EEG Differences Between Eyes-Closed and Eyes-Open Conditions at the Resting Stage for Euthymic Participants

Kan, Donica Pei Xin; Croarkin, Paul E.; Phang, Cheng Kar; Lee, P. F.

doi:10.1007/s11062-018-9706-6

Cited by 34 publications

(23 citation statements)

References 44 publications

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“…The mental state when one closes the eyes is characterized by a maximum amplitude of these alpha waves, and this is called the Berger effect (Berger, 1929). This maximum amplitude of alpha waves when the eyes are closed has been found in many studies (Legewie et al, 1969;Westphal et al, 1993;Barry et al, 2007;Kan et al, 2017), including in children (Barry et al, 2009) and elderly (Barry and De Blasio, 2017). In addition, a recent study by Rimbert et al (2018) showed an increase in alpha waves in a motor task in the closed-eye condition compared to the open-eye condition.…”

Section: Alpha Wavessupporting

confidence: 67%

Eyes Wide Shut: Primary Process Opens Up

et al. 2020

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Freud was the first to invite his patients to lie down on a couch, facilitating the closing of the eyes. If the mere fact of closing the eyes favors access to unconscious materials, it should also favor primary process mentation. Primary process is an associative mode of thought based on superficial similarities including phonology, while secondary process mentation in language is primarily concerned with meaning. Fifty-two participants were given French Word Lists with phonological choices (P) corresponding to primary processes, while semantic choices (S) represent secondary processes when they are in mutual competition (PS). For example, participants were given a first word, such as, e.g. cale (to hold), and then had to choose between lac (P; lake) and fixe (S; fix), which alternative was most similar to the first word, cale. Two control lists, SN and PN, where the other choice is unrelated (N for nothing), verify that the subjects are equally capable of recognizing the phonological and semantics similarity. Results show an (near 10% increase in P choices in PS when participants close their eyes, while results on PN and SN were unchanged. The mere fact of closing the eyes induces a modest increase in primary process mentation. Based on the literature, the eyes closed (EC) condition is linked to increased alpha synchronization, which is thought to induce an inward mental shift. This research contributes to validating the psychoanalytic technique consisting on inviting patients to lie down on a couch and invite them to close their eyes.

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Section: Alpha Wavessupporting

confidence: 67%

Eyes Wide Shut: Primary Process Opens Up

et al. 2020

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“…In fact, the resting state fMRI technique was most widely used in investigating various mental disorders by requesting all subjects in the EO or EC resting state. It is still unknown whether resting state condition with EO or EC is a confounding factor for the comparison between the diseases and healthy controls (Alba et al, 2016 ; Janssen et al, 2016 ; Li W. et al, 2016 ; Kan et al, 2017 ; Nair et al, 2017 ). Future studies using resting state paradigm should clearly indicate whether they are using data with EO or EC.…”

Section: Limitationsmentioning

confidence: 99%

Eyes-Open and Eyes-Closed Resting States With Opposite Brain Activity in Sensorimotor and Occipital Regions: Multidimensional Evidences From Machine Learning Perspective

Jie

Chen

et al. 2018

Front. Hum. Neurosci.

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Studies have demonstrated that there are widespread significant differences in spontaneous brain activity between eyes-open (EO) and eyes-closed (EC) resting states. However, it remains largely unclear whether spontaneous brain activity is effectively related to EO and EC resting states. The amplitude, local functional concordance, inter-hemisphere functional synchronization, and network centrality of spontaneous brain activity were measured by the fraction amplitude of low frequency fluctuation (fALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC) and degree centrality (DC), respectively. Using the public Eyes-open/Eyes-closed dataset, we employed the support vector machine (SVM) and bootstrap technique to establish linking models for the fALFF, ReHo, VMHC and DC dimensions. The classification accuracies of linking models are 0.72 (0.59, 0.82), 0.88 (0.79, 0.97), 0.82 (0.74, 0.91) and 0.70 (0.62, 0.79), respectively. Specifically, we observed that brain activity in the EO condition is significantly greater in attentional system areas, including the fusiform gyrus, occipital and parietal cortex, but significantly lower in sensorimotor system areas, including the precentral/postcentral gyrus, paracentral lobule (PCL) and temporal cortex compared to the EC condition from the four dimensions. The results consistently indicated that spontaneous brain activity is effectively related to EO and EC resting states, and the two resting states are of opposite brain activity in sensorimotor and occipital regions. It may provide new insight into the neural substrate of the resting state and help computational neuroscientists or neuropsychologists to choose an appropriate resting state condition to investigate various mental disorders from the resting state functional magnetic resonance imaging (fMRI) technique.

