Time-varying Sources and Vascular Contributions to Age-accompanied Functional Brain Network Re-organization
Abstract
Abstract
The brain is a complex network of interacting brain areas that can be further divided into
segregated functional systems. Resting-state system segregation is a feature of brain network
organization that has relevance to brain function in both health and disease across adult lifespan.
It is unclear what gives rise to system segregation and the individual differences in this brain
network measure. In this dissertation, two aspects of this important question are investigated: (1)
Do vascular factors contribute to relationships between age and system segregation across the
adult lifespan? and (2) Can sources of time-varying information help account for relationships
between aging and system segregation? The interplay between these questions reveals how the
temporal evolution of system re-configuration at a short time scale impacts more stable
individual features of large-scale network organization, in the context of differences in vascular
health of adult individuals. This dissertation was accomplished by incorporating data from a total
of 894 unique participants, over 3 independent studies (age range: 20 – 100 years) and including
multiple neuroimaging modalities and measures of participant health and demographics.
The contribution of vascular factors towards relationships between age and resting-state system
segregation is first investigated. There exist relationships between age and vascular measures,
including cardiovascular health (CVH) and cerebrovascular reactivity (CVR). Age-related
decreases of system segregation persist after controlling for vascular-related variance. This is
demonstrated by (i) computing system segregation regional CVR-corrected signals within each
participant, and (ii) including CVH as a participant-level covariate in the models. These results
demonstrate that age-related differences in system segregation cannot be fully attributed to
differences in cerebrovascular and cardiovascular factors.
To examine the contribution of time-varying information to system segregation, I examine the
relationship between resting-state BOLD signal variability and system segregation. After
controlling for vascular confounds by (i) estimating BOLD variability using CVR-corrected
signals, (ii) including CVH as a covariate in the model, there is an absence of a relationship
between age and BOLD variability, revealing that vascular factors serve as a major source of
variance explaining previously reported relationships between age and resting-state BOLD signal
variability. Further, with correction of vascular factors, BOLD variability does not relate to
system segregation.
An additional source of time-varying information is evaluated in relation to system segregation,
focused on co-fluctuation amplitude of the resting-state time-series. Moments of greater co-
fluctuation pattern across edges are identified (events), during which functional brain networks
are highly modular relative to non-event moments. I demonstrate that the number of events that
are present in an individual’s resting-state time-series is related to their system segregation.
However, I next demonstrate that age-accompanied decreases of system segregation are evident
across all the moments, irrespective of co-fluctuation amplitude of edges. Collectively, these
findings reveal that while high co-fluctuation moments (events) may contribute towards
establishing an individual’s system segregation, brain network re-organization exists across all
time points of a resting-state scan.
In sum, this dissertation provides important support that resting-state system segregation
measures brain network re-organization across the adult lifespan. This measure is independent
from vascular differences within individuals, and provides critical evidence of brain aging that is
consistently evident across periods of rest. Serving as a biomarker of functional brain network
integrity, system segregation further supports the application of this approach towards measuring
individual brain health across the lifespan.