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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

Influential features and areas for naïve Bayes. Most likely values (learned conditional probabilities) for the naïve Bayes classifier (epilepsy) are shown for each predicted network (executive control not shown). The most influential areas correspond anatomically to their respective network location. Note that the values were shifted to be centered at zero (from the original [1,11] range) to match the perceptron weight appearance.
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Figure 5: Influential features and areas for naïve Bayes. Most likely values (learned conditional probabilities) for the naïve Bayes classifier (epilepsy) are shown for each predicted network (executive control not shown). The most influential areas correspond anatomically to their respective network location. Note that the values were shifted to be centered at zero (from the original [1,11] range) to match the perceptron weight appearance.

Mentions: Examining the weights of the perceptron and the conditional probability estimates of the naive Bayes classifier revealed that the concept learned by the algorithms was an anatomical, spatial representation of the four networks. The weights that most influenced classification were located at the functional and anatomical regions of each of the four (executive control not shown) respective networks (Figures 4, 5).


Classification and Extraction of Resting State Networks Using Healthy and Epilepsy fMRI Data
Influential features and areas for naïve Bayes. Most likely values (learned conditional probabilities) for the naïve Bayes classifier (epilepsy) are shown for each predicted network (executive control not shown). The most influential areas correspond anatomically to their respective network location. Note that the values were shifted to be centered at zero (from the original [1,11] range) to match the perceptron weight appearance.
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Related In: Results  -  Collection

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

Figure 5: Influential features and areas for naïve Bayes. Most likely values (learned conditional probabilities) for the naïve Bayes classifier (epilepsy) are shown for each predicted network (executive control not shown). The most influential areas correspond anatomically to their respective network location. Note that the values were shifted to be centered at zero (from the original [1,11] range) to match the perceptron weight appearance.
Mentions: Examining the weights of the perceptron and the conditional probability estimates of the naive Bayes classifier revealed that the concept learned by the algorithms was an anatomical, spatial representation of the four networks. The weights that most influenced classification were located at the functional and anatomical regions of each of the four (executive control not shown) respective networks (Figures 4, 5).

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