Using connectome-based predictive modeling to predict individual behavior from brain connectivity.
Neuroimaging is a fast-developing research area in which anatomical and functional images of human brains are collected using techniques such as functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and electroencephalography (EEG). Technical advances and large-scale data sets have allowed for the development of models capable of predicting individual differences in traits and behavior using brain connectivity measures derived from neuroimaging data. Here, we present connectome-based predictive modeling (CPM), a data-driven protocol for developing predictive models of brain-behavior relationships from connectivity data using cross-validation. This protocol includes the following steps: (i) feature selection, (ii) feature summarization, (iii) model building, and (iv) assessment of prediction significance. We also include suggestions for visualizing the most predictive features (i.e., brain connections). The final result should be a generalizable model that takes brain connectivity data as input and generates predictions of behavioral measures in novel subjects, accounting for a considerable amount of the variance in these measures. It has been demonstrated that the CPM protocol performs as well as or better than many of the existing approaches in brain-behavior prediction. As CPM focuses on linear modeling and a purely data-driven approach, neuroscientists with limited or no experience in machine learning or optimization will find it easy to implement these protocols. Depending on the volume of data to be processed, the protocol can take 10-100 min for model building, 1-48 h for permutation testing, and 10-20 min for visualization of results.
Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA.
Xilin
X
Shen
Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, Connecticut, USA.
Emily S
ES
Finn
Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA.
Dustin
D
Scheinost
Department of Psychology, Yale University, New Haven, Connecticut, USA.
Monica D
0000-0001-6179-4025
MD
Rosenberg
Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, Connecticut, USA.|Department of Psychology, Yale University, New Haven, Connecticut, USA.|Department of Neuroscience, Yale School of Medicine, New Haven, Connecticut, USA.
Marvin M
MM
Chun
Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA.|Department of Biomedical Engineering, Yale University, New Haven, Connecticut, USA.
Xenophon
X
Papademetris
Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA.|Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, Connecticut, USA.|Department of Neurosurgery, Yale School of Medicine, New Haven, Connecticut, USA.
R Todd
RT
Constable
D001921:Brain / Q000033:anatomy & histology* / Q000166:cytology / Q000502:physiology*; D063132:Connectome / Q000379:methods*; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000033:anatomy & histology* / Q000166:cytology / Q000502:physiology*; D009434:Neural Pathways