Default Mode Network and Neural Phase Synchronization in Healthy Aging: A Resting State EEG Study

Chow, Ricky; Rabi, Rahel; Paracha, Shahier; Hasher, Lynn; CPsych, Nicole D. Anderson; Alain, Claude

doi:10.1016/j.neuroscience.2022.01.008

Cited by 14 publications

(12 citation statements)

References 74 publications

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“…For instance, Scally, Burke, Bunce and Delvenne 65 showed a reduction in the global EEG alpha power and functional connectivity in resting state for older adults. Likewise, Chow, Rabi, Paracha, Hasher, Anderson and Alain 66 reported reduced functional connectivity in alpha band during resting state when comparing older versus young adults. They highlighted especially the impaired connectivity between medial prefrontal cortex (mPFC) and several brain regions forming the default mode network.…”

Section: Discussionmentioning

confidence: 90%

Age-related brain mechanisms underlying short-term recognition of musical sequences: An EEG study

Costa

Vuust

Kringelbach

et al. 2023

Preprint

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Recognition is the ability to correctly identify previously learned information. It is an important part of declarative episodic memory and a vital cognitive function, which declines with ageing. Several studies investigated recognition of visual elements, complex images, spatial patterns, and musical melodies, focusing especially on automatic and long-term recognition. Here, we studied the impact of ageing on the event-related potentials using electroencephalography (EEG) associated with short-term recognition of auditory sequences. To this end, we recruited 54 participants, which were divided into two groups: (i) 29 young adults (20-30 years old), (ii) 25 older adults (60-80 years old). We presented two sequences with an interval of a few seconds. Participants were asked to state how similar the second sequence was with regards to the first one. The neural results indicated a stronger negative, widespread activity associated with the recognition of the same sequence compared to the sequences that were transposed or completely different. This difference was widely distributed across the EEG sensors and involved especially temporo-parietal areas of the scalp. Notably, we reported largely reduced neural responses for the older versus young adults, even when no behavioral differences were observed. In conclusion, our study suggests that the combination of auditory sequences, music, and fast-scale neurophysiology may represent a privileged solution to better understand short-term memory and the cognitive decline associated with ageing.

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Section: Discussionmentioning

confidence: 90%

Age-related brain mechanisms underlying short-term recognition of musical sequences: An EEG study

Costa

Vuust

Kringelbach

et al. 2023

Preprint

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“…Interestingly, there were significant differences between the two groups in 16 lead sites, including F3, C4, T3, and Cz, suggesting that the EEG signal is more complex in the TLE‐CI group and that such alterations could be detected throughout the brain. Previous studies have confirmed that the default network in the α band of the brain may also respond to changes in cognitive functions, indicating a correlation between the reduced cognitive function and altered α band oscillation in the whole brain 38,39 …”

Section: Discussionmentioning

confidence: 88%

The potential of the Lempel–Ziv complexity of the EEG in diagnosing cognitive impairment in patients with temporal lobe epilepsy

Ren

Yue

Han

et al. 2023

Epileptic Disorders

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Aim To analyze whether the Lempel–Ziv Complexity (LZC) in quantitative electroencephalogram differs between the temporal lobe epilepsy (TLE) patients with or without cognitive impairment (CI) and explore the diagnostic value of LZC for identifying CI in TLE patients. Methods Twenty‐two clinical features and 20‐min EEG recordings were collected from 48 TLE patients with CI and 27 cognitively normal (CON) TLE patients. Seventy‐six LZC features were calculated for 19 leads in four frequency bands (alpha, beta, delta, and theta). The clinical and LZC features were compared between the two groups. A support vector machine (SVM) was subsequently constructed using the leave‐one‐out method of cross‐validation for LZC features with statistical differences. Results Regarding the clinical features, the level of education (p < .001), hippocampal atrophy and sclerosis (p = .029), and depression (p = .037) were statistically different between the two groups. For the LZC features, there were statistically significant differences in the alpha (Fp1, Fz, Cz, Pz, C3, C4, T3, T4, T5, T6, F3, F4, F7, F8, O1, and O2), beta (Fp2), and theta (F7) oscillations. The mean LZC in the alpha band was higher in the TLE‐CI group than that in the CON group, and there were no differences in the remaining bands. The SVM model showed 74.51% accuracy, 79.63% sensitivity, 84.30% F1 score, 68.75% specificity, and .85 area under the curve scores. Conclusions The LZC in the alpha band might have the potential to be used as a biomarker for the diagnosis of TLE combined with CI. The TLE‐CI group, on the other hand, exhibited a higher degree of complexity in alpha oscillations, which were widespread and occurred in all brain regions.

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“…By these means, a null distribution of cluster sums was created from the 1000 permutation tests. Based on this distribution, the p-value of the preliminary cluster was estimated 15,[19][20][21] . In addition, a cluster could be identified as positive or negative, depending on the direction of its statistical effect.…”

Section: Methodsmentioning

confidence: 99%

Investigating mechanism of the effect of emotional facial expressions on attentional processing by data clustering approach

Zhao

Peng

2023

Sci Rep

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To explore the mechanism of the effect of emotional facial expression on attentional process, time course and topographic map of Electroencephalographic activities affected by emotional stimuli were investigated. Emotional Stroop task was used to collect 64-channel event-related potentials (ERP) in nonclinical participants, and data clustering was applied to find significant effect of sad and happy facial expression on ERP. Several significant ERP clusters were found in the sad and happy conditions respectively. In the sad condition, the decreased N170 in the bilateral parietooccipital areas, the increased P3 in the right centroparietal region and the increased negative deflection between 600 and 650 ms in the prefrontal regions were observed, these alterations reflected inhibited perceptual processing of sad facial expression, and increased activations of the orienting network and the executive control network in attentional system, respectively. In the happy condition, increased negative slow wave was found in the left centroparietal region indicating strengthened awareness and readiness for successive trials. Importantly, nonpathological attentional bias to sad facial expression in nonclinical participants was associated with inhibited perceptual processing and increased activations of the orienting and executive control networks. It provides the basis for better understanding and application of attentional bias in psychiatric clinical utilization.

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Default Mode Network and Neural Phase Synchronization in Healthy Aging: A Resting State EEG Study

Cited by 14 publications

References 74 publications

Age-related brain mechanisms underlying short-term recognition of musical sequences: An EEG study

Age-related brain mechanisms underlying short-term recognition of musical sequences: An EEG study

The potential of the Lempel–Ziv complexity of the EEG in diagnosing cognitive impairment in patients with temporal lobe epilepsy

Investigating mechanism of the effect of emotional facial expressions on attentional processing by data clustering approach

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