NDLI: Detection of resting-state brain activity in magnetic resonance images through wavelet feature cluster analysis

Content Provider	IEEE Xplore Digital Library
Author	Verdoolaege, G. Vlerick, L. Achten, E.
Copyright Year	2011
Description	Author affiliation: Department of Neuroradiology, Ghent University Hospital, De Pintelaan 185, 9000 Gent, Belgium (Vlerick, L.; Achten, E.) \|\| Department of Applied Physics, Ghent University, Jozef Plateaustraat 22, 9000 Gent, Belgium (Verdoolaege, G.)
Abstract	Magnetic resonance imaging studies of the resting brain have recently revealed the existence of low-frequency fluctuations of the cerebral hemodynamics. It has been suggested that these fluctuations are linked to baseline neural activity, organized in functional networks. This paper presents a novel method for the detection of these resting-state networks from blood-oxygen level dependent signals, through their wavelet representation in the appropriate frequency range. A valley-seeking clustering principle is employed, requiring no a priori knowledge of the number of functional networks. The technique is applied to a data set acquired at rest and is shown to retrieve a number of identifiable functional networks. The method is proposed as an alternative to e.g. independent component analysis and exhibits an enhanced network separation capability and stability against noise.
Starting Page	2661
Ending Page	2664
File Size	1199045
Page Count	4
File Format	PDF
ISBN	9781457713040
ISSN	15224880
e-ISBN	9781457713033
e-ISBN	9781457713026
DOI	10.1109/ICIP.2011.6116215
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-11
Publisher Place	Belgium
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Noise Clustering algorithms Vectors Conferences Magnetic resonance imaging Time series analysis Discrete wavelet transforms clustering fMRI resting state wavelet
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

Activation detection in event-related fMRI through clustering ofwavelet distributions

Interaction-Based Clustering of Multivariate Time Series

A wavelet clustering technique for the identification of functionally connected regions in the rat brain using resting state fMRI

Classification of MRI brain images using k-nearest neighbor and artificial neural network

On edge detection in MRI using the wavelet transform and unsupervised neural networks

Localization of semantic category classification in fMRI images

Empirical Mode Decomposition in Data-Driven fMRI Analysis

Agglomerative clustering for resting state MRI

Resting state fMRI analysis using a spatial regression mixture model

Detection of resting-state brain activity in magnetic resonance images through wavelet feature cluster analysis

Similar Documents

Activation detection in event-related fMRI through clustering ofwavelet distributions

Interaction-Based Clustering of Multivariate Time Series

A wavelet clustering technique for the identification of functionally connected regions in the rat brain using resting state fMRI

Classification of MRI brain images using k-nearest neighbor and artificial neural network

On edge detection in MRI using the wavelet transform and unsupervised neural networks

Localization of semantic category classification in fMRI images

Empirical Mode Decomposition in Data-Driven fMRI Analysis

Agglomerative clustering for resting state MRI

Resting state fMRI analysis using a spatial regression mixture model

Detection of resting-state brain activity in magnetic resonance images through wavelet feature cluster analysis