The Cuban Human Brain Mapping Project, a young and middle age population-based EEG, MRI, and cognition dataset

The Cuban Human Brain Mapping Project (CHBMP) repository is an open multimodal neuroimaging and cognitive dataset from 282 young and middle age healthy participants (31.9 ± 9.3 years, age range 18–68 years). This dataset was acquired from 2004 to 2008 as a subset of a larger stratified random sample of 2,019 participants from La Lisa municipality in La Habana, Cuba. The exclusion criteria included the presence of disease or brain dysfunctions. Participant data that is being shared comprises i) high-density (64–120 channels) resting-state electroencephalograms (EEG), ii) magnetic resonance images (MRI), iii) psychological tests (MMSE, WAIS-III, computerized go-no go reaction time), as well as iv,) demographic information (age, gender, education, ethnicity, handedness, and weight). The EEG data contains recordings with at least 30 minutes in duration including the following conditions: eyes closed, eyes open, hyperventilation, and subsequent recovery. The MRI consists of anatomical T1 as well as diffusion-weighted (DWI) images acquired on a 1.5 Tesla system. The dataset presented here is hosted by Synapse.org and available at https://chbmp-open.loris.ca.

Measurement(s)	functional brain measurement
Technology Type(s)	electroencephalography (EEG) • magnetic resonance imaging (MRI) • neuropsychological testing
Factor Type(s)	age of participants • gender of participants • handedness of participants • educational level of participants
Sample Characteristic - Organism	Homo sapiens
Sample Characteristic - Location	Cuba

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13277348

A Quantitative EEG Toolbox for the MNI Neuroinformatics Ecosystem: Normative SPM of EEG Source Spectra

The Tomographic Quantitative Electroencephalography (qEEGt) toolbox is integrated with the Montreal Neurological Institute (MNI) Neuroinformatics Ecosystem as a docker into the Canadian Brain Imaging Research Platform (CBRAIN). qEEGt produces age-corrected normative Statistical Parametric Maps of EEG log source spectra testing compliance to a normative database. This toolbox was developed at the Cuban Neuroscience Center as part of the first wave of the Cuban Human Brain Mapping Project (CHBMP) and has been validated and used in different health systems for several decades. Incorporation into the MNI ecosystem now provides CBRAIN registered users access to its full functionality and is accompanied by a public release of the source code on GitHub and Zenodo repositories. Among other features are the calculation of EEG scalp spectra, and the estimation of their source spectra using the Variable Resolution Electrical Tomography (VARETA) source imaging. Crucially, this is completed by the evaluation of z spectra by means of the built-in age regression equations obtained from the CHBMP database (ages 5-87) to provide normative Statistical Parametric Mapping of EEG log source spectra. Different scalp and source visualization tools are also provided for evaluation of individual subjects prior to further post-processing. Openly releasing this software in the CBRAIN platform will facilitate the use of standardized qEEGt methods in different research and clinical settings. An updated precis of the methods is provided in Appendix I as a reference for the toolbox. qEEGt/CBRAIN is the first installment of instruments developed by the neuroinformatic platform of the Cuba-Canada-China (CCC) project.

Cyberinfrastructure for Open Science at the Montreal Neurological Institute

Data sharing is becoming more of a requirement as technologies mature and as global research and communications diversify. As a result, researchers are looking for practical solutions, not only to enhance scientific collaborations, but also to acquire larger amounts of data, and to access specialized datasets. In many cases, the realities of data acquisition present a significant burden, therefore gaining access to public datasets allows for more robust analyses and broadly enriched data exploration. To answer this demand, the Montreal Neurological Institute has announced its commitment to Open Science, harnessing the power of making both clinical and research data available to the world (Owens, 2016a,b). As such, the LORIS and CBRAIN (Das et al., 2016) platforms have been tasked with the technical challenges specific to the institutional-level implementation of open data sharing, including: Comprehensive linking of multimodal data (phenotypic, clinical, neuroimaging, biobanking, and genomics, etc.)Secure database encryption, specifically designed for institutional and multi-project data sharing, ensuring subject confidentiality (using multi-tiered identifiers).Querying capabilities with multiple levels of single study and institutional permissions, allowing public data sharing for all consented and de-identified subject data.Configurable pipelines and flags to facilitate acquisition and analysis, as well as access to High Performance Computing clusters for rapid data processing and sharing of software tools.Robust Workflows and Quality Control mechanisms ensuring transparency and consistency in best practices.Long term storage (and web access) of data, reducing loss of institutional data assets.Enhanced web-based visualization of imaging, genomic, and phenotypic data, allowing for real-time viewing and manipulation of data from anywhere in the world.Numerous modules for data filtering, summary statistics, and personalized and configurable dashboards. Implementing the vision of Open Science at the Montreal Neurological Institute will be a concerted undertaking that seeks to facilitate data sharing for the global research community. Our goal is to utilize the years of experience in multi-site collaborative research infrastructure to implement the technical requirements to achieve this level of public data sharing in a practical yet robust manner, in support of accelerating scientific discovery.

The MNI data-sharing and processing ecosystem

Neuroimaging has been facing a data deluge characterized by the exponential growth of both raw and processed data. As a result, mining the massive quantities of digital data collected in these studies offers unprecedented opportunities and has become paramount for today's research. As the neuroimaging community enters the world of "Big Data", there has been a concerted push for enhanced sharing initiatives, whether within a multisite study, across studies, or federated and shared publicly. This article will focus on the database and processing ecosystem developed at the Montreal Neurological Institute (MNI) to support multicenter data acquisition both nationally and internationally, create database repositories, facilitate data-sharing initiatives, and leverage existing software toolkits for large-scale data processing.

