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

10.52586/5047 ◽  
2021 ◽  
Vol 26 (12) ◽  
pp. 1537-1547
Author(s):  
Fu-Jung Hsiao ◽  
Wei-Ta Chen ◽  
Li-Ling Hope Pan ◽  
Hung-Yu Liu ◽  
Yen-Feng Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Resting State ◽  
Pain Sensitivity ◽  
Heat Pain

scholarly journals Resting-state brain oscillations predict cognitive function in psychiatric disorders: A transdiagnostic machine learning approach

2021 ◽  
Vol 30 ◽  
pp. 102617
Author(s):  
Kaia Sargent ◽  
UnYoung Chavez-Baldini ◽  
Sarah L. Master ◽  
Karin J.H. Verweij ◽  
Anja Lok ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cognitive Function ◽  
Psychiatric Disorders ◽  
Resting State ◽  
Learning Approach ◽  
Brain Oscillations ◽  
Machine Learning Approach

Aging effect on head motion: A Machine Learning study on resting state fMRI data

2021 ◽  
Vol 352 ◽  
pp. 109084
Author(s):  
Valeria Saccà ◽  
Alessia Sarica ◽  
Andrea Quattrone ◽  
Federico Rocca ◽  
Aldo Quattrone ◽  
...  
Keyword(s):  
Machine Learning ◽  
Resting State ◽  
Aging Effect ◽  
Resting State Fmri ◽  
Fmri Data ◽  
Head Motion ◽  
Learning Study

scholarly journals MALINI (Machine Learning in NeuroImaging): A MATLAB toolbox for aiding clinical diagnostics using resting-state fMRI data

Data in Brief ◽  
2020 ◽  
Vol 29 ◽  
pp. 105213 ◽  
Author(s):  
Pradyumna Lanka ◽  
D. Rangaprakash ◽  
Sai Sheshan Roy Gotoor ◽  
Michael N. Dretsch ◽  
Jeffrey S. Katz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Resting State ◽  
Resting State Fmri ◽  
Clinical Diagnostics ◽  
Fmri Data ◽  
Matlab Toolbox

scholarly journals Machine Learning Analytics of Resting-State Functional Connectivity Predicts Survival Outcomes of Glioblastoma Multiforme Patients

2021 ◽  
Vol 12 ◽  
Author(s):  
Bidhan Lamichhane ◽  
Andy G. S. Daniel ◽  
John J. Lee ◽  
Daniel S. Marcus ◽  
Joshua S. Shimony ◽  
...  
Keyword(s):  
Machine Learning ◽  
Glioblastoma Multiforme ◽  
Functional Connectivity ◽  
Resting State ◽  
Learning Analytics ◽  
Support Vector ◽  
Resting State Functional Connectivity ◽  
Term Survival ◽  
Long Term Survival

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.

scholarly journals Identifying Methamphetamine Dependence Using Regional Homogeneity in BOLD Signals

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.

Characterizing individual differences in heat-pain sensitivity

Pain ◽  
2005 ◽  
Vol 119 (1-3) ◽  
pp. 65-74 ◽  
Author(s):  
Christopher S. Nielsen ◽  
Donald D. Price ◽  
Olav Vassend ◽  
Audun Stubhaug ◽  
Jennifer R. Harris
Keyword(s):  
Individual Differences ◽  
Pain Sensitivity ◽  
Heat Pain
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