NDLI: A Sticky Weighted Regression Model for Time-Varying Resting-State Brain Connectivity Estimation

Content Provider	IEEE Xplore Digital Library
Author	Aiping Liu Xun Chen McKeown, M.J. Wang, Z.J.
Copyright Year	1964
Abstract	Despite recent progress on brain connectivity modeling using neuroimaging data such as fMRI, most current approaches assume that brain connectivity networks have time-invariant topology/coefficients. This is clearly problematic as the brain is inherently nonstationary. Here, we present a time-varying model to investigate the temporal dynamics of brain connectivity networks. The proposed method allows for abrupt changes in network structure via a fused least absolute shrinkage and selection operator (LASSO) scheme, as well as recovery of time-varying networks with smoothly changing coefficients via a weighted regression technique. Simulations demonstrate that the proposed method yields improved accuracy on estimating time-dependent connectivity patterns when compared to a static sparse regression model or a weighted time-varying regression model. When applied to real resting-state fMRI datasets from Parkinson's disease (PD) and control subjects, significantly different temporal and spatial patterns were found to be associated with PD. Specifically, PD subjects demonstrated reduced network variability over time, which may be related to impaired cognitive flexibility previously reported in PD. The temporal dynamic properties of brain connectivity in PD subjects may provide insights into brain dynamics associated with PD and may serve as a potential biomarker in future studies.
Sponsorship	IEEE Engineering in Medicine and Biology Society
Starting Page	501
Ending Page	510
Page Count	10
File Size	729785
File Format	PDF
ISSN	00189294
Volume Number	62
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vectors Biological system modeling Mathematical model Brain models Data models time varying Brain connectivity network dynamic functional magnetic resonance imaging (fMRI) Parkinson's disease (PD)
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering

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

Time varying brain connectivity modeling using FMRI signals

Analysis of brain connectivity in simultaneous EEG/fMRI

Identifying patterns in temporal variation of functional connectivity using resting state FMRI

Classification of schizophrenia and bipolar patients using static and time-varying resting-state FMRI brain connectivity

Resting State fMRI Functional Connectivity Analysis Using Dynamic Time Warping

Time-varying Dynamic Network Model For Dynamic Resting State Functional Connectivity in fMRI and MEG imaging

Resting State fMRI Functional Connectivity-Based Classification Using a Convolutional Neural Network Architecture

Multiframe Evolving Dynamic Functional Connectivity (EVOdFNC): A Method for Constructing and Investigating Functional Brain Motifs

Time-Resolved Resting-State Functional Magnetic Resonance Imaging Analysis: Current Status, Challenges, and New Directions.

A Sticky Weighted Regression Model for Time-Varying Resting-State Brain Connectivity Estimation

Similar Documents

Time varying brain connectivity modeling using FMRI signals

Analysis of brain connectivity in simultaneous EEG/fMRI

Identifying patterns in temporal variation of functional connectivity using resting state FMRI

Classification of schizophrenia and bipolar patients using static and time-varying resting-state FMRI brain connectivity

Resting State fMRI Functional Connectivity Analysis Using Dynamic Time Warping

Time-varying Dynamic Network Model For Dynamic Resting State Functional Connectivity in fMRI and MEG imaging

Resting State fMRI Functional Connectivity-Based Classification Using a Convolutional Neural Network Architecture

Multiframe Evolving Dynamic Functional Connectivity (EVOdFNC): A Method for Constructing and Investigating Functional Brain Motifs

Time-Resolved Resting-State Functional Magnetic Resonance Imaging Analysis: Current Status, Challenges, and New Directions.

A Sticky Weighted Regression Model for Time-Varying Resting-State Brain Connectivity Estimation