Characteristics of Resting State EEG Power in 80+-Year-Olds of Different Cognitive Status.
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ABSTRACT:
SUBMITTER: Frohlich S
PROVIDER: S-EPMC8387136 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:In the past decade, the fast and transient coupling and uncoupling of functionally related brain regions into networks has received much attention in cognitive neuroscience. Empirical tools to study network coupling include functional magnetic resonance imaging (fMRI)-based functional and/or effective connectivity, and electroencephalography (EEG)/magnetoencephalography-based measures of neuronal synchronization. Here we use simultaneously recorded EEG and fMRI to assess whether fMRI-based connectivity and frequency-specific EEG power are related. Using data collected during resting state, we studied whether posterior EEG alpha power fluctuations are correlated with connectivity within the visual network and between the visual cortex and the rest of the brain. The results show that when alpha power increases, BOLD connectivity between the primary visual cortex and occipital brain regions decreases and that the negative relation of the visual cortex with the anterior/medial thalamus decreases and the ventral-medial prefrontal cortex is reduced in strength. These effects were specific for the alpha band, and not observed in other frequency bands. The decreased connectivity within the visual system may indicate an enhanced functional inhibition during a higher alpha activity. This higher inhibition level also attenuates long-range intrinsic functional antagonism between the visual cortex and the other thalamic and cortical regions. Together, these results illustrate that power fluctuations in posterior alpha oscillations result in local and long-range neural connectivity changes.
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Project description:BackgroundThe Montreal Cognitive Assessment (MoCA) is sensitive to cognitive impairment; however, it is also sensitive to demographic and socio-cultural factors. This necessitates reliable sub-population norms, but these are often lacking for older adults.ObjectiveTo present demographically adjusted regression-based MoCA norms for cognitively healthy Swedish older adults.MethodsA pseudo-random sample of community-dwelling 80- to 94-year-olds, stratified by age and gender, was invited to the study. Initial telephone interviews and medical records searches (n = 218) were conducted to screen for cognitive impairment. N = 181 eligible participants were administered a protocol including the Swedish version of the MoCA and assessments of global cognition (Mini-Mental State Examination, MMSE) and depression (Patient Health Questionnaire-9, PHQ-9). Individuals scoring in the range of possible cognitive impairment on the MMSE or more than mild depression on the PHQ-9 were excluded (n = 23); three discontinued the test-session.ResultsNorms were derived from the remaining n = 158. They were evenly distributed by gender, on average 85 years old, and with a mean education of 11 years. MoCA scores were independently influenced by age and education, together explaining 17.2% of the total variance. Higher age and lower education were associated with lower performance and 46% performed below the original cut-off (< 26/30).ConclusionThe negative impact of increasing age on MoCA performance continues linearly into the nineties in normal aging. Demographic factors should be considered when interpreting MoCA performance and a tool for computing demographically corrected standard scores is provided.
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Project description:IntroductionResting-state oscillatory activity has been extensively studied across a wide array of disorders. Establishing which spectrally- and spatially-specific oscillatory components exhibit test-retest reliability is essential to move the field forward. While studies have shown short-term reliability of MEG resting-state activity, no studies have examined test-retest reliability across an extended period of time to establish the stability of these signals, which is critical for reproducibility.MethodsWe examined 18 healthy adults age 23 - 61 who completed three visits across three years. For each visit, participants completed both a resting state MEG and structural MRI scan. MEG data were source imaged, and the cortical power in canonical frequency bands (delta, theta, alpha, beta, low gamma, high gamma) was computed. Intra-class correlation coefficients (ICC) were then calculated across the cortex for each frequency band.ResultsOver three years, power in the alpha and beta bands displayed the highest reliability estimates, while gamma showed the lowest estimates of three-year reliability. Spatially, delta, alpha, and beta all showed the highest degrees of reliability in the parietal cortex. Interestingly, the peak signal for each of these frequency bands was located outside of the parietal cortex, suggesting that reliability estimates were not solely dependent on the signal-to-noise ratio.ConclusionOscillatory resting-state power in parietal delta, posterior beta, and alpha across most of the cortex are reliable across three years and future MEEG studies may focus on these measures for the development of specific markers.
