The MNI data-sharing and processing ecosystem

AbstractNeuroimaging 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

Advances in studying brain morphology: the benefits of open-access data

Until recently, neuroimaging data for a research study needed to be collected within one’s own lab. However, when studying inter-individual differences in brain structure, a large sample of participants is necessary. Given the financial costs involved in collecting neuroimaging data from hundreds or thousands of participants, large-scale studies of brain morphology could previously only be conducted by well-funded laboratories with access to MRI facilities and to large samples of participants. With the advent of broad open-access data-sharing initiatives, this has recently changed–here the primary goal of the study is to collect large datasets to be shared, rather than sharing of the data as an afterthought. This paradigm shift is evident as increase in the pace of discovery, leading to a rapid rate of advances in our characterization of brain structure. The utility of open-access brain morphology data is numerous, ranging from observing novel patterns of agerelated differences in subcortical structures to the development of more robust cortical parcellation atlases, with these advances being translatable to improved methods for characterizing clinical disorders (see Figure 1 for an illustration). Moreover, structural MRIs are generally more robust than functional MRIs, relative to potential artifacts and in being not task-dependent, resulting in large potential yields. While the benefits of open-access data have been discussed more broadly within the field of cognitive neuroscience elsewhere (Van Horn and Gazzaniga, 2013; Poldrack and Gorgolewski, 2014; Van Horn and Toga, 2014; Vogelstein et al., 2016; Voytek, 2016; Gilmore et al., 2017), as well as in other fields (Choudhury et al., 2014; Ascoli et al., 2017; Davies et al., 2017), this opinion paper is focused specifically on the implications of open data to brain morphology research

Infants later diagnosed with autism have lower canonical babbling ratios in the first year of life

BACKGROUND: Canonical babbling-producing syllables with a mature consonant, full vowel, and smooth transition-is an important developmental milestone that typically occurs in the first year of life. Some studies indicate delayed or reduced canonical babbling in infants at high familial likelihood for autism spectrum disorder (ASD) or who later receive an ASD diagnosis, but evidence is mixed. More refined characterization of babbling in the first year of life in infants with high likelihood for ASD is needed. METHODS: Vocalizations produced at 6 and 12 months by infants (n = 267) taking part in a longitudinal study were coded for canonical and non-canonical syllables. Infants were categorized as low familial likelihood (LL), high familial likelihood diagnosed with ASD at 24 months (HL-ASD) or not diagnosed (HL-Neg). Language delay was assessed based on 24-month expressive and receptive language scores. Canonical babble ratio (CBR) was calculated by dividing the number of canonical syllables by the number of total syllables. Generalized linear (mixed) models were used to assess the relationship between group membership and CBR, controlling for site, sex, and maternal education. Logistic regression was used to assess whether canonical babbling ratios at 6 and 12 months predict 24-month diagnostic outcome. RESULTS: No diagnostic group differences in CBR were detected at 6 months, but HL-ASD infants produced significantly lower CBR than both the HL-Neg and LL groups at 12 months. HL-Neg infants with language delay also showed reduced CBR at 12 months. Neither 6- nor 12-month CBR was significant predictors of 24-month diagnostic outcome (ASD versus no ASD) in logistic regression. LIMITATIONS: Small numbers of vocalizations produced by infants at 6 months may limit the reliability of CBR estimates. It is not known if results generalize to infants who are not at high familial likelihood, or infants from more diverse racial and socioeconomic backgrounds. CONCLUSIONS: Lower canonical babbling ratios are apparent by the end of the first year of life in ASD regardless of later language delay, but are also observed for infants with later language delay without ASD. Canonical babbling may lack specificity as an early marker when used on its own

To the Cloud! A Grassroots Proposal to Accelerate Brain Science Discovery

The revolution in neuroscientific data acquisition is creating an analysis challenge. We propose leveraging cloud-computing technologies to enable large-scale neurodata storing, exploring, analyzing, and modeling. This utility will empower scientists globally to generate and test theories of brain function and dysfunctio

National Neuroinformatics Framework for Canadian Consortium on Neurodegeneration in Aging (CCNA)

The Canadian Institutes for Health Research (CIHR) launched the “International Collaborative Research Strategy for Alzheimer's Disease” as a signature initiative, focusing on Alzheimer's Disease (AD) and related neurodegenerative disorders (NDDs). The Canadian Consortium for Neurodegeneration and Aging (CCNA) was subsequently established to coordinate and strengthen Canadian research on AD and NDDs. To facilitate this research, CCNA uses LORIS, a modular data management system that integrates acquisition, storage, curation, and dissemination across multiple modalities. Through an unprecedented national collaboration studying various groups of dementia-related diagnoses, CCNA aims to investigate and develop proactive treatment strategies to improve disease prognosis and quality of life of those affected. However, this constitutes a unique technical undertaking, as heterogeneous data collected from sites across Canada must be uniformly organized, stored, and processed in a consistent manner. Currently clinical, neuropsychological, imaging, genomic, and biospecimen data for 509 CCNA subjects have been uploaded to LORIS. In addition, data validation is handled through a number of quality control (QC) measures such as double data entry (DDE), conflict flagging and resolution, imaging protocol checks1, and visual imaging quality validation. Site coordinators are also notified of incidental findings found in MRI reads or biosample analyses. Data is then disseminated to CCNA researchers via a web-based Data-Querying Tool (DQT). This paper will detail the wide array of capabilities handled by LORIS for CCNA, aiming to provide the necessary neuroinformatic infrastructure for this nation-wide investigation of healthy and diseased aging

