Brainhack is an innovative meeting format that promotes scientific collaboration and education in an open, inclusive environment. This NeuroView describes the myriad benefits for participants and the research community and how Brainhacks complement conventional formats to augment scientific progress.

Brainhack is an innovative meeting format that promotes scientific collaboration and education in an open and inclusive environment. Departing from the formats of typical scientific workshops, these events are based on grassroots projects and training, and foster open and reproducible scientific practices. We describe here the multifaceted, lasting benefits of Brainhacks for individual participants, particularly early career researchers. We further highlight the unique contributions that Brainhacks can make to the research community, contributing to scientific progress by complementing opportunities available in conventional formats.

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BackgroundThe Comprehensive Assessment of Neurodegeneration and Dementia (COMPASS‐ND) cohort of the pan‐Canadian Consortium on Neurodegeneration in Aging (CCNA) provides imaging on participants following a standardized protocol. This imaging contribution to deep phenotyping is done to study the full spectrum of age‐related dementias in more than 1,100 individuals (50‐90y) with subjective or mild cognitive impairment, Alzheimer’s disease, other dementias, or otherwise cognitively unimpaired. To accelerate discovery, we propose to release biomarkers extracted from these acquisitions. Here we present preliminary (N=385, AD=39) preprocessing methods, quality control, and available derivatives of the resting‐state functional (rsfMRI), diffusion weighted (DWI), and structural magnetic resonance images.MethodfMRI data was preprocessed using fMRIprep v20.2.1. Sørensen–Dice similarity measure was computed between individual masks and group templates using our internal BIDS app. Functional connectome of 101 regions from Dictionary of Functional Modes (DiFuMo) 64 dimension atlas were calculated with confounds removed. Mean network connectivity for the Yeo7 networks was summarized from connectomes. DWI data underwent robust quality control and preprocessing using Tractoflow. Bundle extraction and mean metric computation was done using freewater_flow, rbx_flow and tractometry_flow. Structural scans were preprocessed and segmented using iterativeN3 and MAGeT‐brain to obtain subcortical and cerebellar volumes. As a validation, a subset of variables associated with aging in the literature was tested for association with participant age using simple correlation.ResultThe functional Dice score was 0.915 (SD=0.017, N=390) and structural was 0.988 (SD=0.004, N=381). Age was significantly associated with mean DMN connectivity (r=‐0.226, p<0.001), bilateral inferior fronto‐occipital fasciculus freewater (r=0.533, p<0.001), and bilateral thalamus volume (r=‐0.243, p<0.001).ConclusionDerivatives obtained include: functional connectomes using DiFuMo64, network connectivity from Yeo7, DWI measures for major fiber tracts, and segmented subcortical and cerebellar volumes. The results of the quality and validity checks indicate the derivatives would be useful for the study of age‐related dementias. Full quality control and release of the complete COMPASS‐ND preprocessed imaging dataset and derivatives should be completed in 2022. For information on access see ccna‐ccnv.ca.