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© 2021 American Neurological Association, Objective: Knowing explicitly where we are is an interpretation of our spatial representations. Reduplicative paramnesia is a disrupting syndrome in which patients present a firm belief of spatial mislocation. Here, we studied the largest sample of patients with delusional misidentifications of space (i.e. reduplicative paramnesia) after stroke to shed light on their neurobiology. Methods: In a prospective, cumulative, case-control study, we screened 400 patients with acute right-hemispheric stroke. We included 64 cases and 233 controls. First, lesions were delimited and normalized. Then, we computed structural and functional disconnection maps using methods of lesion-track and network-mapping. The maps were compared, controlling for confounders. Second, we built a multivariate logistic model including clinical, behavioural and neuroimaging data. Finally, we performed a nested cross-validation of the model with a support-vector machine analysis. Results: The most frequent misidentification subtype was confabulatory mislocation (56%), followed by place reduplication (19%) and chimeric assimilation (13%). Our results indicate that structural disconnection is the strongest predictor of the syndrome and included two distinct streams, connecting right fronto-thalamic and right occipito-temporal structures. In the multivariate model, the independent predictors of reduplicative paramnesia were the structural disconnection map, lesion sparing of right dorsal fronto-parietal regions, age and anosognosia. Good discrimination accuracy was demonstrated (area under the curve = 0.80[0.75-0.85]). Interpretation: Our results localize the anatomical circuits that may have a role in the abnormal spatial-emotional binding and in the defective updating of spatial representations underlying reduplicative paramnesia. This novel data may contribute to better understand the pathophysiology of delusional syndromes after stroke., The authors acknowledge our colleagues from the Department of Neurology and from the Language Research Laboratory for their contribution to patient screening. This work received funding from “PRÉMIO JOÃO LOBO ANTUNES” – SCML (grant to P.N.A.), from “Bolsa de Investigação em Doenças Vasculares Cerebrais 2017”–SPAVC (grant to P.N.A.) and from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No. 818521 to M.T.S.).“Data were provided in part bythe Human Connectome Project, WU-Minn Consortium(Principal Investigators: David Van Essen and KamilUgurbil; 1U54 MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.

Introduction We present BIDS-Derivatives, a set of principles for organizing and describing outputs of computations performed on brain imaging data, enabling researchers and tools to understand and reuse those outputs in subsequent processing. BIDS-Derivatives is an extension to the Brain Imaging Data Structure (BIDS), which is a standard for organizing magnetic resonance imaging (MRI) [2], electrophysiological [6, 7, 8] and behavioral data generated by a broad range of neuroscientific experiments. BIDS has facilitated the generation of tools (BIDS-Apps) [3] that may run with minimal intervention on BIDS datasets, adapting to the details of the available data. BIDS also provides a common structure for archiving data, both within labs and in large-scale databases such as OpenNeuro [4] and the NIMH Data Archive [10]. Methods The BIDS specification is hosted on GitHub and published on ReadTheDocs [9]. Significant modifications to BIDS are formulated as BIDS Extension Proposals (BEPs), which may be developed as separate documents or as "forks" of the document source. Derivatives were conceived during early BIDS discussions as a category distinct from raw experimental data, ranging from preprocessed data to publishable results. A BEP was initially drafted in February 2016. Further work defining the scope of derivatives at an August 2017 meeting led to the division of the effort into fine-grained proposals [5]. In July 2018, a survey of the neuroimaging community was taken to establish priorities (essential, desirable or inessential) for structural, functional and diffusion MRI derivatives. The results of the survey were posted [1] in advance of an August 2018 workshop of 31 participants, where sub-proposals were pushed toward completion and common principles were established. In December 2018, Release Candidate 1 was published, including all imaging modalities, for implementation and feedback. In July 2019, a "Common Derivatives" proposal was re-introduced establishing more general principles, to be followed by subsequent modality-specific and non-imaging proposals. Results BIDS-Derivatives are specified in version 1.3.0 of the BIDS standard. This initial release specifies common derivatives, including dataset-level metadata, naming rules for preprocessed data of any modality, and generic imaging derivatives. Dataset metadata and organization follow BIDS conventions, and have been extended to allow the source dataset(s) to be linked and provenance information recorded of software used to generate the dataset. File-level naming rules permit space and desc keywords, allowing pipelines to distinguish files by a reference space or a generic description field. Custom references spaces may also be specified with the SpatialReference metadata field. All derived files must distinguish themselves from original (e.g., raw) data files by some component in the filename, permitting the inclusion of original and derived data in the same dataset, if necessary. Imaging-specific derivatives specified in this initial release include naming conventions for resampling parameters (e.g., resolution and surface mesh density) and specifications of regions of interest as masks or deterministic and probabilistic segmentations. Conclusions A standard for specifying derivatives will simplify the sharing and archiving of preprocessed data and the results of analyses. It will permit data repositories to provide canonical, preprocessed versions of datasets, simplify further automated processing, and facilitate collaboration between researchers and replication of analyses of published datasets. This initial release establishes common principles that guide future derivative specifications. Additional specifications of anatomical, functional and diffusion derivatives are planned within the next year, and electrophysiological, positron emission tomography, and connectomic derivatives are in progress. BIDS is an open effort, and everyone is encouraged to contribute, regardless of level of expertise., {"references":["Feingold, F.W. (2018), 'BIDS-Processed Data Survey Results', Stanford Center for Reproducible Neuroscience, http://reproducibility.stanford.edu/bids-processed-data-survey-results/","Gorgolewski, K.J. (2016), 'The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments' Scientific Data, 3:160044. doi:10.1038/sdata.2016.44","Gorgolewski, K.J. (2017a), 'BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods', PLOS Computational Biology 13(3): e1005209, doi:10.1371/journal.pcbi.1005209","Gorgolewski K.J. (2017b), 'OpenNeuro – a free online platform for sharing and analysis of neuroimaging data [version 1; not peer reviewed]', F1000Research, 6:1055 (poster), doi:10.7490/f1000research.1114354.1","Gorgolewski, K.J. (2017c), 'Restructuring BIDS Derivatives', bids-discussion mailing-list, https://groups.google.com/forum/#!msg/bids-discussion/l74eKXzNX84/4rMQzS_KAwAJ","Holdgraf, C. (2019), 'iEEG-BIDS, extending the Brain Imaging Data Structure specification to human intracranial electrophysiology' Scientific Data, 6:102. doi:10.1038/s41597-019-0105-7","Niso, G. (2018), 'MEG-BIDS, the brain imaging data structure extended to magnetoencephalography' Scientific Data, 5:180110. doi:doi:10.1038/sdata.2018.110","Pernet, C.R. (2019), 'EEG-BIDS, an extension to the brain imaging data structure for electroencephalography' Scientific Data, 6:103. doi:10.1038/s41597-019-0104-8","https://bids-specification.readthedocs.io/en/stable/","https://nda.nih.gov/"]}

