scholarly journals Multi-Class Sparse Bayesian Regression for Neuroimaging Data Analysis

2010 ◽  
pp. 50-57 ◽  
Author(s):  
Vincent Michel ◽  
Evelyn Eger ◽  
Christine Keribin ◽  
Bertrand Thirion
Keyword(s):  
Data Analysis ◽  
Bayesian Regression ◽  
Neuroimaging Data

Software for Neuroimaging Data Analysis

2015 ◽  
pp. 2733-2744
Author(s):  
Jean Baptiste Poline ◽  
David Kennedy
Keyword(s):  
Data Analysis ◽  
Neuroimaging Data

Software for Neuroimaging Data Analysis

2014 ◽  
pp. 1-14
Author(s):  
Jean Baptiste Poline ◽  
David Kennedy
Keyword(s):  
Data Analysis ◽  
Neuroimaging Data

Emerging Shifts in Neuroimaging Data Analysis in the Era of “Big Data”

2019 ◽  
pp. 99-118 ◽  
Author(s):  
Danilo Bzdok ◽  
Marc-Andre Schulz ◽  
Martin Lindquist
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Neuroimaging Data

Ambiguous Results in Functional Neuroimaging Data Analysis Due to Covariate Correlation

NeuroImage ◽  
1999 ◽  
Vol 10 (4) ◽  
pp. 483-486 ◽  
Author(s):  
Alexandre Andrade ◽  
Anne-Lise Paradis ◽  
Stéphanie Rouquette ◽  
Jean-Baptiste Poline
Keyword(s):  
Data Analysis ◽  
Functional Neuroimaging ◽  
Neuroimaging Data

scholarly journals Outsourcing neuroimaging data analysis

2010 ◽  
Vol 14 (1) ◽  
pp. 2-4 ◽  
Author(s):  
Anthony Steven Dick ◽  
Uri Hasson
Keyword(s):  
Data Analysis ◽  
Neuroimaging Data

scholarly journals Vector autoregression, structural equation modeling, and their synthesis in neuroimaging data analysis

2011 ◽  
Vol 41 (12) ◽  
pp. 1142-1155 ◽  
Author(s):  
Gang Chen ◽  
Daniel R. Glen ◽  
Ziad S. Saad ◽  
J. Paul Hamilton ◽  
Moriah E. Thomason ◽  
...  
Keyword(s):  
Structural Equation Modeling ◽  
Data Analysis ◽  
Structural Equation ◽  
Vector Autoregression ◽  
Equation Modeling ◽  
Neuroimaging Data

scholarly journals Is there a problem with methods skills in cognitive neuroscience? Evidence from an online survey

2018 ◽  
Author(s):  
Olaf Hauk
Keyword(s):  
Data Analysis ◽  
Linear Algebra ◽  
Cognitive Neuroscience ◽  
Signal Analysis ◽  
Online Survey ◽  
Complex Data ◽  
Multiple Choice Questions ◽  
Neuroimaging Data ◽  
The Difference ◽  
Training Programmes

AbstractCognitive neuroscience increasingly relies on complex data analysis methods. Researchers in this field come from highly diverse scientific backgrounds, such as psychology, engineering and medicine. This poses challenges with respect to acquisition of appropriate scientific computing and data analysis skills, as well as communication among researchers with different knowledge and skills sets. Are researchers in cognitive neuroscience adequately equipped to address these challenges? Here, we present evidence from an online survey of methods skills. Respondents (n=305) mainly comprised students and post-doctoral researchers working in the cognitive neurosciences. Multiple choice questions addressed a variety of basic and fundamental aspects of neuroimaging data analysis, such as signal analysis, linear algebra, and statistics. We analysed performance with respect to the following factors: undergraduate degree (grouped into Psychology, Methods, Biology), current researcher status (undergraduate student, PhD student, post-doctoral researcher), gender, and self-rated expertise levels. Overall accuracy was 72%. Not surprisingly, the Methods group performed best (87%), followed by Biology (73%) and Psychology (66%). Accuracy increased from undergraduate (59%) to PhD (74%) level, but not from PhD to post-doctoral (74%) level. The difference in performance for the Methods versus non-methods (Psychology/Biology) groups was particularly striking for questions related to signal analysis and linear algebra, two areas especially relevant to neuroimaging research. Self-rated methods expertise was not strongly predictive of performance. The majority of respondents (93%) indicated they would like to receive at least some additional training on the topics covered in this survey. In conclusion, methods skills among junior researchers in cognitive neuroscience can be improved, researchers are aware of this, and there is strong demand for more skills-oriented training opportunities. We hope that this survey will provide an empirical basis for the development of bespoke skills-oriented training programmes in cognitive neuroscience institutions.

scholarly journals Hierarchical Bayesian Regression for Multi-site Normative Modeling of Neuroimaging Data

2020 ◽  
pp. 699-709
Author(s):  
Seyed Mostafa Kia ◽  
Hester Huijsdens ◽  
Richard Dinga ◽  
Thomas Wolfers ◽  
Maarten Mennes ◽  
...  
Keyword(s):  
Bayesian Regression ◽  
Hierarchical Bayesian ◽  
Neuroimaging Data
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