New Insights into Functional Magnetic Resonance Imaging (fMRI)

A special issue of Tomography (ISSN 2379-139X).

Deadline for manuscript submissions: closed (24 October 2024) | Viewed by 2773

Special Issue Editor


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Guest Editor
Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK
Interests: fMRI; image analysis; statistical analysis; cancer; brain

Special Issue Information

Dear Colleagues,

Since the breakthrough of functional magnetic resonance imaging (fMRI) in the late 1980s, it has been applied in countless fields for scientific studies, including (but not limited to) brain encoding and decoding. In brain encoding studies, fMRI has been implemented for brain activation detection, connectivity, and adaptation studies. These methods have been applied in clinical, psychological, and cognitive studies and have led to revolutionary findings that help to explain the normal brain and the brain that is affected by various diseases. However, brain decoding or mind-reading techniques make up a relatively new research area, and algorithms have been designed to read people's thoughts from the brain. Based on fMRI and statistical methods, it is possible to read the mind if there are enough training datasets. This Special Issue aims to present the current findings dedicated to fMRI methods and their application to a wide range of scientific research topics in both clinical and psychological studies. It can be a new method for fMRI studies, and it can also be a new application of fMRI in basic scientific research fields. Our goal is for these works to contribute to the exchange of knowledge and ideas resulting in discoveries in multidisciplinary subjects using fMRI.

Dr. Xingfeng Li
Guest Editor

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Keywords

  • fMRI
  • statistical analysis
  • image analysis
  • brain
  • radiology
  • MRI
  • cognitive
  • neuroscience

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Published Papers (2 papers)

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Research

13 pages, 4825 KiB  
Article
Identifying Brain Network Structure for an fMRI Effective Connectivity Study Using the Least Absolute Shrinkage and Selection Operator (LASSO) Method
by Xingfeng Li and Yuan Zhang
Tomography 2024, 10(10), 1564-1576; https://doi.org/10.3390/tomography10100115 - 30 Sep 2024
Viewed by 830
Abstract
Background: Studying causality relationships between different brain regions using the fMRI method has attracted great attention. To investigate causality relationships between different brain regions, we need to identify both the brain network structure and the influence magnitude. Most current methods concentrate on magnitude [...] Read more.
Background: Studying causality relationships between different brain regions using the fMRI method has attracted great attention. To investigate causality relationships between different brain regions, we need to identify both the brain network structure and the influence magnitude. Most current methods concentrate on magnitude estimation, but not on identifying the connection or structure of the network. To address this problem, we proposed a nonlinear system identification method, in which a polynomial kernel was adopted to approximate the relation between the system inputs and outputs. However, this method has an overfitting problem for modelling the input–output relation if we apply the method to model the brain network directly. Methods: To overcome this limitation, this study applied the least absolute shrinkage and selection operator (LASSO) model selection method to identify both brain region networks and the connection strength (system coefficients). From these coefficients, the causality influence is derived from the identified structure. The method was verified based on the human visual cortex with phase-encoded designs. The functional data were pre-processed with motion correction. The visual cortex brain regions were defined based on a retinotopic mapping method. An eight-connection visual system network was adopted to validate the method. The proposed method was able to identify both the connected visual networks and associated coefficients from the LASSO model selection. Results: The result showed that this method can be applied to identify both network structures and associated causalities between different brain regions. Conclusions: System identification with LASSO model selection algorithm is a powerful approach for fMRI effective connectivity study. Full article
(This article belongs to the Special Issue New Insights into Functional Magnetic Resonance Imaging (fMRI))
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13 pages, 1349 KiB  
Article
Application of Diffusion Kurtosis Imaging and Blood Oxygen Level-Dependent Magnetic Resonance Imaging in Kidney Injury Associated with ANCA-Associated Vasculitis
by Wenhui Yu, Weijie Yan, Jing Yi, Lu Cheng, Peiyi Luo, Jiayu Sun, Shenju Gou and Ping Fu
Tomography 2024, 10(7), 970-982; https://doi.org/10.3390/tomography10070073 - 25 Jun 2024
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Abstract
Objective: Functional magnetic resonance imaging (fMRI) has been applied to assess the microstructure of the kidney. However, it is not clear whether fMRI could be used in the field of kidney injury in patients with Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV). Methods: This [...] Read more.
Objective: Functional magnetic resonance imaging (fMRI) has been applied to assess the microstructure of the kidney. However, it is not clear whether fMRI could be used in the field of kidney injury in patients with Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV). Methods: This study included 20 patients with AAV. Diffusion kurtosis imaging (DKI) and blood oxygen level-dependent (BOLD) scanning of the kidneys were performed in AAV patients and healthy controls. The mean kurtosis (MK), mean diffusivity (MD), and fractional anisotropy (FA) parameters of DKI, the R2* parameter of BOLD, and clinical data were further analyzed. Results: In AAV patients, the cortex exhibited lower MD but higher R2* values compared to the healthy controls. Medullary MK values were elevated in AAV patients. Renal medullary MK values showed a positive correlation with serum creatinine levels and negative correlations with hemoglobin levels and estimated glomerular filtration rate. To assess renal injury in AAV patients, AUC values for MK, MD, FA, and R2* in the cortex were 0.66, 0.67, 0.57, and 0.55, respectively, and those in the medulla were 0.81, 0.77, 0.61, and 0.53, respectively. Conclusions: Significant differences in DKI and BOLD MRI parameters were observed between AAV patients with kidney injuries and the healthy controls. The medullary MK value in DKI may be a noninvasive marker for assessing the severity of kidney injury in AAV patients. Full article
(This article belongs to the Special Issue New Insights into Functional Magnetic Resonance Imaging (fMRI))
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