A5059671113- Functional Brain Connectivity Studies
- Neural dynamics and brain function
- Mental Health Research Topics
- EEG and Brain-Computer Interfaces
- Advanced Neuroimaging Techniques and Applications
- Complex Network Analysis Techniques
- Multiple Sclerosis Research Studies
- Alzheimer's disease research and treatments
- Transcranial Magnetic Stimulation Studies
- Neural and Behavioral Psychology Studies
University of Manchester
2021-2024
Manchester Academic Health Science Centre
2024
The relationship between structural and functional brain networks has been characterised as complex: the two mirror each other show mutual influence but they also diverge in their organisation. This work explored whether a combination of connectivity can improve fit regression models cognitive performance. Principal Component Analysis (PCA) was first applied to data from Human Connectome Project identify latent components: Executive Function, Self-regulation, Language, Encoding Sequence...
Brain connectivity analysis begins with the selection of a parcellation scheme that will define brain regions as nodes network whose connections be studied. has already been used in predictive modelling cognition, but it remains unclear if resolution can systematically impact model performance. In this work, structural, functional and combined were each defined five different schemes. The modality schemes varied. Each was to predict individual differences age, education, sex, executive...
Abstract Graph theory has been used in cognitive neuroscience to understand how organisational properties of structural and functional brain networks relate function. may bridge the gap integration connectivity by introducing common measures network characteristics. However, explanatory predictive value combined graph have not investigated modelling performance healthy adults. In this work, a Principal Component Regression approach with embedded Step‐Wise was fit multiple regression models...
Abstract 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 single□blind sham□controlled cross□over randomised atDCS trial. Dynamic causal modelling was used to estimate between regions that showed from fMRI. Group-level inferences for sessions (pre- and post-stimulation) type (atDCS sham) were carried out using parametric empirical...
Abstract The relationship between structural and functional brain networks has been characterised as complex: the two mirror each other show mutual influence but they also diverge in their organisation. This work explored whether a combination of connectivity can improve predictive models cognitive performance. Principal Component Analysis (PCA) was first applied to data from Human Connectome Project identify components reflecting five domains: Executive Function, Self-regulation, Language,...
ABSTRACT Brain connectivity analysis begins with the selection of a parcellation scheme that will define brain regions as nodes network whose connections be studied. has already been used in predictive modelling cognition, but it remains unclear if resolution can systematically impact model performance. In this work, structural, functional and combined were each defined 5 different schemes. The modality schemes varied. Each was to predict individual differences age, education, sex, Executive...