scholarly journals Deep Transfer Learning for Whole-Brain FMRI Analyses

2019 ◽  
pp. 59-67 ◽  
Author(s):  
Armin W. Thomas ◽  
Klaus-Robert Müller ◽  
Wojciech Samek
Keyword(s):  
Transfer Learning ◽  
Whole Brain ◽  
Brain Fmri

scholarly journals Author Correction: Simultaneous cortex-wide fluorescence Ca2+ imaging and whole-brain fMRI

2021 ◽  
Author(s):  
Evelyn M. R. Lake ◽  
Xinxin Ge ◽  
Xilin Shen ◽  
Peter Herman ◽  
Fahmeed Hyder ◽  
...  
Keyword(s):  
Whole Brain ◽  
Brain Fmri

The Time Course of Changes during Motor Sequence Learning: A Whole-Brain fMRI Study

NeuroImage ◽  
1998 ◽  
Vol 8 (1) ◽  
pp. 50-61 ◽  
Author(s):  
Ivan Toni ◽  
Michael Krams ◽  
Robert Turner ◽  
Richard E. Passingham
Keyword(s):  
Sequence Learning ◽  
Time Course ◽  
Motor Sequence Learning ◽  
Motor Sequence ◽  
Whole Brain ◽  
Fmri Study ◽  
Brain Fmri

scholarly journals Correction: Multiplexed Echo Planar Imaging for Sub-Second Whole Brain FMRI and Fast Diffusion Imaging

PLoS ONE ◽  
2011 ◽  
Vol 6 (9) ◽  
Author(s):  
David A. Feinberg ◽  
Steen Moeller ◽  
Stephen M. Smith ◽  
Edward Auerbach ◽  
Sudhir Ramanna ◽  
...  
Keyword(s):  
Diffusion Imaging ◽  
Echo Planar Imaging ◽  
Fast Diffusion ◽  
Planar Imaging ◽  
Whole Brain ◽  
Echo Planar ◽  
Brain Fmri

Whole-Brain fMRI during Paired-Associate Learning: Neuronal Correlates of Encoding and Retrieval in Episodic Memory

NeuroImage ◽  
1998 ◽  
Vol 7 (4) ◽  
pp. S842
Author(s):  
FM Mottaghy ◽  
BJ Krause ◽  
NJ Shah ◽  
D Schmidt ◽  
L Jäncke ◽  
...  
Keyword(s):  
Episodic Memory ◽  
Paired Associate ◽  
Paired Associate Learning ◽  
Associate Learning ◽  
Whole Brain ◽  
Brain Fmri ◽  
Encoding And Retrieval

Whole Brain fMRI of a Go/ No Go Task

NeuroImage ◽  
1998 ◽  
Vol 7 (4) ◽  
pp. S971 ◽  
Author(s):  
A.M. Smith ◽  
K.A. Kiehl ◽  
A. Mendrek ◽  
B.B. Forster ◽  
R.D. Hare ◽  
...  
Keyword(s):  
Whole Brain ◽  
Brain Fmri

PRESTO-SENSE: An ultrafast whole-brain fMRI technique

2000 ◽  
Vol 43 (6) ◽  
pp. 779-786 ◽  
Author(s):  
Xavier Golay ◽  
Klaas P. Pruessmann ◽  
Markus Weiger ◽  
G�rard R. Crelier ◽  
Paul J.M. Folkers ◽  
...  
Keyword(s):  
Whole Brain ◽  
Brain Fmri

Sparse representation of whole-brain fMRI signals for identification of functional networks

2015 ◽  
Vol 20 (1) ◽  
pp. 112-134 ◽  
Author(s):  
Jinglei Lv ◽  
Xi Jiang ◽  
Xiang Li ◽  
Dajiang Zhu ◽  
Hanbo Chen ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Functional Networks ◽  
Whole Brain ◽  
Brain Fmri

scholarly journals Fat-suppressed alternating-SSFP for whole-brain fMRI using breath-hold and visual stimulus paradigms

