Machine Learning Based Classification of Resting-State fMRI Features Exemplified by Metabolic State (Hunger/Satiety)

Al-Zubaidi, Arkan; Mertins, Alfred; Heldmann, Marcus; Jauch‐Chara, Kamila; Münte, Thomas F.

doi:10.3389/fnhum.2019.00164

Cited by 33 publications

(27 citation statements)

References 120 publications

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“…Since then, the subsequent studies on brain function using resting-state fMRI (rs-fMRI) data has exploded. For example, recent studies have shown that rs-fMRI has become an essential technique to analyze the brain's spontaneous activity and intrinsic functional connectivity [3].…”

Section: Introductionmentioning

confidence: 99%

A Deep Neural Network Study of the ABIDE Repository on Autism Spectrum Classification

Yang¹,

Schrader²,

Zhang³

2020

IJACSA

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The objective of this study is to implement deep neural network (DNN) models to classify autism spectrum disorder (ASD) patients and typically developing (TD) participants. The experimental design utilizes functional connectivity features extracted from resting-state functional magnetic resonance imaging (rs-fMRI) originating in the multisite repository Autism Brain Imaging Data Exchange (ABIDE) over a significant set of training samples. Our methodology and results have two main parts. First, we build DNN models using the TensorFlow framework in python to classify ASD from TD. Here we acquired an accuracy of 75.27%. This is significantly higher than any known accuracy (71.98%) using the same data. We also obtained a recall of 74% and a precision of 78.37%. In summary, and based on our literature review, this study demonstrated that our DNN (128-64) model achieves the highest accuracy, recall, and precision on the ABIDE dataset to date. Second, using the same ABIDE data, we implemented an identical experimental design with four distinct hidden layer configuration DNN models each preprocessed using four different industry accepted strategies. These results aided in identifying the preprocessing technique with the highest accuracy, recall, and precision: the Configurable Pipeline for the Analysis of Connectomes (CPAC).

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Section: Introductionmentioning

confidence: 99%

A Deep Neural Network Study of the ABIDE Repository on Autism Spectrum Classification

Yang¹,

Schrader²,

Zhang³

2020

IJACSA

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“…This method was used to extract optimal features from functional MRI (fMRI) connectivity matrix graph measures, and the features were feed into SVM for classification. Al-Zubaidi et al had a similar result in classifying human with metabolic states (hunger/satiety) [22]. The extracted connectivity parameters from 90 brain regions and used sequential forward floating selection strategy with linear SVM to classify the human with the two states.…”

Section: Multivariate Analysis Methods Introduced Machine Learningmentioning

confidence: 93%

Dilated 3D Convolutional Neural Networks for Brain MRI Data Classification

et al. 2019

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Benefiting from the research of machine learning (ML) and deep learning(DL), multivariate methods based on ML and DL have been the mainstream and successful analysis methods in Neural Engineering or Neuroimaging research, for example, assisting diagnosis based on brain Magnetic Resonance Imaging (MRI). However, many existing methods based on traditional ML methods cannot sufficiently extract discriminative features, especially feature patterns across long-distance brain areas, resulting in unsatisfactory classification performance. Designing an effective and robust classifier for different MRI images remains a challenge. In this paper, we introduced dilated 3D CNN method for classifying 3D MRI images combining CNN structure and dilated convolution with a small number of feature maps. We also presented a methodology framework based on dilated 3D CNN method, which can classify both single MRI images and image sequences. Our method and framework were evaluated on the structural MRI images of ADHD-200 dataset and fMRI images of a Schizophrenia dataset, demonstrating better performances than some other state-of-the-art methods.INDEX TERMS Biomedical image processing, magnetic resonance imaging, machine learning.

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“…Sets C and D contained only seizure-free activity, while set E was the only one that contained seizure activity. In this paper, the full set of the datasets (i.e., A,B,C, D and E) were used, like in [11,12]. Unlike the start/plus fMRI dataset, the dataset for the epilepsy prediction problem has large number of samples (the actual size of the full dataset is 11,501) to train and test for deriving the best prediction problem.…”

Section: Further Analysis Of the Vwm-based Features For Epileptic mentioning

confidence: 99%

“…To improve the analysis of brain diseases, a framework that was proposed in [10] utilizes fMRI data to estimate the states and parameters of the stochastic Metabolic Hemodynamic Model (sMHM). A recent research work [11] investigated the influence of hunger and satiety on rs-fMRI using three connectivity models namely; local connectivity, global connectivity and amplitude rs-fMRI signals.…”

Section: Introductionmentioning

confidence: 99%

Voxel Weight Matrix-Based Feature Extraction for Biomedical Applications

Albalawi

Alshehri²,

Chahid

et al. 2020

IEEE Access

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functional Magnetic Resonance Imaging (fMRI) is an emerging medical tool used to measure brain activities that were induced normally such as cognitive states (e.g., reading a sentence or viewing a picture) or abnormally (e.g., brain activity occurs after a stroke or brain injury). These measured data can be used to construct a model via machine learning techniques to predict the occurrence of a certain cognitive behavior or brain disease. The difficulty of this prediction problem can be summarized in two points: first, the size of the dataset is very small due to the small number of subjects (i.e., patients) who can contribute to these research-based experiments. Second, the size of the feature vector resulted from these medical tools is very large compared to the few number of samples that were collected. One possible way to overcome these obstacles is to develop a feature generation methodology that can produce a smallsized and descriptive feature vector that may improve the overall prediction performance. Motivated by these considerations, this paper proposes a novel feature generation methodology termed Voxel Weight Matrix (VWM) method. This feature generation technique can transform the original high-dimensional feature vector to a two-dimensional discriminative feature domain. The main contribution of this feature generation technique is its ability to represent the statistical measures of the original feature vector via two-dimensional feature vector. After generating the VWM-based feature set, various classification tools such as logistic regression (LR) models and Support Vector Machine (SVM) are used for cognitive state prediction based on publicly available fMRI dataset called state/plus dataset. The classification models with the proposed VWM features outperformed the best two reported prediction models associated with the star/plus dataset with an average accuracy of 99.8%. To further illustrate the effectiveness of the proposed feature generation methodology, another publicly available Electroencephalography (EEG) dataset are used for Epileptic Seizure Prediction.

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Machine Learning Based Classification of Resting-State fMRI Features Exemplified by Metabolic State (Hunger/Satiety)

Cited by 33 publications

References 120 publications

A Deep Neural Network Study of the ABIDE Repository on Autism Spectrum Classification

A Deep Neural Network Study of the ABIDE Repository on Autism Spectrum Classification

Dilated 3D Convolutional Neural Networks for Brain MRI Data Classification

Voxel Weight Matrix-Based Feature Extraction for Biomedical Applications

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