Sliding window correlation analysis: Modulating window shape for dynamic brain connectivity in resting state.

The sliding window correlation (SWC) analysis is a straightforward and common approach for evaluating dynamic functional connectivity. Despite the fact that sliding window analyses have been long used, there are still considerable technical issues associated with the approach. A great effort has recently been dedicated to investigate the window setting effects on dynamic connectivity estimation. In this direction, tapered windows have been proposed to alleviate the effect of sudden changes associated with the edges of rectangular windows. Nevertheless, the majority of the windows exploited to estimate brain connectivity tend to suppress dynamic correlations, especially those with faster variations over time. Here, we introduced a window named modulated rectangular (mRect) to address the suppressing effect associated with the conventional windows. We provided a frequency domain analysis using simulated time series to investigate how sliding window analysis (using the regular window functions, e.g. rectangular and tapered windows) may lead to unwanted spectral modulations, and then we showed how this issue can be alleviated through the mRect window. Moreover, we created simulated dynamic network data with altering states over time using simulated fMRI time series, to examine the performance of different windows in tracking network states. We quantified the state identification rate of different window functions through the Jaccard index, and observed superior performance of the mRect window compared to the conventional window functions. Overall, the proposed window function provides an approach that improves SWC estimations, and thus the subsequent inferences and interpretations based on the connectivity network analyses.

Single-shot coherent noise suppression by spatial interferometric heterodyning.

Coherent noise affects the information content in active imaging systems. Here we show that noise reduction can be accomplished using the intrinsic coherence properties of the electromagnetic fields. We demonstrate numerically and experimentally that a single-shot measurement using interferometric spatial heterodyning detection in conjuncture with computational image processing, permits suppressing coherent noise in conditions of low signal-to-noise ratios, through spectral upshifting of coherent information.

Compressive correlation imaging with random illumination.

Achieving high rates and high resolution in noisy conditions is a main desiderate for computational imaging. Here, we demonstrate both numerically and experimentally that binary illumination based on spatially random distributions provides superior imaging capabilities at high compression ratios while operating in noisy environments. The proposed method permits decreasing the time for image formation without adding complexity to the imaging system or compromising the resolution.