Atlas55+: Brain Functional Atlas of Resting-state Networks for Late Adulthood

Abstract

Currently, several human brain functional atlases are used to define the spatial constituents of the resting-state networks (RSNs). However, the only brain atlases available are derived from samples of young adults. As brain networks are continuously reconfigured throughout life, the lack of brain atlases derived from older populations may influence RSN results in late adulthood. To address this, we aimed to construct a reliable brain atlas derived from older participants only. To do so, we analyzed the resting-state functional MRI data from three cohorts of healthy individuals in late life (total N=563; age=55-95years) and a mid-life cohort (N=322; age=18-54years). First, we identified five major RSNs across the late-life cohorts. Then, we demonstrated: high spatial reproducibility across the late-life cohorts with an average spatial overlap of 67%; and RSNs derived from the late-life cohorts were spatially different from those derived from the mid-life cohort (p=0.016). In response, we constructed a novel brain atlas, called Atlas55+, which includes a consensus of the major RSNs across the late-life cohorts. Atlas55+ provides a reliable age-appropriate template for the major RSNs in late adulthood and is publicly available. Our results confirm the need to construct age-appropriate brain functional atlases for studies investigating aging-related brain mechanisms.

Multivariate patterns of brain-behavior associations across the adult lifespan

“…The STAC-r model supports the idea that preserved cognition in older adults is partially supported by good brain efficiency, which involves compensatory mechanisms through the mediation of primary networks, despite being less efficient than in their youthful state [32,34]. With aging, this transition from executive to visual networks to support healthy behavior may reflect changes in biological mechanisms involving brain networks less impacted by aging [23,34]. In fact, the overall negative impact of age on the functional integrity of the executive networks have been consistently reported [20,23,24,44,61], while the visual network is among the least impacted [23].…”

“…With aging, this transition from executive to visual networks to support healthy behavior may reflect changes in biological mechanisms involving brain networks less impacted by aging [23,34]. In fact, the overall negative impact of age on the functional integrity of the executive networks have been consistently reported [20,23,24,44,61], while the visual network is among the least impacted [23]. This relatively low impact of aging on the visual networks is likely related to their high structuralfunctional coherence [62], low inter-and intraindividual variability in functional activation [63], anatomical morphology [64,65] and resting-state functional connectivity [66][67][68].…”

“…Throughout adulthood, age has been robustly established as one of the strongest negative contributors to brain AGING function using multivariate [7,8] and univariate [17][18][19][20] approaches. Healthy aging has been associated with regional brain atrophy [17,21,22], reduced brain network integrity [20,[23][24][25] and changes in regional connectivity [20,[24][25][26]. Beyond this negative impact, a study by Miller et al (2016) [17] that involved 5,000 participants aged 44-78 years old suggested that aging alters the brainbehavior-environment associations in two ways.…”

Age-dependent changes in the dynamic functional organization of the brain at rest – a cross-cultural replication approach

“…We additionally excluded participants I) that were taking psychoactive drugs (urine drug screening and semi-structured interview for, buprenorphine, amphetamine, benzodiazepine, cocaine, methamphetamine, morphine/heroine, methadone, and marihuana; n=19), 2) with neurological disorders (DSM-5 diagnosis; n=28) and/or alcohol abuse and family history of alcohol addiction (DSM-5 diagnosis and semi-structured interview; n=53), 3) with failed spatial normalization as identified by visual inspection (n=2), and, 4) with missing demographic and/or MRI data (n=8). After applying these criteria a total of 133 participants remained, in line with previous studies (Doucet et al, 2021), both dataset subjects were subdivided into two distinct participant groups (young: n=88, age-range = 20-35, male/female=57/31; older: n=45, age-range=55-80, male/female = 22/23). All participants provided written informed consent.…”

A Framework for Brain Atlases: Lessons from Seizure Dynamics

“…During the course of conducting this study, and while undergoing peer review, other atlases with more accurate or relevant parcellations to the study's population were published in different areas of neuroscience [50][51][52][53][54][55][56][57][58] . Here, we cautiously propose a question: Are e orts to publish more atlases created with di erent algorithms or slightly modified parcellations from existing atlases providing any advantages over already existing atlases?…”