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

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.


Extraction and Classification process. Flowchart of the steps involved in ICA map extraction and classification.
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Figure 2: Extraction and Classification process. Flowchart of the steps involved in ICA map extraction and classification.

Mentions: Data were preprocessed using AFNI (http://afni.nimh.nih.gov/afni, version: AFNI_2011_12_21_1014) and FSL (http://www.fmrib.ox.ac.uk/fsl, version: v5.0) open source software. To be consistent with the RSN independent component (IC) templates used in one classification algorithm, the method's preprocessing followed the standard procedure reported by Allen et al. (2011). The steps were: (1) discarding the first four resting scan volumes to remove T1 equilibrium effects, (2) motion and slice-timing correction, (3) skull stripping, (4) spatial normalization to standard Montreal Neurological Institute (MNI) brain space with resampling to 3 × 3 × 3 mm voxels, (5) spatial smoothing with a Gaussian kernel with a full-width at half-maximum (FWHM) of 10 mm, and (6) removing slices of no signal to match the matrix size of the used templates. Note that the spatial normalization step is used only for classification purposes and that each patient's ICA maps are available in their original “patient space.” The preprocessing script is publicly available from https://dl.dropboxusercontent.com/u/33755383/algorithms_scripts.7z. A flowchart of the steps of the entire method from preprocessing to classification is shown in Figure 2.


Classification and Extraction of Resting State Networks Using Healthy and Epilepsy fMRI Data
Extraction and Classification process. Flowchart of the steps involved in ICA map extraction and classification.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 2: Extraction and Classification process. Flowchart of the steps involved in ICA map extraction and classification.
Mentions: Data were preprocessed using AFNI (http://afni.nimh.nih.gov/afni, version: AFNI_2011_12_21_1014) and FSL (http://www.fmrib.ox.ac.uk/fsl, version: v5.0) open source software. To be consistent with the RSN independent component (IC) templates used in one classification algorithm, the method's preprocessing followed the standard procedure reported by Allen et al. (2011). The steps were: (1) discarding the first four resting scan volumes to remove T1 equilibrium effects, (2) motion and slice-timing correction, (3) skull stripping, (4) spatial normalization to standard Montreal Neurological Institute (MNI) brain space with resampling to 3 × 3 × 3 mm voxels, (5) spatial smoothing with a Gaussian kernel with a full-width at half-maximum (FWHM) of 10 mm, and (6) removing slices of no signal to match the matrix size of the used templates. Note that the spatial normalization step is used only for classification purposes and that each patient's ICA maps are available in their original “patient space.” The preprocessing script is publicly available from https://dl.dropboxusercontent.com/u/33755383/algorithms_scripts.7z. A flowchart of the steps of the entire method from preprocessing to classification is shown in Figure 2.

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.