ArticleMedical image analysis2017
Task fMRI data analysis based on supervised stochastic coordinate coding.
Article in Medical image analysis, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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
18 citing papers in PubMed, 50 citations in OpenAlex.
- Mapping longitudinally consistent intrinsic connectivity networks in macaque brain via longitudinal sparse dictionary learning.IBRO neuroscience reports · 2025Article
- Deep linear matrix approximate reconstruction with integrated BOLD signal denoising reveals reproducible hierarchical brain connectivity networks from multiband multi-echo fMRI.Frontiers in neuroscience · 2025Article
- A novel subject-wise dictionary learning approach using multi-subject fMRI spatial and temporal components.Scientific reports · 2023Article
- Building a tissue-unbiased brain template of fiber orientation distribution and tractography with multimodal registration.Magnetic resonance in medicine · 2023Article
- Decoding Task-Based fMRI Data with Graph Neural Networks, Considering Individual Differences.Brain sciences · 2022Article
- Low-Rank Tucker-2 Model for Multi-Subject fMRI Data Decomposition With Spatial Sparsity Constraint.IEEE transactions on medical imaging · 2022Article
- Predicting future cognitive decline with hyperbolic stochastic coding.Medical image analysis · 2021Article
- A novel pipeline leveraging surface-based features of small subcortical structures to classify individuals with autism spectrum disorder.Progress in neuro-psychopharmacology & biological psychiatry · 2021Article
- Predicting Brain Amyloid Using Multivariate Morphometry Statistics, Sparse Coding, and Correntropy: Validation in 1,101 Individuals From the ADNI and OASIS Databases.Frontiers in neuroscience · 2021Article
- Improved Prediction of Imminent Progression to Clinically Significant Memory Decline Using Surface Multivariate Morphometry Statistics and Sparse Coding.Journal of Alzheimer's disease : JAD · 2021Article
- Modeling task-based fMRI data via deep belief network with neural architecture search.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2020Article
- Shift-Invariant Canonical Polyadic Decomposition of Complex-Valued Multi-Subject fMRI Data With a Phase Sparsity Constraint.IEEE transactions on medical imaging · 2020Article
- Sparse Representation-Based Denoising for High-Resolution Brain Activation and Functional Connectivity Modeling: A Task fMRI Study.IEEE access : practical innovations, open solutions · 2020Article
- Integrating Convolutional Neural Networks and Multi-Task Dictionary Learning for Cognitive Decline Prediction with Longitudinal Images.Journal of Alzheimer's disease : JAD · 2020Article
- A Distributed Computing Platform for fMRI Big Data Analytics.IEEE transactions on big data · 2019Article
- Classifying early and late mild cognitive impairment stages of Alzheimer's disease by fusing default mode networks extracted with multiple seeds.BMC bioinformatics · 2018Article
- Estimation of Dynamic Sparse Connectivity Patterns From Resting State fMRI.IEEE transactions on medical imaging · 2018Article
- MULTI-TASK SPARSE SCREENING FOR PREDICTING FUTURE CLINICAL SCORES USING LONGITUDINAL CORTICAL THICKNESS MEASURES.Proceedings. IEEE International Symposium on Biomedical Imaging · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 5 institutions in 3 countries.
Funding
Abstract
Task functional magnetic resonance imaging (fMRI) has been widely employed for brain activation detection and brain network analysis. Modeling rich information from spatially-organized collection of fMRI time series is challenging because of the intrinsic complexity. Hypothesis-driven methods, such as the general linear model (GLM), which regress exterior stimulus from voxel-wise functional brain activity, are limited due to overlooking the complexity of brain activities and the diversity of concurrent brain networks. Recently, sparse representation and dictionary learning methods have attracted increasing interests in task fMRI data analysis. The major advantage of this methodology is its promise in reconstructing concurrent brain networks systematically. However, this data-driven strategy is, to some extent, arbitrary and does not sufficiently utilize the prior information of task design and neuroscience knowledge. To bridge this gap, we here propose a novel supervised sparse representation and dictionary learning framework based on stochastic coordinate coding (SCC) algorithm for task fMRI data analysis, in which certain brain networks are learned with known information such as pre-defined temporal patterns and spatial network patterns, and at the same time other networks are learned automatically from data. Our proposed method has been applied to two independent task fMRI datasets, and qualitative and quantitative evaluations have shown that our method provides a new and effective framework for task fMRI data analysis.
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.