PERFORM Publications

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Title		Authors	PubMed ID
1	Adaptive sliding mode fault-tolerant control of an over-actuated hybrid VTOL fixed-wing UAV under transition flight	Wang B; Zhao H; Hu X; Shen Y; Li N;	41475926 ENCS
2	Intolerance of uncertainty, psychological symptoms, and pain in long-term childhood cancer survivors: a report from the Childhood Cancer Survivor Study	Alberts NM; Stratton KL; Leisenring WM; Pizzo A; Lamoureux É; Alschuler K; Flynn J; Krull KR; Jibb LA; Nathan PC; Olgin JE; Stinson JN; Armstrong GT;	40699439 PSYCHOLOGY
3	Near-optimal learning of Banach-valued, high-dimensional functions via deep neural networks	Adcock B; Brugiapaglia S; Dexter N; Moraga S;	39454372 MATHSTATS
4	Exploring the effects of anthropogenic disturbance on predator inspection activity in Trinidadian guppies	Brusseau AJP; Feyten LEA; Crane AL; Brown GE;	38476138 BIOLOGY
5	Development and performance assessment of a new opensource Bayesian inference R platform for building energy model calibration	Hou D; Zhan D; Wang L; Hassan IG; Sezer N;	37936825 ENCS
6	How uncertainty affects information search among consumers: a curvilinear perspective	He S; Rucker DD;	36471868 JMSB
7	UncertaintyFuseNet: Robust uncertainty-aware hierarchical feature fusion model with Ensemble Monte Carlo Dropout for COVID-19 detection	Abdar M; Salari S; Qahremani S; Lam HK; Karray F; Hussain S; Khosravi A; Acharya UR; Makarenkov V; Nahavandi S;	36217534 ENCS
8	Development of a DREAM-based inverse model for multi-point source identification in river pollution incidents: Model testing and uncertainty analysis	Zhu Y; Chen Z;	36191500 ENCS
9	Viral Anxiety Mediates the Influence of Intolerance of Uncertainty on Adherence to Physical Distancing Among Healthcare Workers in COVID-19 Pandemic	Chung S; Lee T; Hong Y; Ahmed O; Silva WAD; Gouin JP;	35733798 PSYCHOLOGY
10	Decision-first modeling should guide decision making for emerging risks	Morgan K; Collier ZA; Gilmore E; Schmitt K;	35104915 ENCS
11	Towards a better understanding of deep convolutional neural network processes for recognizing organic chemicals of environmental concern	Sun X; Zhang X; Wang L; Li Y; Muir DCG; Zeng EY;	34388923 CHEMBIOCHEM
12	Assessing the regional biogenic methanol emission from spring wheat during the growing season: A Canadian case study	Cai M; An C; Guy C; Lu C; Mafakheri F;	34182392 ENCS
13	A robust optimization model for tactical capacity planning in an outpatient setting	Aslani N; Kuzgunkaya O; Vidyarthi N; Terekhov D;	33215335 ENCS
14	Qualitative threshold method validation and uncertainty evaluation: A theoretical framework and application to a 40 analytes liquid chromatography-tandem mass spectrometry method	Camirand Lemyre F; Desharnais B; Laquerre J; Morel MA; Côté C; Mireault P; Skinner CD;	32476284 CHEMBIOCHEM
15	Quantifying construction waste reduction through the application of prefabrication: a case study in Anhui, China.	Hao J, Chen Z, Zhang Z, Loehlein G	32358748 ENCS
16	An ecological framework of neophobia: from cells to organisms to populations.	Crane AL, Brown GE, Chivers DP, Ferrari MCO	31599483 BIOLOGY
17	Worldwide contamination of food-crops with mycotoxins: Validity of the widely cited 'FAO estimate' of 25.	Eskola M, Kos G, Elliott CT, Hajšlová J, Mayar S, Krska R	31478403 CHEMBIOCHEM
18	Influence of Head Tissue Conductivity Uncertainties on EEG Dipole Reconstruction.	Vorwerk J, Aydin Ü, Wolters CH, Butson CR	31231178 PERFORM

Title:	Influence of Head Tissue Conductivity Uncertainties on EEG Dipole Reconstruction.
Authors:	Vorwerk J, Aydin Ü, Wolters CH, Butson CR
Link:	https://www.ncbi.nlm.nih.gov/pubmed/31231178?dopt=Abstract
DOI:	10.3389/fnins.2019.00531
Publication:	Frontiers in neuroscience
Keywords:	EEG dipole reconstruction; EEG source analysis; conductivity estimation; conductivity uncertainty; finite element method; generalized polynomial chaos; head modeling; sensitivity analysis;
PMID:	31231178	Category:	Front Neurosci	Date Added:	2019-06-25
Dept Affiliation:	PERFORM 1 Scientific Computing & Imaging (SCI) Institute, University of Utah, Salt Lake City, UT, United States. 2 Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany. 3 Institute of Electrical and Biomedical Engineering, UMIT - University for Health Sciences, Medical Informatics and Technology, Hall in Tirol, Austria. 4 Multimodal Functional Imaging Lab, Department of Physics and PERFORM Centre, Concordia University, Montreal, QC, Canada. 5 Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany. 6 Departments of Biomedical Engineering, Neurology, and Psychiatry, University of Utah, Salt Lake City, UT, United States. 7 Department of Neurosurgery, Clinical Neurosciences Center, University of Utah, Salt Lake City, UT, United States.

Description:

Influence of Head Tissue Conductivity Uncertainties on EEG Dipole Reconstruction.

Front Neurosci. 2019;13:531

Authors: Vorwerk J, Aydin Ü, Wolters CH, Butson CR

Abstract

Reliable EEG source analysis depends on sufficiently detailed and accurate head models. In this study, we investigate how uncertainties inherent to the experimentally determined conductivity values of the different conductive compartments influence the results of EEG source analysis. In a single source scenario, the superficial and focal somatosensory P20/N20 component, we analyze the influence of varying conductivities on dipole reconstructions using a generalized polynomial chaos (gPC) approach. We find that in particular the conductivity uncertainties for skin and skull have a significant influence on the EEG inverse solution, leading to variations in source localization by several centimeters. The conductivity uncertainties for gray and white matter were found to have little influence on the source localization, but a strong influence on the strength and orientation of the reconstructed source, respectively. As the CSF conductivity is most accurately determined of all conductivities in a realistic head model, CSF conductivity uncertainties had a negligible influence on the source reconstruction. This small uncertainty is a further benefit of distinguishing the CSF in realistic volume conductor models.

PMID: 31231178 [PubMed]

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