Non-linear dynamical coupling between different brain areas during evoked emotions: An EEG investigation

Афтанас, Л. И.; Lotova, Natalia V; Koshkarov, Vladimir I.; Popov, S. P.

doi:10.1016/s0301-0511(98)00015-5

Cited by 51 publications

(20 citation statements)

References 41 publications

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“…Coherence decrease may be related to functional separation of brain regions. Aftanas et al (1998) hypothesized that coherence decrease may reflect increased processing between a specific brain region and subcortical regions. However, the coherence changes (increases or decreases) centered around the electrode may be an indicator of the functional importance of this region for the specific task.…”

Section: Discussionmentioning

confidence: 99%

Memory load effect in auditory–verbal short-term memory task: EEG fractal and spectral analysis

et al. 2015

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The objective of this preliminary study was to quantify changes in complexity of EEG using fractal dimension (FD) alongside linear methods of spectral power, event-related spectral perturbations, coherence, and source localization of EEG generators for theta (4-7 Hz), alpha (8-12 Hz), and beta (13-23 Hz) frequency bands due to a memory load effect in an auditory-verbal short-term memory (AVSTM) task for words. We examined 20 healthy individuals using the Sternberg's paradigm with increasing memory load (three, five, and seven words). The stimuli were four-letter words. Artifact-free 5-s EEG segments during retention period were analyzed. The most significant finding was the increase in FD with the increase in memory load in temporal regions T3 and T4, and in parietal region Pz, while decrease in FD with increase in memory load was registered in frontal midline region Fz. Results point to increase in frontal midline (Fz) theta spectral power, decrease in alpha spectral power in parietal region-Pz, and increase in beta spectral power in T3 and T4 region with increase in memory load. Decrease in theta coherence within right hemisphere due to memory load was obtained. Alpha coherence increased in posterior regions with anterior decrease. Beta coherence increased in fronto-temporal regions. Source localization delineated theta activity increase in frontal midline region, alpha decrease in superior parietal region, and beta increase in superior temporal gyrus with increase in memory load. In conclusion, FD as a nonlinear measure may serve as a sensitive index for quantifying dynamical changes in EEG signals during AVSTM tasks.

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Section: Discussionmentioning

confidence: 99%

Memory load effect in auditory–verbal short-term memory task: EEG fractal and spectral analysis

et al. 2015

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“…Emotional activation was associated with an overall increase of complexity in the interaction among cortical regions. On the other hand, emotional valence could not be clearly associated with specific effects [1,3]. Further studies found that emotional arousal led to more complex and less predictable cortical dynamics, where positive emotions were related to a higher complexity in posterior areas and to a lower complexity in frontal areas than that evidenced with negative emotions [2].…”

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confidence: 80%

EEG phase synchronization during emotional response to positive and negative film stimuli

Costa

Rognoni

Galati

2006

Neuroscience Letters

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In the present study the patterns of interdependency between different brain regions were investigated as volunteers looked at emotional and non-emotional film stimuli. The main goal was to evaluate the emotion-related differences and to check their consistency during the elaboration of the same type of stimuli in repeated presentations. A measure called synchronization index (SI) was used to detect interdependencies in EEG signals. The hypotheses were that emotional-information processing could involve variation in synchronized activity and that two valence-specific emotions -happiness and sadness -differ from each other. The SI obtained was compared among the various experimental conditions and significant changes were found. The results demonstrated an overall increase of SI during emotional stimulation and, in particular, during sadness, which yielded a pattern involving a large exchange of information among frontal channels. On the other hand, happiness was associated with a wider synchronization among frontal and occipital sites, although happiness itself was less synchronized. We conclude that the SI can be successfully applied for studying the dynamic cooperation between cortical areas during emotion responses.

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“…AD causes changes in the cortical activity of the brain [93] which impacts the connectivity among cortical regions of the brain [37], which can be reflected in the EEG coherence. EEG coherence can be quantified by assessing the functional coupling between brain regions [37,94]. Coherence measures depend on channel location and the frequency bands of the EEG signal [93,[95][96][97][98].…”

Section: Reduction In Eeg Coherencementioning

confidence: 99%

Electroencephalogram Based Biomarkers for Detection of Alzheimer’s Disease

Al-nuaimi¹

2020

Neuroimaging - Neurobiology, Multimodal and Network Applications

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Alzheimer's disease (AD) is an age-related progressive and neurodegenerative disorder, which is characterized by loss of memory and cognitive decline. It is the main cause of disability among older people. The rapid increase in the number of people living with AD and other forms of dementia due to the aging population represents a major challenge to health and social care systems worldwide. Degeneration of brain cells due to AD starts many years before the clinical manifestations become clear. Early diagnosis of AD will contribute to the development of effective treatments that could slow, stop, or prevent significant cognitive decline. Consequently, early diagnosis of AD may also be valuable in detecting patients with dementia who have not obtained a formal early diagnosis, and this may provide them with a chance to access suitable healthcare facilities. An early diagnosis biomarker capable of measuring brain cell degeneration due to AD would be valuable. Potentially, electroencephalogram (EEG) can play a valuable role in the early diagnosis of AD. EEG is noninvasive and low cost, and provides valuable information about brain dynamics in AD. Thus, EEG-based biomarkers may be used as a first-line decisionsupport tool in AD diagnosis and could complement other AD biomarkers.Keywords: EEG biomarkers, AD biomarkers, slowing of EEG, decrease in EEG coherence, reduction in EEG complexity, detection of Alzheimer's disease Neuroimaging -Neurobiology, Multimodal and Network Applications 2 an enormous financial. However, many dementia sufferers are not diagnosed early [7,8]. It is estimated that up to 50% of individuals with dementia may not have been diagnosed formally [8, 9]. In 2011, 28 million people out of 36 million people with dementia were not diagnosed worldwide [10].Degeneration of brain cells due to AD begins many years before clinical manifestations become noticeable [5,[11][12][13][14][15]. Early diagnosis of AD will help to the development of efficient treatments that could mitigate, stop, or prevent significant cognitive impairment [14, 16, 17]. Early diagnosis of AD may also be valuable in detecting patients with dementia who have not obtained a formal early diagnosis, and this may provide them with a chance to access suitable healthcare facilities [18][19][20].An early diagnosis biomarker that can measure brain cell degeneration due to AD would be valuable [2,[21][22][23]. But this may involve dealing with an extremely large number of individuals, as up to 50% of individuals with dementia may not have been diagnosed formally. Therefore, Simple, non-invasive, low-cost and reliable biomarkers are necessary for early diagnosis that can be accessed in clinical practice [5,24,25]. Recent guidelines support the use of biomarkers for biochemical and neuroimaging to promote AD diagnosis. AD testing of cerebral spinal fluid (CSF) is not commonly used in clinical practice as it involves lumbar puncture, an invasive procedure [2,26,27]. Neuroimaging is expensive, accessible only in specialized centers [28], and may ...

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Non-linear dynamical coupling between different brain areas during evoked emotions: An EEG investigation

Cited by 51 publications

References 41 publications

Memory load effect in auditory–verbal short-term memory task: EEG fractal and spectral analysis

Memory load effect in auditory–verbal short-term memory task: EEG fractal and spectral analysis

EEG phase synchronization during emotional response to positive and negative film stimuli

Electroencephalogram Based Biomarkers for Detection of Alzheimer’s Disease

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