Biology subjects

Litwinczuk, M. C.

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

5 recordsLinked to original sources

bioRxiv2025

Fronto-parietal effective connectivity during working memory in Neurofibromatosis Type 1 adolescents and neurotypical controls

Neurofibromatosis Type 1 (NF1) is a rare, single-gene neurodevelopmental disorder. Atypical brain activation patterns have been linked to working memory difficulties in individuals with NF1. The present work investigates if NF1 has increased inhibitory activity in the frontoparietal network during working memory tasks compared to neurotypical controls. Forty-three adolescents with NF1 and twenty-six age-matched neurotypical controls completed functional magnetic resonance imaging scans during a verbal working memory task. Dynamic causal models (DCMs) were estimated for bilateral frontoparietal network (dorsolateral and ventrolateral prefrontal cortices (dlPFC and vlPFC), superior and inferior parietal gyri (SPG and IPG)). The parametric empirical Bayes approach with Bayesian model reduction was used to test the hypothesis that NF1 diagnosis would be characterised by greater inhibitory self-connections (intrinsic connectivity). Leave-one-out cross-validation (LOO-CV) was performed to test the generalisability of group differences. NF1 participants demonstrated greater average intrinsic connectivity of left dlPFC, IPG, SPG and bilateral vlPFC. The DCM that best explained effects of working memory showed that NF1 group has increased intrinsic connectivity of left vlPFC, but weaker intrinsic connectivity of right vlPFC and left dlPFC. The parameters of these connections showed a modest but positive predictive correlation of r = 0.19 (p = 0.055) with diagnosis status, suggesting a trend toward predictive value. Overall, increased average intrinsic connectivity of left dlPFC, IPG, SPG and bilateral vlPFC in NF1, suggests reduced overall sensitivity of these regions to inputs. Working memory evoked different patterns of input processing in NF1, that cannot be characterised by increased inhibition alone. Instead, modulatory connectivity related to working memory showed less inhibitory self-connectivity of left dlPFC and left vlPFC, and more inhibitory intrinsic connectivity of right vlPFC in NF1. This discrepancy between average and modulatory connectivity suggests that overall NF1 participants are responsive to cognitive task-related inputs but may show atypical adaptation to the task demands of working memory.

neuroscience↗

bioRxiv2025

Functional connectivity reveals increased network segregation and sensorimotor processing during working memory in adolescents with Neurofibromatosis Type 1

Neurofibromatosis type 1 (NF1) is a rare, monogenic condition associated with impairments in working memory processing. However, it is unknown to what extent these impairments are explained by disrupted connectivity and state transitions of the functional brain networks. This study characterised task-state functional connectivity (FC) and network controllability during working memory in NF1 and investigated whether altered controllability distinguished NF1 from neurotypical controls and related to individual differences in working memory performance. We analysed functional MRI data from 53 NF1 adolescents and 36 age-matched neurotypical controls performing a verbal N-back task. Compared to controls, NF1 participants showed reduced FC within frontoparietal network (particularly between the left prefrontal cortex and left intraparietal sulcus), reduced FC between default and limbic networks, and increased FC between control and somatomotor regions. Network controllability analysis revealed increased average controllability but reduced modal controllability and activation energy in prefrontal, parietal, temporal and retrosplenial regions in NF1, indicating that in NF1 these regions have enhanced capacity to drive the network toward easy-to-reach states and are readily activated within the current network organisation. The opposite pattern of reduced average controllability but increased modal controllability and activation energy were observed in somatosensory, auditory, extrastriate and temporal-parietal regions, suggesting enhanced capacity of these regions to drive the network towards difficult-to-reach states. These changes accurately predicted NF1 status, with an area under the ROC curve exceeding 80% across all controllability models. Furthermore, average controllability accurately predicted working memory performance in the NF1 group, generalising to out-of-sample data. In particular, left and right lateral ventral prefrontal cortex, left superior parietal lobule, left intraparietal sulcus consistently emerged as predictors of working memory performance across all measures. By demonstrating that altered regional network controllability in NF1 predicts working memory performance, this study provides a novel mechanistic account of the functional network alterations underlying cognitive difficulties in NF1 and establishes a framework for evaluating future therapeutic interventions through their effects on functional brain network control.

neuroscience↗

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↗