor: 5.038
Authors: Avram J Holmes; Marisa O Hollinshead; Timothy M O'Keefe; Victor I Petrov; Gabriele R Fariello; Lawrence L Wald; Bruce Fischl; Bruce R Rosen; Ross W Mair; Joshua L Roffman; Jordan W Smoller; Randy L Buckner Journal: Sci Data Date: 2015-07-07 Impact factor: 6.444
Authors: Maggie Stark; Haikun Huang; Lap-Fai Yu; Rebecca Martin; Ryan McCarthy; Emily Locke; Chelsea Yager; Ahmed Ali Torad; Ahmed Mahmoud Kadry; Mostafa Ali Elwan; Matthew Lee Smith; Dylan Bradley; Ali Boolani Journal: Sensors (Basel) Date: 2022-04-20 Impact factor: 3.847
Authors: Du Lei; Kun Qin; Walter H L Pinaya; Jonathan Young; Therese Van Amelsvoort; Machteld Marcelis; Gary Donohoe; David O Mothersill; Aiden Corvin; Sandra Vieira; Su Lui; Cristina Scarpazza; Celso Arango; Ed Bullmore; Qiyong Gong; Philip McGuire; Andrea Mechelli Journal: Schizophr Bull Date: 2022-06-21 Impact factor: 7.348