ArticleProceedings of SPIE--the International Society for Optical Engineering2020
Anatomically-Informed Data Augmentation for Functional MRI with Applications to Deep Learning.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning approaches in the therapeutic outcome prediction in major depressive disorder: a systematic review.Frontiers in psychiatry · 2025Pooled it
- Are current machine learning applications comparable to radiologist classification of degenerate and herniated discs and Modic change? A systematic review and meta-analysis.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2023Pooled it
- Patterns of Pretreatment Reward Task Brain Activation Predict Individual Antidepressant Response: Key Results From the EMBARC Randomized Clinical Trial.Biological psychiatry · 2022Trial
- A Comprehensive Review of Multimodal Medical Image Fusion: Techniques, Evaluation, and Future Directions.Sensors (Basel, Switzerland) · 2026Review
- The complementary roles of oversight, education, and collaboration in the responsible integration of artificial intelligence in radiation medicine: White paper of CADRA.Journal of applied clinical medical physics · 2026Review
- ETDACVO: Structural-Fidelity-Aware Evolutionary Co-Optimization for Robust and Explainable Brain Tumor MRI Classification.Biomedicines · 2026Article
- WGTMM: WGAN with Transformer Feature Matching for Generating fMRI Data in MCI Patients.Brain sciences · 2026Article
- Graph-Regularized Manifold-Aware Conditional Wasserstein GAN for Brain Functional Connectivity Generation.Human brain mapping · 2025Article
- AI-powered integration of multimodal imaging in precision medicine for neuropsychiatric disorders.Cell reports. Medicine · 2025Review
- Comprehensive evaluation of U-Net based transcranial magnetic stimulation electric field estimations.Scientific reports · 2025Article
- Towards Clinical Diagnoses: Classifying Alzheimer's Disease Using Single fMRI, Small Datasets, and Transfer Learning.Brain and behavior · 2025Article
- BLENDS: Augmentation of Functional Magnetic Resonance Images for Machine Learning Using Anatomically Constrained Warping.Brain connectivity · 2023Article
- Graph Empirical Mode Decomposition-Based Data Augmentation Applied to Gifted Children MRI Analysis.Frontiers in neuroscience · 2022Article
- Data Augmentation for Brain-Tumor Segmentation: A Review.Frontiers in computational neuroscience · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
The application of deep learning to build accurate predictive models from functional neuroimaging data is often hindered by limited dataset sizes. Though data augmentation can help mitigate such training obstacles, most data augmentation methods have been developed for natural images as in computer vision tasks such as CIFAR, not for medical images. This work helps to fills in this gap by proposing a method for generating new functional Magnetic Resonance Images (fMRI) with realistic brain morphology. This method is tested on a challenging task of predicting antidepressant treatment response from pre-treatment task-based fMRI and demonstrates a 26% improvement in performance in predicting response using augmented images. This improvement compares favorably to state-of-the-art augmentation methods for natural images. Through an ablative test, augmentation is also shown to substantively improve performance when applied before hyperparameter optimization. These results suggest the optimal order of operations and support the role of data augmentation method for improving predictive performance in tasks using fMRI.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.