Biology subjects

Wehrheim, M. H.

Publications and source records attributed to Wehrheim, M. H..

2 recordsLinked to original sources

bioRxiv2024

Camera Paths, Modeling, and Image Processing Tools for ArtiaX

The enhancement of biomolecular image analysis and data interpretation is significantly improved through the application of advanced visualization techniques. Numerous visualization packages are currently available, spanning a broad spectrum of applications. Recently, we have extended the capabilities of UCSF ChimeraX to address the specific demands of cryo-electron tomography. Here, we introduce the evolution of our existing plugin, ArtiaX, designed to generate models that facilitate particle selection, define camera recording paths, and execute particle selection routines. In particular, diverse models can be generated and populated with putative particle positions and orientations. A specifically tailored coarse grained algorithm was developed to rectify overlaps, as encountered in template matching, employing a rapid and efficient approach. In addition, models can be used to drive the camera position, thereby simplifying the process of movie creation. The plugin incorporates fundamental image filtering options for the on-the-fly analysis of tomographic data and also provides compatibility of particle lists with RELION-5 star files. Collectively, this update of ArtiaX comprehensively encompasses essential tools for the analysis and visualization of electron tomograms. It retains its hallmark attributes of speed, reliability, and user-friendliness, fostering seamless human-machine interaction.

bioinformatics↗

bioRxiv2022

How much data do we need? Lower bounds of brain activation states to predict human cognitive ability

Human functional brain connectivity can be temporally decomposed into states of high and low cofluctuation, defined as coactivation of brain regions over time. Rare states of particularly high cofluctuation have been shown to reflect fundamentals of intrinsic functional network architecture and to be highly subject-specific. However, it is unclear whether such network-defining states also contribute to individual variations in cognitive abilities - which strongly rely on the interactions among distributed brain regions. By introducing CMEP, a new eigenvector-based prediction framework, we show that as few as 16 temporally separated time frames (< 1.5% of 10min resting-state fMRI) can significantly predict individual differences in intelligence (N = 263, p < .001). Against previous expectations, individuals network-defining time frames of particularly high cofluctuation do not predict intelligence. Multiple functional brain networks contribute to the prediction, and all results replicate in an independent sample (N = 831). Our results suggest that although fundamentals of person-specific functional connectomes can be derived from few time frames of highest connectivity, temporally distributed information is necessary to extract information about cognitive abilities. This information is not restricted to specific connectivity states, like network-defining high-cofluctuation states, but rather reflected across the entire length of the brain connectivity time series.

neuroscience↗