NDLI: Independent components of oculomotor learning

Content Provider	IEEE Xplore Digital Library
Author	Alkan, Y. Alvarez, T.L. Biswal, B.B. Vicci, V.R.
Copyright Year	2009
Description	Author affiliation: Department of Radiology, University of Medicine and Dentistry of New Jersey, Newark, USA (Biswal, B.B.) \|\| Department of Rehabilitation and Private Practice, Kessler Institute for Rehabilitation, West Orange, Chester, Saddle Brook, Westfield, NJ, USA (Vicci, V.R.) \|\| Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, USA (Alkan, Y.; Alvarez, T.L.)
Abstract	This research is an investigation of the visual circuit for saccadic and vergence eye movements using an oculomotor learning task to study the cerebral and cerebellar areas utilizing fMRI. Data from eight subjects were analyzed with analysis of functional neural images (AFNI) and the Infomax independent component analysis algorithm. This study showed that 1) neural activation is present in the prefrontal, frontal, parietal, occipital cortices, and cerebellar vermis (4/5) and cerebellar vermis (VI/VII) or declive for saccadic and vergence eye movements and 2) there are two distinct pathways involved in an oculomotor learning task. The first is a frontal-parietal and the second is cerebellar (VI/VII) pathway, for short-term oculomotor learning. These findings suggest that oculomotor learning utilizes two distinct circuits to learn a visual stimulus pattern.
Starting Page	163
Ending Page	166
File Size	817050
Page Count	4
File Format	PDF
ISBN	9781424420728
DOI	10.1109/NER.2009.5109260
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-04-29
Publisher Place	Turkey
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Circuits Independent component analysis Glass Magnetic heads saccades vergence fMRI Image analysis USA Councils Magnetic separation Magnetic resonance imaging Informax-ICA Timing AFNI
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

A Modified Infomax ICA Algorithm for fMRI Data Source Separation

Flexible complex ICA of fMRI data

Complex ICA for fMRI analysis: performance of several approaches

Independent subspace analysis with prior information for fMRI data

Phase correction and denoising for ICA of complex FMRI data

Application of independent component analysis for activation detection in functional magnetic resonance imaging (FMRI) data

Reduction of the Ballistocardiogram Artifact in Simultaneous EEG-fMRI using ICA

Partner-matching for the automated identification of reproducible ICA components from fMRI datasets: Algorithm and validation

Decomposition of Vergence Dynamics Using Independent Component Analysis

Independent components of oculomotor learning

Similar Documents

A Modified Infomax ICA Algorithm for fMRI Data Source Separation

Flexible complex ICA of fMRI data

Complex ICA for fMRI analysis: performance of several approaches

Independent subspace analysis with prior information for fMRI data

Phase correction and denoising for ICA of complex FMRI data

Application of independent component analysis for activation detection in functional magnetic resonance imaging (FMRI) data

Reduction of the Ballistocardiogram Artifact in Simultaneous EEG-fMRI using ICA

Partner-matching for the automated identification of reproducible ICA components from fMRI datasets: Algorithm and validation

Decomposition of Vergence Dynamics Using Independent Component Analysis

Independent components of oculomotor learning