Differential contributions of static and time-varying functional connectivity to human behavior

Eichenbaum, Adam; Pappas, Ioannis; Lurie, Daniel J.; Cohen, Jessica R.; D’Esposito, Mark

doi:10.1162/netn_a_00172

Cited by 29 publications

(48 citation statements)

References 74 publications

(104 reference statements)

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“…These reconfigurations can be described as changes in connectivity strength between specific sets of brain region-pairs, forming recurrent connectome states. Such functional connectome states hold great significance as their time-varying (dynamic) characteristics have been linked to behavior and cognition as detailed below (11, 12).…”

Section: Introductionmentioning

confidence: 99%

“…Beyond these spatial features, specific temporal features of connectome dynamics have been very fruitful in the context of behavioral relevance (24). Specifically, the proportion of the total recording time spent in each connectome state (fractional occupancy, or FO) and the probability to transition between specific pairs of discrete states (transition probability, or TP) have been linked to behavior (11, 12). Moreover, the time spent in two “metastates”, identified from hierarchical clustering of FO over fine-grained states, was found to be heritable (12).…”

Section: Introductionmentioning

confidence: 99%

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Dynamic trajectories of connectome state transitions are heritable

Jun

Alderson

Altmann

et al. 2021

Preprint

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The brain's functional connectivity architecture (functional connectome) is dynamic, constantly reconfiguring in an individual-specific manner and contributing to inter-individual variability in cognitive performances. However, to what extent genetic effects shape the dynamic reconfigurations of the functional connectome is largely unknown. This paper assesses whether dynamic connectome features are heritable, quantifies their heritability, and explores their association with cognitive phenotypes. We identified discrete connectome states from resting-state fMRI data (n = 1003 including twins and non-twin siblings) and obtained multivariate features, each describing temporal or spatial characteristics of connectome dynamics. We found strong evidence that the temporal features, particularly fractional occupancy (FO) and transition probability (TP), were heritable. Importantly, a substantial proportion of phenotypic variance of these features (35% of FO and 41% of TP) was explained by genetic effects. The effect was stable after adjusting for head motion, independent of global signal regression and the chosen number of states, and absent in surrogate data. Contrarily, the data did not provide robust support for heritability of spatial features suggesting that genetic effects primarily contribute to how the connectome transitions across states, rather than the precise way in which the states are spatially instantiated. Notably, temporal phenotypes also captured variability in cognitive performance. Overall, our findings demonstrate a link between genetic makeup and temporal reconfigurations of the functional connectome, suggesting that dynamic features may act as endophenotypes for cognitive abilities. In conclusion, we propose that connectome dynamics offer considerable potential as a theoretical, conceptual, and practical framework for linking genetics to behavior.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic trajectories of connectome state transitions are heritable

Jun

Alderson

Altmann

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

“…Whereas previous work has focused mainly on combining SC and FC for the analysis of functional Magnetic Resonance Imaging data (fMRI), similar integrative approaches are missing for the emerging field of time-varying directed FC analysis (Eichenbaum et al, 2021). Time-varying FC characterizes the dynamics of directed neuronal interactions that evolve at the millisecond scale, exploiting high-temporal resolution recordings, such as local field potentials and EEG source imaging data (Milde et al, 2010; Pascucci et al, 2018; Plomp et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Structure supports function: informing directed and dynamic functional connectivity with anatomical priors

Pascucci

Rubega²,

Rué-Queralt³

et al. 2021

Preprint

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The dynamic repertoire of functional brain networks is constrained by the underlying topology of structural connections: the lack of a direct structural link between two brain regions prevents direct functional interactions. Despite the intrinsic relationship between structural (SC) and functional connectivity (FC), integrative and multimodal approaches to combine the two remain limited, especially for electrophysiological data. In the present work, we propose a new linear adaptive filter for estimating dynamic and directed FC using structural connectivity information as priors. We tested the filter in rat epicranial recordings and human event-related EEG data, using SC priors from a meta-analysis of tracer studies and diffusion tensor imaging metrics, respectively. Our results show that SC priors increase the resilience of FC estimates to noise perturbation while promoting sparser networks under biologically plausible constraints. The proposed filter provides intrinsic protection against SC-related false negatives, as well as robustness against false positives, representing a valuable new method for multimodal imaging and dynamic FC analysis.

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“…1,3,4 More recent work in this field has shown that time-varying measures of functional connectivity may account for more, or differential, variability in behavioral outcomes. 5,6 These time-varying measures typically capture aspects of network reorganization, or differential patterns of interactions between networks, that evolve and change over the course of a resting-state or taskbased functional magnetic resonance imaging (fMRI) scan. The most common method for measuring these time-varying patterns is to estimate measures of connectivity during a number of overlapping "sliding windows" in the blood oxygen level-dependent (BOLD) timeseries data.…”

Section: Introductionmentioning

confidence: 99%

The role of neural flexibility in cognitive aging

Varangis

Stern

et al. 2021

Preprint

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Studies assessing relationships between brain and cognitive changes in healthy aging have shown that a variety of aspects of brain structure and function explain a significant portion of the variability in cognitive outcomes throughout adulthood. Many studies assessing relationships between brain function and cognition have utilized time-averaged, or static functional connectivity methods to explore ways in which brain network organization may contribute to aspects of cognitive aging. However, recent studies in this field have suggested that time-varying, or dynamic measures of functional connectivity, which assess changes in functional connectivity throughout a scan session, may play a stronger role in explaining cognitive outcomes in healthy young adults. Further, both static and dynamic functional connectivity studies suggest that there may be differences in patterns of brain-cognition relationships as a function of whether or not the participant is performing a task during the scan. Thus, the goals of the present study were threefold: (1) assess whether dynamic connectivity (neural flexibility) during both resting as well as task-based scans is related to participant age and cognitive performance in a lifespan aging sample, (2) determine whether neural flexibility moderates relationships between age and cognitive performance, and (3) explore differences in neural flexibility between rest and task. Participants in the study were 423 healthy adults between the ages of 20-80 who provided resting state and/or task-based (Matrix Reasoning) functional magnetic resonance imaging (fMRI) scan data as part of their participation in two ongoing studies of cognitive aging. Neural flexibility measures from both resting and task-based scans reflected the number of times each node changed network assignment, and were averaged both across the whole brain (global neural flexibility) as well as within nine somatosensory/cognitive networks. Results showed that neural flexibility during the task was higher in older adults, and that neural flexibility in Default Mode and Visual networks was negatively related to performance on the Matrix Reasoning task. Resting state neural flexibility was not significantly related to either participant age or cognitive performance. Additionally, no neural flexibility measures that significantly moderated relationships between participant age and cognitive outcomes. Further, neural flexibility differed as a function of scan type, with resting state neural flexibility exhibiting significantly more variability than task-based neural flexibility. Thus, neural flexibility measures computed during a cognitive task may be more strongly related to cognitive performance across the adult lifespan, and are more sensitive to the effects of participant age on brain organization.

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Differential contributions of static and time-varying functional connectivity to human behavior

Cited by 29 publications

References 74 publications

Dynamic trajectories of connectome state transitions are heritable

Dynamic trajectories of connectome state transitions are heritable

Structure supports function: informing directed and dynamic functional connectivity with anatomical priors

The role of neural flexibility in cognitive aging

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