Automated network analysis to measure brain effective connectivity estimated from EEG data of patients with alcoholism

Bae, Youngoh; Yoo, Byeong Wook; Lee, Jung Chan; Kim, Hee Chan

doi:10.1088/1361-6579/aa6b4c

Cited by 38 publications

(17 citation statements)

References 42 publications

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“…Specific neurological patterns of brain activation have been reported in schizophrenia [14], including the use of graph theory to find the characteristics of psychiatric disorders [15]. Previous research in alcoholism using the combination of global and local parameters based on whole-brain functional networks has been reported [16]. Whole-brain search based on graph theory to compute global and local features, followed by feature reduction to select the best performing features, is the typical process for building a machine learning-based diagnostic tool.…”

Section: Introductionmentioning

confidence: 99%

Differences Between Schizophrenic and Normal Subjects Using Network Properties from fMRI

et al. 2017

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Schizophrenia has been proposed to result from impairment of functional connectivity. We aimed to use machine learning to distinguish schizophrenic subjects from normal controls using a publicly available functional MRI (fMRI) data set. Global and local parameters of functional connectivity were extracted for classification. We found decreased global and local network connectivity in subjects with schizophrenia, particularly in the anterior right cingulate cortex, the superior right temporal region, and the inferior left parietal region as compared to healthy subjects. Using support vector machine and 10-fold cross-validation, nine features reached 92.1% prediction accuracy, respectively. Our results suggest that there are significant differences between control and schizophrenic subjects based on regional brain activity detected with fMRI.

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Section: Introductionmentioning

confidence: 99%

Differences Between Schizophrenic and Normal Subjects Using Network Properties from fMRI

et al. 2017

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“…We then used PCA to preprocess the data and, with the SVM learning model, it was possible to classify the recordings with an overall accuracy of over 98%, with good sensitivity and specificity for all tissue types. SVM is a method of automatic classification that has been widely applied in the field of medicine, for example, to classify cancers based on tumor markers in the blood [22], to interpret electroencephalography signals [23], to determine subgroups of schizophrenia [24], and to aid in decision-making in patients with symptoms of acute coronary syndrome [25].…”

Section: Discussionmentioning

confidence: 99%

Extended-wavelength diffuse reflectance spectroscopy with a machine-learning method for in vivo tissue classification

Dahlstrand¹,

Sheikh²,

Ansson³

et al. 2019

PLoS ONE

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ObjectivesAn extended-wavelength diffuse reflectance spectroscopy (EWDRS) technique was evaluated for its ability to differentiate between and classify different skin and tissue types in an in vivo pig model.Materials and methodsEWDRS recordings (450–1550 nm) were made on skin with different degrees of pigmentation as well as on the pig snout and tongue. The recordings were used to train a support vector machine to identify and classify the different skin and tissue types.ResultsThe resulting EWDRS curves for each skin and tissue type had a unique profile. The support vector machine was able to classify each skin and tissue type with an overall accuracy of 98.2%. The sensitivity and specificity were between 96.4 and 100.0% for all skin and tissue types.ConclusionEWDRS can be used in vivo to differentiate between different skin and tissue types with good accuracy. Further development of the technique may potentially lead to a novel diagnostic tool for e.g. non-invasive tumor margin delineation.

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“…However, the influence of this problem on EEG signals was not previously known. Although surrogate data method is widely used for EEG analysis ( Breakspear and Terry, 2002 ; Natarajan et al, 2004 ; Spasic, 2010 ; Dimitriadis et al, 2015 , 2017 ; Bae et al, 2017 ; Olejarczyk et al, 2017 ), the cyclic behavior of dominant frequency component is not considered in segmentation. The current study shows the importance of segmenting data according to the alpha component for eyes-closed resting state EEG.…”

Section: Discussionmentioning

confidence: 99%

“…There are two main purposes for surrogate data testing. The first purpose is to test whether the chosen non-linear measure captures non-linear structure in the data, which cannot be detected with spectral density function ( Breakspear and Terry, 2002 ; Natarajan et al, 2004 ; Spasic, 2010 ; Bae et al, 2017 ; Orgo et al, 2017 ). If the data does not have any non-linear structure, a linear method could be used instead.…”

Section: Introductionmentioning

confidence: 99%

Surrogate Data Method Requires End-Matched Segmentation of Electroencephalographic Signals to Estimate Non-linearity

et al. 2018

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The aim of the study is to clarify the impact of the strong cyclic signal component on the results of surrogate data method in the case of resting electroencephalographic (EEG) signals. In addition, the impact of segment length is analyzed. Different non-linear measures (fractality, complexity, etc.) of neural signals have been demonstrated to be useful to infer the non-linearity of brain functioning from EEG. The surrogate data method is often applied to test whether or not the non-linear structure can be captured from the data. In addition, a growing number of studies are using surrogate data method to determine the statistical threshold of connectivity values in network analysis. Current study focuses on the conventional segmentation of EEG signals, which could lead to false results of surrogate data method. More specifically, the necessity to use end-matched segments that contain an integer number of dominant frequency periods is studied. EEG recordings from 80 healthy volunteers during eyes-closed resting state were analyzed using multivariate surrogate data method. The artificial surrogate data were generated by shuffling the phase spectra of original signals. The null hypothesis that time series were generated by a linear process was rejected by statistically comparing the non-linear statistics calculated for original and surrogate data sets. Five discriminating statistics were used as non-linear estimators: Higuchi fractal dimension (HFD), Katz fractal dimension (KFD), Lempel-Ziv complexity (LZC), sample entropy (SampEn) and synchronization likelihood (SL). The results indicate that the number of segments evaluated as non-linear differs in the case of various non-linear measures and changes with the segment length. The main conclusion is that the dependence on the deviation of the segment length from full periods of dominant EEG frequency has non-monotonic character and causes misleading results in the evaluation of non-linearity. Therefore, in the case of the signals with non-monotonic spectrum and strong dominant frequency, the correct use of surrogate data method requires the signal length comprising of full periods of the spectrum dominant frequency. The study is important to understand the influence of incorrect selection of EEG signal segment length for surrogate data method to estimate non-linearity.

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Automated network analysis to measure brain effective connectivity estimated from EEG data of patients with alcoholism

Cited by 38 publications

References 42 publications

Differences Between Schizophrenic and Normal Subjects Using Network Properties from fMRI

Differences Between Schizophrenic and Normal Subjects Using Network Properties from fMRI

Extended-wavelength diffuse reflectance spectroscopy with a machine-learning method for in vivo tissue classification

Surrogate Data Method Requires End-Matched Segmentation of Electroencephalographic Signals to Estimate Non-linearity

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