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Deriving Autism Spectrum Disorder Functional Networks from RS-FMRI Data using Group ICA and Dictionary Learning [article]

Xin Yang, Ning Zhang, Donglin Wang
2021 arXiv   pre-print
In our experiments, the ASD functional networks were derived from resting-state functional magnetic resonance imaging (rs-fMRI) data.  ...  The objective of this study is to derive functional networks for the autism spectrum disorder (ASD) population using the group ICA and dictionary learning model together and to classify ASD and typically  ...  learning from rs-fMRI Explained variance of all 20 dictionary learning components For single group ICA and single dictionary learning, the ROI-based functional connectivity of the extracted 20 functional  ... 
arXiv:2106.09000v1 fatcat:3htmohdmn5g2fexbogfplcfnhm

Review of methods for functional brain connectivity detection using fMRI

Kaiming Li, Lei Guo, Jingxin Nie, Gang Li, Tianming Liu
2009 Computerized Medical Imaging and Graphics  
Since the mid of 1990s, functional connectivity study using fMRI (fcMRI) has drawn increasing attention of neuroscientists and computer scientists, since it opens a new window to explore functional network  ...  Keywords FMRI; Brain connectivity; Brain network He is working on human brain mapping, cortical surface reconstruction and mapping, deformable surface model, hybrid volume and surface registration, and  ...  Independent component analysis: Independent component analysis is a recently developed popular method for functional connectivity detection using fMRI.  ... 
doi:10.1016/j.compmedimag.2008.10.011 pmid:19111443 pmcid:PMC2724763 fatcat:rwdt2pi6o5dfvozmubhlcfqqre

Multi-timescale hybrid components of the functional brain connectome: A bimodal EEG-fMRI decomposition

Jonathan Wirsich, Enrico Amico, Anne-Lise Giraud, Joaquín Goñi, Sepideh Sadaghiani
2020 Network Neuroscience  
This work reveals the existence of joint spatially independent components, suggesting that parts of resting-state connectivity are co-expressed in a linked manner across EEG and fMRI over individuals.  ...  Conversely, the second component is sensitive to different EEG-frequencies and is primarily linked to intra-ICN connectivity in fMRI but to inter-ICN connectivity in EEG.  ...  An interesting feature of this approach is that it is able to identify hybrid joint connectivity components that are linked in terms of explaining subject-specific variance of spatially independent nonlinear  ... 
doi:10.1162/netn_a_00135 pmid:32885120 pmcid:PMC7462430 fatcat:gyh6wq5vxzbdnlfmrk6oqvqvcy

Performance of temporal and spatial ICA in identifying and removing low-frequency physiological and motion effects in resting-state fMRI [article]

Ali Golestani, J. Jean Chen
2021 bioRxiv   pre-print
Conventionally, due to the lower amount of temporal than spatial information in rs-fMRI data, spatial ICA (sICA) is the method of choice.  ...  Independent component analysis (ICA) is an approach for addressing these challenges.  ...  In scenario 3, when the components are temporally correlated and spatially independent, sICA performs better in identifying the components' spatial patterns.  ... 
doi:10.1101/2021.09.19.460965 fatcat:7vhebpc4znh7lbdg2xme6ipwka

Independent component analysis of nondeterministic fMRI signal sources

Vesa Kiviniemi, Juha-Heikki Kantola, Jukka Jauhiainen, Aapo Hyvärinen, Osmo Tervonen
2003 NeuroImage  
Neuronal activation can be separated from other signal sources of functional magnetic resonance imaging (fMRI) data by using independent component analysis (ICA).  ...  The ability of spatial-domain ICA to separate spontaneous physiological signal sources was evaluated in 15 anesthetized children known to present prominent vasomotor fluctuations in the functional cortices  ...  The selectable signal sources in each functional or vascular area were calculated in order to evaluate the number of identifiable independent signal sources in the fMRI data.  ... 
doi:10.1016/s1053-8119(03)00097-1 pmid:12814576 fatcat:haigxfkjcnfvfnsxd7usphahh4

