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

Aoki, Yasunori; Ishii, Ryouhei; Iwase, Masao; Ikeda, Shunichiro; Hata, Masahiro; Canuet, Leonides; Imajo, Kaoru; Tanaka, Mieko; Matsuzaki, Haruyasu; Musha, Toshimitsu; Takeda, Masatoshi

doi:10.1109/embc.2013.6609530

Cited by 13 publications

(26 citation statements)

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“…In the current implementation, the solution space consists of 6,239 voxels in the cortical grey matter at 5 mm spatial resolution, using a realistic head model [50] with the MNI152 template [51]. eLORETA has been widely used to explore cortical electrical activities, and its validity has been proved in healthy subjects and neuropsychiatric patients [19-21, 52, 53]. Pascual-Marqui et al [23] also showed the property of correct localization of eLORETA with human EEG data during various sensory stimulations even in the presence of structured noise.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Exact low-resolution brain electromagnetic tomography (eLORETA) is a linear inverse solution that reconstructs cortical electrical activity from the scalp EEG data with correct localization [19-23]. Thus, eLORETA with EEG data has been widely used in neuroscience research [19, 20, 24-26]. Recent studies on cortical activity using fMRI or eLORETA have revealed that cortical sources work together in distant regions even in the resting state and construct a set of resting state networks (RSNs), which subserve specific brain functions [20, 27-30].…”

Section: Introductionmentioning

confidence: 99%

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EEG Resting-State Networks in Dementia with Lewy Bodies Associated with Clinical Symptoms

et al. 2019

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Background: Dementia with Lewy bodies (DLB) is characterized by progressive cognitive decline, fluctuating cognition, visual hallucinations, rapid eye movement sleep behavior disorder, and parkinsonism. DLB is the second most common type of degenerative dementia of all dementia cases. However, DLB, particularly in the early stage, is underdiagnosed and sometimes misdiagnosed with other types of dementia. Thus, it is of great interest investigating neurophysiological markers of DLB. Method: We introduced exact low-resolution brain electromagnetic tomography (eLORETA)-independent component analysis (ICA) to assess activities of 5 electroencephalography (EEG) resting-state networks (RSNs) in 41 drug-free DLB patients. Results: Compared to 80 healthy controls, DLB patients had significantly decreased activities in occipital visual and sensorimotor networks, where DLB patients and healthy controls showed no age dependences in all EEG-RSN activities. Also, we found correlations between all EEG-RSN activities and DLB symptoms. Specifically, decreased occipital α activity showed correlations with worse brain functions related to attention/concentration, visuospatial discrimination, and global cognition. Enhanced visual perception network activity correlated with milder levels of depression and anxiety. Enhanced self-referential network activity correlated with milder levels of depression. Enhanced memory perception network activity correlated with better semantic memory, visuospatial discrimination function, and global cognitive function as well as with severer visual hallucination. In addition, decreased sensorimotor network activity correlated with a better semantic memory. Conclusion: These results indicate that eLORETA-ICA can detect EEG-RSN activity alterations in DLB related to symptoms. Therefore, eLORETA-ICA with EEG data can be a useful noninvasive tool for sensitive detection of EEG-RSN activity changes characteristic of DLB and for understanding the neurophysiological mechanisms underlying this disease.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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EEG Resting-State Networks in Dementia with Lewy Bodies Associated with Clinical Symptoms

et al. 2019

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“…Unlike fMRI, which measures hemodynamic changes that occur in response to cortical activity, neurophysiological techniques, such as EEG and MEG measure cortical electrical/magnetic activity directly and noninvasively with a high temporal resolution (1–2 ms) (Canuet et al, 2011 ). Thus, EEG has been widely used in clinical practice to support clinical diagnosis and management of neuropsychiatric diseases such as epilepsy, disturbance of consciousness and dementia, and also in neuroscience to investigate cortical electrical activities and functions (Ishii et al, 1999 ; Canuet et al, 2011 ; Kurimoto et al, 2012 ; Aoki et al, 2013a , b ).…”

Section: Introductionmentioning

confidence: 99%

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

Aoki

Ishii

Pascual-Marqui

et al. 2015

Front. Hum. Neurosci.

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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.

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“…Therefore, it is an urgent issue to find predictive factors for the surgical outcome. In a previous study, in order to investigate the mechanisms of functional recovery, we performed normalized power variance (NPV) analysis, which is a sensitive measure of the instability of cortical electrical activity 7 , on electroencephalography (EEG) data before and after CSF tapping, a procedure that drains a small volume of CSF (30 ml) by lumbar puncture. A striking finding was that cortical functional recovery induced by CSF tapping was associated with NPV changes.…”

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confidence: 99%

Noninvasive prediction of shunt operation outcome in idiopathic normal pressure hydrocephalus

Aoki

Kazui

Tanaka

et al. 2015

Sci Rep

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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.

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

Cited by 13 publications

References 13 publications

EEG Resting-State Networks in Dementia with Lewy Bodies Associated with Clinical Symptoms

EEG Resting-State Networks in Dementia with Lewy Bodies Associated with Clinical Symptoms

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

Noninvasive prediction of shunt operation outcome in idiopathic normal pressure hydrocephalus

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