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“…where the alpha band dominates the EEG. However, this study used eyes-open data, in which the alpha network is less powerful and differs in functional network structure [82][83][84]. Therefore here the theta-band was used, motivated by past studies which have demonstrated theta-band alterations to EEG functional connectivity in AD [31] and epilepsy [25,[85][86][87], and a relationship between theta-band dynamics and cognitive impairment in dementia [17,31,88].…”

Section: Methodsmentioning

confidence: 99%

A Large-Scale Brain Network Mechanism for Increased Seizure Propensity in Alzheimer’s Disease

Tait

Lopes

Stothart

et al. 2021

Preprint

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People with Alzheimer’s disease (AD) are 6-10 times more likely to develop seizures than the healthy aging population. Leading hypotheses largely consider increased excitability of local cortical tissue as primarily responsible for increased seizure prevalence in AD. However, both local dynamics and large-scale brain network structure are believed to be crucial for determining seizure likelihood and phenotype. In this study, we combine computational modelling with electrophysiological data to demonstrate a potential large-scale brain network mechanism for increased seizure propensity in people with AD. EEG was recorded from 21 people with probable AD and 26 healthy controls. At the time of EEG acquisition, all participants were free from seizures. Whole brain functional connectivity derived from source-reconstructed EEG recordings was used to build subject-specific brain network models of seizure transitions using an approach previously validated on participants with epilepsy vs controls. As cortical tissue excitability was increased in the simulations, network models of AD simulations were more likely to transition into seizures than simulations from healthy controls. Our results suggest an increased group-level probability of developing seizures at a future time for AD participants. We subsequently used the model to assess seizure propensity of different regions across the cortex. We found the most important regions for seizure generation were those typically burdened by amyloid-beta at the early stages of AD, as previously reported by in-vivo and post-mortem staging of amyloid plaques. These included cingulate, medial temporal, and orbital regions. Analysis of these spatial distributions also give potential insight into mechanisms of increased susceptibility to generalized (as opposed to focal) seizures in AD vs controls. This research suggests avenues for future studies testing patients with seizures, e.g. co-morbid AD/epilepsy patients, and comparisons with PET and MRI scans to relate regional seizure propensity with amyloid/tau pathology and cortical atrophy.Author summaryPeople with Alzheimer’s disease (AD) are more likely to develop seizures than cognitively healthy people. In this study, we aimed to understand whether whole-brain network structure is related to this increased seizure likelihood. We used electroencephalography (EEG) to estimate brain networks from people with AD and healthy controls. We subsequently inserted these networks into a model brain and simulated disease progression by increasing the excitability of brain tissue. We found the simulated AD brains were more likely to develop seizures than the simulated control brains. No participants had seizures when we collected data, so our results suggest an increased probability of developing seizures at a future time for AD participants. Therefore functional brain network structure may play a role in increased seizure likelihood in AD. We also used the model to examine which brain regions were most important for generating seizures, and found that the seizure-generating regions corresponded to those typically affected in early AD. Our results also provide a potential explanation for why people with AD are more likely to have generalized seizures (i.e. seizures involving the whole brain, as opposed to ‘focal’ seizures which only involve certain areas) than the general population with epilepsy.

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EEG Differences Between Eyes-Closed and Eyes-Open Conditions at the Resting Stage for Euthymic Participants

Cited by 34 publications

References 44 publications

Eyes Wide Shut: Primary Process Opens Up

Eyes Wide Shut: Primary Process Opens Up

Eyes-Open and Eyes-Closed Resting States With Opposite Brain Activity in Sensorimotor and Occipital Regions: Multidimensional Evidences From Machine Learning Perspective

A Large-Scale Brain Network Mechanism for Increased Seizure Propensity in Alzheimer’s Disease

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