OMEGA: The Open MEG Archive

In contrast with other imaging modalities, there is presently a scarcity of fully open resources in magnetoencephalography (MEG) available to the neuroimaging community. Here we present a collaborative effort led by the McConnell Brain Imaging Centre of the Montreal Neurological Institute, and the Université de Montréal to build and share a centralised repository to curate MEG data in raw and processed form for open dissemination. The Open MEG Archive (OMEGA, omega.bic.mni.mcgill.ca) is bound to become a continuously expanding repository of multimodal data with a primary focus on MEG, in addition to storing anatomical MRI volumes, demographic participant data and questionnaires, and other forms of electrophysiological data such as EEG. The OMEGA initiative offers both the technological framework for multi-site MEG data aggregation, and serves as one of the largest freely available resting-state and eventually task-related MEG datasets presently available.

Absolute quantification of resting oxygen metabolism and metabolic reactivity during functional activation using QUO2 MRI

We have recently described an extension of calibrated MRI, which we term QUO2 (for QUantitative O(2) imaging), providing absolute quantification of resting oxidative metabolism (CMRO(2)) and oxygen extraction fraction (OEF(0)). By combining BOLD, arterial spin labeling (ASL) and end-tidal O(2) measurements in response to hypercapnia, hyperoxia and combined hyperoxia/hypercapnia manipulations, and the same MRI measurements during a task, a comprehensive set of vascular and metabolic measurements can be obtained using a generalized calibration model (GCM). These include the baseline absolute CBF in units of ml/100g/min, cerebrovascular reactivity (CVR) in units of %Δ CBF/mm Hg, M in units of percent, OEF(0) and CMRO(2) at rest in units of μmol/100g/min, percent evoked CMRO(2) during the task and n, the value for flow-metabolic coupling associated with the task. The M parameter is a calibration constant corresponding to the maximal BOLD signal that would occur upon removal of all deoxyhemoglobin. We have previously shown that the GCM provides estimates of the above resting parameters in grey matter that are in excellent agreement with literature. Here we demonstrate the method using functionally-defined regions-of-interest in the context of an activation study. We applied the method under high and low signal-to-noise conditions, corresponding respectively to a robust visual stimulus and a modified Stroop task. The estimates fall within the physiological range of literature values, showing the general validity of the GCM approach to yield non-invasively an extensive array of relevant vascular and metabolic parameters.

Task-related BOLD responses and resting-state functional connectivity during physiological clamping of end-tidal CO(2)

Carbon dioxide (CO(2)), a potent vasodilator, is known to have a significant impact on the blood-oxygen level dependent (BOLD) signal. With the growing interest in studying synchronized BOLD fluctuations during the resting state, the extent to which the apparent synchrony is due to variations in the end-tidal pressure of CO(2) (PETCO(2)) is an important consideration. CO(2)-related fluctuations in BOLD signal may also represent a potential confound when studying task-related responses, especially if breathing depth and rate are affected by the task. While previous studies of the above issues have explored retrospective correction of BOLD fluctuations related to arterial PCO(2), here we demonstrate an alternative approach based on physiological clamping of the arterial CO(2) level to a near-constant value. We present data comparing resting-state functional connectivity within the default-mode-network (DMN), as well as task-related BOLD responses, acquired in two conditions in each subject: 1) while subject's PETCO(2) was allowed to vary spontaneously; and 2) while controlling subject's PETCO(2) within a narrow range. Strong task-related responses and areas of maximal signal correlation in the DMN were not significantly altered by suppressing fluctuations in PETCO(2). Controlling PETCO(2) did, however, improve the performance of retrospective physiological noise correction techniques, allowing detection of additional regions of task-related response and resting-state connectivity in highly vascularized regions such as occipital cortex. While these results serve to further rule out systemic physiological fluctuations as a significant source of apparent resting-state network connectivity, they also demonstrate that fluctuations in arterial CO(2) are one of the factors limiting sensitivity in task-based and resting-state fMRI, particularly in regions of high vascular density. This must be considered when comparing subject groups who might exhibit differences in respiratory physiology or breathing patterns.

Levodopa influences striatal activity but does not affect cortical hyper-activity in Parkinson's disease

Motor studies of Parkinson's disease (PD) have shown cortical hypo-activity in relation to nigrostriatal dopamine depletion. Cognitive studies also identified increased cortical activity in PD. We have previously suggested that the hypo-activity/hyper-activity patterns observed in PD are related to the striatal contribution. Tasks that recruit the striatum in control participants are associated with cortical hypo-activity in patients with PD, whereas tasks that do not result in cortical hyper-activity. The putamen, a structure affected by the neurodegeneration observed in PD, shows increased activation for externally-triggered (ET) and self-initiated (SI) movements. The first goal of this study was to evaluate the effect of levodopa on the putamen's response to ET and SI movements. Our second goal was to assess the effect of levodopa on the hypo-activity/hyper-activity patterns in cortical areas. Patients with PD on and off levodopa and healthy volunteers performed SI, ET and control finger movements during functional magnetic resonance imaging. Healthy participants displayed significant differences in putamen activity in ET and SI movements. These differences were reduced in patients off medication, with non-task-specific increases in activity after levodopa administration. Furthermore, the ventrolateral prefrontal cortex showed significant increases in activity during SI movements in healthy controls, whereas it was hypo-active in PD. This region showed significantly increased activity during ET movements in patients off medication. Levodopa had no effect on this discrepancy. Our results suggest that dopamine replacement therapy has a non-task-specific effect on motor corticostriatal regions, and support the hypothesis that increases and decreases in cortical activity in PD are related to the mesocortical dopamine pathway imbalance.