scholarly journals A Unified Framework for Inattention Estimation From Resting State Phase Synchrony Using Machine Learning

2021 ◽  
Vol 12 ◽  
Author(s):  
Xun-Heng Wang ◽  
Lihua Li
Keyword(s):  
Machine Learning ◽  
Resting State ◽  
Clinical Symptoms ◽  
Learning Algorithms ◽  
Resting State Fmri ◽  
Machine Learning Algorithms ◽  
Phase Synchrony ◽  
Unified Framework ◽  
Brain Behavior ◽  
Behavior Models

Inattention is one of the most significant clinical symptoms for evaluating attention deficit hyperactivity disorder (ADHD). Previous inattention estimations were performed using clinical scales. Recently, predictive models for inattention have been established for brain-behavior estimation using neuroimaging features. However, the performance of inattention estimation could be improved for conventional brain-behavior models with additional feature selection, machine learning algorithms, and validation procedures. This paper aimed to propose a unified framework for inattention estimation from resting state fMRI to improve the classical brain-behavior models. Phase synchrony was derived as raw features, which were selected with minimum-redundancy maximum-relevancy (mRMR) method. Six machine learning algorithms were applied as regression methods. 100 runs of 10-fold cross-validations were performed on the ADHD-200 datasets. The relevance vector machines (RVMs) based on the mRMR features for the brain-behavior models significantly improve the performance of inattention estimation. The mRMR-RVM models could achieve a total accuracy of 0.53. Furthermore, predictive patterns for inattention were discovered by the mRMR technique. We found that the bilateral subcortical-cerebellum networks exhibited the most predictive phase synchrony patterns for inattention. Together, an optimized strategy named mRMR-RVM for brain-behavior models was found for inattention estimation. The predictive patterns might help better understand the phase synchrony mechanisms for inattention.

Evaluation of machine learning algorithms performance for the prediction of early multiple sclerosis from resting-state FMRI connectivity data

2018 ◽  
Vol 13 (4) ◽  
pp. 1103-1114 ◽  
Author(s):  
Valeria Saccà ◽  
Alessia Sarica ◽  
Fabiana Novellino ◽  
Stefania Barone ◽  
Tiziana Tallarico ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
Resting State ◽  
Learning Algorithms ◽  
Resting State Fmri ◽  
Machine Learning Algorithms

255. Automated classification of Resting State fMRI networks using Machine Learning algorithms

2018 ◽  
Vol 56 ◽  
pp. 219-220 ◽  
Author(s):  
C. Pinardi ◽  
O. Ortenzia ◽  
S. Gardini ◽  
R. Aldigeri ◽  
M. Micheli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Resting State ◽  
Learning Algorithms ◽  
Resting State Fmri ◽  
Machine Learning Algorithms ◽  
Automated Classification

scholarly journals Machine Learning Approach for Automatic Detection of Alzheimers Disease using Resting State fMRI

2021 ◽  
Vol 9 (VI) ◽  
pp. 5399-5409
Author(s):  
K. Emily Esther Rani
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Diagnosis ◽  
Resting State ◽  
Learning Algorithms ◽  
Resting State Fmri ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Discrete Wavelet

Alzheimer’s Disease (AD) is a neurological disease that affects memory and the livelihood of the people that are diagnosed with it. Efficient automated techniques for early diagnosis of AD is very important because early diagnosis is used to prevent a patient from death. In this work, we present a novel computer-aided diagnosis (CAD) techniques using machine learning algorithms for the early diagnosis of AD. The input resting state fMRI(rsfMRI) images are taken from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The input image is pre-processed using Discrete Wavelet Transform(DWT). Automated thresholding algorithm is used to segment the image. Then, the segmented resting state fMRI images are used to extract useful and informative features. The best features are selected by Fisher’s code feature selection algorithm. Finally, an automated Image classification step is performed using machine learning algorithms Support Vector Machine(SVM), Decision Tree , Random Forest and Multi-Layer Perceptron algorithms to distinguish between normal patients and AD patients.

