Why we should use topological data analysis in ageing: Towards defining the “topological shape of ageing”

Fülöp, Tamás; Desroches, Mathieu; Cohen, Alan A.; Santos, F. A. N.; Rodrigues, Serafim

doi:10.1016/j.mad.2020.111390

Cited by 7 publications

(4 citation statements)

References 70 publications

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“…Taking all this together, we can envisage a multistage closed-loop framework between in silico drug screening and drug testing in mini-brains as follows: stage one should involve data mining in existing databases, antimicrobial activity prediction via rational design 377 and quantitative structure-activity relationship (QSAR) should generate analogs with improved activity. This step should also incorporate novel computational methods based on topological data analysis (TDA), which enable us to extract topological and geometrical invariants from candidate molecules (see 378 for a brief introduction to TDA). The overall aim of this stage is to extract the microscopic structure/features of a molecule characterized by physical-chemical descriptors (polarizability, dipole moment, number of atoms, hydrophobicity, toxicity, etc) and uniquely map it to macroscopic experimental observables (ie activity of the molecule, for example, binding kinetic and thermodynamic parameters).…”

Section: Ways To Find New Treatments For Ad: What Could Help To Accelerate the Dmt Discovery?mentioning

confidence: 99%

Targeting Impaired Antimicrobial Immunity in the Brain for the Treatment of Alzheimer’s Disease

Fülöp¹,

Tripathi²,

Rodrigues³

et al. 2021

NDT

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Alzheimer’s disease (AD) is the most common form of dementia and aging is the most common risk factor for developing the disease. The etiology of AD is not known but AD may be considered as a clinical syndrome with multiple causal pathways contributing to it. The amyloid cascade hypothesis, claiming that excess production or reduced clearance of amyloid-beta (Aβ) and its aggregation into amyloid plaques, was accepted for a long time as the main cause of AD. However, many studies showed that Aβ is a frequent consequence of many challenges/pathologic processes occurring in the brain for decades. A key factor, sustained by experimental data, is that low-grade infection leading to production and deposition of Aβ, which has antimicrobial activity, precedes the development of clinically apparent AD. This infection is chronic, low grade, largely clinically silent for decades because of a nearly efficient antimicrobial immune response in the brain. A chronic inflammatory state is induced that results in neurodegeneration. Interventions that appear to prevent, retard or mitigate the development of AD also appear to modify the disease. In this review, we conceptualize further that the changes in the brain antimicrobial immune response during aging and especially in AD sufferers serve as a foundation that could lead to improved treatment strategies for preventing or decreasing the progression of AD in a disease-modifying treatment.

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Section: Ways To Find New Treatments For Ad: What Could Help To Accelerate the Dmt Discovery?mentioning

confidence: 99%

Targeting Impaired Antimicrobial Immunity in the Brain for the Treatment of Alzheimer’s Disease

Fülöp¹,

Tripathi²,

Rodrigues³

et al. 2021

NDT

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“…To summarise, the competition of timescales between immunobiographical variables leads to an immune system that is sensitive to perturbations (or initial conditions) without being chaotic and where several ageing history scenarios compete [212]. The dominant history outcome is, in fact, decided by a small perturbation.…”

Section: Perspective On Mathematical Modelling Illustrating the Role ...mentioning

confidence: 99%

Immunosenescence and Altered Vaccine Efficiency in Older Subjects: A Myth Difficult to Change

et al. 2022

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Organismal ageing is associated with many physiological changes, including differences in the immune system of most animals. These differences are often considered to be a key cause of age-associated diseases as well as decreased vaccine responses in humans. The most often cited vaccine failure is seasonal influenza, but, while it is usually the case that the efficiency of this vaccine is lower in older than younger adults, this is not always true, and the reasons for the differential responses are manifold. Undoubtedly, changes in the innate and adaptive immune response with ageing are associated with failure to respond to the influenza vaccine, but the cause is unclear. Moreover, recent advances in vaccine formulations and adjuvants, as well as in our understanding of immune changes with ageing, have contributed to the development of vaccines, such as those against herpes zoster and SARS-CoV-2, that can protect against serious disease in older adults just as well as in younger people. In the present article, we discuss the reasons why it is a myth that vaccines inevitably protect less well in older individuals, and that vaccines represent one of the most powerful means to protect the health and ensure the quality of life of older adults.

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“…Our analyses assessing high-order functional interactions are based on some specific metrics such as mean values of O-information at each interaction order. Future studies may consider different algebraic or topological properties of the full O-information hypergraph [68][69][70][71][72][73], which may provide complementary insights. It is also worth noting that the reported values of the O-information are not indicative of 'pure' synergy or redundancy, but correspond to the balance between them.…”

Section: Limitations and Future Workmentioning

confidence: 99%

High-order functional interactions in ageing explained via alterations in the connectome in a whole-brain model

Gatica

Rosas

Mediano

et al. 2021

Preprint

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The human brain generates a rich repertoire of spatio-temporal activity patterns, which support a wide variety of motor and cognitive functions. These patterns of activity change with age in a multi-factorial manner. One of these factors is the variations in the brain's connectomics that occurs along the lifespan. However, the precise relationship between high-order functional interactions and connnectomics, as well as their variations with age are largely unknown, in part due to the absence of mechanistic models that can efficiently map brain connnectomics to functional connectivity in aging. To investigate this issue, we have built a neurobiologically-realistic whole-brain computational model using both anatomical and functional MRI data from 161 participants ranging from 10 to 80 years old. We show that the age differences in high-order functional interactions can be largely explained by variations in the connectome. Based on this finding, we propose a simple neurodegeneration model that is representative of normal physiological aging. As such, when applied to connectomes of young participant it reproduces the age-variations that occur in the high-order structure of the functional data. Overall, these results begin to disentangle the mechanisms by which structural changes in the connectome lead to functional differences in the ageing brain. Our model can also serve as a starting point for modelling more complex forms of pathological ageing or cognitive deficits.

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Why we should use topological data analysis in ageing: Towards defining the “topological shape of ageing”

Cited by 7 publications

References 70 publications

Targeting Impaired Antimicrobial Immunity in the Brain for the Treatment of Alzheimer’s Disease

Targeting Impaired Antimicrobial Immunity in the Brain for the Treatment of Alzheimer’s Disease

Immunosenescence and Altered Vaccine Efficiency in Older Subjects: A Myth Difficult to Change

High-order functional interactions in ageing explained via alterations in the connectome in a whole-brain model

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