The effect of global signal regression on DCM estimates of noise and effective connectivity from resting state fMRI

The influence of global BOLD fluctuations on resting state functional connectivity in fMRI data remains a topic of debate, with little consensus. In this study, we assessed the effects of global signal regression (GSR) on effective connectivity within and between resting state networks (RSNs) - as estimated with dynamic causal modelling (DCM) for resting state fMRI (rsfMRI). DCM incorporates a forward (generative) model that quantifies the contribution of different types of noise (including global measurement noise), effective connectivity, and (neuro)vascular processes to functional connectivity measurements. DCM analyses were applied to two different designs; namely, longitudinal and cross-sectional designs. In the modelling of longitudinal designs, we considered four extensive longitudinal resting state fMRI datasets with a total number of 20 subjects. In the analysis of cross-sectional designs, we used rsfMRI data from 361 subjects from the Human Connectome Project. We hypothesized that (1) GSR would have no discernible impact on effective connectivity estimated with DCM, and (2) GSR would be reflected in the parameters representing global measurement noise. Additionally, we performed comparative analyses of information gain with and without GSR. Our results showed negligible to small effects of GSR on effective connectivity within small (separately estimated) RSNs. However, although the effect sizes were small, there was substantial to conclusive evidence for an effect of GSR on connectivity parameters. For between-network connectivity, we found two important effects: the effect of GSR on between-network effective connectivity (averaged over all connections) was negligible to small, while the effect of GSR on individual connections was non-negligible. In the cross-sectional (but not in the longitudinal) data, some connections showed substantial to conclusive evidence for an effect of GSR. Contrary to our expectations, we found either no effect (in the longitudinal designs) or a non-specific (cross-sectional design) effect of GSR on parameters characterising (global) measurement noise. Data without GSR were found to be more informative than data with GSR; however, in small resting state networks the precision of posterior estimates was greater after GSR. In conclusion, GSR is a minor concern in DCM studies; however, quantitative interpretation of between-network connections (as opposed to average between-network connectivity) and noise parameters should be treated with some caution. The Kullback-Leibler divergence of the posterior from the prior (i.e., information gain) - together with the precision of posterior estimates - might offer a useful measure to assess the appropriateness of GSR in resting state fMRI

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 (betweensubject) level model was used to infer which dynamic connectivity parameters are conserved over subjects. Our results indicate that connectivity fluctuations in the default mode network and to a lesser extent the saliency network comprised both subject-specific components and a common component. For both networks, connections to higher order regions appear to monotonically increase during the 1- min 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

Variability and reliability of effective connectivity within the core default mode network : a multi-site 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 applications of spectral DCM have 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, and physiological states. Quantifying these variations may help understand functional brain architectures - and inform clinical applications. 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 also addressed the effects on consistency of standard data processing procedures. DCM analyses were applied to four longitudinal resting state fMRI datasets. Our sample comprised 17 subjects with 589 resting state fMRI sessions in total. These data allowed us to quantify the robustness of connectivity estimates for each subject, and to generalise our conclusions beyond specific data features. We found that subjects 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 the reliability of connectivity estimates for the majority of subjects. Finally, Bayesian model reduction significantly increased the consistency of connectivity patterns

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

Variability in (computational) neuroscience

Presented at NeuroImaging Method Meeting (Ghent University

Reproducibility in (computational) neuroscience

Presentation given as part of Master's course concerning data analysis (28th March 2018

Användbarhet inom människa-datorinteraktion i praktiken: En kartläggning av utvärderingsmetoder

Studiens syfte är att kartlägga yrkesverksammas preferenser och användning av utvärderingsmetoder för användbarhet inom människa-datorinteraktion. En webbenkät distribuerades via epost till företag med minst en anställd som arbetar med användbarhet samt via sociala medier genom intresseorganisationen STIMDI och företaget Inuse. Totalt erhölls 104 svarande varav en räknades som bortfall då den ansågs vara en dubblett. Resultaten visar att ingen enskild eller kombination av metoder används oftast och rankas ge högst effekt på användbarhet. Angående vilka utvärderingsmetoder som används är intervju och tänka högt med användare de två metoder som nämns av flest deltagare. Resultaten visar även att nästan 80 % skattar användning av utvärderingsmetoder och involvering av användare i deras arbete som mycket viktigt. En tydlig preferens för att involvera användare och arbeta med kombinationer av metoder framgår i denna kartläggning. Forskning bör möjligtvis fokusera mer på hur användandet av utvärderingsmetoder sker i kombination.

Användbarhet inom människa-datorinteraktion i praktiken: En kartläggning av utvärderingsmetoder

Studiens syfte är att kartlägga yrkesverksammas preferenser och användning av utvärderingsmetoder för användbarhet inom människa-datorinteraktion. En webbenkät distribuerades via epost till företag med minst en anställd som arbetar med användbarhet samt via sociala medier genom intresseorganisationen STIMDI och företaget Inuse. Totalt erhölls 104 svarande varav en räknades som bortfall då den ansågs vara en dubblett. Resultaten visar att ingen enskild eller kombination av metoder används oftast och rankas ge högst effekt på användbarhet. Angående vilka utvärderingsmetoder som används är intervju och tänka högt med användare de två metoder som nämns av flest deltagare. Resultaten visar även att nästan 80 % skattar användning av utvärderingsmetoder och involvering av användare i deras arbete som mycket viktigt. En tydlig preferens för att involvera användare och arbeta med kombinationer av metoder framgår i denna kartläggning. Forskning bör möjligtvis fokusera mer på hur användandet av utvärderingsmetoder sker i kombination.

Within and between-subject variability of effective connectivity:A spectral DCM study

Presentation presented at BrainModes 2017 (Delhi, India; as part of Daniele Marinazzo's presentation