Biological markers of aging and mental health: A seed and soil model of neurocognitive disorders

McDonough, Ian M.; Allen, Rebecca S.

doi:10.1080/13607863.2018.1531383

Cited by 17 publications

(15 citation statements)

References 63 publications

(61 reference statements)

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“…We also tested the extent that corrections in the HRF shape and timing would alter the results of investigations on aging and vascular risk on the estimations of brain activity using fMRI. We approached this issue by considering that an accumulation of vascular health risks might lead to a crucial tipping point between neuronal AGE AND VASCULAR RISK EFFECTS ON HRF 10 viability or its death (Daulatzai, 2012;McDonough & Allen, 2018). Thus, an increased number of risk factors likely have greater impact on the BOLD signal, potentially representing a cumulative sum of potential neuronal injury on the brain that represents multiple pathways (Huang & Wikswo, 2006;Tyson, Chen, & Novak, 2001).…”

Section: The Present Studymentioning

confidence: 99%

The Trouble Interpreting fMRI Studies in Populations with Cerebrovascular Risk: The Use of a Subject-Specific Hemodynamic Response Function in a Study of Age, Vascular Risk, and Memory

Im¹,

Bender

Patihis

et al. 2019

Preprint

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Functional magnetic resonance imaging (fMRI) is commonly used to investigate the neural bases of behavior ranging from basic cognitive mechanisms to aging to psychological disorders.However, the BOLD signal captured by fMRI is an indirect measure of neural function and is affected by many factors that are non-neural in origin. These non-neural factors, however, do affect brain vasculature such as the shape and timing of the hemodynamic response function (HRF) during task-evoked fMRI that, in turn, can cause inappropriate and/or misleading interpretations of fMRI differences between groups. In this study, we tested the proposition that vascular health risks, which often go unmeasured in neuroimaging studies, and aging interact to modify the shape and/or timing of the HRF (height, time-to-peak, width), which then affect the differences in patterns of brain activity in a task-evoked memory encoding paradigm. Adult participants (aged 20-74) answered questions about their health history and underwent two fMRI tasks: viewing of a flashing checkerboard using a slow event-related design and a paired associates memory encoding task during a fast event-related design. We found that aging and vascular risk had the largest impacts on the maximum peak value of the HRF. Using a subjectspecific HRF resulted in an overall dampening of the estimated brain activity in both taskpositive and task-negative regions due to a reduction in the inter-individual variance of that activity. Across three vascular risk factors, using a subject-specific HRF resulted in more consistent brain regions that reached significance and larger effect sizes compared with the canonical HRF. A slight advantage in the reliability of brain-behavior correlations also was found. The findings from this study have far reaching consequences for the interpretation of taskevoked fMRI activity, especially in populations known to experience alterations to brain vasculature including adults of all ages that have higher vascular risk, the majority of older AGE AND VASCULAR RISK EFFECTS ON HRF 3 adults, and people with neurocognitive disorders in which vasculature differences may play a role including dementia. Highlights• Older age was associated with smaller maximum peak of the hemodynamic response.• Younger and middle-aged adults with more vascular risk had higher HRF peaks.• Using a subject-specific HRF resulted in a "dampening" of brain activity.• A subject-specific HRF resulted in more consistent aging and vascular risk effects.

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Section: The Present Studymentioning

confidence: 99%

The Trouble Interpreting fMRI Studies in Populations with Cerebrovascular Risk: The Use of a Subject-Specific Hemodynamic Response Function in a Study of Age, Vascular Risk, and Memory

Im¹,

Bender

Patihis

et al. 2019

Preprint

Self Cite

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Section: Introductionmentioning

confidence: 99%

“…According to the seed and soil model (McDonough and Allen 2019 ) neurocognitive disorders are only developed, if pathological processes such as cell death and accumulation of neurofibrillary tangles (the seed) meet an unfavorable neuronal environment (the soil). Unfavorable neuronal environments can be health conditions accompanying normal aging such as cardiovascular disease or infections, but also behavior such as a risky lifestyle (McDonough and Allen 2019 ). In contrast, a protective environment, e.g., healthier lifestyle such as higher physical activity and social integration, may promote a more resilient soil and neuroprotection (Anaturk et al 2018 ; Arenaza-Urquijo et al 2015 ; Bittner et al 2019 ; Fratiglioni et al 2004 ; McDonough and Allen 2019 ).…”

