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

2021 ◽  
Author(s):  
Aykut Eken
Keyword(s):  
Machine Learning ◽  
Functional Connectivity ◽  
Resting State ◽  
Dynamic Time Warping ◽  
Support Vector ◽  
Concentration Change ◽  
Time Warping ◽  
Pearson’S Correlation ◽  
Pearson's Correlation ◽  
Dynamic Time

AbstractFlourishing 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.

scholarly journals Resting State fMRI Functional Connectivity Analysis Using Dynamic Time Warping

2017 ◽  
Vol 11 ◽  
Author(s):  
Regina J. Meszlényi ◽  
Petra Hermann ◽  
Krisztian Buza ◽  
Viktor Gál ◽  
Zoltán Vidnyánszky
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Dynamic Time Warping ◽  
Resting State Fmri ◽  
Connectivity Analysis ◽  
Time Warping ◽  
Functional Connectivity Analysis ◽  
Dynamic Time

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 Cartographing dynamic stall with machine learning

2020 ◽  
Vol 5 (2) ◽  
pp. 819-838
Author(s):  
Matthew Lennie ◽  
Johannes Steenbuck ◽  
Bernd R. Noack ◽  
Christian Oliver Paschereit
Keyword(s):  
Machine Learning ◽  
Dynamic Time Warping ◽  
Data Science ◽  
Leading Edge ◽  
Separated Flows ◽  
Dynamic Stall ◽  
Energy Yield ◽  
Time Warping ◽  
Coherent Vortex ◽  
Dynamic Time

Abstract. Once stall has set in, lift collapses, drag increases and then both of these forces will fluctuate strongly. The result is higher fatigue loads and lower energy yield. In dynamic stall, separation first develops from the trailing edge up the leading edge. Eventually the shear layer rolls up, and then a coherent vortex forms and then sheds downstream with its low-pressure core causing a lift overshoot and moment drop. When 50+ experimental cycles of lift or pressure values are averaged, this process appears clear and coherent in flow visualizations. Unfortunately, stall is not one clean process but a broad collection of processes. This means that the analysis of separated flows should be able to detect outliers and analyze cycle-to-cycle variations. Modern data science and machine learning can be used to treat separated flows. In this study, a clustering method based on dynamic time warping is used to find different shedding behaviors. This method captures the fact that secondary and tertiary vorticity vary strongly, and in static stall with surging flow the flow can occasionally reattach. A convolutional neural network was used to extract dynamic stall vorticity convection speeds and phases from pressure data. Finally, bootstrapping was used to provide best practices regarding the number of experimental repetitions required to ensure experimental convergence.

scholarly journals Identification and Therapeutic Outcome Prediction of Cervical Spondylotic Myelopathy Based on the Functional Connectivity From Resting-State Functional MRI Data: A Preliminary Machine Learning Study

2021 ◽  
Vol 12 ◽  
Author(s):  
Qian Su ◽  
Rui Zhao ◽  
ShuoWen Wang ◽  
HaoYang Tu ◽  
Xing Guo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Functional Connectivity ◽  
Functional Mri ◽  
Resting State ◽  
Cervical Spondylotic Myelopathy ◽  
Preliminary Evidence ◽  
Neurological Recovery ◽  
Support Vector ◽  
Learning Analysis ◽  
Cross Site

Currently, strategies to diagnose patients and predict neurological recovery in cervical spondylotic myelopathy (CSM) using MR images of the cervical spine are urgently required. In light of this, this study aimed at exploring potential preoperative brain biomarkers that can be used to diagnose and predict neurological recovery in CSM patients using functional connectivity (FC) analysis of a resting-state functional MRI (rs-fMRI) data. Two independent datasets, including total of 53 patients with CSM and 47 age- and sex-matched healthy controls (HCs), underwent the preoperative rs-fMRI procedure. The FC was calculated from the automated anatomical labeling (AAL) template and used as features for machine learning analysis. After that, three analyses were used, namely, the classification of CSM patients from healthy adults using the support vector machine (SVM) within and across datasets, the prediction of preoperative neurological function in CSM patients via support vector regression (SVR) within and across datasets, and the prediction of neurological recovery in CSM patients via SVR within and across datasets. The results showed that CSM patients could be successfully identified from HCs with high classification accuracies (84.2% for dataset 1, 95.2% for dataset 2, and 73.0% for cross-site validation). Furthermore, the rs-FC combined with SVR could successfully predict the neurological recovery in CSM patients. Additionally, our results from cross-site validation analyses exhibited good reproducibility and generalization across the two datasets. Therefore, our findings provide preliminary evidence toward the development of novel strategies to predict neurological recovery in CSM patients using rs-fMRI and machine learning technique.

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