... in which the order of slices in different multiband sequences is deduced using a silly but informative experiment.
V sodelovanju z Anticevic Lab, Yale School of Medicine, smo razvili programsko okolje Quantitative Neuroimaging Environment & Toolbox (QuNex). Okolje združuje več paketov v prilagodljiv in razširljiv okvir za organizacijo, predobdelavo, zagotavljanje kakovosti in analizo podatkov iz širokega nabora nevroslikovnih metod. Podroben opis je na voljo v nedavno objavljenem članku v
In collaboration with the Anticevic Lab at Yale School of Medicine, we have developed the Quantitative Neuroimaging Environment & Toolbox (QuNex). This toolbox integrates several packages to support a flexible and extensible framework for data organization, preprocessing, quality assurance, and various analyses across neuroimaging modalities. A detailed description of the toolbox
Objavili smo nov članek v reviji Frontiers in Neuroimaging. V članku smo analizirali učinkovitost različnih pristopov za odstranjevanje artefaktov iz EEG meritev, ki so bile sočasno zajete s fMR. Statistična analiza je temeljila na hierarhičnem Bayesovem modeliranju. Članek je dostopen na spletnih straneh Frontiers in Neuroimaging.
A new paper from our lab was recently published in Frontiers in Neuroimaging titled "Evaluation and comparison of most prevalent artifact reduction methods for EEG acquired simultaneously with fMRI". An in-depth evaluation of the most common approaches to MR-induced EEG artifact reduction utilizing Bayesian hierarchical probabilistic modeling. The paper is
Brain activity data inferred from functional MR images (fMRI) are stored in a huge voxel space when acquired, resulting in heavy computational operations and cumbersome data analysis, if no other data reduction approaches are applied. The first common approach to data reduction is to project cortical brain activity onto a
Raziskovalec, doktorski študent, tel.: +386 1 241 1174, email: aleksij.kraljic@ff.uni-lj.si
Researcher, PhD Student, Phone: +386 1 241 1174, email: aleksij.kraljic@ff.uni-lj.si