In this report, we explore the strengths and limitations of principal component analysis (PCA) and independent component analysis (ICA) for clutter and noise filtering in ultrasonic peripheral perfusion imaging. The advantages of pre-filtering spatial registration to reduce the bandwidth of coherent clutter motion is also considered. PCA methods excel when the echo covariance exhibits a significant blood-scattering component orthogonal to the tissue clutter component. This situation exists in peripheral perfusion imaging when the echo signals are temporally stationary and normally distributed. ICA methods separate non-orthogonal blood-clutter echo components often found in moving clutter, but only for echo signals with either non-normal-amplitude distributions or nonstationary normal distributions. When clutter movement is large and spatially coherent, echo registration followed by PCA filtering can be ideal. Effective filtering is essential for contrast-free ultrasonic perfusion imaging of muscle tissues in the extremities of patients at risk for developing peripheral artery diseases. Statistical filter performance is examined using simulation and echo data from an in vivo ischemic hindlimb mouse model.
All-optical systems for stimulating and imaging neuronal activity have served as powerful tools for understanding the underlying circuitry of the brain. Experiments using these setups, however, tend to choose stimulation locations based solely on what brain regions are of interest, and take for granted that stimulation effects may vary even within localized brain regions. We thus have developed an algorithm for acquiring neuronal activity via calcium imaging data to assess network connectivity. These parameters include the signal rise time, decay time, inter-event intervals, and the timing and amplitude of signal peaks. These parameters are then compared between cell clusters for similarities, and used as a basis for establishing interconnectivity. Additionally, we have incorporated both temporal and spatial correlation functions to assess inter-neuronal connectivity based on these parameters. This data is then run through a genetic algorithm, applying weights to cells with similar parameters to learn which are interconnected in a given field-of-view. For this study, hippocampal neurons extracted from 2 day old transgenic mice (GCaMP6s, Jackson Labs), - cultured for 2 weeks and imaged under single and two-photon conditions. Single-photon imaging was performed under a commercial Zeiss microscope, whereas two-photon imaging was performed with an in-house imaging system. Results demonstrate a strong correlation between these parameters and cellular connectivity, making them noteworthy markers for targeted stimulation. This study demonstrates an efficient method of assessing network connectivity for various imaging techniques, and hence directed targeting for optogenetic stimulation.
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