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Project description:BackgroundAmong the most robust neural abnormalities differentiating individuals with Attention-Deficit/Hyperactivity Disorder (ADHD) from typically developing controls are elevated levels of slow oscillatory activity (e.g., theta) and reduced fast oscillatory activity (e.g., alpha and beta) during resting-state electroencephalography (EEG). However, studies of resting state EEG in adults with ADHD are scarce and yield inconsistent findings.MethodsEEG profiles, recorded during a resting-state with eyes-open and eyes-closed conditions, were compared for college students with ADHD (n?=?18) and a nonclinical comparison group (n?=?17).ResultsThe ADHD group showed decreased power for fast frequencies, especially alpha. This group also showed increased power in the slow frequency bands, however, these effects were strongest using relative power computations. Furthermore, the theta/beta ratio measure was reliably higher for the ADHD group. All effects were more pronounced for the eyes-closed compared to the eyes-open condition. Measures of intra-individual variability suggested that brains of the ADHD group were less variable than those of controls.ConclusionsThe findings of this pilot study reveal that college students with ADHD show a distinct neural pattern during resting state, suggesting that oscillatory power, especially alpha, is a useful index for reflecting differences in neural communication of ADHD in early adulthood.
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Project description:Cognitive functions and spontaneous neural activity show significant changes over the life-span, but the interrelations between age, cognition and resting-state brain oscillations are not well understood. Here, we assessed performance on the Trail Making Test and resting-state magnetoencephalographic (MEG) recordings from 53 healthy adults (18-89 years old) to investigate associations between age-dependent changes in spontaneous oscillatory activity and cognitive performance. Results show that healthy aging is accompanied by a marked and linear decrease of resting-state activity in the slow frequency range (0.5-6.5 Hz). The effects of slow wave power on cognitive performance were expressed as interactions with age: For older (>54 years), but not younger participants, enhanced delta and theta power in temporal and central regions was positively associated with perceptual speed and executive functioning. Consistent with previous work, these findings substantiate further the important role of slow wave oscillations in neurocognitive function during healthy aging.
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Project description:IntroductionThe differentiation of Lewy body dementia from other common dementia types clinically is difficult, with a considerable number of cases only being found post-mortem. Consequently, there is a clear need for inexpensive and accurate diagnostic approaches for clinical use. Electroencephalography (EEG) is one potential candidate due to its relatively low cost and non-invasive nature. Previous studies examining the use of EEG as a dementia diagnostic have focussed on the eyes closed (EC) resting state; however, eyes open (EO) EEG may also be a useful adjunct to quantitative analysis due to clinical availability.MethodsWe extracted spectral properties from EEG signals recorded under research study protocols (1024 Hz sampling rate, 10:5 EEG layout). The data stems from a total of 40 dementia patients with an average age of 74.42, 75.81 and 73.88 years for Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD), respectively, and 15 healthy controls (HC) with an average age of 76.93 years. We utilised k-nearest neighbour, support vector machine and logistic regression machine learning to differentiate between groups utilising spectral data from the delta, theta, high theta, alpha and beta EEG bands.ResultsWe found that the combination of EC and EO resting state EEG data significantly increased inter-group classification accuracy compared to methods not using EO data. Secondly, we observed a distinct increase in the dominant frequency variance for HC between the EO and EC state, which was not observed within any dementia subgroup. For inter-group classification, we achieved a specificity of 0.87 and sensitivity of 0.92 for HC vs dementia classification and 0.75 specificity and 0.91 sensitivity for AD vs DLB classification, with a k-nearest neighbour machine learning model which outperformed other machine learning methods.ConclusionsThe findings of our study indicate that the combination of both EC and EO quantitative EEG features improves overall classification accuracy when classifying dementia types in older age adults. In addition, we demonstrate that healthy controls display a definite change in dominant frequency variance between the EC and EO state. In future, a validation cohort should be utilised to further solidify these findings.
