How many resting state networks are there

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The individual structural connectome data of all participants were averaged to obtain a group-averaged structural connectivity matrix and a tract length matrix. The edge weight of the structural connectivity matrix, representing the structural connectivity network organization of the brain, was normalized by the volume of the interconnected ROIs to account for different size of the ROIs We simulated phase time series using the Kuramoto model constrained by the empirical structural connectivity.

N is the total number of regions. Phases were initialized randomly. These computations resulted in 6 within-network and 6 between-network functional connectivity measures per participant that were then Fisher Z-transformed. Network cohesion i. We compared metastability and synchrony resulting from the Kuramoto model against the empirical data.

The model error was quantified in terms of the absolute difference between the simulated and empirical values for a range of coupling strengths. Finally, to ensure robustness of the simulation results, we conducted 5 additional runs of the Kuramoto simulation with varying random initial conditions SI section 2.

The datasets generated during the current study are available from the corresponding author on reasonable request. Biswal, B.

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Woods et al. In brief, participants were between the ages of 65—89, had no history of major psychiatric illness, no history of brain or head injury resulting in loss of consciousness greater than 20 min, and no formal diagnosis or evidence of mild cognitive impairment MCI , dementia, or neurological brain disease.

Possible MCI was defined by 1. All participants were right-handed and had no contraindications for MRI scanning. Before beginning all study procedures, participants signed a consent form approved by the Institutional Review Boards at the University of Florida and the University of Arizona.

At the baseline visit, participants completed a variety of cognitive assessments, medical history and mood questionnaires, and an MRI scan. Three participants were excluded due to incomplete or extreme scores greater than three standard deviations from the mean on the NIH toolbox. Additionally, two participants were excluded as outliers due to extreme network connectivity values resulting in a total sample size of older adults.

In the present study, we used the unadjusted standard scores for the Fluid Cognition Composite and its five subtests that measure cognitive abilities shown to decline with advanced age.

These subtests measure components of executive function, attention, episodic memory, working memory, and processing speed. For instance, the Dimensional Change Card Sort task assesses the set-shifting component of executive function i. Here, a participant must match a target stimulus to a choice stimulus according to the shifting criterion of either shape or color.

The Flanker task is a visuospatial attention task that also requires inhibitory control over automatic responses. The goal of this task is to determine the direction of a central target arrow that is flanked by similar stimuli on the left and right.

The Picture Sequence Memory task targets episodic memory, a cognitive process involved in the retrieval of learned information. In this task, thematically related pictures are displayed in a sequence, and participants must remember and move the pictures into the sequence demonstrated.

The List Sorting task is a measure of working memory, the ability to temporarily hold and manipulate a limited capacity of information. This requires participants to sequence and sort a list of visual and auditory stimuli from smallest to largest increasing in the number of categories and items.

Lastly, the Pattern Comparison task is a measure of processing speed, where participants quickly identify whether or not two visual patterns are the same. Scanner type was included as a covariate in our statistical analyses to control for potential differences in the quality and acquisition of MRI data.

Both study sites followed the same scanning procedures and used identical sequences. Participant head motion was constrained by foam padding, and participants were provided with earplugs to reduce the adverse effects of scanner noise. We followed a preprocessing pipeline which included the functional realignment and unwarping, functional centering of the image to 0, 0, 0 coordinates, slice-timing correction, structural centering to 0, 0, 0 coordinates, structural segmentation and normalization to MNI space, functional normalization to MNI space, and spatial smoothing with a smoothing kernel of 8 mm FWHM.

During preprocessing, the Conn toolbox implements an anatomical, component-based, noise correction strategy aCompCor for spatial and temporal processing to remove physiological noise factors from the data Behzadi et al.

The implementation of aCompCor combined with the quantification of participant motion and the identification of outlier scans through the Artifact Rejection Toolbox ART 2 allows for better interpretation of functional connectivity results Behzadi et al.

Due to potential confounding effects, the resulting motion information and frame-wise outliers were included as covariates in our first-level analyses Behzadi et al. Applying linear regression and using a band-pass filter of 0.

For the rs-fMRI analyses, we used a publicly available network parcellation of the brain defined by Yeo et al. Figure 1. Each network is color-coded; however, the colors do not depict different levels of correlation strength. First, to assess how the four networks contribute to the general domain of cognitive aging, we ran a multiple linear regression evaluating the unique effect of within-network connectivity on Fluid Cognition Composite scores. In the secondary analyses, we only evaluated networks that significantly contributed to Fluid Cognition Composite scores, since our primary question concerns identifying important resting-state networks in the cognitive aging process overall.

