Deconstructing multivariate decoding for the study of brain function
Multivariate decoding methods were developed originally as tools to enable accurate
predictions in real-world applications. The realization that these methods can also be …
predictions in real-world applications. The realization that these methods can also be …
Cortical specialization for attended versus unattended working memory
Items held in working memory can be either attended or not, depending on their current
behavioral relevance. It has been suggested that unattended contents might be solely …
behavioral relevance. It has been suggested that unattended contents might be solely …
[HTML][HTML] How to control for confounds in decoding analyses of neuroimaging data
L Snoek, S Miletić, HS Scholte - Neuroimage, 2019 - Elsevier
Over the past decade, multivariate “decoding analyses” have become a popular alternative
to traditional mass-univariate analyses in neuroimaging research. However, a fundamental …
to traditional mass-univariate analyses in neuroimaging research. However, a fundamental …
Controlling for effects of confounding variables on machine learning predictions
Machine learning predictive models are being used in neuroimaging to predict information
about the task or stimuli or to identify potentially clinically useful biomarkers. However, the …
about the task or stimuli or to identify potentially clinically useful biomarkers. However, the …
Population heterogeneity in clinical cohorts affects the predictive accuracy of brain imaging
Brain imaging research enjoys increasing adoption of supervised machine learning for
single-participant disease classification. Yet, the success of these algorithms likely depends …
single-participant disease classification. Yet, the success of these algorithms likely depends …
Decoding the contents and strength of imagery before volitional engagement
R Koenig-Robert, J Pearson - Scientific reports, 2019 - nature.com
Is it possible to predict the freely chosen content of voluntary imagery from prior neural
signals? Here we show that the content and strength of future voluntary imagery can be …
signals? Here we show that the content and strength of future voluntary imagery can be …
Tools of the Trade Multivoxel pattern analysis in fMRI: a practical introduction for social and affective neuroscientists
ME Weaverdyck, MD Lieberman… - Social Cognitive and …, 2020 - academic.oup.com
The family of neuroimaging analytical techniques known as multivoxel pattern analysis
(MVPA) has dramatically increased in popularity over the past decade, particularly in social …
(MVPA) has dramatically increased in popularity over the past decade, particularly in social …
Machine learning of brain-specific biomarkers from EEG
Background Electroencephalography (EEG) has a long history as a clinical tool to study
brain function, and its potential to derive biomarkers for various applications is far from …
brain function, and its potential to derive biomarkers for various applications is far from …
Structural differences in adolescent brains can predict alcohol misuse
Alcohol misuse during adolescence (AAM) has been associated with disruptive
development of adolescent brains. In this longitudinal machine learning (ML) study, we …
development of adolescent brains. In this longitudinal machine learning (ML) study, we …
Shared neural representations of cognitive conflict and negative affect in the medial frontal cortex
L Vermeylen, D Wisniewski… - Journal of …, 2020 - Soc Neuroscience
Influential theories of Medial Frontal Cortex (MFC) function suggest that the MFC registers
cognitive conflict as an aversive signal, but no study directly tested this idea. Instead, recent …
cognitive conflict as an aversive signal, but no study directly tested this idea. Instead, recent …