scholarly journals Resting-state slow wave power, healthy aging and cognitive performance

2014 ◽  
Vol 4 (1) ◽  
Author(s):  
Eleni L. Vlahou ◽  
Franka Thurm ◽  
Iris-Tatjana Kolassa ◽  
Winfried Schlee
Keyword(s):  
Cognitive Performance ◽  
Slow Wave ◽  
Resting State ◽  
Healthy Aging ◽  
Wave Power

scholarly journals Resting State Networks Related to the Maintenance of Good Cognitive Performance During Healthy Aging

2021 ◽  
Vol 15 ◽  
Author(s):  
Satoshi Maesawa ◽  
Satomi Mizuno ◽  
Epifanio Bagarinao ◽  
Hirohisa Watanabe ◽  
Kazuya Kawabata ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Cognitive Performance ◽  
Negative Correlation ◽  
Resting State ◽  
Healthy Aging ◽  
Cognitive Networks ◽  
Cognitive Test ◽  
Resting State Networks ◽  
Mr Images ◽  
Dual Regression

Purpose: Maintenance of cognitive performance is important for healthy aging. This study aims to elucidate the relationship between brain networks and cognitive function in subjects maintaining relatively good cognitive performance.Methods: A total of 120 subjects, with equal number of participants from each age group between 20 and 70 years, were included in this study. Only participants with Addenbrooke’s Cognitive Examination – Revised (ACE-R) total score greater than 83 were included. Anatomical T1-weighted MR images and resting-state functional MR images (rsfMRIs) were taken from all participants using a 3-tesla MRI scanner. After preprocessing, several factors associated with age including the ACE-R total score, scores of five domains, sub-scores of ACE-R, and brain volumes were tested. Morphometric changes associated with age were analyzed using voxel based morphometry (VBM) and changes in resting state networks (RSNs) were examined using dual regression analysis.Results: Significant negative correlations with age were seen in the total gray matter volume (GMV, r = −0.58), and in the memory, attention, and visuospatial domains. Among the different sub-scores, the score of the delayed recall (DR) showed the highest negative correlation with age (r = −0.55, p < 0.001). In VBM analysis, widespread regions demonstrated negative correlation with age, but none with any of the cognitive scores. Quadratic approximations of cognitive scores as functions of age showed relatively delayed decline compared to total GMV loss. In dual regression analysis, some cognitive networks, including the dorsal default mode network, the lateral dorsal attention network, the right / left executive control network, the posterior salience network, and the language network, did not demonstrate negative correlation with age. Some regions in the sensorimotor networks showed positive correlation with the DR, memory, and fluency scores.Conclusion: Some domains of the cognitive test did not correlate with age, and even the highly correlated sub-scores such as the DR score, showed delayed decline compared to the loss of total GMV. Some RSNs, especially involving cognitive control regions, were relatively maintained with age. Furthermore, the scores of memory, fluency, and the DR were correlated with the within-network functional connectivity values of the sensorimotor network, which supported the importance of exercise for maintenance of cognition.

scholarly journals Slow Wave Sleep and EEG Delta Spectral Power are Associated with Cognitive Function in Parkinson’s Disease

2020 ◽  
pp. 1-12
Author(s):  
Kimberly H. Wood ◽  
Adeel A. Memon ◽  
Raima A. Memon ◽  
Allen Joop ◽  
Jennifer Pilkington ◽  
...  
Keyword(s):  
Executive Function ◽  
Cognitive Function ◽  
Cognitive Performance ◽  
Slow Wave ◽  
Effect Size ◽  
Processing Speed ◽  
Slow Wave Sleep ◽  
Spectral Power ◽  
Z Scores ◽  
Domain Scores

Background: Cognitive and sleep dysfunction are common non-motor symptoms in Parkinson’s disease (PD). Objective: Determine the relationship between slow wave sleep (SWS) and cognitive performance in PD. Methods: Thirty-two PD participants were evaluated with polysomnography and a comprehensive level II neurocognitive battery, as defined by the Movement Disorders Society Task Force for diagnosis of PD-mild cognitive impairment. Raw scores for each test were transformed into z-scores using normative data. Z-scores were averaged to obtain domain scores, and domain scores were averaged to determine the Composite Cognitive Score (CCS), the primary outcome. Participants were grouped by percent of SWS into High SWS and Low SWS groups and compared on CCS and other outcomes using 2-sided t-tests or Mann-Whitney U. Correlations of cognitive outcomes with sleep architecture and EEG spectral power were performed. Results: Participants in the High SWS group demonstrated better global cognitive function (CCS) (p = 0.01, effect size: r = 0.45). In exploratory analyses, the High SWS group showed better performance in domains of executive function (effect size: Cohen’s d = 1.05), language (d = 0.95), and processing speed (d = 1.12). Percentage of SWS was correlated with global cognition and executive function, language, and processing speed. Frontal EEG delta power during N3 was correlated with the CCS and executive function. Cognition was not correlated with subjective sleep quality. Conclusion: Increased SWS and higher delta spectral power are associated with better cognitive performance in PD. This demonstrates the significant relationship between sleep and cognitive function and suggests that interventions to improve sleep might improve cognition in individuals with PD.

