Measuring the chronological age of brain in-vivo

Abstract

The great challenge of research into ageing rests in the need of truly predictive biomarkers that can mark the health status and characterize effective modulations without the wait for the whole ageing trajectory.

The key observation inherited from the third law of physics is that organs, systems and subjects do not go through time in the same way but age differently and that this process is driven by diverse rates of entropy. 

Hence, using EEG and fMRI data we will develop instantaneous estimators of chronological brain age using entropy estimators that will be validated by ancillary measures of mitochondrial efficiency.




References:
[1]

Hayflick L (2007) Entropy explains aging, genetic determinism explains longevity, and undefined terminology explains misunderstanding both. PLoS Genet. 3(12):e220

[2]

Miquel J (1998) An update on the oxygen stress-mitochondrial mutation theory of aging: genetic and evolutionary implications. Exp Gerontol. 33(1-2):113-26

[3]

Khansari N, Shakiba Y, Mahmoudi M (2009) Chronic inflammation and oxidative stress as a major cause of age-related diseases and cancer. Recent Pat Inflamm Allergy Drug Discov. 3(1):73-80.

[4]

Toussaint PJ, et al. (2014) Characteristics of the default mode functional connectivity in normal ageing and Alzheimer's disease using resting state fMRI with a combined approach of entropy-based and graph theoretical measurements. Neuroimage. 101:778-86.

[5]

Blokh D, Stambler I. (2016)The application of information theory for the research of aging and aging-related diseases. Prog Neurobiol. pii: S0301-0082(15)30059-9.


Biological Areas:

Neurobiology
Ageing

BBSRC Area:

Animal disease, health and welfare