Biology subjects

Labudda, K.

Publications and source records attributed to Labudda, K..

4 recordsLinked to original sources

bioRxiv2026

A fast and accurate calculation method for light induced isomerization of retinal proteins in real time

Retinal is a chromophore covalently bound to various photoreceptors. Its photo-induced isomerization triggers a series of structural changes named photocycle, leading to diverse biological functions. Despite tremendous advances in structural biology and artificial intelligence-driven structure prediction, it remains challenging to analyze all photocyclic intermediates. Here, we present an optimized computational approach to calculate RSBH+ isomerization and its induced structural changes based on a classical molecular mechanics approach using quantum mechanically improved retinal force field parameters. Isomerization is induced by an excited state restraint which is subsequently relaxed to allow the return to the electronic ground state. We applied this approach to the key protein of optogenetics, Channelrhodopsin-2 from Chlamydomonas reinhardtii (CrChR2). Besides the reformation of the alltrans/CN-anti ground state, we observed the production of a mixture of two isomeric states 13-cis/CN- anti and 13-cis/CN-syn. These findings agree with the previously found branched photocycle model based on experimental data. Our calculations show an asymmetric potential energy landscape of the excited state leading to a corresponding isomerization state distribution. Unlike earlier publications, our procedure describes the retinal photoisomerization on the natural timescale of 500 fs. As our newly derived retinal force field parameter set precisely relies on quantum biological knowledge, it assists to improve the refinement of experimental structure biological data. Our readily customizable strategy provides mechanistic insights at high spatio-temporal resolution, which permits accurate structural predictions of early photocycle intermediates. These insights will stimulate the rational design of optogenetic tools thus providing improved diagnostic and therapeutic approaches for neuronal and other diseases. HighlightsO_LIuniversal method to study molecular mechanism of optogenetic tools C_LIO_LIretinal photo-isomerization calculation in real time C_LIO_LIprediction of branched photo cycle agrees with experimental IR spectroscopic results C_LIO_LIdetected asymmetric excited state potential energy landscape C_LIO_LIassists to improve structural model refinement of retinal proteins C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=116 HEIGHT=200 SRC="FIGDIR/small/707937v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@71f7fdorg.highwire.dtl.DTLVardef@503482org.highwire.dtl.DTLVardef@1a77120org.highwire.dtl.DTLVardef@1f410a0_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2019

Volume-wise analysis of fMRI time series allows accurate prediction of language lateralization

Using fMRI as a clinical tool, for example for lateralizing language, requires that it provides accurate results on the individual level. However, using a single voxel-wise activity map per patient limits how well the uncertainty associated with a decision can be estimated. Here, we explored how using a "volume-wise" analysis, where the lateralization of each time point of a patients fMRI session is evaluated independently, could support clinical decision making. Ninety-six patients with epilepsy who performed a language fMRI were analyzed retrospectively. Results from Wada testing were used as an indication of true language lateralization. Each patients 200 fMRI volumes were correlated with an independent template of prototypical lateralization. Depending on the strength of correlation with the template, each volume was classified as indicating either left-lateralized, bilateral or right-lateralized language. A decision about the patients language lateralization was then made based on how most volumes were classified. The results show that, using a simple majority vote, accuracies of 84% were reached in a sample of 63 patients with high-quality data. When 33 patients with datasets previously deemed inconclusive were added, the same accuracy was reached when more than 43% of a patients volumes were in agreement with each other. Increasing this cutoff to 51% volumes with agreeing classifications allowed for excluding all inconclusive cases and reaching accuracies over 90% for the remaining cases. Further increasing the cutoff to 65% agreeing volumes resulted in correct predictions for all remaining patients. The study confirms the usefulness of fMRI for language lateralization in patients with epilepsy, by demonstrating high accuracies. Furthermore, it illustrates how the diagnostic yield of individual volumes of fMRI data can be increased using simple similarity measures. The accuracy of our approach increased with the number of agreeing volumes, and thus allowed estimating the uncertainty associated with each individual diagnosis.

neuroscience↗

bioRxiv2019

Quantifying the Confidence in fMRI-based Language Lateralisation through Laterality Index Deconstruction

In epilepsy patients, language lateralisation is an important part of the presurgical diagnostic process. Using task-based fMRI, language lateralisation can be determined by visual inspection of activity patterns or by quantifying the difference in left- and right-hemisphere activity using variations of a basic formula [(L-R)/(L+R)]. However, the values of this laterality index (LI) depend on the choice of activity thresholds and regions of interest. The diagnostic utility of the LI also depends on how its continuous values are translated into categorical decisions about a patients language lateralisation. Here, we analysed fMRI data from 712 epilepsy patients who performed a verbal fluency task. Each fMRI data set was evaluated by a trained human rater as depicting left-sided, right-sided, or bilateral lateralisation or as being inconclusive. We used data-driven methods to define the activity thresholds and regions of interest used for LI computation and to define a classification scheme that allowed us to translate the LI values into categorical decisions. By deconstructing the LI into measures of laterality (L-R) and strength (L+R), we also modelled the relationship between activation strength and conclusiveness of a data set. In a held-out data set, predictions reached 91% correct when using only conclusive data and 82% when inconclusive data were included. Although only trained on human evaluations of fMRIs, the approach generalised to the prediction of language Wada test results, allowing for significant above-chance accuracies. Compared against different existing methods of LI-computation, our approach improved the identification of inconclusive cases and increased the accuracy with which decisions for the remaining data could be made. We discuss how this approach can support clinicians in assessing fMRI data on a single-case level, deciding whether lateralisation can be determined with sufficient certainty or whether additional information is needed.

neuroscience↗

bioRxiv2018

Thought experiment: Decoding cognitive processes from the fMRI data of one individual

Cognitive processes, such as the generation of language, can be mapped onto the brain using fMRI. These maps can in turn be used for decoding the respective processes from the brain activation patterns. Given individual variations in brain anatomy and organization, analyzes on the level of the single person are important to improve our understanding of how cognitive processes correspond to patterns of brain activity. They also allow to advance clinical applications of fMRI, because in the clinical setting making diagnoses for single cases is imperative. In the present study, we used mental imagery tasks to investigate language production, motor functions, visuo-spatial memory, face processing, and resting-state activity in a single person. Analysis methods were based on similarity metrics, including correlations between training and test data, as well as correlations with maps from the NeuroSynth meta-analysis. The goal was to make accurate predictions regarding the cognitive domain (e.g. language) and the specific content (e.g. animal names) of single 30-second blocks. Four teams used the dataset, each blinded regarding the true labels of the test data. Results showed that the similarity metrics allowed to reach the highest degrees of accuracy when predicting the cognitive domain of a block. Overall, 23 of the 25 test blocks could be correctly predicted by three of the four teams. Excluding the unspecific rest condition, up to 10 out of 20 blocks could be successfully decoded regarding their specific content. The study shows how the information contained in a single fMRI session and in each of its single blocks can allow to draw inferences about the cognitive processes an individual engaged in. Simple methods like correlations between blocks of fMRI data can serve as highly reliable approaches for cognitive decoding. We discuss the implications of our results in the context of clinical fMRI applications, with a focus on how decoding can support functional localization.

neuroscience↗