From Connectomic to Task-evoked Fingerprints: Individualized Prediction of Task Contrasts from Resting-state Functional Connectivity

Autor:	Ngo, Gia H., Khosla, Meenakshi, Jamison, Keith, Kuceyeski, Amy, Sabuncu, Mert R.
Rok vydání:	2020
Předmět:	Computer Science - Machine Learning Quantitative Biology - Neurons and Cognition Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Resting-state functional MRI (rsfMRI) yields functional connectomes that can serve as cognitive fingerprints of individuals. Connectomic fingerprints have proven useful in many machine learning tasks, such as predicting subject-specific behavioral traits or task-evoked activity. In this work, we propose a surface-based convolutional neural network (BrainSurfCNN) model to predict individual task contrasts from their resting-state fingerprints. We introduce a reconstructive-contrastive loss that enforces subject-specificity of model outputs while minimizing predictive error. The proposed approach significantly improves the accuracy of predicted contrasts over a well-established baseline. Furthermore, BrainSurfCNN's prediction also surpasses test-retest benchmark in a subject identification task. Comment: Accepted to MICCAI 2020
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2008.02961 Zobrazit plný text záznamu View this record from Arxiv