Impact of automated ICA-based denoising of fMRI data in acute stroke patients

Impact of automated ICA-based denoising of fMRI data in acute stroke patients

Different strategies have been developed using Independent Component Analysis (ICA) to automatically de-noise fMRI data, either focusing on removing only certain components (e.g. motion-ICA-AROMA, Pruim et al., 2015a) or using more complex classifiers to remove multiple types of noise components (e....

Descripció completa

Dades bibliogràfiques
Autors principals:	Carone, D, Licenik, R, Suri, S, Griffanti, L, Filippini, N, Kennedy, J
Format:	Journal article
Idioma:	English
Publicat:	Elsevier 2017

Ítems similars

Impact of automated ICA-based denoising of fMRI data in acute stroke patients
per: D. Carone, et al.
Publicat: (2017-01-01)

Hand classification of fMRI ICA noise components
per: Griffanti, L, et al.
Publicat: (2016)

Evaluating the efficacy of multi-echo ICA denoising on model-based fMRI
per: Adam Steel, et al.
Publicat: (2022-12-01)

ICA-based denoising for ASL perfusion imaging
per: Carone, D, et al.
Publicat: (2019)

An automated method for identifying artifact in ICA of resting-state fMRI
per: Kaushik eBhaganagarapu, et al.
Publicat: (2013-07-01)

Temporally and spatially constrained ICA of fMRI data analysis.
per: Zhi Wang, et al.
Publicat: (2014-01-01)

Exploratory motion analysis in fMRI using ICA
per: Bannister, P, et al.
Publicat: (2001)

Temporally constrained ICA with threshold and its application to fMRI data
per: Zhiying Long, et al.
Publicat: (2019-01-01)

ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging.
per: Griffanti, L, et al.
Publicat: (2014)

Comparing the reliability of different ICA algorithms for fMRI analysis.
per: Pengxu Wei, et al.
Publicat: (2022-01-01)

Comparing the reliability of different ICA algorithms for fMRI analysis
per: Pengxu Wei, et al.
Publicat: (2022-01-01)

Automated Classification of Resting-State fMRI ICA Components Using a Deep Siamese Network
per: Yiyu Chou, et al.
Publicat: (2022-03-01)

Effect of Spatial Smoothing on Task fMRI ICA and Functional Connectivity
per: Zikuan Chen, et al.
Publicat: (2018-02-01)

Denoising spinal cord fMRI data: Approaches to acquisition and analysis
per: Eippert, F, et al.
Publicat: (2016)

NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders
per: Yuhui Du, et al.
Publicat: (2020-01-01)

Functional subdivision of group-ICA results of fMRI data collected during cinema viewing.
per: Siina Pamilo, et al.
Publicat: (2012-01-01)

Clustering-Constrained ICA for Ballistocardiogram Artifacts Removal in Simultaneous EEG-fMRI
per: Kai Wang, et al.
Publicat: (2018-02-01)

A Constrained ICA-EMD Model for Group Level fMRI Analysis
per: Simon Wein, et al.
Publicat: (2020-04-01)

Classification of temporal ICA components for separating global noise from fMRI data: reply to power
per: Glasser, MF, et al.
Publicat: (2019)

Task-driven ICA feature generation for accurate and interpretable prediction using fMRI
per: Duff, E, et al.
Publicat: (2012)

Task-driven ICA feature generation for accurate and interpretable prediction using fMRI.
per: Duff, E, et al.
Publicat: (2012)

Non-linear ICA Analysis of Resting-State fMRI in Mild Cognitive Impairment
per: Xia-an Bi, et al.
Publicat: (2018-06-01)

ICA-based artifact removal diminishes scan site differences in multi-center resting-state fMRI.
per: Rogier Alexander Feis, et al.
Publicat: (2015-10-01)

Combination of Resting state fMRI, DTI and sMRI Data to Discriminate Schizophrenia by N-way MCCA+jICA
per: Jing eSui, et al.
Publicat: (2013-05-01)

Analysis of group ICA-based connectivity measures from fMRI: application to Alzheimer's disease.
per: Shanshan Li, et al.
Publicat: (2012-01-01)

Optimized Temporal Denoised Convolutional Autoencoder for Enhanced ADHD Classification Using fMRI Data
per: Zarina Begum, et al.
Publicat: (2025-01-01)

Intersubject MVPD: Empirical comparison of fMRI denoising methods for connectivity analysis
per: Li, Yichen, et al.
Publicat: (2021)

Hybrid ICA-Seed-Based Methods for fMRI Functional Connectivity Assessment: A Feasibility Study
per: Robert E. Kelly, et al.
Publicat: (2010-01-01)

Dimensionality of ICA in Resting-State fMRI Investigated by Feature Optimized Classification of Independent Components with SVM
per: Yanlu eWang, et al.
Publicat: (2015-05-01)

Spatial ICA reveals functional activity hidden from traditional fMRI GLM-based analyses
per: Jiansong eXu, et al.
Publicat: (2013-08-01)

Investigating the intrinsic dimensionality of FMRI data for ICA
per: Beckmann, C, et al.
Publicat: (2001)

Comparing data-driven physiological denoising approaches for resting-state fMRI: implications for the study of aging
per: Ali M. Golestani, et al.
Publicat: (2024-02-01)

Comparing the efficacy of data-driven denoising methods for a multi-echo fMRI acquisition at 7T
per: Abraham B. Beckers, et al.
Publicat: (2023-10-01)

MP-PCA denoising of fMRI time-series data can lead to artificial activation “spreading”
per: Francisca F. Fernandes, et al.
Publicat: (2023-06-01)

Removal of artifacts from resting-state fMRI data in stroke
per: Grigori Yourganov, et al.
Publicat: (2018-01-01)

ICA extracts epileptic sources from fMRI in EEG-negative patients: a retrospective validation study.
per: Borbála Hunyadi, et al.
Publicat: (2013-01-01)

Continuous evaluation of denoising strategies in resting-state fMRI connectivity using fMRIPrep and Nilearn.
per: Hao-Ting Wang, et al.
Publicat: (2024-03-01)

Challenges in the reproducibility of clinical studies with resting state fMRI: An example in early Parkinson's disease.
per: Griffanti, L, et al.
Publicat: (2016)

A Comparison of Denoising Approaches for Spoken Word Production Related Artefacts in Continuous Multiband fMRI Data
per: Angelique Volfart, et al.
Publicat: (2024-09-01)

Deep attentive spatio-temporal feature learning for automatic resting-state fMRI denoising
per: Keun-Soo Heo, et al.
Publicat: (2022-07-01)