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

Improving the reliability of model-based decision-making estimates in the two-stage decision task with reaction-times and drift-diffusion modeling

A well-established notion in cognitive neuroscience proposes that multiple brain systems contribute to choice behaviour. These include: (1) a model-free system that uses values cached from the outcome history of alternative actions, and (2) a model-based system that considers action outcomes and the transition structure of the environment. The widespread use of this distinction, across a range of applications, renders it important to index their distinct influences with high reliability. Here we consider the two-stage task, widely considered as a gold standard measure for the contribution of model-based and model-free systems to human choice. We tested the internal/temporal stability of measures from this task, including those estimated via an established computational model, as well as an extended model using drift-diffusion. Drift-diffusion modeling suggested that both choice in the first stage, and RTs in the second stage, are directly affected by a model-based/free trade-off parameter. Both parameter recovery and the stability of model-based estimates were poor but improved substantially when both choice and RT were used (compared to choice only), and when more trials (than conventionally used in research practice) were included in our analysis. The findings have implications for interpretation of past and future studies based on the use of the two-stage task, as well as for characterising the contribution of model-based processes to choice behaviour

Using stratified medicine to understand, diagnose, and treat neuropathic pain

Neuropathic pain (NeuP) is defined as pain arising from a lesion or disease of the somatosensory nervous system. NeuP is common, affecting approximately 6-8% of the general population and currently treatment is inadequate due to both poor drug efficacy and tolerability. Many different types of injury can cause neuropathic pain including genetic (e.g. SCN9A gain of function variants), metabolic (e.g. diabetic polyneuropathy), infective (e.g. HIV associated neuropathy, hepatitis), traumatic and toxic (e.g. chemotherapy induced neuropathy) causes. Such injurious events can impact on anatomically distinct regions of the somatosensory nervous system ranging from the terminals of nociceptive afferents (in small fiber neuropathy) to the thalamus (in post-stroke pain). Classification of neuropathic pain using etiology and location remains an important aspect of routine clinical practice; however, pain medicine is coming to the realization that we need more precision in this classification. The hope is that improved classification will lead to better understanding of risk, prognosis and optimal treatment of NeuP