2015 ◽  
Vol 75 (5) ◽  
pp. 1978-1988 ◽  
Author(s):  
Tiffany Jou ◽  
Steve Patterson ◽  
John M. Pauly ◽  
Chris V. Bowen
Keyword(s):  
Visual Stimulus ◽  
Breath Hold ◽  
Whole Brain ◽  
Brain Fmri

scholarly journals Mass univariate testing biases the detection of interaction effects in whole-brain analysis of variance

2017 ◽  
Author(s):  
Robert S. Chavez ◽  
Dylan D. Wagner
Keyword(s):  
Analysis Of Variance ◽  
Signal To Noise Ratio ◽  
Brain Activity ◽  
Interaction Effects ◽  
Sample Sizes ◽  
Whole Brain ◽  
Two Factors ◽  
Independent Variable ◽  
Simulated Analysis ◽  
Brain Fmri

AbstractWhole-brain analysis of variance (ANOVA) is a common analytic approach in cognitive neuroscience. Researchers are often interested in exploring whether brain activity reflects to the interaction of two factors. Disordinal interactions — where there is a reversal of the effect of one independent variable at a level of a second independent variable — are common in the literature. It is well established in power-analyses of factorial ANOVAs that certain patterns of interactions, such as disordinal (e.g., cross-over interactions) require less power than others to detect. This fact, combined with the perils of mass univariate testing suggests that testing for interactions in whole-brain ANOVAs, may be biased towards the detection of disordinal interactions. Here, we report on a series of simulated analysis --including whole-brain fMRI data using realistic multi-source noise parameters-- that demonstrate a bias towards the detection of disordinal interactions in mass-univariate contexts. Moreover, results of these simulations indicated that spurious disordinal interactions are found at common thresholds and cluster sizes at the group level. Moreover, simulations based on implanting true ordinal interaction effects can nevertheless appear like crossover effects at realistic levels of signal-to-noise ratio (SNR) when performing mass univariate testing at the whole-brain level, potentially leading to erroneous conclusions when interpreted as is. Simulations of varying sample sizes and SNR levels show that this bias is driven primarily by SNR and larger sample sizes do little to ameliorate this issue. Together, the results of these simulations argue for caution when searching for ordinal interactions in whole-brain ANOVA.

scholarly journals Improving Whole-Brain Neural Decoding of fMRI with Domain Adaptation

2018 ◽  
Author(s):  
Shuo Zhou ◽  
Christopher R. Cox ◽  
Haiping Lu
Keyword(s):  
Domain Adaptation ◽  
Feature Space ◽  
Neural Decoding ◽  
Whole Brain ◽  
Classifier Adaptation ◽  
Size Discrepancy ◽  
Training Examples ◽  
Psychological Similarity ◽  
Brain Fmri ◽  
Decoding Accuracy

AbstractIn neural decoding, there has been a growing interest in machine learning on whole-brain functional magnetic resonance imaging (fMRI). However, the size discrepancy between the feature space and the training set poses serious challenges. Simply increasing the number of training examples is infeasible and costly. In this paper, we proposed a domain adaptation framework for whole-brain fMRI (DawfMRI) to improve whole-brain neural decoding on target data leveraging pre-existing source data. DawfMRI consists of three steps: 1) feature extraction from whole-brain fMRI, 2) source and target feature adaptation, and 3) source and target classifier adaptation. We evaluated its eight possible variations, including two non-adaptation and six adaptation algorithms, using a collection of seven task-based fMRI datasets (129 unique subjects and 11 cognitive tasks in total) from the OpenNeuro project. The results demonstrated that appropriate source domain can help improve neural decoding accuracy for challenging classification tasks. The best-case improvement is 8.94% (from 78.64% to 87.58%). Moreover, we discovered a plausible relationship between psychological similarity and adaptation effectiveness. Finally, visualizing and interpreting voxel weights showed that the adaptation can provide additional insights into neural decoding.

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