Global fitting for high-accuracy multi-channel single-molecule localization

Multi-channel detection in single-molecule localization microscopy (SMLM) greatly increases information content for various biological applications. Here, we present globLoc, a graphics processing unit (GPU) based global fitting algorithm with flexible PSF modeling and parameter sharing, to extract maximum information from multi-channel single molecule data. We show, both in simulations and experiments, that global fitting can substantially improve the 3D localization precision for biplane and 4Pi SMLM and color assignment for ratiometric multicolor imaging.

The HAPPE plus Event-Related (HAPPE+ER) Software: A Standardized Processing Pipeline for Event-Related Potential Analyses

Event-Related Potential (ERP) designs are a common method for interrogating neurocognitive function with electroencephalography (EEG). However, the gold standard of preprocessing ERP data is manual-editing, a subjective, time-consuming processes. A number of automated pipelines have recently been created to address the need for standardization, automation, and quantification of EEG data processing; however, few are optimized for ERP analyses (especially in developmental or clinical populations). To fill this need, we propose and validate the HAPPE plus Event-Related (HAPPE+ER) software, a standardized and automated processing pipeline optimized for ERP analyses in EEG data. HAPPE+ER processes event-related potential data from raw files through a series of filtering, line noise reduction, bad channel detection, artifact rejection from continuous data, segmentation, and bad segment rejection methods. HAPPE+ER also includes post-processing reports of both data quality and pipeline quality metrics to facilitate the evaluation and reporting of data processing in a standardized manner. Finally, HAPPE+ER includes a post-processing script to facilitate generating ERP figures and measures for statistical analysis. We describe multiple approaches with both adult and developmental data to optimize and validate pipeline performance. HAPPE+ER software is freely available under the terms of GNU General Public License at https://github.com/PINE-Lab/HAPPE.

HAPPILEE: The Harvard Automated Processing Pipeline In Low Electrode Electroencephalography, a standardized software for low density EEG and ERP data

Low-density Electroencephalography (EEG) recordings (e.g. fewer than 32 electrodes) are widely-used in research and clinical practice and enable scalable brain function measurement across a variety of settings and populations. Though a number of automated pipelines have recently been proposed to standardize and optimize EEG preprocessing for high-density systems with state-of-the-art methods, few solutions have emerged that are compatible with low-density systems. However, low-density data often include long recording times and/or large sample sizes that would benefit from similar standardization and automation with contemporary methods. To address this need, we propose the HAPPE In Low Electrode Electroencephalography (HAPPILEE) pipeline as a standardized, automated pipeline optimized for EEG recordings with low density channel layouts of any size. HAPPILEE processes task-free (e.g. resting-state) and task-related EEG, and event-related potential (ERP) data, from raw files through a series of processing steps including filtering, line noise reduction, bad channel detection, artifact rejection from continuous data, segmentation, and bad segment rejection that have all been optimized for low density data. HAPPILEE also includes post-processing reports of data and pipeline quality metrics to facilitate the evaluation and reporting of data quality and processing-related changes to the data in a standardized manner. We describe multiple approaches with both recorded and simulated EEG data to optimize and validate pipeline performance. The HAPPILEE pipeline is freely available as part of HAPPE 2.0 software under the terms of the GNU General Public License at: https://github.com/PINE-Lab/HAPPE.