scholarly journals Discriminating stress from rest based on resting‐state connectivity of the human brain: A supervised machine learning study

2020 ◽  
Vol 41 (11) ◽  
pp. 3089-3099 ◽  
Author(s):  
Wei Zhang ◽  
Alberto Llera ◽  
Mahur M. Hashemi ◽  
Reinoud Kaldewaij ◽  
Saskia B. J. Koch ◽  
...  
Keyword(s):  
Machine Learning ◽  
Human Brain ◽  
Resting State ◽  
Supervised Machine Learning ◽  
Learning Study ◽  
Resting State Connectivity

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 Integrating Optimized Multiscale Entropy Model with Machine Learning for the Localization of Epileptogenic Hemisphere in Temporal Lobe Epilepsy Using Resting-State fMRI

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiaoxuan Fu ◽  
Youhua Wang ◽  
Abdelkader Nasreddine Belkacem ◽  
Qirui Zhang ◽  
Chong Xie ◽  
...  
Keyword(s):  
Machine Learning ◽  
Temporal Lobe Epilepsy ◽  
Temporal Lobe ◽  
Roc Curve ◽  
Resting State ◽  
Supervised Machine Learning ◽  
Multiscale Entropy ◽  
Support Vector ◽  
Brain Areas ◽  
Entropy Model

The bottleneck associated with the validation of the parameters of the entropy model has limited the application of this model to modern functional imaging technologies such as the resting-state functional magnetic resonance imaging (rfMRI). In this study, an optimization algorithm that could choose the parameters of the multiscale entropy (MSE) model was developed, while the optimized effectiveness for localizing the epileptogenic hemisphere was validated through the classification rate with a supervised machine learning method. The rfMRI data of 20 mesial temporal lobe epilepsy patients with positive indicators (the indicators of epileptogenic hemisphere in clinic) in the hippocampal formation on either left or right hemisphere (equally divided into two groups) on the structural MRI were collected and preprocessed. Then, three parameters in the MSE model were statistically optimized by both receiver operating characteristic (ROC) curve and the area under the ROC curve value in the sensitivity analysis, and the intergroup significance of optimized entropy values was utilized to confirm the biomarked brain areas sensitive to the epileptogenic hemisphere. Finally, the optimized entropy values of these biomarked brain areas were regarded as the feature vectors input for a support vector machine to classify the epileptogenic hemisphere, and the classification effectiveness was cross-validated. Nine biomarked brain areas were confirmed by the optimized entropy values, including medial superior frontal gyrus and superior parietal gyrus ( p  < .01). The mean classification accuracy was greater than 90%. It can be concluded that combination of the optimized MSE model with the machine learning model can accurately confirm the epileptogenic hemisphere by rfMRI. With the powerful information interaction capabilities of 5G communication, the epilepsy side-fixing algorithm that requires computing power can be integrated into a cloud platform. The demand side only needs to upload patient data to the service platform to realize the preoperative assessment of epilepsy.

Thalamic hyperconnectivity as neurophysiological signature of major depressive disorder in two multicenter studies

2021 ◽  
Author(s):  
Selene Gallo ◽  
Ahmed ElGazzar ◽  
Paul Zhutovsky ◽  
Rajat Mani Thomas ◽  
Nooshin Javaheripour ◽  
...  
Keyword(s):  
Machine Learning ◽  
Major Depressive Disorder ◽  
Deep Learning ◽  
Depressive Disorder ◽  
Resting State ◽  
Support Vector ◽  
Major Depressive ◽  
Multicenter Studies ◽  
Disease Heterogeneity ◽  
Resting State Connectivity

The promise of machine learning has fueled the hope for developing diagnostic tools for psychiatry. Initial studies showed high accuracy for the identification of major depressive disorder (MDD) with resting-state connectivity, but progress has been hampered by the absence of large datasets. Here we used regular machine learning and advanced deep learning algorithms to differentiate patients with MDD from healthy controls and identify neurophysiological signatures of depression in two of the largest resting-state datasets for MDD. Resting-state functional magnetic resonance imaging data were obtained from the REST-meta-MDD (N=2338) and PsyMRI (N=1039) consortia. Classification of functional connectivity matrices was done using support vector machines (SVM) and graph convolutional neural networks (GCN) and performance was evaluated using 5-fold cross-validation. Results were visualized using GCN-Explainer, an ablation study and univariate t-testing.Mean classification accuracy was 61% for MDD versus controls. Mean accuracy for classifying (non-)medicated subgroups was 62%. Visualization of the results showed that classifications were driven by stronger thalamic connections in both datasets, while nearly all other connections were weaker with small univariate effect sizes.Whole brain resting-state connectivity is a reliable though poor biomarker for MDD, presumably due to disease heterogeneity. Deep learning revealed thalamic hyperconnectivity as a prominent neurophysiological signature of depression in both multicenter studies, which may guide the development of biomarkers in future studies.

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