Simultaneous EEG-fMRI during a neurofeedback task, a brain imaging dataset for multimodal data integration

AbstractThe rapid advances in brain imaging technology over the past 20 years are affording new insights into cortical processing hierarchies in the human brain. These new data provide a complementary front in seeking to understand the links between perceptual and physiological states. Here we review some of the challenges associated with incorporating brain imaging data into such “linking hypotheses,” highlighting some of the considerations needed in brain imaging data acquisition and analysis. We discuss work that has sought to link human brain imaging signals to existing electrophysiological data and opened up new opportunities in studying the neural basis of complex perceptual judgments. We consider a range of approaches when using human functional magnetic resonance imaging to identify brain circuits whose activity changes in a similar manner to perceptual judgments and illustrate these approaches by discussing work that has studied the neural basis of 3D perception and perceptual learning. Finally, we describe approaches that have sought to understand the information content of brain imaging data using machine learning and work that has integrated multimodal data to overcome the limitations associated with individual brain imaging approaches. Together these approaches provide an important route in seeking to understand the links between physiological and psychological states.

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Bayesian analysis of multimodal data and brain imaging

10.1117/12.387621 ◽

2000 ◽

Author(s):

Amir H. Assadi ◽

Hamid Eghbalnia ◽

Miroslav Backonja ◽

Ronald T. Wakai ◽

Paul Rutecki ◽

...

Keyword(s):

Bayesian Analysis ◽

Brain Imaging ◽

Multimodal Data

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Deep Learning-Based Annotation Transfer between Molecular Imaging Modalities: An Automated Workflow for Multimodal Data Integration

Analytical Chemistry ◽

10.1021/acs.analchem.0c02726 ◽

2021 ◽

Vol 93 (6) ◽

pp. 3061-3071

Author(s):

Alan M. Race ◽

Daniel Sutton ◽

Gregory Hamm ◽

Gareth Maglennon ◽

Jennifer P. Morton ◽

...

Keyword(s):

Deep Learning ◽

Molecular Imaging ◽

Data Integration ◽

Imaging Modalities ◽

Multimodal Data

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PRATEEK: Integration of Multimodal Neuroimaging Data to Facilitate Advanced Brain Research

Journal of Alzheimer s Disease ◽

10.3233/jad-210440 ◽

2021 ◽

pp. 1-13

Author(s):

Anshika Goel ◽

Saurav Roy ◽

Khushboo Punjabi ◽

Ritwick Mishra ◽

Manjari Tripathi ◽

...

Keyword(s):

Magnetic Resonance ◽

Data Integration ◽

Brain Research ◽

Integration Scheme ◽

Resonance Spectroscopy ◽

Processing Scheme ◽

Multimodal Data ◽

Neuroimaging Data ◽

User Friendly

Background: In vivo neuroimaging modalities such as magnetic resonance imaging (MRI), functional MRI (fMRI), magnetoencephalography (MEG), magnetic resonance spectroscopy (MRS), and quantitative susceptibility mapping (QSM) are useful techniques to understand brain anatomical structure, functional activity, source localization, neurochemical profiling, and tissue susceptibility respectively. Integrating unique and distinct information from these neuroimaging modalities will further help to enhance the understanding of complex neurological disease. Objective: To develop a processing scheme for multimodal data integration in seamless manner on healthy young population, thus establishing a generalized framework for various clinical conditions (e.g., Alzheimer’s disease). Methods: A multimodal data integration scheme has been developed to integrate the outcomes from multiple neuroimaging data (fMRI, MEG, MRS, and QSM) spatially. Furthermore, the entire scheme has been incorporated into a user-friendly toolbox- “PRATEEK”. Results: The proposed methodology and toolbox has been tested for viability among fourteen healthy young participants for bilateral occipital cortices as the region of interest. This scheme can also be extended to other anatomical regions of interest. Overlap percentage from each combination of two modalities (fMRI-MRS, MEG-MRS, fMRI-QSM, and fMRI-MEG) has been computed and also been qualitatively assessed for combinations of the three (MEG-MRS-QSM) and four (fMRI-MEG-MRS-QSM) modalities. Conclusion: This user-friendly toolbox minimizes the need of an expertise in handling different neuroimaging tools for processing and analyzing multimodal data. The proposed scheme will be beneficial for clinical studies where geometric information plays a crucial role in advance brain research.

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