CORSICA: correction of structured noise in fMRI by automatic identification of ICA components

Vincent Perlbarg, Pierre Bellec, Jean-Luc Anton, Mélanie Pélégrini-Issac, Julien Doyon, Habib Benali
2007 Magnetic Resonance Imaging  
When applied to functional Magnetic Resonance Imaging (fMRI) data, spatial Independent Component Analysis (sICA), a data-driven technique that adresses the blind source separation problem, seems able to  ...  We propose a new automatic method called CORSICA to identify the components related to physiological noise, using prior information on the spatial localization of the main physiological fluctuations in  ...  it is expected to be an important preprocessing step for functional connectivity studies in fMRI.  ... 
doi:10.1016/j.mri.2006.09.042 pmid:17222713 fatcat:l7wh6zvhmraprahflrw36662oy

fMRI for the Assessment of Functional Connectivity [chapter]

Till Nierhaus, Daniel Margulies, Xiangyu Long, Arno Villringer
2012 Neuroimaging - Methods  
History of BOLD in functional neuroimaging, and the beginnings of functional connectivity in both task-states and rest fMRI is the most widely used imaging technique in modern cognitive neuroscience.  ...  T 2 . fMRI for the Assessment of Functional Connectivity 31 As a diamagnetic molecule, [oxy-Hb] does not produce the same dephasing.  ...  Acknowledgment The authors would like to thank The Neuro Bureau ( and its affiliated team of researchers for continued creative support.  ... 
doi:10.5772/23864 fatcat:ptda4zrn2rafbgewzrqvfarzty

Exploring connectivity with large-scale Granger causality on resting-state functional MRI

Adora M. DSouza, Anas Z. Abidin, Lutz Leistritz, Axel Wismüller
2017 Journal of Neuroscience Methods  
0.76) and (ii) independent component analysis (ICA) of resting-state fMRI (Accuracy=0.86).  ...  Subsequently, functional subnetworks are extracted from lsGC connectivity measures for both datasets and validated quantitatively. We also provide guidelines to select lsGC free parameters.  ...  This work was conducted as a Practice Quality Improvement (PQI) project related to American Board of Radiology (ABR) Maintenance of Certificate (MOC) for Prof. Dr. Axel Wismüller.  ... 
doi:10.1016/j.jneumeth.2017.06.007 pmid:28629720 pmcid:PMC5555849 fatcat:bue6x7zof5hxvizxt3xd5hefoa

Multimodal fusion analysis of functional, cerebrovascular and structural neuroimaging in healthy ageing subjects [article]

Xulin Liu, Lorraine K Tyler, James B Rowe, Kamen A Tsvetanov, Cam-CAN
2021 bioRxiv   pre-print
Here, we used linked independent component analysis as a data-driven multimodal approach to jointly analyze magnetic resonance imaging of grey matter density, cerebrovascular, and functional network topographies  ...  interpretation of neural correlates of cognitive decline in ageing.  ...  Calhoun, 2018) , to represent data in a small number of independent components (ICs) which here are spatial maps that describe the temporal and spatial characteristics of underlying signals (V. D.  ... 
doi:10.1101/2021.12.22.473894 fatcat:dibz35s7ffgg5hgnar7eh5bfzi

Multimodal Functional Network Connectivity: An EEG-fMRI Fusion in Network Space

Xu Lei, Dirk Ostwald, Jiehui Hu, Chuan Qiu, Camillo Porcaro, Andrew P. Bagshaw, Dezhong Yao, Pedro Antonio Valdes-Sosa
2011 PLoS ONE  
First, functional networks (FNs) are extracted using spatial independent component analysis (ICA) in each modality separately.  ...  In this paper, multimodal functional network connectivity (mFNC) is proposed for the fusion of EEG and fMRI in network space.  ...  Spatial ICA of EEG is used here to extract statistically independent spatial components.  ... 
doi:10.1371/journal.pone.0024642 pmid:21961040 pmcid:PMC3178514 fatcat:4t3i6k6mvvdnlojyxdr5dukhka

