scholarly journals Meta-analytic activation maps can help identify affective processes captured by contrast-based task fMRI: the case of threat-related facial expressions

2019 ◽  
Author(s):  
M. Justin Kim ◽  
Annchen R. Knodt ◽  
Ahmad R. Hariri
Keyword(s):  
Facial Expressions ◽  
Brain Activity ◽  
Meta Analysis ◽  
Fmri Data ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Independent Dataset ◽  
Pattern Similarity ◽  
Affective Processes ◽  
Amygdala Activity

AbstractMeta-analysis of functional magnetic resonance imaging (fMRI) data is an effective method for capturing the distributed patterns of brain activity supporting discrete cognitive and affective processes. One opportunity presented by the resulting meta-analysis maps (MAMs) is as a reference for better understanding the nature of individual contrast maps (ICMs) derived from specific task fMRI data. Here, we compared MAMs from 148 neuroimaging studies representing the broad emotion categories of fear, anger, disgust, happiness, and sadness with ICMs from fearful > neutral and angry > neutral facial expressions from an independent dataset of task fMRI (n = 1263). Analyses revealed that both fear and anger ICMs exhibited the greatest pattern similarity to fear MAMs. As the number of voxels included for the computation of pattern similarity became more selective, the specificity of MAM-ICM correspondence decreased. Notably, amygdala activity long considered critical for processing threat-related facial expressions was neither sufficient nor necessary for detecting MAM-ICM pattern similarity effects. Our analyses suggest that both fearful and angry facial expressions are best captured by distributed patterns of brain activity associated with fear. More generally, our analyses demonstrate how MAMs can be leveraged to better understand affective processes captured by ICMs in task fMRI data.

Methods of Predicting the Brain Activity Based on Noun

2013 ◽  
Vol 347-350 ◽  
pp. 2516-2520
Author(s):  
Jian Hua Jiang ◽  
Xu Yu ◽  
Zhi Xing Huang
Keyword(s):  
Brain Activity ◽  
Past Research ◽  
Neural Representation ◽  
Fmri Data ◽  
Semantic Features ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
The Past ◽  
Machine Learning Methods ◽  
The Brain

Over the last decade, functional magnetic resonance imaging (fMRI) has become a primary tool to predict the brain activity.During the past research, researchers transfer the focus from the picture to the word.The results of these researches are relatively successful. In this paper, several typical methods which are machine learning methods are introduced. And most of the methods are by using fMRI data associated with words features. The semantic features (properties or factors) support words neural representation, and have a certain commonality in the people.The purpose of the application of these methods is used for prediction or classification.

scholarly journals Understanding multivariate brain activity: evaluating the effect of voxelwise noise correlations on population codes in functional magnetic resonance imaging

2019 ◽  
Author(s):  
Ru-Yuan Zhang ◽  
Xue-Xin Wei ◽  
Kendrick Kay
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Brain Activity ◽  
Neuronal Population ◽  
Fmri Data ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Correlated Activity ◽  
Population Codes

ABSTRACTPrevious studies have shown that neurons exhibit trial-by-trial correlated activity and that such noise correlations (NCs) greatly impact the accuracy of population codes. Meanwhile, multivariate pattern analysis (MVPA) has become a mainstream approach in functional magnetic resonance imaging (fMRI), but it remains unclear how NCs between voxels influence MVPA performance. Here, we tackle this issue by combining voxel-encoding modeling and MVPA. We focus on a well-established form of NC, tuning-compatible noise correlation (TCNC), whose sign and magnitude are systematically related to the tuning similarity between two units. We first replicate the classical finding that TCNCs impair population codes in a standard neuronal population. We then extend our analysis to fMRI data, and show that voxelwise TCNCs do not impair and can even improve MVPA performance when TCNCs are strong or the number of voxels is large. We also confirm these results using standard information-theoretic analyses in computational neuroscience. Further computational analyses demonstrate that the discrepancy between the effect of TCNCs in neuronal and voxel populations can be explained by tuning heterogeneity and pool sizes. Our results provide a theoretical foundation to understand the effect of correlated activity on population codes in macroscopic fMRI data. Our results also suggest that future fMRI research could benefit from a closer examination of the correlational structure of multivariate responses, which is not directly revealed by conventional MVPA approaches.

scholarly journals Bayesian MEG time courses with fMRI priors

2021 ◽  
Author(s):  
Yingying Wang ◽  
Scott K. Holland
Keyword(s):  
Spatial Resolution ◽  
Brain Activity ◽  
Fmri Data ◽  
Source Reconstruction ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Good Spatial Resolution ◽  
Excellent Spatial Resolution ◽  
Inverse Solutions ◽  
Time Courses

