Standardization of electroencephalography for multi-site, multi-platform and multi-investigator studies: Insights from the canadian biomarker integration network in depression

Subsequent to global initiatives in mapping the human brain and investigations of neurobiological markers for brain disorders, the number of multi-site studies involving the collection and sharing of large volumes of brain data, including electroencephalography (EEG), has been increasing. Among the complexities of conducting multi-site studies and increasing the shelf life of biological data beyond the original study are timely standardization and documentation of relevant study parameters. We presentthe insights gained and guidelines established within the EEG working group of the Canadian Biomarker Integration Network in Depression (CAN-BIND). CAN-BIND is a multi-site, multi-investigator, and multiproject network supported by the Ontario Brain Institute with access to Brain-CODE, an informatics platform that hosts a multitude of biological data across a growing list of brain pathologies. We describe our approaches and insights on documenting and standardizing parameters across the study design, data collection, monitoring, analysis, integration, knowledge-translation, and data archiving phases of CAN-BIND projects. We introduce a custom-built EEG toolbox to track data preprocessing with open-access for the scientific community. We also evaluate the impact of variation in equipment setup on the accuracy of acquired data. Collectively, this work is intended to inspire establishing comprehensive and standardized guidelines for multi-site studies

EEG/MEG Source Imaging: Methods, Challenges, and Open Issues

We present the four key areas of research—preprocessing, the volume conductor, the forward problem, and the inverse problem—that affect the performance of EEG and MEG source imaging. In each key area we identify prominent approaches and methodologies that have open issues warranting further investigation within the community, challenges associated with certain techniques, and algorithms necessitating clarification of their implications. More than providing definitive answers we aim to identify important open issues in the quest of source localization

Multimodal imaging of language perception

This Thesis draws together several lines of research by examining language perception in the same individuals using three neuroimaging methods: magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), and electroencephalography (EEG). The MEG experiments conducted in this Thesis demonstrated that neural processing of written and spoken words converges to the superior temporal cortex following initial modality-specific analysis. In both reading and speech perception, the superior temporal cortex is involved in processing word meaning at ∼250-450 ms in the left hemisphere and after ∼450 ms bilaterally. The data thus support a view of a largely shared semantic system in auditory and visual language perception, in line with the assumption that reading acquisition makes use of the neural systems originally developed for speech perception during evolution and in individual language development. The MEG experiments on reading morphologically complex words showed that the left superior temporal activation was enhanced for the morphologically complex words at ∼200-700 ms. The results suggest that the majority of inflected words in the highly inflected Finnish language are represented in a decomposed form and that the decomposition process requires additional neural resources. Only very high-frequency inflected words may acquire full-form representations. The MEG results on parafoveal preview in reading indicated that neural processing of written words in the left hemisphere is affected by a preview of words in the right visual field. The underlying neural mechanism may facilitate reading of connected text in natural conditions. In a direct comparison, MEG and fMRI showed diverging activation patterns in a reading task although the same individuals were performing the same task. Based on the similarity of the EEG responses recorded simultaneously with both MEG and fMRI, the participants were performing the task similarly during the two recordings. The divergent MEG and fMRI results cannot be attributed to differences in the experimental procedures or language since these factors were controlled. Rather, they are likely to reflect actual dissimilarities in the way neural activity in a high-level cognitive task is picked up by MEG evoked responses and fMRI signals

Age-related delay in information accrual for faces: Evidence from a parametric, single-trial EEG approach

Background: In this study, we quantified age-related changes in the time-course of face processing by means of an innovative single-trial ERP approach. Unlike analyses used in previous studies, our approach does not rely on peak measurements and can provide a more sensitive measure of processing delays. Young and old adults (mean ages 22 and 70 years) performed a non-speeded discrimination task between two faces. The phase spectrum of these faces was manipulated parametrically to create pictures that ranged between pure noise (0% phase information) and the undistorted signal (100% phase information), with five intermediate steps. Results: Behavioural 75% correct thresholds were on average lower, and maximum accuracy was higher, in younger than older observers. ERPs from each subject were entered into a single-trial general linear regression model to identify variations in neural activity statistically associated with changes in image structure. The earliest age-related ERP differences occurred in the time window of the N170. Older observers had a significantly stronger N170 in response to noise, but this age difference decreased with increasing phase information. Overall, manipulating image phase information had a greater effect on ERPs from younger observers, which was quantified using a hierarchical modelling approach. Importantly, visual activity was modulated by the same stimulus parameters in younger and older subjects. The fit of the model, indexed by R2, was computed at multiple post-stimulus time points. The time-course of the R2 function showed a significantly slower processing in older observers starting around 120 ms after stimulus onset. This age-related delay increased over time to reach a maximum around 190 ms, at which latency younger observers had around 50 ms time lead over older observers. Conclusion: Using a component-free ERP analysis that provides a precise timing of the visual system sensitivity to image structure, the current study demonstrates that older observers accumulate face information more slowly than younger subjects. Additionally, the N170 appears to be less face-sensitive in older observers