scholarly journals Altered Weibull Degree Distribution in Resting-State Functional Brain Networks Is Associated With Cognitive Decline in Mild Cognitive Impairment

2021 ◽  
Vol 12 ◽  
Author(s):  
Yifei Zhang ◽  
Xiaodan Chen ◽  
Xinyuan Liang ◽  
Zhijiang Wang ◽  
Teng Xie ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Human Brain ◽  
Cognitive Performance ◽  
Power Law ◽  
Resting State ◽  
Brain Networks ◽  
Functional Networks ◽  
Whole Brain ◽  
Connectivity Degree

The topological organization of human brain networks can be mathematically characterized by the connectivity degree distribution of network nodes. However, there is no clear consensus on whether the topological structure of brain networks follows a power law or other probability distributions, and whether it is altered in Alzheimer's disease (AD). Here we employed resting-state functional MRI and graph theory approaches to investigate the fitting of degree distributions of the whole-brain functional networks and seven subnetworks in healthy subjects and individuals with amnestic mild cognitive impairment (aMCI), i.e., the prodromal stage of AD, and whether they are altered and correlated with cognitive performance in patients. Forty-one elderly cognitively healthy controls and 30 aMCI subjects were included. We constructed functional connectivity matrices among brain voxels and examined nodal degree distributions that were fitted by maximum likelihood estimation. In the whole-brain networks and all functional subnetworks, the connectivity degree distributions were fitted better by the Weibull distribution [f(x)~x(β−1)e(−λxβ)] than power law or power law with exponential cutoff. Compared with the healthy control group, the aMCI group showed lower Weibull β parameters (shape factor) in both the whole-brain networks and all seven subnetworks (false-discovery rate-corrected, p < 0.05). These decreases of the Weibull β parameters in the whole-brain networks and all subnetworks except for ventral attention were associated with reduced cognitive performance in individuals with aMCI. Thus, we provided a short-tailed model to capture intrinsic connectivity structure of the human brain functional networks in health and disease.

scholarly journals A comparison of resting state EEG and structural MRI for classifying Alzheimer’s disease and mild cognitive impairment

2019 ◽  
Author(s):  
FR Farina ◽  
DD Emek-Savaş ◽  
L Rueda-Delgado ◽  
R Boyle ◽  
H Kiiski ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Resting State ◽  
Healthy Aging ◽  
Neurodegenerative Disorder ◽  
Low Cost ◽  
Structural Mri ◽  
Lower Accuracy

AbstractAlzheimer’s disease (AD) is a neurodegenerative disorder characterised by severe cognitive decline and loss of autonomy. AD is the leading cause of dementia. AD is preceded by mild cognitive impairment (MCI). By 2050, 68% of new dementia cases will occur in low- and middle-income countries. In the absence of objective biomarkers, psychological assessments are typically used to diagnose MCI and AD. However, these require specialist training and rely on subjective judgements. The need for low-cost, accessible and objective tools to aid AD and MCI diagnosis is therefore crucial. Electroencephalography (EEG) has potential as one such tool: it is relatively inexpensive (cf. magnetic resonance imaging; MRI) and is portable. In this study, we collected resting state EEG, structural MRI and rich neuropsychological data from older adults (55+ years) with AD, with MCI and from healthy controls (n~60 per group). Our goal was to evaluate the utility of EEG, relative to MRI, for the classification of MCI and AD. We also assessed the performance of combined EEG and behavioural (Mini-Mental State Examination; MMSE) and structural MRI classification models. Resting state EEG classified AD and HC participants with moderate accuracy (AROC=0.76), with lower accuracy when distinguishing MCI from HC participants (AROC=0.67). The addition of EEG data to MMSE scores had no additional value compared to MMSE alone. Structural MRI out-performed EEG (AD vs HC, AD vs MCI: AROCs=1.00; HC vs MCI: AROC=0.73). Resting state EEG does not appear to be a suitable tool for classifying AD. However, EEG classification accuracy was comparable to structural MRI when distinguishing MCI from healthy aging, although neither were sufficiently accurate to have clinical utility. This is the first direct comparison of EEG and MRI as classification tools in AD and MCI participants.

