Brainhack: a collaborative workshop for the open neuroscience community

Craddock, R. Cameron; Margulies, Daniel S.; Bellec, Pierre; Nichols, B. Nolan; Alcauter, Sarael; Barrios, Fernando A.; Burnod, Yves; Cannistraci, Christopher J.; Cohen‐Adad, Julien; Leener, Benjamin De; Dery, Sebastien; Downar, Jonathan; Dunlop, Katharine; Franco, Alexandre R.; Froehlich, Caroline; Gerber, Andrew J.; Ghosh, Samik; Grabowski, Thomas J.; Hill, Sean; Heinsfeld, Anibal Sólon; Hutchison, R. Matthew; Kundu, Prantik; Laird, Angela R.; Liew, Sook‐Lei; Lurie, Daniel J; McLaren, Donald G.; Meneguzzi, Felipe; Mennes, Maarten; Mesmoudi, Salma; O’Connor, David; Pasaye, Erick H.; Peltier, Scott; Poline, Jean‐Baptiste; Prasad, G. Naga Raja; Pereira, Ramon Fraga; Quirion, Pierre-Olivier; Rokem, Ariel; Saad, Ziad S.; Shi, Yonggang; Strother, Stephen C.; Toro, Roberto; Uddin, Lucina Q.; Horn, John D. Van; Meter, John W. Van; Welsh, Robert C.; Xu, Ting

doi:10.1186/s13742-016-0121-x

Cited by 43 publications

(34 citation statements)

References 13 publications

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“…However, a clear consensus on data cleaning standards has not yet emerged, despite worthy efforts in that direction. [63][64][65] This is largely attributable to the fact that defining inclusion/exclusion criteria of an MRI scan rests largely upon the research question at hand and on the imaging modality. Standards in fMRI may be more explicit on this point; for instance, Power and colleagues 17 proposed a widely cited method of "scrubbing" fMRI data to remove frames with a high degree of motion and significant amplitude changes in the blood oxygen level-dependent (BOLD) signal.…”

Section: Correcting For Motion After Image Acquisitionmentioning

confidence: 99%

Head motion: the dirty little secret of neuroimaging in psychiatry

Makowski

Lepage

Evans

2019

jpn

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Section: Correcting For Motion After Image Acquisitionmentioning

confidence: 99%

Head motion: the dirty little secret of neuroimaging in psychiatry

Makowski

Lepage

Evans

2019

jpn

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“…Methodological workshops in the form of hackathons provide excellent opportunities for ongoing training. These meeting formats emphasize the full participation of all attendees from different career stages (e.g., the Brainhack format; Craddock et al, 2016). In addition, working closely with statisticians from the conception of a study onwards can likely improve the use of statistics to a large extent.…”

Section: Opportunities For Increased Reproducibility Of Dcn 32mentioning

confidence: 99%

Opportunities for increased reproducibility and replicability of developmental neuroimaging

Klapwijk¹,

Bos²,

Tamnes³

et al. 2019

Preprint

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Recently, many workflows and tools that aim to increase the reproducibility and replicability of research findings have been suggested. In this review, we discuss the opportunities that these efforts offer for the field of developmental cognitive neuroscience. We focus on issues broadly related to statistical power and to flexibility and transparency in data analyses. Critical considerations relating to statistical power include challenges in recruitment and testing of young populations, how to increase the value of studies with small samples, and the opportunities and challenges related to working with large-scale datasets. Developmental studies also involve challenges such as choices about age groupings, modelling across the lifespan, the analyses of longitudinal changes, and neuroimaging data that can be processed and analyzed in a multitude of ways. Flexibility in data acquisition, analyses and description may thereby greatly impact results. We discuss methods for improving transparency in developmental cognitive neuroscience, and how preregistration of studies can improve methodological rigor in the field. While outlining challenges and issues that may arise before, during, and after data collection, solutions and resources are highlighted aiding to overcome some of these. Since the number of useful tools and techniques is ever-growing, we highlight the fact that many practices can be implemented stepwise.

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“…Those from nonimaging, computational disciplines will need to be educated on the fundamentals of brain imaging data, while clinical researchers will need to improve their computational and analytical skills to promote the cohesiveness and quality of their research. This is being addressed by educational workshops such as Brainhack (Craddock et al, 2016) and Neurohackweek ("Neurohackweek," 2016), where interdisciplinary researchers come together to learn from one another as they work to address open problems in neuroscience.…”

Section: Scaling Up Data Resourcesmentioning

confidence: 99%

Clinically useful brain imaging for neuropsychiatry: How can we get there?

2017

Self Cite

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Despite decades of research, visions of transforming neuropsychiatry through the development of brain imaging-based "growth charts" or "lab tests" have remained out of reach. In recent years, there is renewed enthusiasm about the prospect of achieving clinically useful tools capable of aiding the diagnosis and management of neuropsychiatric disorders. The present work explores the basis for this enthusiasm. We assert that there is no single advance that currently has the potential to drive the field of clinical brain imaging forward. Instead, there has been a constellation of advances that, if combined, could lead to the identification of objective brain imaging-based markers of illness. In particular, we focus on advances that are helping to (1) elucidate the research agenda for biological psychiatry (e.g., neuroscience focus, precision medicine), (2) shift research models for clinical brain imaging (e.g., big data exploration, standardization), (3) break down research silos (e.g., open science, calls for reproducibility and transparency), and (4) improve imaging technologies and methods. Although an arduous road remains ahead, these advances are repositioning the brain imaging community for long-term success.

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Brainhack: a collaborative workshop for the open neuroscience community

Cited by 43 publications

References 13 publications

Head motion: the dirty little secret of neuroimaging in psychiatry

Head motion: the dirty little secret of neuroimaging in psychiatry

Opportunities for increased reproducibility and replicability of developmental neuroimaging

Clinically useful brain imaging for neuropsychiatry: How can we get there?

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