OpenPBTA: An Open Pediatric Brain Tumor Atlas | bioRxiv

Abstract:  Pediatric brain and spinal cancer are the leading disease-related cause of death in children, thus we urgently need curative therapeutic strategies for these tumors. To accelerate such discoveries, the Children’s Brain Tumor Network and Pacific Pediatric Neuro-Oncology Consortium created a systematic process for tumor biobanking, model generation, and sequencing with immediate access to harmonized data. We leverage these data to create OpenPBTA, an open collaborative project which establishes over 40 scalable analysis modules to genomically characterize 1,074 pediatric brain tumors. Transcriptomic classification reveals that TP53 loss is a significant marker for poor overall survival in ependymomas and H3 K28-altered diffuse midline gliomas and further identifies universal TP53 dysregulation in mismatch repair-deficient hypermutant high-grade gliomas. OpenPBTA is a foundational analysis platform actively being applied to other pediatric cancers and inform molecular tumor board decision-making, making it an invaluable resource to the pediatric oncology community.

 

Towards Robust, Reproducible, and Clinically Actionable EEG Biomarkers: Large Open Access EEG Database for Discovery and Out-of-sample Validation – Hanneke van Dijk, Mark Koppenberg, Martijn Arns, 2022

“To aid researchers in development and validation of EEG biomarkers, and development of new (AI) methodologies, we hereby also announce our open access EEG dataset: the Two Decades Brainclinics Research Archive for Insights in Neuroscience (TDBRAIN)….

The whole raw EEG dataset as well as python code to preprocess the raw data is available at www.brainclinics.com/resources and can freely be downloaded using ORCID credentials….”

Frontiers | neuPrint: An open access tool for EM connectomics

Abstract:  Due to advances in electron microscopy and deep learning, it is now practical to reconstruct a connectome, a description of neurons and the chemical synapses between them, for significant volumes of neural tissue. Smaller past reconstructions were primarily used by domain experts, could be handled by downloading data, and performance was not a serious problem. But new and much larger reconstructions upend these assumptions. These networks now contain tens of thousands of neurons and tens of millions of connections, with yet larger reconstructions pending, and are of interest to a large community of non-specialists. Allowing other scientists to make use of this data needs more than publication—it requires new tools that are publicly available, easy to use, and efficiently handle large data. We introduce neuPrint to address these data analysis challenges. Neuprint contains two major components—a web interface and programmer APIs. The web interface is designed to allow any scientist worldwide, using only a browser, to quickly ask and answer typical biological queries about a connectome. The neuPrint APIs allow more computer-savvy scientists to make more complex or higher volume queries. NeuPrint also provides features for assessing reconstruction quality. Internally, neuPrint organizes connectome data as a graph stored in a neo4j database. This gives high performance for typical queries, provides access though a public and well documented query language Cypher, and will extend well to future larger connectomics databases. Our experience is also an experiment in open science. We find a significant fraction of the readers of the article proceed to examine the data directly. In our case preprints worked exactly as intended, with data inquiries and PDF downloads starting immediately after pre-print publication, and little affected by formal publication later. From this we deduce that many readers are more interested in our data than in our analysis of our data, suggesting that data-only papers can be well appreciated and that public data release can speed up the propagation of scientific results by many months. We also find that providing, and keeping, the data available for online access imposes substantial additional costs to connectomics research.

 

Data Trove Released by Seattle Alzheimer’s Disease Brain Cell Atlas | Inside Precision Medicine

“Neuroscientists at the Allen Institute for Brain Science and their collaborators have released their first research data set on Alzheimer’s disease, in which they categorized cell types based on gene activity. The team hope this approach could ultimately identify new targets for better therapies.

The publicly available dataset captures large-scale cellular and molecular information gleaned from more than 1.2 million neurons and other brain cells from 84 people who donated their brains to science after their deaths. It includes detailed microscopy images of amyloid-? and other disease-related proteins in the patients’ brains….”

Journal Selection Primer for Neuroradiology Researchers – ScienceDirect

“Authors can also benefit from the open-access model. Open access articles are freely available to all, including physicians, researchers, and patients. Thus, it can potentially lead to an increase in visibility, use, and citation of your article (8). However, researchers must distinguish reputable journals from predatory journals in the open-access publication model. Unfortunately, predatory journals have managed to bleed into PubMed and PubMed central databases in recent years. Therefore, we also recommend that authors check their target journals’ affiliations with reputable scholarly organizations such as the DOAJ, Open Access Scholarly Publishers Association (OASPA), and the Committee on Publication Ethics (COPE) (6).  Some journals (indexed or non-indexed) may also be affiliated with a reputable National Society; for instance, the American Journal of Neuroradiology (AJNR) is affiliated with the American Society of Neuroradiology (Table 4)….”

