ISOMAP and machine learning algorithms for the construction of embedded functional connectivity networks of anatomically separated brain regions from resting state fMRI data of patients with Schizophrenia

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.

Download Full-text

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

Brain Imaging and Behavior ◽

10.1007/s11682-018-9926-9 ◽

2018 ◽

Vol 13 (4) ◽

pp. 1103-1114 ◽

Cited By ~ 9

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

Download Full-text

Altered Functional Connectivity of the Primary Visual Cortex in Patients With Iridocyclitis and Assessment of Its Predictive Value Using Machine Learning

Frontiers in Immunology ◽

10.3389/fimmu.2021.660554 ◽

2021 ◽

Vol 12 ◽

Author(s):

Yan Tong ◽

Xin Huang ◽

Chen-Xing Qi ◽

Yin Shen

Keyword(s):

Machine Learning ◽

Visual Cortex ◽

Functional Connectivity ◽

Resting State ◽

Permutation Test ◽

Resting State Fmri ◽

Fmri Data ◽

Svm Classifier ◽

Postcentral Gyrus ◽

The Right

PurposeTo explore the intrinsic functional connectivity (FC) alteration of the primary visual cortex (V1) between individuals with iridocyclitis and healthy controls (HCs) by the resting-state functional magnetic resonance imaging (fMRI) technique, and to investigate whether FC findings be used to differentiate patients with iridocyclitis from HCs.MethodsTwenty-six patients with iridocyclitis and twenty-eight well-matched HCs were recruited in our study and underwent resting-state fMRI examinations. The fMRI data were analyzed by Statistical Parametric Mapping (SPM12), Data Processing and Analysis for Brain Imaging (DPABI), and Resting State fMRI Data Analysis Toolkit (REST) software. Differences in FC signal values of the V1 between the individuals with iridocyclitis and HCs were compared using independent two-sample t-tests. Significant differences in FC between two groups were chosen as classification features for distinguishing individuals with iridocyclitis from HCs using a support vector machine (SVM) classifier that involved machine learning. Classifier performance was evaluated using permutation test analysis.ResultsCompared with HCs, patients with iridocyclitis displayed significantly increased FC between the left V1 and left cerebellum crus1, left cerebellum 10, bilateral inferior temporal gyrus, right hippocampus, and left superior occipital gyrus. Moreover, patients with iridocyclitis displayed significantly lower FC between the left V1 and both the bilateral calcarine and bilateral postcentral gyrus. Patients with iridocyclitis also exhibited significantly higher FC values between the right V1 and left cerebellum crus1, bilateral thalamus, and left middle temporal gyrus; while they displayed significantly lower FC between the right V1 and both the bilateral calcarine and bilateral postcentral gyrus (voxel-level P<0.01, Gaussian random field correction, cluster-level P<0.05). Our results showed that 63.46% of the participants were correctly classified using the leave-one-out cross-validation technique with an SVM classifier based on the FC of the left V1; and 67.31% of the participants were correctly classified based on the FC of the right V1 (P<0.001, non-parametric permutation test).ConclusionPatients with iridocyclitis displayed significantly disturbed FC between the V1 and various brain regions, including vision-related, somatosensory, and cognition-related regions. The FC variability could distinguish patients with iridocyclitis from HCs with substantial accuracy. These findings may aid in identifying the potential neurological mechanisms of impaired visual function in individuals with iridocyclitis.

Download Full-text

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

Physica Medica ◽

10.1016/j.ejmp.2018.04.266 ◽

2018 ◽

Vol 56 ◽

pp. 219-220 ◽

Cited By ~ 1

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

Download Full-text

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

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.36170 ◽

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.

Download Full-text