The data volumes associated with image processing in astronomy can range from small sets of images taken by individual observers to large survey telescopes generating tens of petabytes of data per year. The tools used by researchers to analyze their images are often bespoke, tailored to specific tasks or science use cases. As part of an initiative to share analysis tools across astronomy (and broader communities) we are developing a cloud-aware analysis framework (the astronomy commons). We demonstrate here an image analysis system (built to process data from the Large Synoptic Survey Telescope; LSST) that can be deployed on the cloud using Amazon's S3, RDS, Lambda, and EBS services together with HTCondor and Pegasus to manage the overall workflow. We demonstrate the scaling of this system (and associated processing costs) to the size of nightly data volumes expected from the LSST.

The data volumes associated with image processing in astronomy can range from small sets of images taken by individual observers to large survey telescopes generating tens of petabytes of data per year. The tools used by researchers to analyze their images are often bespoke, tailored to specific tasks or science use cases. As part of an initiative to share analysis tools across astronomy (and broader communities) we are developing a cloud-aware analysis framework (the astronomy commons). We demonstrate here an image analysis system (built to process data from the Legacy Survey of Space and Time; LSST) that can be deployed on the cloud using Amazon's S3, RDS, Lambda, and EBS services together with HTCondor and Pegasus to manage the overall workflow. We demonstrate the scaling of this system (and associated processing costs) to the size of nightly data volumes expected from the LSST.

The white matter contains long-range connections between different brain regions and the organization of these connections holds important implications for brain function in health and disease. Tractometry uses diffusion-weighted magnetic resonance imaging (dMRI) data to quantify tissue properties (e.g. fractional anisotropy (FA), mean diffusivity (MD), etc.), along the trajectories of these connections [1]. Statistical inference from tractometry usually either (a) averages these quantities along the length of each bundle in each individual, or (b) performs analysis point-by-point along each bundle, with group comparisons or regression models computed separately for each point along every one of the bundles. These approaches are limited in their sensitivity, in the former case, or in their statistical power, in the latter. In the present work, we developed a method based on the sparse group lasso (SGL) [2] that takes into account tissue properties measured along all of the bundles, and selects informative features by enforcing sparsity, not only at the level of individual bundles, but also across the entire set of bundles and all of the measured tissue properties. The sparsity penalties for each of these constraints is identified using a nested cross-validation scheme that guards against over-fitting and simultaneously identifies the correct level of sparsity. We demonstrate the accuracy of the method in two settings: i) In a classification setting, patients with amyotrophic lateral sclerosis (ALS) are accurately distinguished from matched controls [3]. Furthermore, SGL automatically identifies FA in the corticospinal tract as important for this classification – correctly finding the parts of the white matter known to be affected by the disease. ii) In a regression setting, dMRI is used to accurately predict “brain age” [4, 5]. In this case, the weights are distributed throughout the white matter indicating that many different regions of the white matter change with development and contribute to the prediction of age. Thus, SGL makes it possible to leverage the multivariate relationship between diffusion properties measured along multiple bundles to make accurate predictions of subject characteristics while simultaneously discovering the most relevant features of the white matter for the characteristic of interest.

