Thresholding Wavelet-Based Statistical Parametric Maps of Functional Brain Imaging Data

NeuroImage ◽  
2009 ◽  
Vol 47 ◽  
pp. S124
Author(s):  
Radu Mutihac
2019 ◽  
Vol 13 ◽  
Author(s):  
Philippe Albouy ◽  
Anne Caclin ◽  
Sam V. Norman-Haignere ◽  
Yohana Lévêque ◽  
Isabelle Peretz ◽  
...  

2010 ◽  
Vol 62 (2) ◽  
pp. 183-196 ◽  
Author(s):  
Jessica Albrecht ◽  
Rainer Kopietz ◽  
Johannes Frasnelli ◽  
Martin Wiesmann ◽  
Thomas Hummel ◽  
...  

2018 ◽  
Author(s):  
Christopher R. Cox ◽  
Timothy T. Rogers

ABSTRACTArtificial neural network models have long proven useful for understanding healthy, disordered, and developing cognition, but this work has often proceeded with little connection to functional brain imaging. We consider how analysis of functional brain imaging data is best approached if the representational assumptions embodied by neural networks are valid. Using a simple model to generate synthetic data, we show that four contemporary methods each have critical and complementary blind-spots for detecting distributed signal. The pattern suggests a new approach based on structured sparsity that, in simulation, retains the strengths of each method while avoiding its weaknesses. When applied to functional magnetic resonance imaging data the new approach reveals extensive distributed signal missed by the other methods, suggesting radically different conclusions about how brains encode cognitive information in the well-studied domain of visual face perception.


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