ArticleMedical image analysis2020
A robust deep neural network for denoising task-based fMRI data: An application to working memory and episodic memory.
Article in Medical image analysis, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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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.
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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.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A technical review of canonical correlation analysis for neuroscience applications.Human brain mapping · 2020Pooled it
- Circuit-based neuromodulation enhances delayed recall in amnestic mild cognitive impairment.Journal of neurology, neurosurgery, and psychiatry · 2024Trial
- DeepCor: denoising fMRI data with contrastive autoencoders.Nature methods · 2026Article
- 3D Wasserstein Generative Adversarial Network with Dense U-Net-Based Discriminator for Preclinical fMRI Denoising.Journal of imaging informatics in medicine · 2025Article
- Physiological Confounds in BOLD-fMRI and Their Correction.NMR in biomedicine · 2025Review
- Nonlinear kernel-based fMRI activation detection.Frontiers in neuroimaging · 2025Article
- A deep neural network for adaptive spatial smoothing of task fMRI data.Frontiers in neuroimaging · 2025Article
- The Untapped Potential of Dimension Reduction in Neuroimaging: Artificial Intelligence-Driven Multimodal Analysis of Long COVID Fatigue.Brain sciences · 2024Article
- Self-supervised learning for denoising of multidimensional MRI data.Magnetic resonance in medicine · 2024Article
- Natural Image Reconstruction from fMRI Based on Node-Edge Interaction and Multi-Scale Constraint.Brain sciences · 2024Article
- Systematic evaluation of head motion on resting-state functional connectivity MRI in the neonate.Human brain mapping · 2023Article
- Deep learning-based motion artifact removal in functional near-infrared spectroscopy.Neurophotonics · 2022Article
- Article
- Sex Differences of Brain Functional Topography Revealed in Normal Aging and Alzheimer's Disease Cohort.Journal of Alzheimer's disease : JAD · 2021Article
- Deep Generative Analysis for Task-Based Functional MRI Experiments.Proceedings of machine learning research · 2021Article
- Single-scale time-dependent window-sizes in sliding-window dynamic functional connectivity analysis: A validation study.NeuroImage · 2020Article
- A 3D Convolutional Encapsulated Long Short-Term Memory (3DConv-LSTM) Model for Denoising fMRI Data.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2020Article
- Nonlinear KCCA in fMRI activation analysis: Self-supervised optimization and robust back-reconstruction.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
In this study, a deep neural network (DNN) is proposed to reduce the noise in task-based fMRI data without explicitly modeling noise. The DNN artificial neural network consists of one temporal convolutional layer, one long short-term memory (LSTM) layer, one time-distributed fully-connected layer, and one unconventional selection layer in sequential order. The LSTM layer takes not only the current time point but also what was perceived in a previous time point as its input to characterize the temporal autocorrelation of fMRI data. The fully-connected layer weights the output of the LSTM layer, and the output denoised fMRI time series is selected by the selection layer. Assuming that task-related neural response is limited to gray matter, the model parameters in the DNN network are optimized by maximizing the correlation difference between gray matter voxels and white matter or ventricular cerebrospinal fluid voxels. Instead of targeting a particular noise source, the proposed neural network takes advantage of the task design matrix to better extract task-related signal in fMRI data. The DNN network, along with other traditional denoising techniques, has been applied on simulated data, working memory task fMRI data acquired from a cohort of healthy subjects and episodic memory task fMRI data acquired from a small set of healthy elderly subjects. Qualitative and quantitative measurements were used to evaluate the performance of different denoising techniques. In the simulation, DNN improves fMRI activation detection and also adapts to varying hemodynamic response functions across different brain regions. DNN efficiently reduces physiological noise and generates more homogeneous task-response correlation maps in real data.
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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.