scholarly journals Optimized Naive-Bayes and Decision Tree Approaches for fMRI Smoking Cessation Classification

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-24 ◽  
Author(s):  
Amirhessam Tahmassebi ◽  
Amir H. Gandomi ◽  
Mieke H. J. Schulte ◽  
Anna E. Goudriaan ◽  
Simon Y. Foo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Smoking Cessation ◽  
Decision Tree ◽  
Data Reduction ◽  
Resting State ◽  
Naive Bayes ◽  
Resting State Fmri ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Bayes Classifier

This paper aims at developing new theory-driven biomarkers by implementing and evaluating novel techniques from resting-state scans that can be used in relapse prediction for nicotine-dependent patients and future treatment efficacy. Two classes of patients were studied. One class took the drug N-acetylcysteine and the other class took a placebo. Then, the patients underwent a double-blind smoking cessation treatment and the resting-state fMRI scans of their brains before and after treatment were recorded. The scientific research goal of this study was to interpret the fMRI connectivity maps based on machine learning algorithms to predict the patient who will relapse and the one who will not. In this regard, the feature matrix was extracted from the image slices of brain employing voxel selection schemes and data reduction algorithms. Then, the feature matrix was fed into the machine learning classifiers including optimized CART decision tree and Naive-Bayes classifier with standard and optimized implementation employing 10-fold cross-validation. Out of all the data reduction techniques and the machine learning algorithms employed, the best accuracy was obtained using the singular value decomposition along with the optimized Naive-Bayes classifier. This gave an accuracy of 93% with sensitivity-specificity of 99% which suggests that the relapse in nicotine-dependent patients can be predicted based on the resting-state fMRI images. The use of these approaches may result in clinical applications in the future.

scholarly journals Accuracy of diagnostic classification algorithms using cognitive-, electrophysiological-, and neuroanatomical data in antipsychotic-naïve schizophrenia patients

2018 ◽  
Vol 49 (16) ◽  
pp. 2754-2763 ◽  
Author(s):  
Bjørn H. Ebdrup ◽  
Martin C. Axelsen ◽  
Nikolaj Bak ◽  
Birgitte Fagerlund ◽  
Bob Oranje ◽  
...  
Keyword(s):  
Machine Learning ◽  
Diagnostic Accuracy ◽  
Clinical Symptoms ◽  
Diffusion Tensor ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
First Episode ◽  
Full Potential ◽  
Symptom Remission

AbstractBackgroundA wealth of clinical studies have identified objective biomarkers, which separate schizophrenia patients from healthy controls on a group level, but current diagnostic systems solely include clinical symptoms. In this study, we investigate if machine learning algorithms on multimodal data can serve as a framework for clinical translation.MethodsForty-six antipsychotic-naïve, first-episode schizophrenia patients and 58 controls underwent neurocognitive tests, electrophysiology, and magnetic resonance imaging (MRI). Patients underwent clinical assessments before and after 6 weeks of antipsychotic monotherapy with amisulpride. Nine configurations of different supervised machine learning algorithms were applied to first estimate the unimodal diagnostic accuracy, and next to estimate the multimodal diagnostic accuracy. Finally, we explored the predictability of symptom remission.ResultsCognitive data significantly classified patients from controls (accuracies = 60–69%;pvalues = 0.0001–0.009). Accuracies of electrophysiology, structural MRI, and diffusion tensor imaging did not exceed chance level. Multimodal analyses with cognition plus any combination of one or more of the remaining three modalities did not outperform cognition alone. None of the modalities predicted symptom remission.ConclusionsIn this multivariate and multimodal study in antipsychotic-naïve patients, only cognition significantly discriminated patients from controls, and no modality appeared to predict short-term symptom remission. Overall, these findings add to the increasing call for cognition to be included in the definition of schizophrenia. To bring about the full potential of machine learning algorithms in first-episode, antipsychotic-naïve schizophrenia patients, carefula priorivariable selection based on independent data as well as inclusion of other modalities may be required.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

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