Hydra vulgaris, a relatively uncomplicated species, is of extreme interest for its capacity to perform complex behavior patterns, despite the lack of a central nervous system. Various patterns of Hydra movement have been studied, but the potential effects of prey movement on its locomotion are not well characterized. Previous research shows that the head of the Hydra, where the tentacles are located, is the most mechanically sensitive part of the Hydra’s body. This is believed to be because the mouth is located at the base of the tentacles and proximity to the mechanosensitive areas is beneficial for feeding and, therefore, survival. Our study used a modified version of lab standard feeding procedures, which necessarily included reducing the concentration of prey introduced during feeding. The prey of choice, 2-3 day old brine shrimp, are known to be very locomotive. We aimed to uncover if the mechanical stimuli generated by the shrimp is enough to observe changes in the Hydra’s feeding behavior, such as an overall increase in locomotion and the appearance of probing or tracking movements. Utilizing directional indices and behavioral analysis techniques, we hope to find evidence of behavioral adjustment in response to prey stimuli. This study contributes to the understanding of flexibility and modulation of survival-related behaviors in even the simplest of creatures.

This study explores how the human brain processes faces versus words, focusing on whether these processes are spatially or non-spatially dependent. Drawing inspiration from artificial intelligence systems like ChatGPT, which process language via one-dimensional positional encoding, we hypothesize that language may be processed in a similar one-dimensional “hyperspace,” while face recognition relies on three-dimensional spatial scaling. Using 3D virtual reality, participants memorized a set of faces or words and were later asked to recognize them under varying size and distance conditions. Reaction time data revealed that face recognition exhibited a V-shaped pattern, with the fastest responses occurring when stimuli matched the ideal size / distance. In contrast, word recognition reaction times remained consistently flat, regardless of changes in size or distance. These findings suggest that facial recognition operates through a spatial scaling mechanism, whereas language recognition functions independently of spatial variables. This divergence highlights distinct cognitive frameworks in the brain and suggests that human language processing may mirror the non-spatial architecture seen in large language models.

Biological sex has a large impact on Autism Spectrum Disorder (ASD) prevalence, with diagnosis occuring in a ratio of 4:1 for males vs. females. Rare genetic variants, such as 22q Deletion (22qDel) markedly increase ASD risk (10-40%). However, sex differences in both ASD diagnosis, behavioral symptoms and neural traits remain poorly understood in 22qDel. We examined sex differences in ASD diagnosis, associated dimensional measures, and resting-state functional connectivity (FC); (n=82, 48.8% F; age M=12.9, SD =5.96). T-tests were performed for sex differences in the Repetitive Behavior Scale(RBS), Social Responsiveness Scale(SRS), and Short Sensory Profile (SSP). Linear regression was used to investigate within-network FC in the somato-motor and auditory brain networks. 61.9% males vs. 47.5% females with 22qDel had ASD diagnosis. Significant sex differences in sensory processing were observed. Females endorsed fewer behaviors on measures of auditory filtering (p =.017,CI[0.55-5.35]) and sensory under-responsiveness (p=.0001,CI[3.19-9.16]). FC analysis found no sex differences in connectivity within auditory or somatomotor networks, nor was connectivity associated with the SSP sex difference results. Unlike idiopathic ASD, there are no significant sex differences in rates of ASD diagnosis and 22qDel however, under-responsiveness and auditory processing difficulties may be more pronounced in males. Future analyses will explore how sex differences in sensory processing map onto neural activity in 22qDel.

Alzheimer’s disease (AD) is a neurodegenerative disease characterized by progressively worsening memory loss and cognitive decline, with prevalence increasing markedly with age from 1.7% in individuals aged 65–74 to 13.1% in those aged 85 and older. Moreover, women are disproportionately affected at a rate of 2:1 as compared to men. Some literature suggests this inter-sex disparity could be attributed to the decrease in estrogen production during menopause and longer lifespans women tend to have. With that, this study examined how lifetime estrogen exposure in women, estimated through comprehensive medical and reproductive histories, could impact AD risk and prevention. As AD spreads through the brain, neurodegeneration occurs in affected areas and results in a thinner cortex. MRI scans from women who participated in the Women’s Health Initiative Memory Study (WHIMS-MRI) were analyzed in order to determine mean cortical thickness (CT) as a measure of AD effects. Data regarding age, parity, and reproductive span was also gathered and controlled for in general linear models assessing cumulative estrogen exposure. Analyses of the models showed that greater cumulative estrogen exposure in a woman’s lifetime was significantly correlated with increased CT. These findings suggest that a greater amount of estrogen exposure in a lifetime can offer a greater protective effect against AD, and that motherhood causes instrumental changes to the brain that come back into play later in life in beneficial ways.

The GGCCCC hexanucleotide repeat expansion in C9ORF72 is strongly associated with ALS and FTD, yet its distribution and genomic context in the general population remain unexplored. This study leverages ExpansionHunter to estimate repeat sizes and ClassicMendel for a pedigree-based GWAS to identify genetic variants linked to repeat expansion variation. Using genomic data from the SPARK cohort and SSC comprised of quad families with healthy parents, proband, and unaffected sibling, a 133-kb SNP association peak on chromosome 9 was identified, with phased haplotypes constructed using SHAPEIT5. Linear regression analyses revealed several SNPs significantly associated with repeat length (with different directions of effects), but multivariate modeling suggested collinearity among significant variants. One haplotype block spanning the association peak was identified, suggesting that these associated SNPs were in high LD structures. By integrating phasing, GWAS, and statistical modeling, this study provides insights into the genetic architecture of repeat expansions beyond known disease contexts. Future work will focus on fine mapping such as using conditional analysis to identify causal SNPs and validation of candidate variants and haplotypes to determine their role in repeat instability and potential phenotypic consequences. Understanding the factors influencing repeat expansions in the general population can enhance our knowledge of genomic instability and its potential role in human traits and disease susceptibility.