Magnetoencephalography (MEG) records brain activity with excellent temporal and good spatial resolution, while functional magnetic resonance imaging (fMRI) offers good temporal and excellent spatial resolution. The aim of this study is to implement a Bayesian framework to use fMRI data as spatial priors for MEG inverse solutions. We used simulated MEG data with both evoked and induced activity and experimental MEG data from sixteen participants to examine the effectiveness of using fMRI spatial priors in MEG source reconstruction. Our results provide empirical evidence that the use of fMRI spatial priors improves the accuracy of MEG source reconstruction.

scholarly journals Bayesian MEG time courses with fMRI priors

2021 ◽  
Author(s):  
Yingying Wang ◽  
Scott K. Holland
Keyword(s):  
Spatial Resolution ◽  
Brain Activity ◽  
Fmri Data ◽  
Source Reconstruction ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Good Spatial Resolution ◽  
Excellent Spatial Resolution ◽  
Inverse Solutions ◽  
Time Courses

Abstract Magnetoencephalography (MEG) records brain activity with excellent temporal and good spatial resolution, while functional magnetic resonance imaging (fMRI) offers good temporal and excellent spatial resolution. The aim of this study is to implement a Bayesian framework to use fMRI data as spatial priors for MEG inverse solutions. We used simulated MEG data with both evoked and induced activity and experimental MEG data from sixteen participants to examine the effectiveness of using fMRI spatial priors in MEG source reconstruction. Our results provide empirical evidence that the use of fMRI spatial priors improves the accuracy of MEG source reconstruction.

Amygdala activation to masked happy facial expressions

2009 ◽  
Vol 16 (2) ◽  
pp. 383-387 ◽  
Author(s):  
MARIO F. JURUENA ◽  
VINCENT P. GIAMPIETRO ◽  
STEPHEN D. SMITH ◽  
SIMON A. SURGULADZE ◽  
JEFFREY A. DALTON ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Facial Expressions ◽  
Backward Masking ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Emotional Stimuli ◽  
Amygdala Activation ◽  
Amygdala Activity ◽  
Conscious Processing

AbstractThe amygdala has a key role in automatic non-conscious processing of emotions. Highly salient emotional stimuli elicit amygdala activity, and happy faces are among the most rapidly perceived facial expressions. In backward masking paradigms, an image is presented briefly and then masked by another stimulus. However, reports of amygdala responses to masked happy faces have been mixed. In the present study, we used functional magnetic resonance imaging (fMRI) to examine amygdala activation to masked happy, sad, and neutral facial expressions. Masked happy faces elicited greater amygdala activation bilaterally as compared to masked sad faces. Our findings indicate that the amygdala is highly responsive to non-consciously perceived happy facial expressions. (JINS, 2010, 16, 383–387.)

scholarly journals Informative Biomarkers for Autism Spectrum Disorder Diagnosis in Functional Magnetic Resonance Imaging Data on the Default Mode Network

2021 ◽  
Vol 11 (13) ◽  
pp. 6216
Author(s):  
Aikaterini S. Karampasi ◽  
Antonis D. Savva ◽  
Vasileios Ch. Korfiatis ◽  
Ioannis Kakkos ◽  
George K. Matsopoulos
Keyword(s):  
Magnetic Resonance Imaging ◽  
Autism Spectrum Disorder ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Default Mode Network ◽  
Autism Spectrum ◽  
Fmri Data ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Dynamic Connectivity

Effective detection of autism spectrum disorder (ASD) is a complicated procedure, due to the hundreds of parameters suggested to be implicated in its etiology. As such, machine learning methods have been consistently applied to facilitate diagnosis, although the scarcity of potent autism-related biomarkers is a bottleneck. More importantly, the variability of the imported attributes among different sites (e.g., acquisition parameters) and different individuals (e.g., demographics, movement, etc.) pose additional challenges, eluding adequate generalization and universal modeling. The present study focuses on a data-driven approach for the identification of efficacious biomarkers for the classification between typically developed (TD) and ASD individuals utilizing functional magnetic resonance imaging (fMRI) data on the default mode network (DMN) and non-physiological parameters. From the fMRI data, static and dynamic connectivity were calculated and fed to a feature selection and classification framework along with the demographic, acquisition and motion information to obtain the most prominent features in regard to autism discrimination. The acquired results provided high classification accuracy of 76.63%, while revealing static and dynamic connectivity as the most prominent indicators. Subsequent analysis illustrated the bilateral parahippocampal gyrus, right precuneus, midline frontal, and paracingulate as the most significant brain regions, in addition to an overall connectivity increment.

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