Biology subjects

Carpenter, C. M.

Publications and source records attributed to Carpenter, C. M..

3 recordsLinked to original sources

bioRxiv2022

Neural Distinctiveness and Reinstatement of Hippocampal Representations Support Unitization for Associations

The medial temporal lobe (MTL) is critical to associative memory success. Yet not all types of associations may be processed in a similar manner within MTL subregions. In particular, work suggests that intra- and inter-item associations not only exhibit differences in overall rates of recollection, but also recruit different MTL subregions. Whereas intra-item associations, akin to unitization, take advantage of associations between within-item features, inter-item associations form links across discrete items. The current work aimed to examine the neural differences between these two types of associations using multivariate neural analyses. Specifically, the current study examined differences across face-occupation as a function of whether the pairing was viewed as a person performing the given job (intra-item binding) or a person saying that they knew someone who had a particular job (inter-item binding). The results show that at encoding, successfully recollected intra- and inter-item associations are discriminable from one another in the hippocampus, parahippocampal cortex, and perirhinal cortex. Additionally, the two trial types are reinstated distinctly such that inter-item trials have higher neural reinstatement from encoding to retrieval compared to intra-item trials in the hippocampus. We conclude that intra- and inter-associative pairs may utilize similar neural regions that represent patterns of activation differentially at encoding. However, in order to reinstate information to the same degree (i.e., subsequently successfully recollected) inter-item associations may act in a compensatory manner, while it is not necessary for intra-item associations to be reinstated to the same degree. This may indicate that intra-item associations promote more efficient reinstatement.

neuroscience↗

bioRxiv2021

Understanding associative false memories in aging using multivariate analyses

Age-related declines in associative memory are ubiquitous, having been observed across a wide array of stimuli and experimental paradigms. Research further shows that such decreases in behavioral discriminability arise from increases in false memories for recombined lures. The current study examined the underlying neural basis of associative false memories by using representational similarity analyses to examine both age differences in the reactivation of encoded representations during retrieval and the neural overlap in the similarity of neural patterns underlying targets and related lures during retrieval. Behaviorally, we observed an age-related reduction in d, which was shown to be driven by increased false alarms in the older adults. While we found no age difference in the relationship between patterns of neural activity underlying hits across memory phases (as measured by ERS), the similarity of neural patterns underlying targets and lures was affected by age. Specifically, while younger and older adults exhibited overall higher pattern similarity between hits and CRs compared to hits and FAs (as measured by RSA), this difference was reduced in older adults within occipital cortices. Additionally, greater Hit-FA representational similarity correlated with increases in associative FAs across occipital, frontal, and parietal cortices. Results suggest that while neural representations underlying targets may not differ across age, greater pattern similarity between the neural representation of targets and lures may reflect reduced distinctiveness of the information encoded in memory, such that old and new items are more difficult to discriminate, leading to more false alarms.

neuroscience↗

bioRxiv2021

PaIRKAT: A pathway integrated regression-based kernel association test with applications to metabolomics and COPD phenotypes

High-throughput data such as metabolomics, genomics, transcriptomics, and proteomics have become familiar data types within the "-omics" family. For this work, we focus on subsets that interact with one another and represent these "pathways" as graphs. Observed pathways often have disjoint components, i.e. nodes or sets of nodes (metabolites, etc.) not connected to any other within the pathway which notably lessens testing power. In this paper we propose the Pathway Integrated Regression-based Kernel Association Test (PaIRKAT), a new kernel machine regression method for incorporating known pathway information into the semi-parametric kernel regression framework. This paper also contributes an application of a graph kernel regularization method for overcoming disconnected pathways. By incorporating a regularized or "smoothed" graph into a score test, PaIRKAT is capable of providing more powerful tests for associations between biological pathways and phenotypes of interest and will be helpful in identifying novel pathways for targeted clinical research. We evaluate this method through several simulation studies and an application to real metabolomics data from the COPDGene study. Our simulation studies illustrate the robustness of this method to incorrect and incomplete pathway knowledge, and the real data analysis shows meaningful improvements of testing power in pathways. PaIRKAT was developed for application to metabolomic pathway data, but the techniques are easily generalizable to other data sources with a graph-like structure. Author SummaryPaIRKAT is a tool for improving testing power on high dimensional data by including graph topography in the kernel machine regression setting. Studies on high dimensional data can struggle to include the complex relationships between variables. The semi-parametric kernel machine regression model is a powerful tool for capturing these types of relationships. They provide a framework for testing for relationships between outcomes of interest and high dimensional data such as metabolomic, genomic, or proteomic pathways. Our paper proposes PaIRKAT, a method for including known biological connections between high dimensional variables by representing them as edges of graphs or networks. It is common for nodes (e.g. metabolites) to be disconnected from all others within the graph, which leads to meaningful decreases in testing power whether or not the graph information is included. We include a graph regularization or smoothing approach for managing this issue. We demonstrate the benefits of this approach through simulation studies and an application to the metabolomic data from the COPDGene study.

bioinformatics↗