Dual regression physiological modeling of resting-state EPI power spectra: Effects of healthy aging

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.

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Age-related differences in resting state functional connectivity in pediatric migraine

The Journal of Headache and Pain ◽

10.1186/s10194-021-01274-y ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Tiffany Bell ◽

Akashroop Khaira ◽

Mehak Stokoe ◽

Megan Webb ◽

Melanie Noel ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Power Spectra ◽

Interaction Effects ◽

Resting State Functional Connectivity ◽

Default Mode ◽

Pediatric Migraine ◽

Age Related ◽

Status Interaction ◽

Underlying Mechanisms

Abstract Background Migraine affects roughly 10% of youth aged 5–15 years, however the underlying mechanisms of migraine in youth are poorly understood. Multiple structural and functional alterations have been shown in the brains of adult migraine sufferers. This study aims to investigate the effects of migraine on resting-state functional connectivity during the period of transition from childhood to adolescence, a critical period of brain development and the time when rates of pediatric chronic pain spikes. Methods Using independent component analysis, we compared resting state network spatial maps and power spectra between youth with migraine aged 7–15 and age-matched controls. Statistical comparisons were conducted using a MANCOVA analysis. Results We show (1) group by age interaction effects on connectivity in the visual and salience networks, group by sex interaction effects on connectivity in the default mode network and group by pubertal status interaction effects on connectivity in visual and frontal parietal networks, and (2) relationships between connectivity in the visual networks and the migraine cycle, and age by cycle interaction effects on connectivity in the visual, default mode and sensorimotor networks. Conclusions We demonstrate that brain alterations begin early in youth with migraine and are modulated by development. This highlights the need for further study into the neural mechanisms of migraine in youth specifically, to aid in the development of more effective treatments.

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Fornix White Matter is Correlated with Resting-State Functional Connectivity of the Thalamus and Hippocampus in Healthy Aging but Not in Mild Cognitive Impairment â€“ A Preliminary Study

Frontiers in Aging Neuroscience ◽

10.3389/fnagi.2015.00010 ◽

2015 ◽

Vol 7 ◽

Cited By ~ 11

Author(s):

Elizabeth G. Kehoe ◽

Dervla Farrell ◽

Claudia Metzler-Baddeley ◽

Brian A. Lawlor ◽

Rose Anne Kenny ◽

...

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

White Matter ◽

Functional Connectivity ◽

Resting State ◽

Healthy Aging ◽

Resting State Functional Connectivity ◽

Preliminary Study

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Cardiac Vagus and Exercise

Physiology ◽

10.1152/physiol.00041.2018 ◽

2019 ◽

Vol 34 (1) ◽

pp. 71-80 ◽

Cited By ~ 6

Author(s):

Alexander V. Gourine ◽

Gareth L. Ackland

Keyword(s):

Heart Rate ◽

Healthy Aging ◽

Exercise Physiology ◽

Experimental Studies ◽

Genomic Data ◽

Well Being ◽

Vagal Activity ◽

Physiological Modeling ◽

Resting Heart Rate ◽

Molecular Neuroscience

Lower resting heart rate and high autonomic vagal activity are strongly associated with superior exercise capacity, maintenance of which is essential for general well-being and healthy aging. Recent evidence obtained in experimental studies using the latest advances in molecular neuroscience, combined with human exercise physiology, physiological modeling, and genomic data suggest that the strength of cardiac vagal activity causally determines our ability to exercise.

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

10.1101/711465 ◽

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.

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