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Project description:In this paper, we explore the utility of resting-state EEG measures as potential biomarkers for the detection and assessment of cognitive decline in mild cognitive impairment (MCI) and Alzheimer's disease (AD). Neurophysiological biomarkers of AD derived from EEG and FDG-PET, once characterized and validated, would expand the set of existing diagnostic molecular biomarkers of AD pathology with associated biomarkers of disease progression and neural dysfunction. Since symptoms of AD often begin to appear later in life, successful identification of EEG-based biomarkers must account for age-related neurophysiological changes that occur even in healthy individuals. To this end, we collected EEG data from individuals with AD (n = 26), MCI (n = 53), and cognitively normal healthy controls stratified by age into three groups: 18-40 (n = 129), 40-60 (n = 62) and 60-90 (= 55) years old. For each participant, we computed power spectral density at each channel and spectral coherence between pairs of channels. Compared to age matched controls, in the AD group, we found increases in both spectral power and coherence at the slower frequencies (Delta, Theta). A smaller but significant increase in power of slow frequencies was observed for the MCI group, localized to temporal areas. These effects on slow frequency spectral power opposed that of normal aging observed by a decrease in the power of slow frequencies in our control groups. The AD group showed a significant decrease in the spectral power and coherence in the Alpha band consistent with the same effect in normal aging. However, the MCI group did not show any significant change in the Alpha band. Overall, Theta to Alpha ratio (TAR) provided the largest and most significant differences between the AD group and controls. However, differences in the MCI group remained small and localized. We proposed a novel method to quantify these small differences between Theta and Alpha bands' power using empirically derived distributions of spectral power across the time domain as opposed to averaging power across time. We defined Power Distribution Distance Measure (PDDM) as a distance measure between probability distribution functions (pdf) of Theta and Alpha power. Compared to average TAR, using PDDF enhanced the statistical significance, the effect size, and the spatial distribution of significant effects in the MCI group. We designed classifiers for differentiating individual MCI and AD participants from age-matched controls. The classification performance measured by the area under ROC curve after cross-validation were AUC = 0.85 and AUC = 0.6, for AD and MCI classifiers, respectively. Posterior probability of AD, TAR, and the proposed PDDM measure were all significantly correlated with MMSE score and neuropsychological tests in the AD group.
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Project description:Here we present a test-retest dataset of electroencephalogram (EEG) acquired at two resting (eyes open and eyes closed) and three subject-driven cognitive states (memory, music, subtraction) with both short-term (within 90 mins) and long-term (one-month apart) designs. 60 participants were recorded during three EEG sessions. Each session includes EEG and behavioral data along with rich samples of behavioral assessments testing demographic, sleep, emotion, mental health and the content of self-generated thoughts (mind wandering). This data enables the investigation of both intra- and inter-session variability not only limited to electrophysiological changes, but also including alterations in resting and cognitive states, at high temporal resolution. Also, this dataset is expected to add contributions to the reliability and validity of EEG measurements with open resource.
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Project description:The present study investigates the relationship between individual differences in verbal and non-verbal cognitive abilities and resting-state EEG network characteristics. We used a network neuroscience approach to analyze both large-scale topological characteristics of the whole brain as well as local brain network characteristics. The characteristic path length, modularity, and cluster coefficient for different EEG frequency bands (alpha, high and low; beta1 and beta2, and theta) were calculated to estimate large-scale topological integration and segregation properties of the brain networks. Betweenness centrality, nodal clustering coefficient, and local connectivity strength were calculated as local network characteristics. We showed that global network integration measures in the alpha band were positively correlated with non-verbal intelligence, especially with the more difficult part of the test (Raven's total scores and E series), and the ability to operate with verbal information (the "Conclusions" verbal subtest). At the same time, individual differences in non-verbal intelligence (Raven's total score and C series), and vocabulary subtest of the verbal intelligence tests, were negatively correlated with the network segregation measures. Our results show that resting-state EEG functional connectivity can reveal the functional architecture associated with an individual difference in cognitive performance.
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