When the time is right: Temporal dynamics of brain activity in healthy aging and dementia

Courtney, Susan; Hinault, Thomas

doi:10.1016/j.pneurobio.2021.102076

Cited by 30 publications

(30 citation statements)

References 213 publications

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“…Whether amplitude- and phase-based connectivity are two interacting modalities or are sensitive to different disease effects is as of yet uncertain, although recent work suggests that they may reflect at least partially distinct neuronal mechanisms [ 63 ]. Possibly, these two measures might be sensitive to disease effects at different stages of the trajectory, with phase synchrony changes in earlier stages compared to amplitude correlation changes [ 36 ]. While the current study is unable to answer this question, this is an interesting consideration for future work .…”

Section: Discussionmentioning

confidence: 99%

“…These metrics were selected based on their performance in previous work [ 24 , 27 , 28 ], and their use in AD literature [ 11 , 29 – 35 ]. These two measures likely reflect different aspects of neural activity and show differential sensitivity to changes in neural dynamics [ 36 ]. Power envelope correlations have shown a good correlation to BOLD signals, which, in turn, have been matched with known structural connectivity [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

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Sensitive and reproducible MEG resting-state metrics of functional connectivity in Alzheimer’s disease

et al. 2022

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Background Analysis of functional brain networks in Alzheimer’s disease (AD) has been hampered by a lack of reproducible, yet valid metrics of functional connectivity (FC). This study aimed to assess both the sensitivity and reproducibility of the corrected amplitude envelope correlation (AEC-c) and phase lag index (PLI), two metrics of FC that are insensitive to the effects of volume conduction and field spread, in two separate cohorts of patients with dementia due to AD versus healthy elderly controls. Methods Subjects with a clinical diagnosis of AD dementia with biomarker proof, and a control group of subjective cognitive decline (SCD), underwent two 5-min resting-state MEG recordings. Data consisted of a test (AD = 28; SCD = 29) and validation (AD = 29; SCD = 27) cohort. Time-series were estimated for 90 regions of interest (ROIs) in the automated anatomical labelling (AAL) atlas. For each of five canonical frequency bands, the AEC-c and PLI were calculated between all 90 ROIs, and connections were averaged per ROI. General linear models were constructed to compare the global FC differences between the groups, assess the reproducibility, and evaluate the effects of age and relative power. Reproducibility of the regional FC differences was assessed using the Mann-Whitney U tests, with correction for multiple testing using the false discovery rate (FDR). Results The AEC-c showed significantly and reproducibly lower global FC for the AD group compared to SCD, in the alpha (8–13 Hz) and beta (13–30 Hz) bands, while the PLI revealed reproducibly lower FC for the AD group in the delta (0.5–4 Hz) band and higher FC for the theta (4–8 Hz) band. Regionally, the beta band AEC-c showed reproducibility for almost all ROIs (except for 13 ROIs in the frontal and temporal lobes). For the other bands, the AEC-c and PLI did not show regional reproducibility after FDR correction. The theta band PLI was susceptible to the effect of relative power. Conclusion For MEG, the AEC-c is a sensitive and reproducible metric, able to distinguish FC differences between patients with AD dementia and cognitively healthy controls. These two measures likely reflect different aspects of neural activity and show differential sensitivity to changes in neural dynamics.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sensitive and reproducible MEG resting-state metrics of functional connectivity in Alzheimer’s disease

et al. 2022

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“…We report that one session of tDCS improves WM in OA by tapping into two distinct but complementary mechanisms of task performance, anterior-posterior theta network synchrony and theta-gamma PAC. Thus, in heathy OA at risk of cognitive decline, a benefit of tDCS to WM may be in strengthening functional brain networks that naturally weaken during aging ( Courtney and Hinault, 2021 ). Our findings inform models of the electrophysiological basis of WM in aging and serve to identify target mechanisms for interventions involving tDCS.…”

Section: Discussionmentioning

confidence: 99%

“…Unfortunately, WM declines as we age, with cognitive performance peaking in our 20 s ( Park et al, 2002 ; Reuter-Lorenz and Sylvester, 2005 ). This age-related WM decline arises from age-related reductions in the brain’s cortical thickness ( Salat et al, 2004 ), structural connectivity ( Cabeza, 2001 ), and functional connectivity ( Courtney and Hinault, 2021 ). Recent efforts augmenting cognitive training with non-invasive neuromodulatory approaches, such as transcranial direct current stimulation (tDCS), have demonstrated that multi-session tDCS can improve WM with low risk and cost ( Berryhill, 2017 ; Berryhill and Martin, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Individual predictors and electrophysiological signatures of working memory enhancement in aging

et al. 2022

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“…The loss of complexity was first postulated as an age-related phenomenon such that impairment of functional components and/or altered coupling between these components would lead to a progressive decline in adaptability to physiologic stress 38 . Since then, mounting evidence in various physiological outputs showed complexity reductions with age and disease 24,26,31,37,39 . Our measurements of retinal µERG bring additional evidence of age-related loss of complexity, a feature that, in this case, emerged for diverse visual inputs.…”

Section: Discussionmentioning

confidence: 99%

Multiscale Entropy Analysis of Retinal Signals Reveals Reduced Complexity in a Mouse Model of Alzheimer’s Disease

Araya

Garay

Rojas

et al. 2022

Preprint

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Alzheimer's disease (AD) is one of the most significant health challenges of our time, affecting a growing number of the elderly population. In recent years, the retina has received increased attention as a candidate for AD biomarkers since it appears to manifest the pathological signatures of the disease. Therefore, its electrical activity may hint at AD-related physiological changes. However, it is unclear how AD affects retinal electrophysiology and what tools are more appropriate to detect these possible changes. In this study, we used entropy tools to estimate the complexity of the dynamics of healthy and diseased retinas at different ages. We recorded microelectroretinogram responses to visual stimuli of different nature from retinas of young and adult, wild-type and 5xFAD -an animal model of AD- mice. To estimate the complexity of signals, we used the multiscale entropy approach, which calculates the entropy at several time scales using a coarse graining procedure. We found that young retinas had more complex responses to different visual stimuli. Further, the responses of young, wild-type retinas to natural-like stimuli exhibited significantly higher complexity than young, 5xFAD retinas. Our findings support a theory of complexity-loss with aging and disease and can have significant implications for early AD diagnosis.

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When the time is right: Temporal dynamics of brain activity in healthy aging and dementia

Cited by 30 publications

References 213 publications

Sensitive and reproducible MEG resting-state metrics of functional connectivity in Alzheimer’s disease

Sensitive and reproducible MEG resting-state metrics of functional connectivity in Alzheimer’s disease

Individual predictors and electrophysiological signatures of working memory enhancement in aging

Multiscale Entropy Analysis of Retinal Signals Reveals Reduced Complexity in a Mouse Model of Alzheimer’s Disease

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