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Classification and Extraction of Resting State Networks Using Healthy and Epilepsy fMRI Data

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ABSTRACT

Functional magnetic resonance imaging studies have significantly expanded the field's understanding of functional brain activity of healthy and patient populations. Resting state (rs-) fMRI, which does not require subjects to perform a task, eliminating confounds of task difficulty, allows examination of neural activity and offers valuable functional mapping information. The purpose of this work was to develop an automatic resting state network (RSN) labeling method which offers value in clinical workflow during rs-fMRI mapping by organizing and quickly labeling spatial maps into functional networks. Here independent component analysis (ICA) and machine learning were applied to rs-fMRI data with the goal of developing a method for the clinically oriented task of extracting and classifying spatial maps into auditory, visual, default-mode, sensorimotor, and executive control RSNs from 23 epilepsy patients (and for general comparison, separately for 30 healthy subjects). ICA revealed distinct and consistent functional network components across patients and healthy subjects. Network classification was successful, achieving 88% accuracy for epilepsy patients with a naïve Bayes algorithm (and 90% accuracy for healthy subjects with a perceptron). The method's utility to researchers and clinicians is the provided RSN spatial maps and their functional labeling which offer complementary functional information to clinicians' expert interpretation.

No MeSH data available.


Related in: MedlinePlus

Example epilepsy patient's IC maps classification. IC spatial maps (t-statistics > 2.0) identified into networks by the viewers and classified by the Naïve Bayes algorithm. IC #22 is the only one misclassified for this patient. The underlay is a standard MNI_avg152T1 AFNI template.
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Figure 6: Example epilepsy patient's IC maps classification. IC spatial maps (t-statistics > 2.0) identified into networks by the viewers and classified by the Naïve Bayes algorithm. IC #22 is the only one misclassified for this patient. The underlay is a standard MNI_avg152T1 AFNI template.

Mentions: Figure 6 shows an example patient's IC maps labeled by the classifier, with only a single (out of ten) misclassified component with regard to the expert viewers. It is motivating to see such high accuracy results for a multi-class classifier on a dataset of 23 patients. Mathematically and statistically speaking, the performance will improve as the amount of training data increases—the classifier will be able to learn a better model representation of the task. Also, improvement of the functional accuracy or appropriateness of the provided, labeled RSNs (e.g., higher model order for more spatial detail) will improve the clinical utility of the method.


Classification and Extraction of Resting State Networks Using Healthy and Epilepsy fMRI Data
Example epilepsy patient's IC maps classification. IC spatial maps (t-statistics > 2.0) identified into networks by the viewers and classified by the Naïve Bayes algorithm. IC #22 is the only one misclassified for this patient. The underlay is a standard MNI_avg152T1 AFNI template.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5037187&req=5

Figure 6: Example epilepsy patient's IC maps classification. IC spatial maps (t-statistics > 2.0) identified into networks by the viewers and classified by the Naïve Bayes algorithm. IC #22 is the only one misclassified for this patient. The underlay is a standard MNI_avg152T1 AFNI template.
Mentions: Figure 6 shows an example patient's IC maps labeled by the classifier, with only a single (out of ten) misclassified component with regard to the expert viewers. It is motivating to see such high accuracy results for a multi-class classifier on a dataset of 23 patients. Mathematically and statistically speaking, the performance will improve as the amount of training data increases—the classifier will be able to learn a better model representation of the task. Also, improvement of the functional accuracy or appropriateness of the provided, labeled RSNs (e.g., higher model order for more spatial detail) will improve the clinical utility of the method.

View Article: PubMed Central - PubMed

ABSTRACT

Functional magnetic resonance imaging studies have significantly expanded the field's understanding of functional brain activity of healthy and patient populations. Resting state (rs-) fMRI, which does not require subjects to perform a task, eliminating confounds of task difficulty, allows examination of neural activity and offers valuable functional mapping information. The purpose of this work was to develop an automatic resting state network (RSN) labeling method which offers value in clinical workflow during rs-fMRI mapping by organizing and quickly labeling spatial maps into functional networks. Here independent component analysis (ICA) and machine learning were applied to rs-fMRI data with the goal of developing a method for the clinically oriented task of extracting and classifying spatial maps into auditory, visual, default-mode, sensorimotor, and executive control RSNs from 23 epilepsy patients (and for general comparison, separately for 30 healthy subjects). ICA revealed distinct and consistent functional network components across patients and healthy subjects. Network classification was successful, achieving 88% accuracy for epilepsy patients with a naïve Bayes algorithm (and 90% accuracy for healthy subjects with a perceptron). The method's utility to researchers and clinicians is the provided RSN spatial maps and their functional labeling which offer complementary functional information to clinicians' expert interpretation.

No MeSH data available.


Related in: MedlinePlus