Section: Introductionmentioning

confidence: 99%

“…Unfavorable neuronal environments can be health conditions accompanying normal aging such as cardiovascular disease or infections, but also behavior such as a risky lifestyle (McDonough and Allen 2019 ). In contrast, a protective environment, e.g., healthier lifestyle such as higher physical activity and social integration, may promote a more resilient soil and neuroprotection (Anaturk et al 2018 ; Arenaza-Urquijo et al 2015 ; Bittner et al 2019 ; Fratiglioni et al 2004 ; McDonough and Allen 2019 ). For example, socially integrated Alzheimer’s disease patients show higher cognitive stability compared to not integrated patients, even when suffering from a similar degree of pathology (Bennett et al 2006 ).…”

Section: Introductionmentioning

confidence: 99%

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When your brain looks older than expected: combined lifestyle risk and BrainAGE

et al. 2021

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Lifestyle may be one source of unexplained variance in the great interindividual variability of the brain in age-related structural differences. While physical and social activity may protect against structural decline, other lifestyle behaviors may be accelerating factors. We examined whether riskier lifestyle correlates with accelerated brain aging using the BrainAGE score in 622 older adults from the 1000BRAINS cohort. Lifestyle was measured using a combined lifestyle risk score, composed of risk (smoking, alcohol intake) and protective variables (social integration and physical activity). We estimated individual BrainAGE from T1-weighted MRI data indicating accelerated brain atrophy by higher values. Then, the effect of combined lifestyle risk and individual lifestyle variables was regressed against BrainAGE. One unit increase in combined lifestyle risk predicted 5.04 months of additional BrainAGE. This prediction was driven by smoking (0.6 additional months of BrainAGE per pack-year) and physical activity (0.55 less months in BrainAGE per metabolic equivalent). Stratification by sex revealed a stronger association between physical activity and BrainAGE in males than females. Overall, our observations may be helpful with regard to lifestyle-related tailored prevention measures that slow changes in brain structure in older adults.

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“…Biological science that studies life and living organisms has become one of the emerging disciplines and is progressively actuated by its medical potential for resolving health issues and improving health care . Investigations into the enormous biosystems, including their physical conformations, biochemical interactions, internal physiological mechanisms, and so on, not only provide opportunities to thoroughly understand lives from the scientific perspective, but also offer paths to novel technologies for biomedical diagnostics and therapies.…”

Section: Introductionmentioning

confidence: 99%

3D electronic and photonic structures as active biological interfaces

Wang

Sun

Yin

et al. 2019

InfoMat

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Biocompatible materials and structures with three-dimensional (3D) architectures establish an ideal platform for the integration of living cells and tissues, serving as desirable interfaces between biotic and abiotic systems. While conventional 3D bioscaffolds provide a mechanical support for biomatters, emerging developments of micro-, nano-, and mesoscale electronic and photonic devices offer new paradigms in analyzing and interrogating biosystems. In this review, we summarize recent advances in the development of 3D functional biointerfaces, with a particular focus on electrically and optically active materials, devices, and structures. We first give an overview of representative methods for manufacturing 3D biointegrated structures, such as chemical synthesis, microfabrication, mechanical assembly, and 3D printing. Subsequently, exemplary 3D nano-, micro-, and mesostructures based on various materials, including semiconductors, metals, and polymers are presented. Finally, we highlight the latest progress on versatile applications of such active 3D structures in the biomedical field, like cell culturing, biosignal sensing/modulation, and tissue regeneration. We believe future 3D micro-, nano-, and mesostructures that incorporate electrical and/or optical functionalities will not only profoundly advance the fundamental studies in biological sciences, but also create enormous opportunities for medical diagnostics and therapies. K E Y W O R D S

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Biological markers of aging and mental health: A seed and soil model of neurocognitive disorders

Cited by 17 publications

References 63 publications

The Trouble Interpreting fMRI Studies in Populations with Cerebrovascular Risk: The Use of a Subject-Specific Hemodynamic Response Function in a Study of Age, Vascular Risk, and Memory

The Trouble Interpreting fMRI Studies in Populations with Cerebrovascular Risk: The Use of a Subject-Specific Hemodynamic Response Function in a Study of Age, Vascular Risk, and Memory

When your brain looks older than expected: combined lifestyle risk and BrainAGE

3D electronic and photonic structures as active biological interfaces

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