Biology subjects

Almgren, H. B. J.

Publications and source records attributed to Almgren, H. B. J..

2 recordsLinked to original sources

bioRxiv2018

Dynamic causal modelling of fluctuating connectivity in resting-state EEG

Functional and effective connectivity are known to change systematically over time. These changes might be explained by several factors, including intrinsic fluctuations in activity-dependent neuronal coupling and contextual factors, like experimental condition and time. Furthermore, contextual effects may be subject-specific or conserved over subjects. To characterize fluctuations in effective connectivity, we used dynamic causal modelling (DCM) of cross spectral responses over 1 min of electroencephalogram (EEG) recordings during rest, divided into 1-sec windows. We focused on two intrinsic networks: the default mode and the saliency network. DCM was applied to estimate connectivity in each time-window for both networks. Fluctuations in DCM connectivity parameters were assessed using hierarchical parametric empirical Bayes (PEB). Within-subject, between-window effects were modelled with a second-level linear model with temporal basis functions as regressors. This procedure was conducted for every subject separately. Bayesian model reduction was then used to assess which (combination of) temporal basis functions best explain dynamic connectivity over windows. A third (between-subject) level model was used to infer which dynamic connectivity parameters are conserved over subjects. Our results indicate that connectivity fluctuations in the saliency network comprised both subject-specific components and a common component. For the default mode network, connectivity trajectories only showed a common component. For both networks, connections to higher order regions appear to monotonically increase during the one minute period. These results not only establish the predictive validity of dynamic connectivity estimates - in virtue of detecting systematic changes over subjects - they also suggest a network-specific dissociation in the relative contribution of fluctuations in connectivity that depend upon experimental context. We envisage these procedures could be useful for characterizing brain state transitions that may be explained by their cognitive or neuropathological underpinnings.

neuroscience↗

bioRxiv2018

Variability and reliability of effective connectivity within the core default mode network: A longitudinal spectral DCM study

Dynamic causal modelling (DCM) for resting state fMRI - namely spectral DCM - is a recently developed and widely adopted method for inferring effective connectivity in intrinsic brain networks. Most research applying spectral DCM has focused on group-averaged connectivity within large-scale intrinsic brain networks; however, the consistency of subject- and session-specific estimates of effective connectivity has not been evaluated. Establishing reliability (within subjects) is crucial for its clinical use; e.g., as a neurophysiological phenotype of disease progression. Effective connectivity during rest is likely to vary due to changes in cognitive, behavioural, and physical states. Determining the sources of fluctuations in effective connectivity may yield greater understanding of brain processes and inform clinical applications about potential confounds. In the present study, we investigated the consistency of effective connectivity within and between subjects, as well as potential sources of variability (e.g., hemispheric asymmetry). We further investigated how standard procedures for data processing and signal extraction affect this consistency. DCM analyses were applied to four longitudinal resting state fMRI datasets. Our sample consisted of 20 subjects with 653 resting state fMRI sessions in total. These data allowed to quantify the robustness of connectivity estimates for each subject, and to draw conclusions beyond specific data features. We found that subjects contributing to all datasets showed systematic and reliable patterns of hemispheric asymmetry. When asymmetry was taken into account, subjects showed very similar connectivity patterns. We also found that various processing procedures (e.g. global signal regression and ROI size) had little effect on inference and reliability of connectivity for the majority of subjects. Bayesian model reduction increased reliability (within-subjects) and stability (between-subjects) of connectivity patterns.

neuroscience↗