Age- and gender-specific characteristics of the resting-state brain activity: a magnetoencephalography study

Hoshi, Hideyuki; Shigihara, Yoshihito

doi:10.18632/aging.103956

Cited by 32 publications

(41 citation statements)

References 115 publications

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“…In accordance with previous findings, MF and IAF exhibited a partial positive correlation with age ( Gómez et al, 2013 ; Hoshi and Shigihara, 2020 ). However, we observed no significant correlation between participant age and cycle length.…”

Section: Discussionsupporting

confidence: 92%

“…Advancements in neuroimaging techniques have led to their widespread adoption in the clinical examination of functional brain diseases (Brammer, 2009;Khanna et al, 2015;Hoshi and Shigihara, 2020;Tanoue et al, 2021). In functional neuroimaging, brain function is primarily assessed based on changes in metabolism (i.e., position emission tomography), blood flow (i.e., functional magnetic resonance imaging, fMRI), or electrophysiology [i.e., magnetoencephalography (MEG) and electroencephalography (EEG)].…”

Section: Introductionmentioning

confidence: 99%

“…Both MEG and EEG record brain activity in terms of “spontaneous neural oscillations,” which are altered by diverse brain disorders such as epilepsy and dementia ( Fernández et al, 2013 ; Ahmed and Rutka, 2016 ). However, physiological factors such as age and sex can also affect the frequency and regional patterns of spontaneous neural oscillations, representing potential confounding factors in relevant studies ( Dustman et al, 1993 ; Vysata et al, 2012 ; Barry and De Blasio, 2017 ; Hoshi and Shigihara, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

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The Menstrual Cycle Alters Resting-State Cortical Activity: A Magnetoencephalography Study

Haraguchi

Hoshi

Ichikawa

et al. 2021

Front. Hum. Neurosci.

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Resting-state neural oscillations are used as biomarkers for functional diseases such as dementia, epilepsy, and stroke. However, accurate interpretation of clinical outcomes requires the identification and minimisation of potential confounding factors. While several studies have indicated that the menstrual cycle also alters brain activity, most of these studies were based on visual inspection rather than objective quantitative measures. In the present study, we aimed to clarify the effect of the menstrual cycle on spontaneous neural oscillations based on quantitative magnetoencephalography (MEG) parameters. Resting-state MEG activity was recorded from 25 healthy women with normal menstrual cycles. For each woman, resting-state brain activity was acquired twice using MEG: once during their menstrual period (MP) and once outside of this period (OP). Our results indicated that the median frequency and peak alpha frequency of the power spectrum were low, whereas Shannon spectral entropy was high, during the MP. Theta intensity within the right temporal cortex and right limbic system was significantly lower during the MP than during the OP. High gamma intensity in the left parietal cortex was also significantly lower during the MP than during the OP. Similar differences were also observed in the parietal and occipital regions between the proliferative (the late part of the follicular phase) and secretory phases (luteal phase). Our findings suggest that the menstrual cycle should be considered to ensure accurate interpretation of functional neuroimaging in clinical practice.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Menstrual Cycle Alters Resting-State Cortical Activity: A Magnetoencephalography Study

Haraguchi

Hoshi

Ichikawa

et al. 2021

Front. Hum. Neurosci.

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“…If we can demonstrate that the previously reported age-related decrease in alpha power is in fact confounded by alterations in the aperiodic signal (i.e., a decreased aperiodic intercept and a flattening of the aperiodic slope), as hypothesized in our present study, these results would challenge current neurophysiological interpretations of age-related decreases in alpha power (e.g., refs [13,[35][36][37][38]).…”

Section: Introductionsupporting

confidence: 42%

Decomposing age effects in EEG alpha power

Tröndle

Popov

Pedroni

et al. 2021

Preprint

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Increasing life expectancy is prompting the need to understand how the brain changes during healthy aging. Research utilizing Electroencephalography (EEG) has found that the power of alpha oscillations decrease from adulthood on. However, non-oscillatory (aperiodic) components in the data may confound results and thus require re-investigation of these findings. The present report aims at analyzing a pilot and two additional independent samples (total N = 533) of resting-state EEG from healthy young and elderly individuals. A newly developed algorithm will be utilized that allows the decomposition of the measured signal into aperiodic and aperiodic-adjusted signal components. By using multivariate sequential Bayesian updating of the age effect in each signal component, evidence across the datasets will be accumulated. It is hypothesized that previously reported age-related alpha power differences will disappear when absolute power is adjusted for the aperiodic signal component. Consequently, age-related differences in the intercept and slope of the aperiodic signal component are expected. Importantly, using a battery of neuropsychological tests, we will assess how the previously reported relationship between cognitive functions and alpha oscillations changes when taking the aperiodic signal into account; this will be done on data of the young and aged individuals separately. The aperiodic signal components and adjusted alpha parameters could potentially offer a promising biomarker for cognitive decline, thus finally the test–retest reliability of the aperiodic and aperiodic-adjusted signal components will be assessed.

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“…Age and sex are possible confounding factors because they affect cardiac functions 32 , brain activities 33 , and cognitive status 34 . After considering these potential biases, our results changed slightly.…”

Section: Discussionmentioning

confidence: 99%

The association between carotid blood flow and resting-state brain activity in patients with cerebrovascular diseases

Matsumoto

Hoshi

Hirata

et al. 2021

Sci Rep

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Cerebral hypoperfusion impairs brain activity and leads to cognitive impairment. Left and right common carotid arteries (CCA) are the major source of cerebral blood supply. It remains unclear whether blood flow in both CCA contributes equally to brain activity. Here, CCA blood flow was evaluated using ultrasonography in 23 patients with cerebrovascular diseases. Resting-state brain activity and cognitive status were also assessed using magnetoencephalography and a cognitive subscale of the Functional Independence Measure, respectively, to explore the relationships between blood flow, functional brain activity, and cognitive status. Our findings indicated that there was an association between blood flow and resting-state brain activity, and between resting-state brain activity and cognitive status. However, blood flow was not significantly associated with cognitive status directly. Furthermore, blood velocity in the right CCA correlated with resting-state brain activity, but not with the resistance index. In contrast, the resistance index in the left CCA correlated with resting-state brain activity, but not with blood velocity. Our findings suggest that hypoperfusion is important in the right CCA, whereas cerebral microcirculation is important in the left CCA for brain activity. Hence, this asymmetry should be considered when designing appropriate therapeutic strategies.

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Age- and gender-specific characteristics of the resting-state brain activity: a magnetoencephalography study

Cited by 32 publications

References 115 publications

The Menstrual Cycle Alters Resting-State Cortical Activity: A Magnetoencephalography Study

The Menstrual Cycle Alters Resting-State Cortical Activity: A Magnetoencephalography Study

Decomposing age effects in EEG alpha power

The association between carotid blood flow and resting-state brain activity in patients with cerebrovascular diseases

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