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Ng, Z.

Publications and source records attributed to Ng, Z..

2 recordsLinked to original sources

Developmental changes in individual alpha frequency: Recording EEG data during public engagement events

Statistical power in cognitive neuroimaging experiments is often very low. Low sample size can reduce the likelihood of detecting real effects (false negatives) and increase the risk of detecting non-existing effects by chance (false positives). Here we document our experience of leveraging a relatively unexplored method of collecting a large sample size for simple electroencephalography (EEG) studies: by recording EEG in the community during public engagement and outreach events. We collected data from 346 participants (189 females, age range 6-76 years) over 6 days, totalling 29 hours, at local science festivals. Alpha activity (6-15 Hz) was filtered from 30 seconds of signal, recorded from a single electrode placed between the occipital midline (Oz) and inion (Iz) while participants rested with their eyes closed. A total of 289 good quality datasets were obtained. Using this community-based approach, we were able to replicate controlled, lab-based findings: IAF increased during childhood, reaching a peak frequency of 10.28 Hz at 28.1 years old, and slowed again in middle and older age. Total alpha power decreased linearly, but the aperiodic-adjusted alpha power did not change over the lifespan. Aperiodic slopes and intercepts were highest in the youngest participants. There were no associations between these EEG indexes and self-reported fatigue, measured by the Multidimensional Fatigue Inventory. Finally, we present a set of important considerations for researchers who wish to collect EEG data within public engagement and outreach environments.

neuroscience↗

Benchmarking workflows to assess performance and suitability of germline variant calling pipelines in clinical diagnostic assays.

Benchmarking the performance of complex analytical pipelines is an essential part of developing Laboratory Developed Assays (LDT). Reference samples and benchmark calls published by Genome in a Bottle (GIAB) Consortium have enabled the evaluation of analytical methods. However, the performance of such methods is not uniform across the different regions of the genome/exome and different variant types and lengths. Here we present a scalable and reproducible, cloud-based benchmarking workflow that can be used by clinical laboratories to rapidly access and validate the performance of LDT assays, across their regions of interest and reportable range, using a broad set of benchmarking samples.

bioinformatics↗