PRATEEK
Integration of Multimodal Neuroimaging Data to Facilitate Advanced Brain Research
PRATEEK
Integration of Multimodal Neuroimaging Data to Facilitate Advanced Brain Research
In vivo neuroimaging techniques such as Magnetic Resonance Imaging (MRI), functional MRI (fMRI), Magnetoencephalography (MEG), Magnetic Resonance Spectroscopy (MRS), and Quantitative Susceptibility Mapping (QSM) are generally applied either individually or in combination for understanding brain structure, functional performance, localization of intracranial activity, neurochemical profile and susceptibility changes respectively. Integration of data gathered from multimodal brain imaging at various Regions of Interest (ROIs) provide wealth of information about tissue microenvironment and its association with structural and functional changes in healthy or diseased condition. In this work, we put forward a comprehensive scheme to spatially integrate the results from each of these modalities specifically obtained from the respective spatially coregistered ROIs. This scheme was validated on 14 healthy young participants (mean age = 25.86±3.59 years) focusing on bilateral occipital cortex as ROIs. Combined results are presented in combination for different bimodal (fMRI-MRS, MEG-MRS, fMRI-QSM and fMRI-MEG) as well as multimodal (MEG-MRS-QSM and fMRI-MEG-MRS-QSM) neuroimaging data to integrate, analyse and interpret the outcomes from each of them. For each combination, individual masks of specific ROI for respective modalities have been integrated to get a common region information. To quantify the common region of interest, percentage overlap between ROI specific masks from each modality has been computed for each combination with respect to each participant. A schematic of such overlap percentage estimations is immensely helpful in assessing the correlation of metabolic and functional activity which is shared between the modalities and evaluating concordance between them. The entire methodology is also incorporated into an interactive Graphical User Interface (GUI). The proposed framework provides a step forward for coalescence of information obtained from multimodal neuroimaging data and can be extended to any anatomical region depending on the focus of study.