Functional brain age prediction suggests accelerated aging in preclinical familial Alzheimer’s disease, irrespective of fibrillar amyloid-beta pathology

Abstract

We aimed at developing a model able to predict brain aging from resting state functional connectivity (rs-fMRI) and assessing whether genetic risk/determinants of Alzheimer's disease (AD) and amyloid (Aβ) pathology contributes to accelerated brain aging. Using data collected in 1340 cognitively unimpaired participants from 18 to 94 years old selected across multi-site cohorts, we showed that chronological age can be predicted across the whole lifespan from topological properties of graphs constructed from rs-fMRI. We subsequently used the difference between the model-predicted age and the chronological age in pre-symptomatic autosomal dominant AD (ADAD) mutation carriers and asymptomatic individuals at risk of sporadic AD and assessed the influence of genetics and Aβ pathology on brain age. Applying our predictive model in the context of preclinical AD revealed that the presymptomatic phase of ADAD is characterized by accelerated functional brain aging. This phenomenon is independent from, and might precede, detectable fibrillar Aβ deposition.

Population modeling with machine learning can enhance measures of mental health

“…Emerging results validate the usefulness of age as one such proxy measure, leading to the so called brain age delta: the difference between predicted and actual age (Cole et al, 2015;Dosenbach et al, 2010;Smith et al, 2019a). The delta has been shown to reflect physical and cognitive impairment in adults and gives an index of neurodegenerative processes (Gonneaud et al, 2020;Liem et al, 2017). Can this strategy of biomarker-like proxy measures be extended beyond the construct of aging?…”

“…Indeed, a public health perspective calls for targeting individual differences in health, not only pathology. Psychological constructs such as IQ and neuroticsm are important factors of the epidemiology of psychiatric disorders [38, 30, 29, 67], and accelerated brain aging is associated with various neurological conditions [18, 17, 25]. Yet, few cohorts come with extensive neuropsychological testing.…”

“…The brain of a person can look similar to the brains commonly seen in older (or younger) people. The resulting brain-predicted age reflects physical and cognitive impairment in adults [24, 17, 16] and reveals neurodegenerative processes [22, 25]. Can this strategy of biomarker-like proxy measures be extended to other targets beyond the construct of aging?…”

Bridging Scales in Alzheimer's Disease: Biological Framework for Brain Simulation With The Virtual Brain

“…Indeed, functional connectivity and structural connectivity are now being investigated to better differentiate AD patients, MCI, and controls, moving toward the goal of identifying prodromal AD patients and the possibility of developing early intervention strategies (Phillips et al, 2015 ; Pereira et al, 2016 ; de Vos et al, 2018 ; Ye et al, 2019 ). A recent study suggests altered functional connectivity corresponding to accelerated aging in preclinical AD (Gonneaud et al, 2020 ).…”

From genes to network models of Alzheimer's disease: Biological framework for multi-scale brain simulation with The Virtual Brain

“…Indeed, FC and SC are now being investigated to better differentiate AD patients, MCI, and controls moving towards the goal of identifying prodromal AD patients and the possibility of developing early intervention strategies (187,(194)(195)(196). A recent study suggests altered FC corresponding to accelerated aging in preclinical AD (197).…”