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

arXiv: https://arxiv.org/abs/2008.02961 · HTML: https://mlsanity.com/paper/2008.02961/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2020.

## Built on by (most cited first, 4 of 4)

- [Rest2Visual: Predicting Visually Evoked fMRI from Resting-State Scans](https://mlsanity.com/paper/2509.13612/) (2025)
- [Brain Cortical Functional Gradients Predict Cortical Folding Patterns via Attention Mesh Convolution](https://mlsanity.com/paper/2205.10605/) (2022)
- [Zero-shot Learning of Individualized Task Contrast Prediction from Resting-state Functional Connectomes](https://mlsanity.com/paper/2310.14105/) (2023)
- [FM-fMRI: Event Conditioned Flow Matching for Rest-to-Task fMRI Time-Series Synthesis](https://mlsanity.com/paper/2605.26423/) (2026)

## Abstract (arXiv)

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.