Perceptual learning is a pervasive and specific improvement in the performance of a perceptual task with training. This dissertation examines the role of the neurotransmitter acetylcholine(ACh) in perceptual learning in a series of behavioral and pharmacological studies in healthy human subjects. ACh plays a role in cognitive functions such as attention and in animal models it has been found to play a role in the facilitation of neural plasticity.The work described here focused on the learning of a visual motion direction discrimination task. In the first study described, I provide a theoretical framework for the study of learning of this task. This part examined the "oblique effect", an advantage in performing this task when stimuli are presented in cardinal, rather than oblique directions. I present both experimental evidence and a population coding model that indicate the oblique effect in behavior may rely on the unequal representation of oblique and cardinal directions in visual areas in cortex. The model suggests that the oblique effect relies on an interplay of this representation with the decoding of the stimulus in higher cortical regions.In the second part of this thesis, participants were administered the cholinesterase inhibitor donepezil while training on the motion direction discrimination task, performed in oblique directions. As previously described, this training abolishes the behavioral oblique effect. Moreover, donepezil increased the effects of training on performance and the specificity of these effects to the oblique direction and the visual field location in which learning took place, suggesting that ACh directs learning towards cells encoding behaviorally relevant features of the stimulus.The third part presents a study investigating the role of ACh in the allocation of voluntary visual spatial attention (which can be allocated in a goal-oriented manner) and involuntary attention (which is automatically captured by salient events). We used an anti-predictive spatial cueing task to assess the effects of pharmacological enhancement of cholinergic transmission on behavioral measures of voluntary and involuntary attention. We found that cholinergic enhancement with donepezil augments the benefits of voluntary attention but does not affect involuntary attention, suggesting that they rely on different neurochemical mechanisms.Taken together, the results of the second and third parts of this thesis provide converging evidence for a potential mechanism of learning: ACh mediates the allocation of voluntary attention, which in turn provides a necessary substrate for learning to occur.

This poster describes the progress of the Journal of Open Source Software (JOSS), a free, open-access journal designed to publish brief papers about research software. The primary purpose of JOSS is to enable developers of research software to receive citation credit equivalent to typical archival publications. JOSS papers are deliberately short and required to include a summary describing the purpose and high-level functionality of the software (written for a diverse, non-specialist audience), a statement of need, authors and their affiliations, and key references, as well as link to an archived version of the software (e.g., DOI obtained from Zenodo). Upon acceptance, papers receive a CrossRef DOI. Rather than a review of a lengthy software paper (including, e.g., methodology, validation, sample results), JOSS submissions undergo rigorous peer review of the article and software, including documentation, tests, continuous integration, and licensing. The JOSS review process is modeled on the established approach of the rOpenSci collaboration. The entire submission and review process occurs openly on GitHub; papers not yet accepted remain visible and under review until the authors make appropriate changes for acceptance—unlike other journals, papers requiring major revision are not rejected. JOSS was founded in May 2016, and in its first year published 111 articles in a variety of fields. Since then, JOSS has published over 494 articles (as of 23 Feb 2019), and submissions continue to grow.

This is the open peer reviewers comments and recommendations regarding the submitted GigaScience article and/or dataset.

Talk presented at COSYNE 2018, 2/6/2018, Breckenridge, CO.Workshop: "Recent computational advances in neuroengineering: From theory to applications"http://www.cosyne.org/c/index.php?title=Workshops2018_01_08

The translation of personal genomics to precision medicine depends on the accurate interpretation of the multitude of genetic variants observed for each individual. However, even when genetic variants are predicted to modify a protein, their functional implications may be unclear. Many diseases are caused by genetic variants affecting important protein features, such as enzyme active sites or interaction interfaces. The scientific community has catalogued millions of genetic variants in genomic databases and thousands of protein structures in the Protein Data Bank. Mapping mutations onto three-dimensional (3D) structures enables atomic-level analyses of protein positions that may be important for the stability or formation of interactions; these may explain the effect of mutations and in some cases even open a path for targeted drug development. To accelerate progress in the integration of these data types, we held a two-day Gene Variation to 3D (GVto3D) workshop to report on the latest advances and to discuss unmet needs. The overarching goal of the workshop was to address the question: what can be done together as a community to advance the integration of genetic variants and 3D protein structures that could not be done by a single investigator or laboratory? Here we describe the workshop outcomes, review the state of the field, and propose the development of a framework with which to promote progress in this arena. The framework will include a set of standard formats, common ontologies, a common application programming interface to enable interoperation of the resources, and a Tool Registry to make it easy to find and apply the tools to specific analysis problems. Interoperability will enable integration of diverse data sources and tools and collaborative development of variant effect prediction methods.