Diffusion map for clustering fMRI spatial maps extracted by independent component analysis

Tuomo Sipola, Fengyu Cong, Tapani Ristaniemi, Vinoo Alluri, Petri Toiviainen, Elvira Brattico, Asoke K. Nandi
2013 2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)  
Functional magnetic resonance imaging (fMRI) produces data about activity inside the brain, from which spatial maps can be extracted by independent component analysis (ICA).  ...  In datasets, there are n spatial maps that contain p voxels. The number of voxels is very high compared to the number of analyzed spatial maps.  ...  Diffusion map for clustering fMRI spatial maps extracted by independent component analysis.  ... 
doi:10.1109/mlsp.2013.6661923 dblp:conf/mlsp/SipolaCRATBN13 fatcat:7oae6vazozbarfs7eojmmmmevq

Functional Connectivity in Default Mode Network During Resting State: An Evaluation of the Effects of Data Pre-processing [article]

Pouya Ghaemmaghami
2016 arXiv   pre-print
In the present study, we examine the influence of two aspects of data pre- processing in resting state functional connectivity analysis: the effect of criteria used to select nodes in the default mode  ...  Three different strategies of region of interest (ROI) selection were compared to define DMN node coordinates: (1) ROIs centered on atlas-based coordinates, (2) ROIs based on the result of group independent  ...  ACKNOWLEDGEMENTS The authors would like to acknowledge Nicola De Pisapia for sharing with us the data used for this Thesis.  ... 
arXiv:1603.01077v1 fatcat:dakhjbelbbh4vn3oxvodcfmvaq

Modeling of Circuits within Networks by fMRI

G. de Marco, A. le Pellec
2010 Wireless Sensor Network  
In this review, the authors describe the most recent functional imaging approaches used to explore and identify circuits within networks and model spatially and anatomically interconnected regions.  ...  After defining the concept of functional and effective connectivity, the authors describe various methods of identification and modeling of circuits within networks.  ...  Each independent component extracted by applying a spatial ICA is spatially independent of all other independent components [35] .  ... 
doi:10.4236/wsn.2010.23028 fatcat:uqudzrg23nhmjosonbw6tptx5q

A Window into the Brain: Advances in Psychiatric fMRI

Xiaoyan Zhan, Rongjun Yu
2015 BioMed Research International  
Functional magnetic resonance imaging (fMRI) plays a key role in modern psychiatric research.  ...  Future research may need to integrate neuroimaging information drawn from different analysis methods and delineate spatial and temporal patterns of brain responses that are specific to certain types of  ...  Acknowledgments The authors acknowledge the Foundation for High-Level Talents in Higher Education of Guangdong (no.  ... 
doi:10.1155/2015/542467 pmid:26413531 pmcid:PMC4564608 fatcat:3hat4unyozdzhgx366jgj2fq2y

Combining fMRI and SNP data to investigate connections between brain function and genetics using parallel ICA

Jingyu Liu, Godfrey Pearlson, Andreas Windemuth, Gualberto Ruano, Nora I. Perrone-Bizzozero, Vince Calhoun
2009 Human Brain Mapping  
The method was aimed to identify simultaneously independent components of each modality and the relationships between them.  ...  Our target is the linkage of these genomic factors to normal/abnormal brain functionality. We explored parallel independent component analysis (paraICA) as a new method for analyzing multimodal data.  ...  This research was supported by the National Institutes of Health, under grants 1 R01 EB 000840, 1 R01 EB 005846 (to VDC), and 2 RO1 MH43775 and 5 RO1 MH52886 (to GP) and a grant from the MIND Institute  ... 
doi:10.1002/hbm.20508 pmid:18072279 pmcid:PMC2668960 fatcat:p4337yhcpbeunmwbaxzk4x3jv4
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