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 INTRODUCTION TO THE SPECIAL ISSUE OF ECONOMICS AND PHILOSOPHY ON NEUROECONOMICS

2008 ◽  
Vol 24 (3) ◽  
pp. 301-302 ◽  
Author(s):  
Giacomo Bonanno ◽  
Christian List ◽  
Bertil Tungodden ◽  
Peter Vallentyne
Keyword(s):  
Decision Making ◽  
Imaging Techniques ◽  
Brain Activity ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Special Issue ◽  
Individual Decision ◽  
The Past ◽  
Economic Choices ◽  
The Brain

The past fifteen years or so have witnessed considerable progress in our understanding of how the human brain works. One of the objectives of the fast-growing field of neuroscience is to deepen our knowledge of how the brain perceives and interacts with the external world. Advances in this direction have been made possible by progress in brain imaging techniques and by clinical data obtained from patients with localized brain lesions. A relatively new field within neuroscience is neuroeconomics, which focuses on individual decision making and aims to systematically classify and map the brain activity that correlates with decision-making that pertains to economic choices. Neuroeconomic studies rely heavily on functional magnetic resonance imaging (fMRI), which measures the haemodynamic response (that is, changes in the blood flow) related to neural activity in the brain.

scholarly journals Adaptive brain activity changes during tongue movement with palatal coverage from fMRI data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yuka Inamochi ◽  
Kenji Fueki ◽  
Nobuo Usui ◽  
Masato Taira ◽  
Noriyuki Wakabayashi
Keyword(s):  
Spatial Information ◽  
Brain Activity ◽  
Motor Impairment ◽  
Angular Gyrus ◽  
Tongue Movement ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Oral Environment ◽  
Motor Dexterity ◽  
The Brain

AbstractSuccessful adaptation to wearing dentures with palatal coverage may be associated with cortical activity changes related to tongue motor control. The purpose was to investigate the brain activity changes during tongue movement in response to a new oral environment. Twenty-eight fully dentate subjects (mean age: 28.6-years-old) who had no experience with removable dentures wore experimental palatal plates for 7 days. We measured tongue motor dexterity, difficulty with tongue movement, and brain activity using functional magnetic resonance imaging during tongue movement at pre-insertion (Day 0), as well as immediately (Day 1), 3 days (Day 3), and 7 days (Day 7) post-insertion. Difficulty with tongue movement was significantly higher on Day 1 than on Days 0, 3, and 7. In the subtraction analysis of brain activity across each day, activations in the angular gyrus and right precuneus on Day 1 were significantly higher than on Day 7. Tongue motor impairment induced activation of the angular gyrus, which was associated with monitoring of the tongue’s spatial information, as well as the activation of the precuneus, which was associated with constructing the tongue motor imagery. As the tongue regained the smoothness in its motor functions, the activation of the angular gyrus and precuneus decreased.

scholarly journals GPT-2’s activations predict the degree of semantic comprehension in the human brain

2021 ◽  
Author(s):  
Charlotte Caucheteux ◽  
Alexandre Gramfort ◽  
Jean-Rémi King
Keyword(s):  
Brain Activity ◽  
Planum Temporale ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Brain Responses ◽  
The Brain ◽  
Semantic Comprehension ◽  
Empirical Framework ◽  
Superior Frontal Cortex ◽  
Language Network

Language transformers, like GPT-2, have demonstrated remarkable abilities to process text, and now constitute the backbone of deep translation, summarization and dialogue algorithms. However, whether these models actually understand language is highly controversial. Here, we show that the representations of GPT-2 not only map onto the brain responses to spoken stories, but also predict the extent to which subjects understand the narratives. To this end, we analyze 101 subjects recorded with functional Magnetic Resonance Imaging while listening to 70 min of short stories. We then fit a linear model to predict brain activity from GPT-2 activations, and correlate this mapping with subjects’ comprehension scores as assessed for each story. The results show that GPT-2’s brain predictions significantly correlate with semantic comprehension. These effects are bilaterally distributed in the language network and peak with a correlation above 30% in the infero-frontal and medio-temporal gyri as well as in the superior frontal cortex, the planum temporale and the precuneus. Overall, this study provides an empirical framework to probe and dissect semantic comprehension in brains and deep learning algorithms.

