Journal: PLOS Biology

Article Title: A guide to the BRAIN Initiative Cell Census Network data ecosystem

doi: 10.1371/journal.pbio.3002133

Figure Lengend Snippet: Primary techniques (right annotation) used in profiling cell types by BICCN investigators (top) are colored by major modality (left) and primary species . Investigator awards are ordered by techniques common to laboratories. BRAIN Initiative data archives store primary data shown by modality; NeMO, Neuroscience Multi-Omic Archive; BIL, Brain Imaging Library; DANDI, Distributed Archives for Neurophysiology Data Integration; BossDB, Brain Observatory Storage Service and Database (see Data archives for the BICCN); The NIH UM1, cooperative agreements involving large-scale research activities; U19, multidisciplinary with specific major objective; U01, discrete, specified, circumscribed project; RF1, discrete, specific project by named investigator ( NIH Grants ).

Article Snippet: DANDI works with BICCN and other BRAIN Initiative groups to curate data using community data standards such as Neurodata without Borders (NWB; [ ]) and Brain Imaging Data Structure (BIDS; [ ]) and to make data and software for cellular neurophysiology FAIR.

Techniques: Imaging

Journal: PLOS Biology

Article Title: A guide to the BRAIN Initiative Cell Census Network data ecosystem

doi: 10.1371/journal.pbio.3002133

Figure Lengend Snippet: ( A) Multimodal cell type data generation by UM1/U01/19, RF1 centers produce high-resolution Level 1 multimodal data. (B) Data are submitted to one of 4 BRAIN archives depending on data type(s): Neuroscience Multi-Omic Data Archive (NeMO), Brain Imaging Library (BIL), Distributed Archives for Neurophysiology Data Integration (DANDI) for neurophysiology data, and Brain Observatory Storage Service and Database (BossDB) for electron microscopy ultrastructural datasets. Datasets are indexed and referenced (C) by the Brain Cell Data Center (BCDC; biccn.org ), which provides a portal for accessing the consortium’s data, tools, and knowledge. (D) Laboratories engage in collaborative cross-modality interpretation of data and results. (E) Terra cloud-based platform for standardized omics processing accessible through BCDC. (F) An infrastructure working group oversees architectural development and workflow management.

Article Snippet: DANDI works with BICCN and other BRAIN Initiative groups to curate data using community data standards such as Neurodata without Borders (NWB; [ ]) and Brain Imaging Data Structure (BIDS; [ ]) and to make data and software for cellular neurophysiology FAIR.

Techniques: Imaging, Electron Microscopy

Journal: Journal of the American Statistical Association

Article Title: Network Reconstruction From High-Dimensional Ordinary Differential Equations

doi: 10.1080/01621459.2016.1229197

Figure Lengend Snippet: Estimated functional connectivities among neuronal populations from the calcium imaging data described in Section 6.2. Each node is positioned near the center of the neuronal population it represents, with jitter added for ease of display. The three red edges are shared between the estimated networks at 1 Hz and 2 Hz; the two blue edges are shared between estimated networks at 2 Hz and 4 Hz; the single green edge is shared between the estimated networks at 1 Hz and 4 Hz. For reference, given two Erdös-Rènyi graphs consisting of 25 nodes and 25 edges, the probability of having three or more shared edges is 0.07, and the probability of having two or more shared edges is 0.26.

Article Snippet: We investigate the calcium imaging recording data from the Allen Brain Observatory project conducted by the Allen Institute for Brain Science (available at http://observatory.brain-map.org .).

Techniques: Functional Assay, Imaging

Journal: NeuroImage : Clinical

Article Title: NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders

doi: 10.1016/j.nicl.2020.102375

Figure Lengend Snippet: Results of study 2, which supports that SZ and ASD show common alterations in sFNC. (A): Mean sFNC pattern across all subjects for ABIDEI. (B) and (C): T-values of all sFNCs and T-values of the sFNCs passing the multiple comparisons correction (p < 0.05 with Bonferroni correction), obtained from two-sample t-tests of HC vs. ASD for ABIDEI. “BFN” denotes Bonferroni correction. (D): Mean sFNC strength of each group (ASD, SZ and HC) in the common impairments between SZ and ASD. For each commonly impaired sFNC, the averaged connectivity values in ASD patients of ABIDEI, SZ patients of FBIRN and MPRC, and HCs of the three datasets are shown, respectively. (E) and (F): The significant correlations (r and p values) between FNC measures and clinical symptoms, with p < 0.05. The T-value from testing group difference between HC and disorder by two-sample t -test is also included in each subfigure. Taking (F) for an instance, it shows the correlation between FNC measure (corresponding to IC 69 and IC 21) and ADOS score in ASD patients. The T-value of FNC measure from two-sample t -test between HC and ASD is also shown in the title part.

Article Snippet: In study 2, we identified the functional changes in ASD using the release 1 of Autism Brain Imaging Data Exchange (ABIDEI) data, and then linked the results to study 1, aiming to show the ability of NeuroMark in the cross-disorder comparison.

Techniques:

Journal: Human Brain Mapping

Article Title: Comparative validation of automated presurgical tractography based on constrained spherical deconvolution and diffusion tensor imaging with direct electrical stimulation

doi: 10.1002/hbm.26662

Figure Lengend Snippet: Summarized demographics, pathological type, distribution, and volumes.

Article Snippet: All acquired images were converted to the brain imaging data structure (BIDS) (Gorgolewski et al., ) format using the KU Leuven Neuroimaging suite (KUL_NIS) ( KULeuven Neuro Imaging Suite (KUL_NIS) , 2018/ ) ( https://github.com/treanus/KUL_NIS ) and dcm2bids (Bedetti et al., ).

Techniques:

Journal: Human Brain Mapping

Article Title: Comparative validation of automated presurgical tractography based on constrained spherical deconvolution and diffusion tensor imaging with direct electrical stimulation

doi: 10.1002/hbm.26662

Figure Lengend Snippet: Schematic representation of the data preprocessing and analysis workflow used to compare different tractography results to intraoperative mapping outcome. BIDS, brain imaging data structure; DSC, dice similarity coefficient; DTI, diffusion tensor imaging; FACT, fiber assignment by continuous tracking; JI, Jaccard index; KUL_FWT, KU Leuven fun with tracts; KUL_NIS, KU Leuven neuroimaging suite; KUL_VBG, KU Leuven virtual brain grafting; MRI, magnetic resonance imaging.

Article Snippet: All acquired images were converted to the brain imaging data structure (BIDS) (Gorgolewski et al., ) format using the KU Leuven Neuroimaging suite (KUL_NIS) ( KULeuven Neuro Imaging Suite (KUL_NIS) , 2018/ ) ( https://github.com/treanus/KUL_NIS ) and dcm2bids (Bedetti et al., ).

Techniques: Imaging, Diffusion-based Assay, Magnetic Resonance Imaging