Donaldson, P. | bioRxiv Science

bioRxiv2023

Resting state aperiodic slope predicts Research Domain Criteria Social Processes in young typically developing adults

The aperiodic exponent of the electrophysiological signal has been utilised to demonstrate differences in brain excitation-inhibition in ageing, cognition, and neuro- and psycho-pathology. Furthermore, excitation-inhibition imbalance has been associated with social communication difficulties in clinical and non-clinical cohorts. No work to date, however, has explored the association between aperiodic activity and social communication difficulties. A total of 40 neurotypical young adults aged 18-24 (24 female; age mean = 21.6, SD = 1.9) each underwent 5 minutes of eyes open and eyes closed resting state electroencephalography using a 64-channel HydroCel Geodesic Sensor Net. Participants also completed Research Domain Criteria social processes measures: Social Responsiveness Scale, Reading the Mind in the Eyes task, and Penn Emotional Recognition Task. Sex differences in aperiodic exponent and offset were observed, with larger exponent and greater offset observed in males, suggestive of greater inhibitory tone. Whole-brain, as well as left and right social brain, aperiodic exponent were moderately correlated with Social Responsiveness Scale Social Communication Index, but not Reading the Mind in the Eyes or Penn Emotional Recognition tasks. However, the correlations between aperiodic exponent and Social Responsiveness Scale Social Communication Index did not survive multiple comparisons correction, and regression models demonstrated that neither aperiodic exponent or offset were significantly predictive of social processes outcomes. These preliminary data provide some evidence that increased aperiodic activity may be associated with social communication difficulties, however, larger samples are needed to validate these initial findings.

neuroscience↗

bioRxiv2021

High throughput measurement of Arabidopsis thaliana fitness traits using transfer learning

O_LIRevealing the contributions of genes to plant phenotype is frequently challenging because the effects of loss of gene function may be subtle or be masked by genetic redundancy. Such effects can potentially be detected by measuring plant fitness, which reflects the cumulative effects of genetic changes over the lifetime of a plant. However, fitness is challenging to measure accurately, particularly in species with high fecundity and relatively small propagule sizes such as Arabidopsis thaliana. C_LIO_LIAn image segmentation-based (ImageJ) and a Faster Region Based Convolutional Neural Network (R-CNN) approach were used for measuring two Arabidopsis fitness traits: seed and fruit counts. C_LIO_LIAlthough straightforward to use, ImageJ was error-prone (correlation between true and predicted seed counts, r2=0.849) because seeds touching each other were undercounted. In contrast, Faster R-CNN yielded near perfect seed counts (r2=0.9996) and highly accurate fruit counts (r2=0.980). C_LIO_LIBy examining seed counts, we were able to reveal fitness effects for genes that were previously reported to have no or condition-specific loss-of-function phenotypes. C_LIO_LIOur study provides models to facilitate the investigation of Arabidopsis fitness traits and demonstrates the importance of examining fitness traits in the study of gene functions. C_LI

bioinformatics↗