ArticleMagnetic resonance imaging2017
Learning-based structurally-guided construction of resting-state functional correlation tensors.
Article in Magnetic resonance imaging, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed, 19 citations in OpenAlex.
- White matter structure-function coupling alteration is associated with plasma biomarkers and cognition in Alzheimer's disease.European radiology · 2025Article
- Structure-function coupling in white matter uncovers the abnormal brain connectivity in Schizophrenia.Translational psychiatry · 2023Article
- Imaging functional neuroplasticity in human white matter tracts.Brain structure & function · 2022Article
- Functional connectivity between white matter and gray matter based on fMRI for Alzheimer's disease classification.Brain and behavior · 2019Article
- Weighted Graph Regularized Sparse Brain Network Construction for MCI Identification.Pattern recognition · 2019Article
- Resting-state network complexity and magnitude changes in neonates with severe hypoxic ischemic encephalopathy.Neural regeneration research · 2019Article
- Functional tractography of white matter by high angular resolution functional-correlation imaging (HARFI).Magnetic resonance in medicine · 2019Article
- Multi-Channel 3D Deep Feature Learning for Survival Time Prediction of Brain Tumor Patients Using Multi-Modal Neuroimages.Scientific reports · 2019Article
- Task-evoked functional connectivity does not explain functional connectivity differences between rest and task conditions.Human brain mapping · 2018Article
- Resting-State Functional MRI: Everything That Nonexperts Have Always Wanted to Know.AJNR. American journal of neuroradiology · 2018Review
- Exploring diagnosis and imaging biomarkers of Parkinson's disease via iterative canonical correlation analysis based feature selection.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 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
6 authors at 2 institutions in 3 countries.
Funding
Abstract
Functional magnetic resonance imaging (fMRI) measures changes in blood-oxygenation-level-dependent (BOLD) signals to detect brain activities. It has been recently reported that the spatial correlation patterns of resting-state BOLD signals in the white matter (WM) also give WM information often measured by diffusion tensor imaging (DTI). These correlation patterns can be captured using functional correlation tensor (FCT), which is analogous to the diffusion tensor (DT) obtained from DTI. In this paper, we propose a noise-robust FCT method aiming at further improving its quality, and making it eligible for further neuroscience study. The novel FCT estimation method consists of three major steps: First, we estimate the initial FCT using a patch-based approach for BOLD signal correlation to improve the noise robustness. Second, by utilizing the relationship between functional and diffusion data, we employ a regression forest model to learn the mapping between the initial FCTs and the corresponding DTs using the training data. The learned forest can then be applied to predict the DTI-like tensors given the initial FCTs from the testing fMRI data. Third, we re-estimate the enhanced FCT by utilizing the DTI-like tensors as a feedback guidance to further improve FCT computation. We have demonstrated the utility of our enhanced FCTs in Alzheimer's disease (AD) diagnosis by identifying mild cognitive impairment (MCI) patients from normal subjects.
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.