- published: 30 Apr 2017
- views: 8
In the past decade, the fusion between diffusion magnetic resonance imaging (dMRI) and functional magnetic resonance imaging (fMRI) has opened the way for exploring structure-function relationships in-vivo. As it stands, the common approach usually consists of analysing fMRI and dMRI datasets separately or using one to inform the other, such as using fMRI activation sites to reconstruct dMRI streamlines that interconnect them. Moreover, given the large inter-individual variability of the healthy human brain, it is possible that valuable information is lost when a fixed set of dMRI/fMRI analysis parameters such as threshold values are assumed constant across subjects. By allowing one to modify such parameters while viewing the results in real-time, one can begin to fully explore the sensiti...
Full Title: Illustrative Hybrid Visualization and Exploration of Anatomical and Functional Brain Data Common practice in brain research and brain surgery involves the multi-modal acquisition of brain anatomy and brain activation data. These highly complex three-dimensional data have to be displayed simultaneously in order to convey spatial relationships. Unique challenges in information and interaction design have to be solved in order to keep the visualization sufficiently complete and uncluttered at the same time. The visualization method presented in this paper addresses these issues by using a hybrid combination of polygonal rendering of brain structures and direct volume rendering of activation data. Advanced rendering techniques including illustrative display styles and ambient o...
The video shows an interface for interactive functional connectivity analysis of fMRI data, the yellow voxel is the reference voxel. The interface was implemented with MevisLab (www.mevislab.de) and OpenCL. All the calculations are done on the GPU. http://liu.diva-portal.org/smash/get/diva2:445120/FULLTEXT01 fMRI analysis on the GPU: http://www.sciencedirect.com/science/article/pii/S0169260711001957
Spontaneous brain activity as revealed by temporal Non-Local Means (tNLM) filtering of resting functional MRI data. The video shows real-time brain activity on the cortex as captured by blood oxygenation level dependent (BOLD) signal. Red represents a BOLD signal larger and than the average, while blue color represents a BOLD signal smaller than the average (white color represents the average BOLD signal). tNLM filtering enables direct visualization of the brain activity in real-time. Side by side comparison of filtering: https://youtu.be/yC_mjRgYryE and https://youtu.be/em2GD9nJgzc For more details and Matlab code: http://neuroimage.usc.edu/neuro/tNLM Reference paper: Bhushan et al., Non-local means filtering reveals real-time whole-brain cortical interactions in resting fMRI. PLOS ...
Children's Healthcare of Atlanta uses many different imaging tests to uphold the mission of providing excellent patient care. One of those tests is functional magnetic resonance imaging (fMRI). Using a strong magnetic field, the fMRI takes real-time photos of the brain's activity. While being scanned, patients will be asked to perform simple tasks, such as reading, thinking or just moving a body part.
Multi-subject fMRI data is critical for evaluating the generality and validity of findings across subjects, and its effective utilization helps improve analysis sensitivity. We develop a shared response model for aggregating multi-subject fMRI data that accounts for different functional topographies among anatomically aligned datasets. Our model demonstrates improved sensitivity in identifying a shared response for a variety of datasets and anatomical brain regions of interest. Furthermore, by removing the identified shared response, it allows improved detection of group differences. The ability to identify what is shared and what is not shared opens the model to a wide range of multi-subject fMRI studies.
3D model of my brain using fMRI scans.
Where exactly are the words in your head? Scientists have created an interactive map showing which brain areas respond to hearing different words. The map reveals how language is spread throughout the cortex and across both hemispheres, showing groups of words clustered together by meaning. The beautiful interactive model allows us to explore the complex organisation of the enormous dictionaries in our heads. Explore the brain model for yourself here: http://gallantlab.org/huth2016 Read the paper here: http://www.nature.com/doifinder/10.1038/nature17637 28th April 2016
The application is part of my master thesis of the Sound and Music Computing master's degree. The fMRI player application helps to explore multivariate data coming from the brain by means of data reduction, visualization and sonification.
Elizabeth Norton, Ph.D. Using simultaneous EEG-fMRI to characterize human face processing in space and time A key feature of the human social brain is its specialized ability to process faces. EEG and fMRI brain imaging studies have yielded conflicting results about whether the brain's response to faces is qualitatively or quantitatively different in individuals with autism spectrum disorders (ASDs). Here, I present data from a study using simultaneous EEG-fMRI to relate the EEG/ERP time course of face processing to the associated fMRI activation in adults with and without ASD. This approach is novel in its focus on relationships between multiple brain measures and behavioral indices. Taken together, these technologies can shed new light on the brain's specialized processing of faces in AS...
Kinesio induced changes in cortical activation by EDF pplication in right hand.
Part 2 of the eighteenth lecture from the class BCS 513 Introduction to fMRI: Imaging, Computational Analysis and Neural Representations, in the Department of Brain & Cognitive Sciences at the University of Rochester. http://www.bcs.rochester.edu/ Instructor: Prof. Rajeev Raizada. http://raizadalab.org