Detection of EEG-resting state independent networks by eLORETA-ICA method.

Recent functional magnetic resonance imaging (fMRI) studies have shown that functional networks can be extracted even from resting state data, the so called "Resting State independent Networks" (RS-independent-Ns) by applying independent component analysis (ICA). However, compared to fMRI, electroencephalography (EEG) and magnetoencephalography (MEG) have much higher temporal resolution and provide a direct estimation of cortical activity. To date, MEG studies have applied ICA for separate frequency bands only, disregarding cross-frequency couplings. In this study, we aimed to detect EEG-RS-independent-Ns and their interactions in all frequency bands. We applied exact low resolution brain electromagnetic tomography-ICA (eLORETA-ICA) to resting-state EEG data in 80 healthy subjects using five frequency bands (delta, theta, alpha, beta and gamma band) and found five RS-independent-Ns in alpha, beta and gamma frequency bands. Next, taking into account previous neuroimaging findings, five RS-independent-Ns were identified: (1) the visual network in alpha frequency band, (2) dual-process of visual perception network, characterized by a negative correlation between the right ventral visual pathway (VVP) in alpha and beta frequency bands and left posterior dorsal visual pathway (DVP) in alpha frequency band, (3) self-referential processing network, characterized by a negative correlation between the medial prefrontal cortex (mPFC) in beta frequency band and right temporoparietal junction (TPJ) in alpha frequency band, (4) dual-process of memory perception network, functionally related to a negative correlation between the left VVP and the precuneus in alpha frequency band; and (5) sensorimotor network in beta and gamma frequency bands. We selected eLORETA-ICA which has many advantages over the other network visualization methods and overall findings indicate that eLORETA-ICA with EEG data can identify five RS-independent-Ns in their intrinsic frequency bands, and correct correlations within RS-independent-Ns.

Noninvasive prediction of shunt operation outcome in idiopathic normal pressure hydrocephalus.

Idiopathic normal pressure hydrocephalus (iNPH) is a syndrome characterized by gait disturbance, cognitive deterioration and urinary incontinence in elderly individuals. These symptoms can be improved by shunt operation in some but not all patients. Therefore, discovering predictive factors for the surgical outcome is of great clinical importance. We used normalized power variance (NPV) of electroencephalography (EEG) waves, a sensitive measure of the instability of cortical electrical activity, and found significantly higher NPV in beta frequency band at the right fronto-temporo-occipital electrodes (Fp2, T4 and O2) in shunt responders compared to non-responders. By utilizing these differences, we were able to correctly identify responders and non-responders to shunt operation with a positive predictive value of 80% and a negative predictive value of 88%. Our findings indicate that NPV can be useful in noninvasively predicting the clinical outcome of shunt operation in patients with iNPH.

EEG and Neuronal Activity Topography analysis can predict effectiveness of shunt operation in idiopathic normal pressure hydrocephalus patients.

Idiopathic normal pressure hydrocephalus (iNPH) is a neuropsychiatric syndrome characterized by gait disturbance, cognitive impairment and urinary incontinence that affect elderly individuals. These symptoms can potentially be reversed by cerebrospinal fluid (CSF) drainage or shunt operation. Prior to shunt operation, drainage of a small amount of CSF or "CSF tapping" is usually performed to ascertain the effect of the operation. Unfortunately, conventional neuroimaging methods such as single photon emission computed tomography (SPECT) and functional magnetic resonance imaging (fMRI), as well as electroencephalogram (EEG) power analysis seem to have failed to detect the effect of CSF tapping on brain function. In this work, we propose the use of Neuronal Activity Topography (NAT) analysis, which calculates normalized power variance (NPV) of EEG waves, to detect cortical functional changes induced by CSF tapping in iNPH. Based on clinical improvement by CSF tapping and shunt operation, we classified 24 iNPH patients into responders (N = 11) and nonresponders (N = 13), and performed both EEG power analysis and NAT analysis. We also assessed correlations between changes in NPV and changes in functional scores on gait and cognition scales before and after CSF tapping. NAT analysis showed that after CSF tapping there was a significant decrease in alpha NPV at the medial frontal cortex (FC) (Fz) in responders, while nonresponders exhibited an increase in alpha NPV at the right dorsolateral prefrontal cortex (DLPFC) (F8). Furthermore, we found correlations between cortical functional changes and clinical symptoms. In particular, delta and alpha NPV changes in the left-dorsal FC (F3) correlated with changes in gait status, while alpha and beta NPV changes in the right anterior prefrontal cortex (PFC) (Fp2) and left DLPFC (F7) as well as alpha NPV changes in the medial FC (Fz) correlated with changes in gait velocity. In addition, alpha NPV changes in the right DLPFC (F8) correlated with changes in WMS-R Mental Control scores in iNPH patients. An additional analysis combining the changes in values of alpha NPV over the left-dorsal FC (∆alpha-F3-NPV) and the medial FC (∆alpha-Fz-NPV) induced by CSF tapping (cut-off value of ∆alpha-F3-NPV + ∆alpha-Fz-NPV = 0), could correctly identified "shunt responders" and "shunt nonresponders" with a positive predictive value of 100% (10/10) and a negative predictive value of 66% (2/3). In contrast, EEG power spectral analysis showed no function related changes in cortical activity at the frontal cortex before and after CSF tapping. These results indicate that the clinical changes in gait and response suppression induced by CSF tapping in iNPH patients manifest as NPV changes, particularly in the alpha band, rather than as EEG power changes. Our findings suggest that NAT analysis can detect CSF tapping-induced functional changes in cortical activity, in a way that no other neuroimaging methods have been able to do so far, and can predict clinical response to shunt operation in patients with iNPH.

Normalized power variance change between pre-ictal and ictal phase of an epilepsy patient using NAT analysis: a case study.

Variance of state variables shifts due to phase-instability and may serve as an early-warning signal of phase transition of complex systems such as an epileptic seizure of brain cortical activity. Neuronal Activity Topology (NAT) analysis calculates a normalized-power-variance (NPV) of electroencephalogram (EEG) data in each frequency band to obtain relative values comparable among different power states.