Machine learning–based prediction of heat pain sensitivity by using resting-state EEG

Glioblastoma multiforme (GBM) is the most frequently occurring brain malignancy. Due to its poor prognosis with currently available treatments, there is a pressing need for easily accessible, non-invasive techniques to help inform pre-treatment planning, patient counseling, and improve outcomes. In this study we determined the feasibility of resting-state functional connectivity (rsFC) to classify GBM patients into short-term and long-term survival groups with respect to reported median survival (14.6 months). We used a support vector machine with rsFC between regions of interest as predictive features. We employed a novel hybrid feature selection method whereby features were first filtered using correlations between rsFC and OS, and then using the established method of recursive feature elimination (RFE) to select the optimal feature subset. Leave-one-subject-out cross-validation evaluated the performance of models. Classification between short- and long-term survival accuracy was 71.9%. Sensitivity and specificity were 77.1 and 65.5%, respectively. The area under the receiver operating characteristic curve was 0.752 (95% CI, 0.62–0.88). These findings suggest that highly specific features of rsFC may predict GBM survival. Taken together, the findings of this study support that resting-state fMRI and machine learning analytics could enable a radiomic biomarker for GBM, augmenting care and planning for individual patients.

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Machine learning approach to identify a resting-state functional connectivity pattern serving as an endophenotype of autism spectrum disorder

Brain Imaging and Behavior ◽

10.1007/s11682-018-9973-2 ◽

2018 ◽

Vol 13 (6) ◽

pp. 1689-1698 ◽

Cited By ~ 4

Author(s):

Bun Yamagata ◽

Takashi Itahashi ◽

Junya Fujino ◽

Haruhisa Ohta ◽

Motoaki Nakamura ◽

...

Keyword(s):

Machine Learning ◽

Autism Spectrum Disorder ◽

Functional Connectivity ◽

Resting State ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Connectivity Pattern ◽

Learning Approach ◽

Resting State Functional Connectivity ◽

Machine Learning Approach

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Identifying Methamphetamine Dependence Using Regional Homogeneity in BOLD Signals

Computational and Mathematical Methods in Medicine ◽

10.1155/2020/3267949 ◽

2020 ◽

Vol 2020 ◽

pp. 1-5

Author(s):

Hufei Yu ◽

Shucai Huang ◽

Xiaojie Zhang ◽

Qiuping Huang ◽

Jun Liu ◽

...

Keyword(s):

Machine Learning ◽

Resting State ◽

Resting State Fmri ◽

Regional Homogeneity ◽

Learning Approaches ◽

Control Subjects ◽

Methamphetamine Dependence ◽

Adaboost Classifier ◽

The Right ◽

Leave One Out

Methamphetamine is a highly addictive drug of abuse, which will cause a series of abnormal consequences mentally and physically. This paper is aimed at studying whether the abnormalities of regional homogeneity (ReHo) could be effective features to distinguish individuals with methamphetamine dependence (MAD) from control subjects using machine-learning methods. We made use of resting-state fMRI to measure the regional homogeneity of 41 individuals with MAD and 42 age- and sex-matched control subjects and found that compared with control subjects, individuals with MAD have lower ReHo values in the right medial superior frontal gyrus but higher ReHo values in the right temporal inferior fusiform. In addition, AdaBoost classifier, a pretty effective ensemble learning of machine learning, was employed to classify individuals with MAD from control subjects with abnormal ReHo values. By utilizing the leave-one-out cross-validation method, we got the accuracy more than 84.3%, which means we can almost distinguish individuals with MAD from the control subjects in ReHo values via machine-learning approaches. In a word, our research results suggested that the AdaBoost classifier-neuroimaging approach may be a promising way to find whether a person has been addicted to methamphetamine, and also, this paper shows that resting-state fMRI should be considered as a biomarker, a noninvasive and effective assistant tool for evaluating MAD.

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