Automatic schizophrenic discrimination on fNIRS by using complex brain network analysis and SVM

Song, Hong; Chen, Lei; Gao, Ruiqi; Bogdan, Iordachescu Ilie Mihaita; Yang, Jian; Wang, Shuliang; Dong, Wei; Quan, Wenxiang; Dang, Weimin; Yu, Xin

doi:10.1186/s12911-017-0559-5

Cited by 41 publications

(41 citation statements)

References 19 publications

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“…The disadvantage of PCA was that the principle components could not be attributed to the data from the specific channel, thus concealing the regional neurophysiological changes. Using the FCS from three channels, the achieved performance was comparable to the current results: accuracy at 70–86%, sensitivity at 70–84%, and specificity at 65–93% (Arbabshirani et al, 2013 ; Chuang et al, 2014 ; Li et al, 2015 ; Pina-Camacho et al, 2015 ; Song et al, 2017 ). The method was not calculated from the time-domain values on single or multiple channels.…”

Section: Discussionsupporting

confidence: 82%

Classification of Schizophrenia by Functional Connectivity Strength Using Functional Near Infrared Spectroscopy

Yang

Quan

et al. 2020

Front. Neuroinform.

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Functional near-infrared spectroscopy (fNIRS) has been widely employed in the objective diagnosis of patients with schizophrenia during a verbal fluency task (VFT). Most of the available methods depended on the time-domain features extracted from the data of single or multiple channels. The present study proposed an alternative method based on the functional connectivity strength (FCS) derived from an individual channel. The data measured 100 patients with schizophrenia and 100 healthy controls, who were used to train the classifiers and to evaluate their performance. Different classifiers were evaluated, and support machine vector achieved the best performance. In order to reduce the dimensional complexity of the feature domain, principal component analysis (PCA) was applied. The classification results by using an individual channel, a combination of several channels, and 52 ensemble channels with and without the dimensional reduced technique were compared. It provided a new approach to identify schizophrenia, improving the objective diagnosis of this mental disorder. FCS from three channels on the medial prefrontal and left ventrolateral prefrontal cortices rendered accuracy as high as 84.67%, sensitivity at 92.00%, and specificity at 70%. The neurophysiological significance of the change at these regions was consistence with the major syndromes of schizophrenia.

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Section: Discussionsupporting

confidence: 82%

Classification of Schizophrenia by Functional Connectivity Strength Using Functional Near Infrared Spectroscopy

Yang

Quan

et al. 2020

Front. Neuroinform.

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“…Both linear and nonlinear data can be processed by the SVM method with superior generalization performance [15]. It has been successfully applied to explore morphological neuroimaging biomarkers for the classification and diagnosis of different subsets of neurological diseases, including Alzheimer's disease (AD) and schizophrenia [16,17]. Based on published morphological studies of patients with tinnitus, the SVM method could also effectively extract neuroimaging biomarkers for tinnitus.…”

Section: Introductionmentioning

confidence: 99%

Morphological Neuroimaging Biomarkers for Tinnitus: Evidence Obtained by Applying Machine Learning

Liu

Huang

Zhu³

et al. 2019

Neural Plasticity

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According to previous studies, many neuroanatomical alterations have been detected in patients with tinnitus. However, the results of these studies have been inconsistent. The objective of this study was to explore the cortical/subcortical morphological neuroimaging biomarkers that may characterize idiopathic tinnitus using machine learning methods. Forty-six patients with idiopathic tinnitus and fifty-six healthy subjects were included in this study. For each subject, the gray matter volume of 61 brain regions was extracted as an original feature pool. From this feature pool, a hybrid feature selection algorithm combining the F-score and sequential forward floating selection (SFFS) methods was performed to select features. Then, the selected features were used to train a support vector machine (SVM) model. The area under the curve (AUC) and accuracy were used to assess the performance of the classification model. As a result, a combination of 13 cortical/subcortical brain regions was found to have the highest classification accuracy for effectively differentiating patients with tinnitus from healthy subjects. These brain regions include the bilateral hypothalamus, right insula, bilateral superior temporal gyrus, left rostral middle frontal gyrus, bilateral inferior temporal gyrus, right inferior parietal lobule, right transverse temporal gyrus, right middle temporal gyrus, right cingulate gyrus, and left superior frontal gyrus. The accuracy in the training and test datasets was 80.49% and 80.00%, respectively, and the AUC was 0.8586. To the best of our knowledge, this is the first study to elucidate brain morphological changes in patients with tinnitus by applying an SVM classifier. This study provides validated cortical/subcortical morphological neuroimaging biomarkers to differentiate patients with tinnitus from healthy subjects and contributes to the understanding of neuroanatomical alterations in patients with tinnitus.

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“…On the other hand, compared to fMRI, fNIRS has several advantages such as ease of use, mobility, being inexpensive and there are several studies that utilizes fNIRS data to classify several psychiatric disorders such as depression (Husain et al, 2020; Takizawa et al, 2014; Zhu et al, 2020), schizophrenia (Azechi et al, 2010; Chuang et al, 2014; Dadgostar et al, 2018; Einalou et al, 2016; Hahn et al, 2013; Ji et al, 2020; Koike et al, 2017; Z. Li et al, 2015; Song et al, 2017) using machine learning techniques. High classification accuracies revealed that fNIRS might be a promising tool to identify the levels of flourishing and also closely related to other psychiatric disorders (Baskak, 2018; Ehlis et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

Assessment of Flourishing Levels of Individuals by Using Resting State fNIRS with Different Functional Connectivity Measures

Eken

2021

Preprint

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Flourishing is an important criterion to assess wellbeing, however, controversies remain, particularly around assessing it with self-report measures. Due to this reason, to be able to understand the underlying neural mechanisms of well-being, researchers often utilize neuroimaging techniques. However, rather than individual answers, previous neuroimaging studies using statistical approaches provided an answer in average sense. To overcome these problems, we applied machine learning techniques to discriminate 43 highly flourishing from regular flourishing individuals by using a publicly available resting state functional near infrared spectroscopy (rs-fNIRS) dataset to get an answer in individual level. We utilized both Pearson’s correlation (CC) and Dynamic Time Warping (DTW) algorithm to estimate functional connectivity from rs-fNIRS data on temporo-parieto-occipital region as input to nine different machine learning algorithms. Our results revealed that by utilizing oxyhemoglobin concentration change with Pearson’s correlation (CC – ΔHbO) and deoxy hemoglobin concentration change with dynamic time warping (DTW – ΔHb), we could be able to classify flourishing individuals with 90 % accuracy with AUC 0.90 and 0.93 using nearest neighbor and Radial Basis Kernel Support Vector Machine. This finding suggests that temporo-parieto-occipital regional based resting state connectivity might be a potential biomarker to identify the levels of flourishing and using both connectivity measures might allow us to find different potential biomarkers.

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Automatic schizophrenic discrimination on fNIRS by using complex brain network analysis and SVM

Cited by 41 publications

References 19 publications

Classification of Schizophrenia by Functional Connectivity Strength Using Functional Near Infrared Spectroscopy

Classification of Schizophrenia by Functional Connectivity Strength Using Functional Near Infrared Spectroscopy

Morphological Neuroimaging Biomarkers for Tinnitus: Evidence Obtained by Applying Machine Learning

Assessment of Flourishing Levels of Individuals by Using Resting State fNIRS with Different Functional Connectivity Measures

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