Modalities, Modes, and Models in Functional Neuroimaging

By Karl J. Friston. 2009

479 citations recorded by the source corpus.

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In Cognitive Neuroscience

Citations in the corpus

Theory of Mind: A Neural Prediction Problem (2013, neutral): “…coding models need make no assumption about the distribution of these two kinds of neurons within a population; in practice, aggregate population activity is often dominated by error neurons (Friston, 2009; Wacongne et al., 2012; Egner et al., 2010; Keller et al., 2012; Meyer and Sauerland, 2009).”
On the interpretation of weight vectors of linear models in multivariate neuroimaging (2014, neutral): “HemodynamicmeasurementsobtainedbyfMRIarethemostpopular neuroimaging modality (Friston, 2009).”
Encoding and decoding in fMRI (2010, methodology): “(Note that with the conventional approach, it makes little difference if the hypothesis is tested using an encoding model or a linear classifier; the experimental design and the interpretation of a significant result will be the same (Friston, 2009)).”
Tensor Regression with Applications in Neuroimaging Data Analysis (2012, methodology): “It also provides a systematic solution for the integrative analysis of multi-modality imaging data and imaging genetics data (Friston, 2009; Casey et al., 2010).”
An Introduction to Model-Based Cognitive Neuroscience (2015, background): “The shape and relative locations of subcortical structures also differ between individuals and change with age [24, 47, 48].”
The Segregation and Integration of Distinct Brain Networks and Their Relationship to Cognition (2016, background): “Despite recognition that both segregation and integration of brain networks are critical for cognitive function (Friston, 2009; Sporns, 2013; Deco et al., 2015), existing literature relating network reconfiguration across tasks rarely interprets findings within this dual segregation-integration…”
Structure and function of complex brain networks (2013, background): “Technological developments in noninvasive neuroimaging have opened up new avenues towards studying the structure and function of the human brain.(1,2) These advances are increasingly combined with powerful network modeling tools developed in the course of a broader research effort to understand the ”