scholarly journals Some Points to Consider in a Task-Based fMRI Study: A Guideline for Beginners

2020 ◽  
Author(s):  
Seyed Amir Hossein Batouli ◽  
Minoo Sisakhti
Keyword(s):  
Magnetic Resonance Imaging ◽  
Data Analysis ◽  
Magnetic Resonance ◽  
Brain Function ◽  
Clinical Applications ◽  
Careful Consideration ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Fmri Study ◽  
Data Analysis Methods

Functional Magnetic Resonance Imaging (fMRI) is a technique widely used to probe brain function, and has shown many research and clinical applications. Despite its popularity and strength, performing an fMRI study needs careful consideration of the design of the experiment, as well as the techniques and methodologies implemented in it, due to the high potential of these factors to alter the outputs of the study. The influences of the demographics of the participants, stimuli design, image acquisition, and data analysis methods on the fMRI results are illustrated previously. Therefore, it is of utmost significance to have an understanding of the critical considerations when designing an fMRI study. In this manuscript, by reviewing the methodology of over one hundred task-based fMRI studies, around 300 substantial tips regarding the different stages of an fMRI experiment are gathered. These could only be found scattered through the literature, and such a collection would act as a guideline for the beginners in the field of fMRI.

scholarly journals Hierarchical Spatio-Temporal Modeling of Naturalistic Functional Magnetic Resonance Imaging Signals via Two-Stage Deep Belief Network With Neural Architecture Search

2021 ◽  
Vol 15 ◽  
Author(s):  
Yudan Ren ◽  
Shuhan Xu ◽  
Zeyang Tao ◽  
Limei Song ◽  
Xiaowei He
Keyword(s):  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Brain Function ◽  
Learning Models ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Two Stage ◽  
Functional Activities ◽  
Neural Architecture

Naturalistic functional magnetic resonance imaging (NfMRI) has become an effective tool to study brain functional activities in real-life context, which reduces the anxiety or boredom due to difficult or repetitive tasks and avoids the problem of unreliable collection of brain activity caused by the subjects’ microsleeps during resting state. Recent studies have made efforts on characterizing the brain’s hierarchical organizations from fMRI data by various deep learning models. However, most of those models have ignored the properties of group-wise consistency and inter-subject difference in brain function under naturalistic paradigm. Another critical issue is how to determine the optimal neural architecture of deep learning models, as manual design of neural architecture is time-consuming and less reliable. To tackle these problems, we proposed a two-stage deep belief network (DBN) with neural architecture search (NAS) combined framework (two-stage NAS-DBN) to model both the group-consistent and individual-specific naturalistic functional brain networks (FBNs), which reflected the hierarchical organization of brain function and the nature of brain functional activities under naturalistic paradigm. Moreover, the test-retest reliability and spatial overlap rate of the FBNs identified by our model reveal better performance than that of widely used traditional methods. In general, our model provides a promising method for characterizing hierarchical spatiotemporal features under the natural paradigm.

Sign in / Sign up

Export Citation Format

Share Document