Biology subjects

Litwinczuk, M. C.

Publications and source records attributed to Litwinczuk, M. C..

3 recordsLinked to original sources

bioRxiv2024

Effects of non-invasive brain stimulation on effective connectivity during working memory task in Neurofibromatosis Type 1 patients

This study examined the effects of anodal transcranial direct current stimulation (atDCS) on effective connectivity during a working memory task. Eighteen adolescents with Neurofibromatosis Type 1 (NF1) completed a single{square}blind sham{square}controlled cross{square}over randomised atDCS trial. Dynamic causal modelling was used to estimate the effective connectivity between regions that showed working memory effects from the fMRI. Group-level inferences for between sessions (pre- and post-stimulation) and stimulation type (atDCS and sham) effects were carried out using the parametric empirical Bayes approach. A correlation analysis was performed to relate the estimated effective connectivity parameters of left dlPFC pre-atDCS and post-atDCS to the concentration of gamma-aminobutyric acid (GABA) measured via magnetic resonance spectroscopy (MRS-GABA). Next, correlation analysis was repeated using all working memory performance and all pre-atDCS and post-atDCS connectivity parameters. It was found that atDCS decreased average excitatory connectivity from left dorsolateral prefrontal cortex (dlPFC) to left superior frontal gyrus and increased average excitatory connectivity to left globus pallidus. Further, reduced average intrinsic (inhibitory) connectivity of left dlPFC was associated with lower MRS-GABA. However, none of the connectivity parameters of dlPFC showed any association with performance on a working memory task. These findings suggest that atDCS reorganised connectivity from frontal to fronto-striatal connectivity. As atDCS-related changes were not specific to the effect of working memory, they may have impacted general cognitive control processes. In addition, by reducing MRS-GABA, atDCS might make dlPFC more sensitive and responsive to external stimulation, such as performance of cognitive tasks. Highlights- atDCS was applied to left dlPFC in NF1 patients during working memory - After atDCS, no effect on modulatory connectivity - Evidence for increased N-back average connectivity from dlPFC to globus pallidus - Less dlPFC MRS-GABA was associated with less dlPFC inhibition

neuroscience↗

bioRxiv2023

Impact of brain parcellation on prediction error in models of cognition and demographics

Brain connectivity analysis begins with the selection of a parcellation scheme that will define brain regions as nodes of a network whose connections will be studied. Brain connectivity has already been used in predictive modelling of cognition, but it remains unclear if the resolution of the parcellation used can systematically impact the predictive model performance. In this work, structural, functional and combined connectivity were each defined with 5 different parcellation schemes. The resolution and modality of the parcellation schemes were varied. Each connectivity defined with each parcellation was used to predict individual differences in age, education, sex, Executive Function, Self-regulation, Language, Encoding and Sequence Processing. It was found that low-resolution functional parcellation consistently performed above chance at producing generalisable models of both demographics and cognition. However, no single parcellation scheme proved superior at predictive modelling across all cognitive domains and demographics. In addition, although parcellation schemes impacted the global organisation of each connectivity type, this difference could not account for the out-of-sample prediction performance of the models. Taken together, these findings demonstrate that while high-resolution parcellations may be beneficial for modelling specific individual differences, partial voluming of signals produced by higher resolution of parcellation likely disrupts model generalisability.

neuroscience↗

bioRxiv2021

Combination of structural and functional connectivity explains unique variation in specific domains of cognitive function.

The relationship between structural and functional brain networks has been characterised as complex: the two networks mirror each other and show mutual influence but they also diverge in their organisation. This work explored whether a combination of structural and functional connectivity can improve predictive models of cognitive performance. Principal Component Analysis (PCA) was first applied to cognitive data from the Human Connectome Project to identify components reflecting five cognitive domains: Executive Function, Self-regulation, Language, Encoding and Sequence Processing. A Principal Component Regression (PCR) approach was then used to fit predictive models of each cognitive domain based on structural (SC), functional (FC) or combined structural-functional (CC) connectivity. Self-regulation, Encoding and Sequence Processing were best modelled by FC, whereas Executive Function and Language were best modelled by CC. The present study demonstrates that integrating structural and functional connectivity can help predict cognitive performance, but that the added explanatory value may be (cognitive) domain-specific. Implications of these results for studies of the brain basis of cognition in health and disease are discussed. HighlightsO_LIWe assessed the relationship between cognitive domains and structural, functional and combined structural-functional connectivity. C_LIO_LIWe found that Executive Function and Language components were best predicted by combined models of functional and structural connectivity. C_LIO_LISelf-regulation, Encoding and Sequence Processing were best predicted by functional connectivity alone. C_LIO_LIOur findings provide insight into separable contributions of functional, structural and combined connectivity to different cognitive domains. C_LI

neuroscience↗