scholarly journals Regional Cerebrovascular Reactivity and Cognitive Performance in Healthy Aging

2018 ◽  
Vol 12 ◽  
pp. 117906951878515 ◽  
Author(s):  
Sarah J Catchlove ◽  
Todd B Parrish ◽  
Yufen Chen ◽  
Helen Macpherson ◽  
Matthew E Hughes ◽  
...  
Keyword(s):  
Cognitive Performance ◽  
Healthy Aging ◽  
Cerebrovascular Reactivity

Diagnosis and Assessment of Dementia Using Functional Brain Imaging

1989 ◽  
Vol 1 (1) ◽  
pp. 63-72 ◽  
Author(s):  
Andrew F. Leuchter ◽  
Donald O. Walter
Keyword(s):  
Electrical Activity ◽  
Brain Imaging ◽  
Slow Wave ◽  
Low Frequency ◽  
Functional Brain Imaging ◽  
Wave Power ◽  
Spectral Ratios ◽  
Functional Brain ◽  
Moderate Dementia ◽  
Severity Of Dementia

Functional brain imaging using computer-analyzed electroencephalography was performed in 40 subjects: 15 with mild-to-moderate dementia of the Alzheimer's type (DAT), 13 with mild-to-moderate multi-infarct dementia (MID), and 12 age-matched controls. We examined three different parameters of brain electrical activity in these subjects: absolute slow-wave power, proportional power in all frequency bands, and ratios of high-frequency/low-frequency electrical activity (so-called “spectral ratios”). Spectral ratios were significantly more powerful in discriminating among groups than the other measures. Functional images using spectral ratios revealed that subjects with DAT have a characteristic left temporo-parietal defect which clearly distinguished them from subjects with MID and from control subjects. The severity of dementia was best assessed by examining absolute slow-wave power, which had the strongest linear correlation with mental status testing. Serial images from one subject with DAT over 3 years demonstrate both quantitative and qualitative shifts in slow-wave activity in the course of DAT. The study suggests that functional imaging may be more useful than either simple EEG or computer-analyzed EEG in assessing and diagnosing patients with suspected dementia.

scholarly journals Effects of Blood Pressure on Cognitive Performance in Aging: A Systematic Review

2020 ◽  
Vol 10 (12) ◽  
pp. 919
Author(s):  
Giuseppe Forte ◽  
Maria Casagrande
Keyword(s):  
Blood Pressure ◽  
Cognitive Impairment ◽  
Longitudinal Studies ◽  
Cognitive Performance ◽  
High Blood Pressure ◽  
Pressure Measurement ◽  
Healthy Aging ◽  
Blood Pressure Measurement ◽  
Cross Sectional

Introduction: Cognitive functions play a crucial role in daily functioning. Unfortunately, some cognitive abilities decline in the process of healthy aging. An increasing body of evidence has highlighted the role of lifestyle habits and cardiovascular diseases, such as high blood pressure, in increasing the risk of cognitive decline. Surprisingly, although hypertension is a modifiable risk factor for cerebrovascular damage, the role of hypertension on cognitive impairment development is not still clear. Several key questions remain unresolved, and there are many inconsistent results in studies considering this topic. This review is aimed to systematically analyze the results found by the studies that investigated whether high blood pressure, in both hypertensive and healthy people, is related to cognitive performance. Furthermore, it points to evaluate the role of age in this relationship. Method: The review process was conducted according to the PRISMA statement. Restrictions were made, selecting the studies in English and published in peer-review journals, including at least one cognitive measure and blood pressure measurement. Studies that included participants with medical conditions, dementia, psychiatric disorders, strokes, and brain injury were excluded. Cross-sectional and longitudinal studies were analyzed separately. Finally, blood pressure measured at young life (18–39 years), midlife (age 40–64 years), elderly (65–74 years), and old age (≥75 years) were considered. Results: The review allows 68 studies to be selected, which include 154,935 participants. The results provided evidence of an adverse effect of exposure to high blood pressure on cognitive performance. High blood pressure in midlife was linked with poorer cognitive functioning; this evidence was found in cross-sectional and longitudinal studies. However, this association declines with increasing age and tends to become inconsistent. In older people, the relationship between blood pressure and cognitive performance is non-linear, highlighting a beneficial effect of high blood pressure on cognition. Conclusions: Despite some limitations, this review showed that cardiovascular and neuro-cognitive systems do not operate in isolation, but they are related. Blood pressure can be considered an early biomarker of cognitive impairment, and the necessity of early blood pressure measurement and control was underlined.

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