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 Regions and Connections: Complementary Approaches to Characterize Brain Organization and Function

2019 ◽  
Vol 26 (2) ◽  
pp. 117-133 ◽  
Author(s):  
Corey Horien ◽  
Abigail S. Greene ◽  
R. Todd Constable ◽  
Dustin Scheinost
Keyword(s):  
Functional Organization ◽  
Brain Activity ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Brain Organization ◽  
Neural Processes ◽  
Relative Utility ◽  
And Function ◽  
Brain Behavior ◽  
The Brain

Functional magnetic resonance imaging has proved to be a powerful tool to characterize spatiotemporal patterns of human brain activity. Analysis methods broadly fall into two camps: those summarizing properties of a region and those measuring interactions among regions. Here we pose an unappreciated question in the field: What are the strengths and limitations of each approach to study fundamental neural processes? We explore the relative utility of region- and connection-based measures in the context of three topics of interest: neurobiological relevance, brain-behavior relationships, and individual differences in brain organization. In each section, we offer illustrative examples. We hope that this discussion offers a novel and useful framework to support efforts to better understand the macroscale functional organization of the brain and how it relates to behavior.

scholarly journals How the Brain Converts Negative Evaluation into Performance Facilitation

2017 ◽  
Vol 28 (2) ◽  
pp. 602-611 ◽  
Author(s):  
Charlotte Prévost ◽  
Hakwan Lau ◽  
Dean Mobbs
Keyword(s):  
Brain Regions ◽  
Negative Evaluation ◽  
Neural Representation ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Success Stories ◽  
Goal Directed Behavior ◽  
The Brain ◽  
Insight Into ◽  
Recurrent Theme

Abstract Surpassing negative evaluation is a recurrent theme of success stories. Yet, there is little evidence supporting the counterintuitive idea that negative evaluation might not only motivate people, but also enhance performance. To address this question, we designed a task that required participants to decide whether taking up a risky challenge after receiving positive or negative evaluations from independent judges. Participants believed that these evaluations were based on their prior performance on a related task. Results showed that negative evaluation caused a facilitation in performance. Concurrent functional magnetic resonance imaging revealed that the motivating effect of negative evaluation was represented in the insula and striatum, while the performance boost was associated with functional positive connectivity between the insula and a set of brain regions involved in goal-directed behavior and the orienting of attention. These findings provide new insight into the neural representation of negative evaluation-induced facilitation.

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.

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.

scholarly journals What part of the brain is involved in graphic design thinking in landscape architecture?

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0258413
Author(s):  
Yu-Ping Tsai ◽  
Shih-Han Hung ◽  
Tsung-Ren Huang ◽  
William C. Sullivan ◽  
Shih-An Tang ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Design Process ◽  
Graphic Design ◽  
Landscape Architecture ◽  
Design Thinking ◽  
Brain Activity ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Specific Activities ◽  
The Brain

Graphic design thinking is a key skill for landscape architects, but little is known about the links between the design process and brain activity. Based on Goel’s frontal lobe lateralization hypothesis (FLLH), we used functional magnetic resonance imaging (fMRI) to scan the brain activity of 24 designers engaging in four design processes—viewing, copy drawing, preliminary ideas, and refinement—during graphic design thinking. The captured scans produced evidence of dramatic differences between brain activity when copying an existing graphic and when engaging in graphic design thinking. The results confirm that designs involving more graphic design thinking exhibit significantly more activity in the left prefrontal cortex. These findings illuminate the design process and suggest the possibility of developing specific activities or exercises to promote graphic design thinking in landscape architecture.

Sign in / Sign up

Export Citation Format

Share Document