CCMuPWA SIGNED

Project "CCMuPWA" data sheet

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 Partnership

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 Project objective

The structural anomalies of corpus callosum (CC) in patients are found highly-correlated with a wide range of disorders, e.g., epilepsy, autism, schizophrenia and mental retardation. However, it remains unclear about the causal contributions of CC-mediated functional changes to these disorders and exactly how the changes influence the local cortical circuitry. Lately, we have successfully combined fMRI with fiber optic mediated calcium recordings and optogenetics, i.e., multi-modal fMRI, to study the balance of excitation/inhibition in the barrel cortex in rats by pairing optogenetic corpus callosum activation with ascending thalamocortical activation. However, it remains challenging to maintain high sensitivity to the brain dynamic signal and better decipher CC-mediated unique cellular (neuron/astrocyte) or layer-specific contributions to the local cortical or global whole-brain fMRI signals. Therefore, the goal of this proposal is to optimize the multi-modal fMRI platform and to characterize the brain activity upon optogenetic callosal activation with higher spatial/temporal resolution using two cutting edge technologies, wireless amplified nuclear MR detector (WAND) and photonic crystal fiber (PCF). Previously, we have implanted a wireless RF coil into the rat body to achieve a high signal-to-noise ratio and spatial resolution for in vivo kidney imaging. The modified WAND will be incorporated into the multi-modal fMRI platform to achieve brain dynamic signal with enhanced sensitivity from the barrel cortex. Next, we will merge it with a novel PCF-based probe integrated calcium recording, optogenetic manipulation and fluid injection function. This proposal will merge the neuronal and astrocytic dynamic signals to the functional mapping, solve the challenges for CC study at multiple scales in the brain, enable novel applications of the multi-modal fMRI platform to better decipher the neuroglial interactions in normal and diseased animal models for future studies.