Navigating Open scholarship for neurodivergent researchers | FORRT – Framework for Open and Reproducible Research Training

“We are a group of early-career neurotypical and neurodivergent researchers that are a part of the Framework of Open Reproducible Research and Training (FORRT) community, aiming to make academia and the open scholarship community more open to neurodiversity. Everyone, no matter what they identify with, is welcome in this group. We aim to discuss how open scholarship can be intersected with the neurodiversity movement, and emphasise how differences should be highlighted and accepted, whilst supporting the idea of accessibility. Our neurodiversity team is a group that currently consists of individuals that have autism, dyspraxia/DCD, speech-language differences, ADHD, dyslexia, or are neurotypical allies. If you have these or other neurominorities and wish to be part of the team, you are more than welcome to join!…

Discussions have been, however, scarce regarding not only how open scholarship can advance the neurodiverse movement, but also how it can benefit from it. It is thus a priority to build community to discuss how the neurodiversity movement can be included in open scholarship, as the lived experience of neurodivergent individuals (including encountered barriers) may help to enhance accessibility, allowing open scholarship to be truly open (Whitaker & Guest, 2020)….”

 

Survey on Open Science Practices in Functional Neuroimaging – ScienceDirect

Abstract:  Replicability and reproducibility of scientific findings is paramount for sustainable progress in neuroscience. Preregistration of the hypotheses and methods of an empirical study before analysis, the sharing of primary research data, and compliance with data standards such as the Brain Imaging Data Structure (BIDS), are considered effective practices to secure progress and to substantiate quality of research. We investigated the current level of adoption of open science practices in neuroimaging and the difficulties that prevent researchers from using them.

Email invitations to participate in the survey were sent to addresses received through a PubMed search of human functional magnetic resonance imaging studies that were published between 2010 and 2020. 283 persons completed the questionnaire.

Although half of the participants were experienced with preregistration, the willingness to preregister studies in the future was modest. The majority of participants had experience with the sharing of primary neuroimaging data. Most of the participants were interested in implementing a standardized data structure such as BIDS in their labs. Based on demographic variables, we compared participants on seven subscales, which had been generated through factor analysis. Exploratory analyses found that experienced researchers at lower career level had higher fear of being transparent and researchers with residence in the EU had a higher need for data governance. Additionally, researchers at medical faculties as compared to other university faculties reported a more unsupportive supervisor with regards to open science practices and a higher need for data governance.

The results suggest growing adoption of open science practices but also highlight a number of important impediments.

Recommendations for repositories and scientific gateways from a neuroscience perspective | Scientific Data

“Digital services such as repositories and science gateways have become key resources for the neuroscience community, but users often have a hard time orienting themselves in the service landscape to find the best fit for their particular needs. INCF has developed a set of recommendations and associated criteria for choosing or setting up and running a repository or scientific gateway, intended for the neuroscience community, with a FAIR neuroscience perspective….”

An open-access accelerated adult equivalent of the ABCD Study neuroimaging dataset (a-ABCD) – ScienceDirect

Abstract:  As public access to longitudinal developmental datasets like the Adolescent Brain Cognitive Development StudySM (ABCD Study®) increases, so too does the need for resources to benchmark time-dependent effects. Scan-to-scan changes observed with repeated imaging may reflect development but may also reflect practice effects, day-to-day variability in psychological states, and/or measurement noise. Resources that allow disentangling these time-dependent effects will be useful in quantifying actual developmental change. We present an accelerated adult equivalent of the ABCD Study dataset (a-ABCD) using an identical imaging protocol to acquire magnetic resonance imaging (MRI) structural, diffusion-weighted, resting-state and task-based data from eight adults scanned five times over five weeks. We report on the task-based imaging data (n?=?7). In-scanner stop-signal (SST), monetary incentive delay (MID), and emotional n-back (EN-back) task behavioral performance did not change across sessions. Post-scan recognition memory for emotional n-back stimuli, however, did improve as participants became more familiar with the stimuli. Functional MRI analyses revealed that patterns of task-based activation reflecting inhibitory control in the SST, reward success in the MID task, and working memory in the EN-back task were more similar within individuals across repeated scan sessions than between individuals. Within-subject, activity was more consistent across sessions during the EN-back task than in the SST and MID task, demonstrating differences in fMRI data reliability as a function of task. The a-ABCD dataset provides a unique testbed for characterizing the reliability of brain function, structure, and behavior across imaging modalities in adulthood and benchmarking neurodevelopmental change observed in the open-access ABCD Study.

 

Empowering Data Sharing and Analytics through the Open Data Commons for Traumatic Brain Injury Research | Neurotrauma Reports

Abstract:  Traumatic brain injury (TBI) is a major public health problem. Despite considerable research deciphering injury pathophysiology, precision therapies remain elusive. Here, we present large-scale data sharing and machine intelligence approaches to leverage TBI complexity. The Open Data Commons for TBI (ODC-TBI) is a community-centered repository emphasizing Findable, Accessible, Interoperable, and Reusable data sharing and publication with persistent identifiers. Importantly, the ODC-TBI implements data sharing of individual subject data, enabling pooling for high-sample-size, feature-rich data sets for machine learning analytics. We demonstrate pooled ODC-TBI data analyses, starting with descriptive analytics of subject-level data from 11 previously published articles (N?=?1250 subjects) representing six distinct pre-clinical TBI models. Second, we perform unsupervised machine learning on multi-cohort data to identify persistent inflammatory patterns across different studies, improving experimental sensitivity for pro- versus anti-inflammation effects. As funders and journals increasingly mandate open data practices, ODC-TBI will create new scientific opportunities for researchers and facilitate multi-data-set, multi-dimensional analytics toward effective translation.

