Phan, K. L. | bioRxiv Science

bioRxiv2024

Alterations in large-scale resting-state network nodes following transcranial focused ultrasound of deep brain structures

BackgroundLow-intensity transcranial focused ultrasound (tFUS) is a brain stimulation approach that holds immense promise for the treatment of brain-based disorders. Several studies in humans have shown that tFUS can successfully modulate perfusion in focal sonication targets including the amygdala; however, limited research has explored how tFUS impacts the function of large-scale neural networks. ObjectiveThe aim of the current study was to address this gap and examine changes in resting-state connectivity between large-scale network nodes using a randomized, double-blind, within-subject crossover study design. MethodsHealthy adults (n=18) completed two tFUS sessions, 14 days apart. Each session included tFUS of either the right amygdala or the left entorhinal cortex (ErC). The inclusion of two active targets allowed for within-subjects comparisons as a function of the locus of sonication. Resting-state functional magnetic resonance imaging was collected before and after each tFUS session. ResultstFUS altered resting-state functional connectivity (rsFC) within and between rs-network nodes. Specifically, pre-to-post sonication of the right amygdala modulated connectivity within nodes of the salience network (SAN) and between nodes of the SAN and the default-mode network (DMN) and fronto-parietal network (FRP). A decrease in SAN to FPN connectivity was specific to the amygdala target. Pre-to-post sonication of the left ErC was found to modulate connectivity between the dorsal attention network (DAN) and FPN and DMN. An increase in DAN to DMN connectivity was specific to the ErC target. ConclusionThese preliminary findings may suggest that tFUS induces neuroplastic changes beyond the immediate sonication target.

neuroscience↗

bioRxiv2020

A simple technique to classify diffraction data from dynamic proteins according to individual polymorphs

One often observes small but measurable differences in diffraction data measured from different crystals of a single protein. These differences might reflect structural differences in the protein and potentially reflect the natural dynamism of the molecule in solution. Partitioning these mixed-state data into single-state clusters is a critical step to extract information about the dynamic behavior of proteins from hundreds or thousands of single-crystal data sets. Mixed-state data can be obtained deliberately (through intentional perturbation) or inadvertently (while attempting to measure highly redundant single-crystal data). State changes may be expressed as changes in morphology, so that a subset of the polystates may be observed as polymorphs. After mixed-state data are deliberately or inadvertently measured, the challenge is to sort the data into clusters that may represent relevant biological polystates. Here we address this problem using a simple multi-factor clustering approach that classifies each data set using independent observables in order to assign each data set to the correct location in conformation space. We illustrate this method using two independent observables (unit cell constants and intensities) to cluster mixed-state data from chymotrypsinogen (ChTg) crystals. We observe that the data populate an arc of the reaction trajectory as ChTg is converted into chymotrypsin.

molecular biology↗