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Electrical Impedance Tomography (EIT) is a medical imaging technique that reconstructs impedance distribution inside a target object by injecting electrical currents into pairs of electrodes and measuring induced voltages on the remaining electrodes. Since neural signals result from the activity of ion channels causing impedance changes in the cell membrane, EIT can image these neural activities for understanding brain function and medical purposes. In our research, our self-developed electronic prototype board was used to generate high-quality electrical current and collect the data on electrodes with a high sampling rate and bit-resolution. In image reconstruction, a preprocessing data analysis algorithm was newly developed and applied to improve the accuracy of our EIT imaging. The human head has complex anatomical geometry and non-uniform resistivity distribution along with the highly resistive skull, which makes brain-EIT remains challenging inaccurate image reconstruction. To mimic the human head, a multi-layered human head phantom was designed and tested to investigate the effect of the skull structure on imaging. In this presentation, comparison studies for measurements and simulation results will be introduced to discuss the source of errors and improve the accuracy and efficiency of our brain-EIT system. 

Electrical impedance tomography (EIT) is a rising and emerging imaging technique with great potential in many areas, especially in functional brain imaging applications. An EIT system with high speed and accuracy can have many applications to medical devices supporting in diagnosis and treatment of neurological disorders and diseases. In this research, EIT algorithms and hardware are developed and improved to increase reconstructed images' accuracy and decrease the reconstruction time. Due to multiplexer design limitations, EIT measurements are subject to strong capacitive effects from charging and discharging in switching cycles around 300 to 400 samples per 1280 samples (in 10 milliseconds sampling). We developed an algorithm to choose data in steady-state condition only selectively. This method improves the signal-to-noise ratio and results in better reconstruction images. An algorithm to effectively synchronize the beginning points of data was developed to increase the system's speed. This presentation also presents the EIT system's hardware architecture based on Texas Instruments Fixed-Point Digital Signal Processor - TMS320VC5509A, which is low-cost, high potential in popularity the community in the future. For high operation speed, we propose the EIT system used Sitara™ AM57x processors of Texas Instruments. 

Measuring and analyzing local field potential (LFP) signals from basolateral amygdala (BLA), hippocampus (HPC) and medial prefrontal cortex (mPFC) may help understand how they communicate with each other during fear memory formation and extinction. In our research, we have formulated a computationally simple and noise immune instantaneous amplitude cross correlation technique which can deduce lead and lag of LFPs generated in BLA, HPC, and mPFC and the directionality of brain signals exchanged between regions. LFP signals are recorded using depth electrodes in the rat brain and cross correlation analysis is applied to theta wave signals after filtering. We found that rats resilient to traumatic conditions (based on post-stress rapid eye movement sleep (REM)) showed a decrease in LFP signal correlation in REM and non-REM (NREM) sleep cycles between BLA-HPC regions after shock training and one day post shock training compared to vulnerable rats that show stress-induced reductions in REM. It is presumed this difference in neural network behavior may be related to REM sleep differences between resilient and vulnerable rats and may provide clues to help understand how traumatic conditions are processed by the brain.