 

Neurosurgical publication – Should we publish at any cost? An in-depth analysis of costs incurred in publication. – ScienceDirect

Abstract:  Objectives

With the recent paradigm shift in neurosurgical publications, open access (OA) publishing is burgeoning along with traditional publishing methods. We aimed to explore costs of publication across 53 journals.

Methods

We identified 53 journals publishing neurosurgical work. Journal type, submission and open access charges, colour print fees, impact indicators, publisher, and subscription prices were obtained from journal and publisher websites. Costs were unified in American Dollars. Mean prices per journal were used to equilibrate membership and subscription discounts. Correlations were performed using Spearman Rho (?), p<0.05.

Results

Of the 53 journals, 12 are OA-only, 40 are hybrid, and 1 is traditional. 22 and 43 journals provide their submission costs by end of phase 1 and 2, respectively (prices always for phase 2; 26 free of charge, 4 under $500, and 1 under $1000). Median OA charge is $3286 (49 journals; range $0-$7827). 36 of the 53 journals did not list print fees for colour figures (29 in phase 2). Median fee estimate per figure is $422 (range $25-$1060). Median personal subscription for 1 year is $344 (range $60-$1158; 48 journals). Median institutional subscription for 1 year is $2082 (Range $38-$5510; 34 journals). There is mild positive correlation between journal impact factor and OA fees (?=0.287, p=0.046).

Conclusions

The lack of easily accessible information about Neurosurgical publications, such as submission costs, or OA charges create unnecessary hurdle and should be remedied. Publishing in Neurosurgery should be a pleasant learning experience for the cost anxiety should not be a limiting factor.

The Digital Brain Bank, an open access platform for post-mortem datasets | eLife

Abstract:  Post-mortem MRI provides the opportunity to acquire high-resolution datasets to investigate neuroanatomy, and validate the origins of image contrast through microscopy comparisons. We introduce the Digital Brain Bank (open.win.ox.ac.uk/DigitalBrainBank), a data release platform providing open access to curated, multimodal post-mortem neuroimaging datasets. Datasets span three themes – Digital Neuroanatomist: datasets for detailed neuroanatomical investigations; Digital Brain Zoo: datasets for comparative neuroanatomy; Digital Pathologist: datasets for neuropathology investigations. The first Digital Brain Bank release includes twenty one distinctive whole-brain diffusion MRI datasets for structural connectivity investigations, alongside microscopy and complementary MRI modalities. This includes one of the highest-resolution whole-brain human diffusion MRI datasets ever acquired, whole-brain diffusion MRI in fourteen non-human primate species, and one of the largest post-mortem whole-brain cohort imaging studies in neurodegeneration. The Digital Brain Bank is the culmination of our lab’s investment into post-mortem MRI methodology and MRI-microscopy analysis techniques. This manuscript provides a detailed overview of our work with post-mortem imaging to date, including the development of diffusion MRI methods to image large post-mortem samples, including whole, human brains. Taken together, the Digital Brain Bank provides cross-scale, cross-species datasets facilitating the incorporation of post-mortem data into neuroimaging studies.

 

The Digital Brain Bank, an open access platform for post-mortem datasets | eLife

Abstract:  Post-mortem MRI provides the opportunity to acquire high-resolution datasets to investigate neuroanatomy, and validate the origins of image contrast through microscopy comparisons. We introduce the Digital Brain Bank (open.win.ox.ac.uk/DigitalBrainBank), a data release platform providing open access to curated, multimodal post-mortem neuroimaging datasets. Datasets span three themes – Digital Neuroanatomist: datasets for detailed neuroanatomical investigations; Digital Brain Zoo: datasets for comparative neuroanatomy; Digital Pathologist: datasets for neuropathology investigations. The first Digital Brain Bank release includes twenty one distinctive whole-brain diffusion MRI datasets for structural connectivity investigations, alongside microscopy and complementary MRI modalities. This includes one of the highest-resolution whole-brain human diffusion MRI datasets ever acquired, whole-brain diffusion MRI in fourteen non-human primate species, and one of the largest post-mortem whole-brain cohort imaging studies in neurodegeneration. The Digital Brain Bank is the culmination of our lab’s investment into post-mortem MRI methodology and MRI-microscopy analysis techniques. This manuscript provides a detailed overview of our work with post-mortem imaging to date, including the development of diffusion MRI methods to image large post-mortem samples, including whole, human brains. Taken together, the Digital Brain Bank provides cross-scale, cross-species datasets facilitating the incorporation of post-mortem data into neuroimaging studies.