Biology subjects

Yoo, K.

Publications and source records attributed to Yoo, K..

5 recordsLinked to original sources

bioRxiv2021

Adult neuromarkers of sustained attention and working memory predict inter- and intra-individual differences in these processes in youth

Sustained attention (SA) and working memory (WM) are critical processes, but the brain networks supporting these abilities in development are unknown. We characterized the functional brain architecture of SA and WM in 9-11-year-old children and adults. First, we found that adult network predictors of SA generalized to predict individual differences and fluctuations in SA in youth. A WM network model predicted WM performance both across and within children--and captured individual differences in later recognition memory--but underperformed in youth relative to adults. We next characterized functional connections differentially related to SA and WM in youth compared to adults. Results revealed two network configurations: a dominant architecture predicting performance in both age groups and a secondary architecture, more prominent for WM than SA, predicting performance in one. Thus, functional connectivity predicts SA and WM in youth, with networks predicting WM changing more from preadolescence to adulthood than those predicting SA.

neuroscience↗

bioRxiv2021

Invasive Earthworms Alter Forest Soil Microbiomes and Nitrogen Cycling

Northern hardwood forests in formerly glaciated areas had been free of earthworms until exotic European earthworms were introduced by human activities. The invasion of exotic earthworms is known to dramatically alter soil physical, geochemical, and biological properties, but its impacts on soil microbiomes are still unclear. Here we show that the invasive earthworms alter soil microbiomes and ecosystem functioning, especially for nitrogen cycling. We collected soil samples at different depths from three sites across an active earthworm invasion chronosequence in a hardwood forest in Minnesota, USA. We analyzed the structures and the functional potentials of the soil microbiomes by using amplicon sequencing, high-throughput nitrogen cycle gene quantification (NiCE chip), and shotgun metagenomics. Both the levels of earthworm invasion and soil depth influenced the microbiome structures. In the most recently and minimally invaded soils, Nitrososphaera and Nitrospira as well as the genes related to nitrification were more abundant than in the heavily invaded soils. By contrast, genes related to denitrification and nitrogen fixation were more abundant in the heavily invaded than the minimally invaded soils. Our results suggest that the N cycling in forest soils is mostly nitrification driven before earthworm invasion, whereas it becomes denitrification driven after earthworm invasion.

microbiology↗

bioRxiv2021

A brain-based universal measure of attention: predicting task-general and task-specific attention performance and their underlying neural mechanisms from task and resting state fMRI

Attention is central for many aspects of cognitive performance, but there is no singular measure of a persons overall attentional functioning across tasks. To develop a universal measure that integrates multiple components of attention, we collected data from more than 90 participants performing three different attention-demanding tasks during fMRI. We constructed a suite of whole-brain models that can predict a profile of multiple attentional components - sustained attention, divided attention and tracking, and working memory capacity - from a single fMRI scan type within novel individuals. Multiple brain regions across the frontoparietal, salience, and subcortical networks drive accurate predictions, supporting a universal (general) attention factor across tasks, which can be distinguished from task-specific attention factors and their neural mechanisms. Furthermore, connectome-to-connectome transformation modeling enhanced predictions of an individuals attention-task connectomes and behavioral performance from their rest connectomes. These models were integrated to produce a new universal attention measure that generalizes best across multiple, independent datasets, and which should have broad utility for both research and clinical applications.

neuroscience↗

bioRxiv2020

A cognitive state transformation model for task-general and task-specific subsystems of the brain connectome

The human brain flexibly controls different cognitive behaviors, such as memory and attention, to satisfy contextual demands. Much progress has been made to reveal task-induced modulations in the whole-brain functional connectome, but we still lack a way to model changes in the brains functional organization. Here, we present a novel connectome-to-connectome (C2C) state transformation framework that enables us to model the brains functional reorganization in response to specific task goals. Using functional magnetic resonance imaging data from the Human Connectome Project, we demonstrate that the C2C model accurately generates an individuals task-specific connectomes from their task-free connectome with a high degree of specificity across seven different cognitive states. Moreover, the C2C model amplifies behaviorally relevant individual differences in the task-free connectome, thereby improving behavioral predictions. Finally, the C2C model reveals how the connectome reorganizes between cognitive states. Previous studies have reported that task-induced modulation of the brain connectome is domain-specific as well as domain-general, but did not specify how brain systems reconfigure to specific cognitive states. Our observations support the existence of reliable state-specific systems in the brain and indicate that we can quantitatively describe patterns of brain reorganization, common across individuals, in a computational model.

neuroscience↗

bioRxiv2020

A connectome-based prediction model of long-term memory

Although many studies have investigated the neural basis of intra-individual fluctuations in long-term memory (LTM), few have explored the differences across individuals. Here, we characterize a whole-brain functional connectivity (FC) network based on fMRI data in an n-back task that robustly predicts individual differences in LTM. Critically, although FC during the n-back task also predicted working memory (WM) performance and the two networks had some shared components, they are also largely distinct from each other: the LTM model performance remained robust when we controlled for WM and vice versa. Furthermore, regions important for LTM such as the medial temporal lobe did contribute, but only partially, to predicting LTM. These results demonstrate that individual differences in LTM are dependent on the configuration of a whole-brain functional network including but not limited to regions traditionally associated with LTM during encoding and that such a network is separable from what supports WM.

neuroscience↗