Multi-modal neuroimaging technique: Innovations and applications

1 College of Computer Science and Technology, Taiyuan University of Technology, Jinzhong 030600, Shanxi, China 2 School of Life Science, Beijing Institute of Technology, Beijing 100081, China In the last two decades, neuroimaging techniques have made quite a splash in not only our general understanding of healthy brain working mechanisms but also in gaining a better understanding of cognitive system alterations in brain disorders, such as Alzheimer’s disease (AD), Parkinson’s disease (PD) and schizophrenia (SZ), bipolar disorder (BD), etc. Multi-modal neuroimaging techniques usually includes electroencephalography (EEG), magnetic resonance imaging (MRI), magnetoencephalography (MEG), positron emission tomography (PET), near-infrared spectroscopy (NIRS). Compared with singlemodal neuroimaging technique, multi-modal neuroimaging techniques should significantly contribute to the brain working mechanisms, and promote to identify more valuable information of potential neurobiological markers, and improve the diagnosis accuracy of neurological diseases. The special session includes five papers contributed by experts who have been studying the conceptual and methodological innovations as well as practical applications of the multimodal neuroimaging techniques. Niu and his colleague [1] focused on how the network complexity changes driving spontaneous functional MRI (fMRI) activity in SZ and BD patients. Functional entropy (FE) is a novel way of measuring the dispersion (or spread) of functional connectivities inside the brain. The FE of SZ and BD patients was considerably lower than that of normal control (NC). At the intramodule level, the FE of SZ was substantially higher than that of BD in the cingulo-opercular network. Moreover, a strong negative association between FE and clinical measures was discovered in patient groups. This paper proposed that network connectivity’s complexity analyses using FE can provide important insights for the diagnosis of mental illness. Top-down attention mechanisms require the selection of specific objects or locations; however, the brain mechanism involved when attention is allocated across different modalities is not well understood. Guan and his colleague [2] define the neural mechanisms underlying divided and selective spatial attention by fMRI and Posner paradigm with concurrent audiovisual. They explored the audiovisual top-down allocation of attention and observed the differences in neural mechanisms under endogenous attention modes, which revealed the differences in cross-modal expression in visual and auditory attention under attentional modulation. Specially, the differences in the activation level of the frontoparietal network, visual/auditory cortex, the putamen and the