Research published in the journal Cerebral Cortex has shown that stronger functional connectivity – that is, communication among neurons in various networks of the brain – is linked to youthful memory in older adults.
Those with superior memories – called superagers – have the strongest connectivity.
The work is the second in a series of three studies undertaken to unlock the secret of something researchers already knew: that some adults in their 80s and 90s function cognitively as well as or better than much younger people.
The first study showed that when compared with typical older adults, the brains of superagers are larger in certain areas that are important for processes that contribute to memory, including learning, storing, and retrieving information.
But brain regions are not isolated islands; they form networks that “talk” to one another to allow for complex behaviors.
“This communication between brain regions is disrupted during normal aging,” said Alexandra Touroutoglou, Ph.D., an investigator in the MGH Department of Neurologyand the Athinoula A. Martinos Center for Biomedical Imaging.
“Superagers show not just youthful brain structure, but youthful connectivity as well.”
The current study looked at superagers, typical adults from 60 to 80 years old, and young adults 18 to 35.
It used functional magnetic resonance imaging (fMRI) to examine the synchronization of brain waves in the default mode network (DMN) and salience network (SN) of participants in a resting state.
“These networks ebb and flow, or oscillate, whether you’re in a resting state or engaged in a task,” said Bradford C. Dickerson, MD, director of MGH’sFrontotemporal Disorders Unit.
“Our prediction was that typical older adults would have less synchronization in these brain waves – less efficient networks – but that superagers would have networks as efficient as the young adults. And that’s what we found.”
The current study looked at superagers, typical adults from 60 to 80 years old, and young adults 18 to 35.
It used functional magnetic resonance imaging (fMRI) to examine the synchronization of brain waves in the default mode network (DMN) and salience network (SN) of participants in a resting state.
The research team’s next study will analyze fMRI data from brains engaged in memory and other cognitive tasks.
t is hoped that taken together, the studies will “provide basics for future researchers to develop biomarkers of successful aging,” said Touroutoglou, who is also an instructor in neurology at Harvard Medical School, noting that one of the mysteries scientists hope to tease out is whether superagers start off with “bigger and better” brain structure and communication than other people or if they are somehow more resilient to the declines of normal aging.
Future research may then measure the effects of genetics as well as exercise, diet, social connectedness, and other lifestyle factors that have been shown to affect resilience in older adults.
“We hope to identify things we can prescribe for people that would help them be more like a superager,” said Dickerson, who is also an associate professor of neurology at Harvard Medical School.
“It’s not as likely to be a pill as more likely to be recommendations for lifestyle, diet, and exercise. That’s one of the long-term goals of this study – to try to help people become superagers if they want to.”
Normal aging is associated with brain changes that can be linked to neurodegeneration (Peters, 2006).
Non-invasive imaging techniques (e.g., MRI) have enabled us to study structural brain changes such as grey matter atrophy and white matter lesions in relation to aging and dementia (Brant-Zawadzki et al., 1985).
More recently, it has been hypothesized that these anatomical brain changes are preceded by changes in the brain’s functional organization (Jack et al., 2010).
Developed over three decades ago, functional MRI (fMRI) is a non-invasive method for investigating the functional dynamics of the brain.
fMRI indirectly reflects neural activity by measuring MRI signal fluctuations induced by changes in blood oxygenation and flow resulting from changes in neural metabolic demand (Logothetis, 2002).
In the absence of an explicit stimulus, resting-state fMRI quantifies the synchronization of spontaneous signal fluctuations over time, or functional connectivity, across multiple brain regions (Fox and Raichle, 2007).
Measures of functional connectivity have been shown to differ between patients with Alzheimer’s disease and controls (Dennis and Thompson, 2014; Wang et al., 2007).
In parallel, many studies of aging have shown reduced functional connectivity within resting-state networks such as the default mode network (DMN), the salience network, and the motor network (Betzel et al., 2014; Chan et al., 2014; Ferreira et al., 2016; Geerligs et al., 2015; Grady et al., 2016).
In contrast, functional connectivity between networks has been found to increase with age, which may reflect decreased segregation (Andrews-Hanna et al., 2007; Chan et al., 2014; Ferreira and Busatto, 2013; Ng et al., 2016).
These age-related decreases in within-network connectivity and increases in between-network connectivity have also been demonstrated to be related to, for example, cognitive performance and motor ability (Andrews-Hanna et al., 2007; Chan et al., 2014; Ferreira and Busatto, 2013; Geerligs et al., 2015; Keller et al., 2015; Wu et al., 2007).
Importantly, previous studies on functional connectivity in normal aging were conducted with relatively small samples, or included wide age ranges rather than middle-aged and elderly persons who are at greatest risk for neurodegeneration.
Also, the lack of a population-based design in most studies may hamper the generalizability of the findings (Chan et al., 2014; Ferreira et al., 2016; Grady et al., 2016; Sala-Llonch et al., 2015; Siman-Tov et al., 2016).
Moreover, brain function depends on the segregation and integration of brain networks. Limiting analyses to an individual resting-state network, such as the DMN, may be inadequate in gaining a more comprehensive understanding of the functional organization of the aging brain (Baldassarre A, 2015; Greicius et al., 2004; Hafkemeijer et al., 2012; Koch et al., 2010; Ng et al., 2016; Tsvetanov et al., 2016).
Finally, previous studies defined networks based on anatomical parcellations that do not necessarily conform to the true functional architecture of the human brain (Song et al., 2014; Wang et al., 2010).
Additional knowledge about the aging brain in the healthy elderly may increase our insight into the neural basis of neurodegenerative diseases.
Based on the current literature, we hypothesized that older age in the general population is negatively associated with within-network connectivity, and positively associated with between-network connectivity.
Given previous literature, we more specifically hypothesized that in middle-aged and elderly persons from the general population, networks showing greatest decreases in functional connectivity would most likely be those that have been previously implicated in aging or neurodegeneration in smaller (clinical) studies, i.e. the DMN, salience network and motor network.
Yet, to allow for changes in other networks to be detected, as well as to avoid a bias towards network decreases, we deployed an exploratory approach, analyzing large-scale networks in the entire brain as well as allowing for both decreases and increases in connectivity. In addition, we explored how various factors such as sex, cardiovascular risk, and apolipoprotein E ε4 carrier status associate with functional connectivity in an aging population.
Source:
Mass General
Media Contacts:
Terri Janos – Mass General
Image Source:
The image is in the public domain.
Original Research: Closed access
“Stronger Functional Connectivity in the Default Mode and Salience Networks Is Associated With Youthful Memory in Superaging “. Jiahe Zhang, Joseph M Andreano, Bradford C Dickerson, Alexandra Touroutoglou, Lisa Feldman Barrett.
Cerebral Cortex doi:10.1093/cercor/bhz071
