NDLI: Decoding Spontaneous Brain Activity from fMRI Using Gaussian Processes: Tracking Brain Reactivation

Content Provider	IEEE Xplore Digital Library
Author	Schrouff, J. Kusse, C. Wehenkel, L. Maquet, P. Phillips, C.
Copyright Year	2012
Abstract	While Multi-Variate Pattern Analysis techniques based on machine learning have now been regularly applied to neuroimaging data, decoding brain activity is usually performed in highly controlled experimental paradigms. In more realistic conditions, the number, sequence and duration of mental states are unpredictably generated by the individual, resulting in complex and imbalanced fMRI data sets. Moreover, in the case of spontaneous brain activity, the mental states can not be linked to any external or internal stimulation, which makes it a highly difficult condition to decode. This study tests the classification of brain activity, acquired on 14 volunteers using fMRI, during mental imagery, a condition in which the number and duration of mental events were not externally imposed but self-generated. Application of the obtained model on rest sessions allowed classifying spontaneous brain activity linked to the task which, overall, correlated with their behavioural performance to the task.
Starting Page	61
Ending Page	64
File Size	333761
Page Count	4
File Format	PDF
ISBN	9781467321822
DOI	10.1109/PRNI.2012.19
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-02
Publisher Place	United Kingdom
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Brain Correlation Accuracy Brain decoding Humans Gaussian processes Educational institutions Decoding Semi-constrained activity Spontaneous activity FMRI
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Decoding Semi-Constrained Brain Activity from fMRI Using Support Vector Machines and Gaussian Processes

Fast voxel selection of fMRI data based on Smoothed 10 norm

Decoding Semi-Constrained Brain Activity from fMRI Using Support Vector Machines and Gaussian Processes

Improving brain decoding through constrained and parametrized temporal smoothing

Retrospective detection of interleaved slice acquisition parameters from fMRI data

Regularized hyperalignment of multi-set fMRI data

Structured Sparsity Models for Brain Decoding from fMRI Data

Classifying Judging States from fMRI Data of Visual Recognition Task

Socialized Gaussian Process Model for Human Behavior Prediction in a Health Social Network

Decoding Spontaneous Brain Activity from fMRI Using Gaussian Processes: Tracking Brain Reactivation

Similar Documents

Decoding Semi-Constrained Brain Activity from fMRI Using Support Vector Machines and Gaussian Processes

Fast voxel selection of fMRI data based on Smoothed 10 norm

Decoding Semi-Constrained Brain Activity from fMRI Using Support Vector Machines and Gaussian Processes

Improving brain decoding through constrained and parametrized temporal smoothing

Retrospective detection of interleaved slice acquisition parameters from fMRI data

Regularized hyperalignment of multi-set fMRI data

Structured Sparsity Models for Brain Decoding from fMRI Data

Classifying Judging States from fMRI Data of Visual Recognition Task

Socialized Gaussian Process Model for Human Behavior Prediction in a Health Social Network

Decoding Spontaneous Brain Activity from fMRI Using Gaussian Processes: Tracking Brain Reactivation