We controlled for age, education, sex, and scanner type in all of our models. All statistical analyses were performed using SPSS version Conversely, sex and scanner type were not significantly associated with Fluid Cognition Composite scores. Figure 2. A scatterplot depicting the primary regression analysis with the standardized predicted values X-axis resulting from regressing age, sex, education, scanner, and within-network connectivity values of the cingulo-opercular network CON , frontoparietal control network FPCN , default mode network DMN , and dorsal attention network DAN on the Fluid Cognition Composite unadjusted standard scores Y-axis.

Figure 3. Scatterplots depicting the unique relationships between each network and the Fluid Cognition Composite controlling for the rest of the predictors in the regression. The X and Y axes represent the standardized residuals for the independent and dependent variables, partialling out the effects of the remaining predictors. As such, the slopes reflect partial correlations. To better characterize the relationship between CON connectivity and cognitive aging, we ran secondary analyses to identify which specific fluid cognitive subtests were associated with the CON network.

The distribution of scores on the Flanker subtest was positively skewed; therefore, we performed a square root transformation to meet normality assumptions before analyses. Notably, CON within-network connectivity was related to better performance across three of the five fluid cognition subtests, suggesting the network has a relatively broad relationship with cognitive aging rather than a relationship-driven by a specific domain.

Figure 4. Scatterplots showing the significant relationships between A Picture Sequence Memory B Flanker and C Dimensional Change Card Sort subtest scores and CON within-network resting-state connectivity controlling for age, education, sex, and scanner covariates. The Y-axes reflect the unadjusted standard scores for Picture Sequence Memory and Dimensional Change Card Sort and the square root transformed scores for the Flanker task. Aging is associated with disruptions in the functional architecture of the brain.

However, due to previously mixed findings, it is unclear if age-related changes in resting-state network functional connectivity are linked to the cognitive aging process.

The present study offers important new insights by uncovering a specific relationship between resting-state network functional connectivity and cognitive performance in a large sample of healthy older adults.

Here, we identified a resting-state network involved in general fluid cognition. Additionally, we outlined the cognitive scope of this network by mapping connectivity onto processing speed, episodic memory, working memory, attention, and executive function subdomains. By linking resting-state network connectivity to various aspects of the cognitive aging process, we hope to create a foundation for future targeted intervention strategies.

Cognitive control is necessary for flexibly allocating mental resources to produce goal-directed behavior. Examples of control processes include attending to stimuli, preparing and initiating a response, and adapting to feedback Cole and Schneider, These components of cognition are necessary for the successful completion of a variety of tasks in daily life.

Beyond the matter of significance, CON intra-network coherence explained more of the variance in composite scores than both age and education. These results suggest that CON connectivity is an important factor that influences fluid cognition and may be a compelling target for novel interventions e. Additionally, CON within-network connectivity was positively associated with performance on three out of the five subtest domains typically vulnerable to the aging process: episodic memory, attention, and executive function.

This pattern of findings suggests the CON network has a relatively global relationship with the cognitive aging process rather than a relationship driven by a specific domain.

These results support the notion of the general widespread involvement of the CON network in cognitive control Dosenbach et al. Broadly, CON is involved in the implementation and maintenance of the perceptual and attentional information in a task Dosenbach et al. Therefore, our findings suggest that greater functional connectivity within CON at rest may reflect a better ability to properly activate this important network during the execution of fluid cognitive tasks in older adults.

Additionally, CON consists of brain regions important for fluid cognitive abilities like decision-making, planning, target and error detection, updating, and switching i. These regions are susceptible to gray matter atrophy and white matter disruptions with age, which contribute to functional activation alterations and declines in cognitive performance Salat et al. He et al. The resting-state functional connectivity of the left insula specifically has also been shown to mediate the association between age and visual processing speed in healthy older adults Ruiz-Rizzo et al.

Greater CON resting-state connectivity may signify greater maintenance of structural integrity in these regions involved in fluid cognition. Future research should utilize multimodal imaging to further assess the relationship between age-related structural changes and resting-state network functional connectivity.

CON is a part of a larger group of networks referred to as the higher-order cognitive networks i. Functional connectivity within these networks typically decreases with age along the same trajectory of age-related structural deterioration and cognitive decline Park and Reuter-Lorenz, ; Giorgio et al.

Conversely, connectivity within sensory and motor resting-state networks remains relatively stable in advanced age. However, relating resting-state characteristics of the DMN and other networks to actual cognitive performance in older adults has resulted in mixed findings Andrews-Hanna et al.

This distinction between the roles of DMN and CON in the cognitive aging process may help facilitate better differentiation of nonpathological aging from disease states.

The present study is not without limitations regarding cohort characteristics and methodology.