Testing Candidate Cerebellar Presymptomatic Biomarkers for Autism Spectrum Disorder

Background: Autism spectrum disorder (ASD) is a neurodevelopmental disorder diagnosed on the basis of social impairment, restricted interests, and repetitive behaviors. Contemporary theories posit that cerebellar-mediated error signaling impairments contribute to the causation of ASD. However, the relationship between infant cerebellar functional connectivity (fcMRI) and later ASD behaviors and outcomes has not been investigated. Such work is critical to establish early (presymptomatic) cerebellar correlates of ASD. Methods: Data from the Infant Brain Imaging Study (n=94, 68 male) were used to evaluate cerebellar fcMRI as a presymptomatic biomarker for ASD. Specifically, brain-behavior associations were analyzed for 6-month cerebellar connections in relation to later (12- and 24-month) ASD behaviors and outcomes using univariate tests of association, multivariate machine learning prediction, and fcMRI enrichment. Univariate and multivariate approaches focused on cerebellar-frontoparietal network (FPN is implicated in error-signaling) and cerebellar-default mode network (DMN is implicated in adult studies of ASD) connections, while enrichment afforded a data-driven test of whole-brain connectivity. Results: Univariate tests of cerebellar-FPN and cerebellar-DMN connections failed to implicate the cerebellum in ASD, despite \u3e 80% power to detect medium-sized effects. Multivariate tests in high-risk infants using cerebellar-FPN and cerebellar-DMN connections similarly failed to achieve above-chance classification accuracy for ASD diagnosis, despite replicating procedures that achieved \u3e 80% positive predictive value in whole-brain data. FcMRI enrichment identified correlates of ASD-associated behaviors in brain networks of a priori interest (FPN, DMN), as well as in cingulo-opercular (CO) and medial visual (mVis) networks. However, post-hoc tests did not support a unique role for cerebellar connectivity within these networks. Conclusions: Contrary to contemporary theories, we failed to observe a relationship between infant cerebellar fcMRI and ASD. Instead—in the first-known application of fcMRI enrichment to temporally lagged, early developmental brain-behavior associations—we identified infant control (FPN, CO), visual, and default mode correlates of later ASD behaviors. Future work may investigate whether connectivity involving these networks prospectively predicts ASD diagnosis, thereby expediting intervention and furthering etiologic understanding

A Framework To Evaluate Pipeline Reproducibility Across Operating Systems

The lack of computational reproducibility threatens data science in several domains. In particular, it has been shown that different operating systems can lead to different analysis results. This study identifies and quantifies the effect of the operating system on neuroimaging analysis pipelines. We developed a framework to evaluate the reproducibility of these neuroimaging pipelines across operating systems. The framework themselves leverages software containerization and system-call interception to record results provenance without having to instrument the pipelines. A tool (Repro-tools) compares results obtained under different conditions. We used our framework to evaluate the effect of the operating system on results produced by pipelines from the Human Connectome Project (HCP), a large open-data initiative to study the human brain. In particular, we focused on pre-processing pipelines for anatomical and functional data, namely PreFreeSurfer, FreeSurfer, PostFreeSurfer, and fMRIVolume. We used data from five subjects released by the HCP. Results highlight substantial differences in the output of the HCP pipelines obtained in two versions of Linux (CentOS6 and CentOS7). Inter-OS differences corresponding to normalized root mean square errors of up to 0.27 were observed, which corresponds to visually important differences. We provide visualizations of the most important differences for various pipeline steps. No meaningful inter-run differences were observed, which shows that the inter-OS differences do not originate from the use of pseudo-random numbers or silent crashes of the pipelines. We hypothesize that the observed inter-OS differences come from numerical instabilities in the pipelines, triggered by rounding and truncation differences that originate in the update of mathematical libraries in different systems. An apparent solution to this issue is to freeze the execution environment using, for example, software containers. However, this would only mask instabilities while they should ultimately be corrected in the pipelines

Enabling Scalable Neurocartography: Images to Graphs for Discovery

In recent years, advances in technology have enabled researchers to ask new questions predicated on the collection and analysis of big datasets that were previously too large to study. More specifically, many fundamental questions in neuroscience require studying brain tissue at a large scale to discover emergent properties of neural computation, consciousness, and etiologies of brain disorders. A major challenge is to construct larger, more detailed maps (e.g., structural wiring diagrams) of the brain, known as connectomes. Although raw data exist, obstacles remain in both algorithm development and scalable image analysis to enable access to the knowledge within these data volumes. This dissertation develops, combines and tests state-of-the-art algorithms to estimate graphs and glean other knowledge across six orders of magnitude, from millimeter-scale magnetic resonance imaging to nanometer-scale electron microscopy. This work enables scientific discovery across the community and contributes to the tools and services offered by NeuroData and the Open Connectome Project. Contributions include creating, optimizing and evaluating the first known fully-automated brain graphs in electron microscopy data and magnetic resonance imaging data; pioneering approaches to generate knowledge from X-Ray tomography imaging; and identifying and solving a variety of image analysis challenges associated with building graphs suitable for discovery. These methods were applied across diverse datasets to answer questions at scales not previously explored