The clinical use of quantitative EEG in cognitive disorders

Kanda, Paulo Afonso De Medeiros; Anghinah, Renato; Smidth, Magali Taino; Silva, Jorge Mario

doi:10.1590/s1980-57642009dn30300004

Cited by 47 publications

(25 citation statements)

References 80 publications

(96 reference statements)

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“…It is an important method for evaluation of cortical processing and physiological changes. 36 A decrease in alpha and beta rhythms and increase in delta and theta frequencies are related to brain lesions and cognitive decline. 37 Babiloni et al.…”

Section: Discussionmentioning

confidence: 99%

Comparative analysis of the electroencephalogram in patients with Alzheimer's disease, diffuse axonal injury patients and healthy controls using LORETA analysis

Ianof

Fraga

Ferreira

et al. 2017

Dement. neuropsychol.

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Alzheimer's disease (AD) is a dementia that affects a large contingent of the elderly population characterized by the presence of neurofibrillary tangles and senile plaques. Traumatic brain injury (TBI) is a non-degenerative injury caused by an external mechanical force. One of the main causes of TBI is diffuse axonal injury (DAI), promoted by acceleration-deceleration mechanisms.ObjectiveTo understand the electroencephalographic differences in functional mechanisms between AD and DAI groups.MethodsThe study included 20 subjects with AD, 19 with DAI and 17 healthy adults submitted to high resolution EEG with 128 channels. Cortical sources of EEG rhythms were estimated by exact low-resolution electromagnetic tomography (eLORETA) analysis.ResultsThe eLORETA analysis showed that, in comparison to the control (CTL) group, the AD group had increased theta activity in the parietal and frontal lobes and decreased alpha 2 activity in the parietal, frontal, limbic and occipital lobes. In comparison to the CTL group, the DAI group had increased theta activity in the limbic, occipital sublobar and temporal areas.ConclusionThe results suggest that individuals with AD and DAI have impairment of electrical activity in areas important for memory and learning.

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Section: Discussionmentioning

confidence: 99%

Comparative analysis of the electroencephalogram in patients with Alzheimer's disease, diffuse axonal injury patients and healthy controls using LORETA analysis

Ianof

Fraga

Ferreira

et al. 2017

Dement. neuropsychol.

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“…Diagnostic imaging tools like Computational Tomography (CT) or Functional magnetic resonance imaging (fMRI) are normally used to evaluate brain damage in the acute and sub-acute phases, offering valuable information about the diagnostic and functional prognosis for each case. Recent studies explored new methods to process and analyze brain signals acquired by conventional techniques like electroencephalography (EEG) (Kanda et al, 2009;Leon-Carrion et al, 2009;Foreman and Claassen, 2012;Rabiller et al, 2015;Wu et al, 2016) or magnetoencephalography (MEG) (Mäkelä et al, 2015;Ikkai et al, 2016;Krauss et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

EEG Biomarkers Related With the Functional State of Stroke Patients

et al. 2020

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Introduction: Recent studies explored promising new quantitative methods to analyze electroencephalography (EEG) signals. This paper analyzes the correlation of two EEG parameters, Brain Symmetry Index (BSI) and Laterality Coefficient (LC), with established functional scales for the stroke assessment. Methods: Thirty-two healthy subjects and thirty-six stroke patients with upper extremity hemiparesis were recruited for this study. The stroke patients where subdivided in three groups according to the stroke location: Cortical, Subcortical, and Cortical + Subcortical. The participants performed assessment visits to record the EEG in the resting state and perform functional tests using rehabilitation scales. Then, stroke patients performed 25 sessions using a motor-imagery based Brain Computer Interface system (BCI). BSI was calculated with the EEG data in resting state and LC was calculated with the Event-Related Synchronization maps. Results: The results of this study demonstrated significant differences in the BSI between the healthy group and Subcortical group (P = 0.001), and also between the healthy and Cortical+Subcortical group (P = 0.019). No significant differences were found between the healthy group and the Cortical group (P = 0.505). Furthermore, the BSI analysis in the healthy group based on gender showed statistical differences (P = 0.027). In the stroke group, the correlation between the BSI and the functional state of the upper extremity assessed by Fugl-Meyer Assessment (FMA) was also significant, ρ = −0.430 and P = 0.046. The correlation between the BSI and the FMA-Lower extremity was not significant (ρ = −0.063, P = 0.852). Similarly, the LC calculated in the alpha band has significative correlation with FMA of upper extremity (ρ = −0.623 and P < 0.001) and FMA of lower extremity (ρ = −0.509 and P = 0.026). Other important significant correlations between LC and functional scales were observed. In addition, the patients showed an improvement in the FMA-upper extremity after the BCI therapy (FMA = 1 median [IQR: 0-8], P = 0.002). Conclusion: The quantitative EEG tools used here may help support our understanding of stroke and how the brain changes during rehabilitation therapy. These tools can help identify changes in EEG biomarkers and parameters during therapy that might lead to improved therapy methods and functional prognoses.

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“…For a century, task-independent ongoing oscillations have been the hallmark of EEG (Berger 1930). The spectral properties of such ongoing activity ("quantitative EEG") and their cross-areal connectivity during resting state have proven informative for understanding brain function and dysfunction (Kanda et al 2009;Sadaghiani et al 2019). The omnipresence and functional importance of electrophysiological oscillations and FC across all cognitive states therefore begs the question of whether they are governed by a state-invariant, i.e.…”

Section: Introductionmentioning

confidence: 99%

Oscillation-based connectivity architecture is dominated by an intrinsic spatial organization, not cognitive state or frequency

Mostame

Sadaghiani

2020

Preprint

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Functional connectivity (FC) of neural oscillations (~1-150Hz) is thought to facilitate neural information exchange across brain areas by forming malleable neural ensembles in the service of cognitive processes. However, neural oscillations and their FC are not restricted to certain cognitive demands and continuously unfold in all cognitive states. To what degree is the spatial organization of oscillation-based FC affected by cognitive state or governed by an intrinsic architecture? And what is the impact of oscillation frequency and FC mode (phase-versus amplitude coupling)? Using ECoG recordings of 18 presurgical patients, we quantified the state-dependency of oscillation-based FC in five canonical frequency bands and across an array of 6 task states. For both phase- and amplitude coupling, static FC analysis revealed a spatially largely state-invariant (i.e. intrinsic) component in all frequency bands. Further, the observed intrinsic FC pattern was spatially similar across all frequency bands. However, temporally independent FC dynamics in each frequency band allow for frequency-specific malleability in information exchange. In conclusion, the spatial organization of oscillation-based FC is largely stable over cognitive states, i.e. primarily intrinsic in nature, and shared across frequency bands. The state-invariance is in line with prior findings at the other temporal extreme of brain activity, the infraslow range (~<0.1Hz) observed in fMRI. Our observations have implications for conceptual frameworks of oscillation-based FC and the analysis of task-related FC changes.

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The clinical use of quantitative EEG in cognitive disorders

Cited by 47 publications

References 80 publications

Comparative analysis of the electroencephalogram in patients with Alzheimer's disease, diffuse axonal injury patients and healthy controls using LORETA analysis

Comparative analysis of the electroencephalogram in patients with Alzheimer's disease, diffuse axonal injury patients and healthy controls using LORETA analysis

EEG Biomarkers Related With the Functional State of Stroke Patients

Oscillation-based connectivity architecture is dominated by an intrinsic spatial organization, not cognitive state or frequency

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