Reconfiguration of cortical network connectivity with increasing cognitive complexity
Creators: name: Nestor, Kimberly
name: Rasero, Javier
name: Betzel, Richard
name: Gianaros, Peter
name: Verstynen, Timothy
Publisher: name: Open Science Framework
Year: 2022
Subjects: subject: Computational Neuroscience
subject: Cognitive Neuroscience
subject: Life Sciences
subject: Quantitative Psychology
subject: Cognitive Psychology
subject: Neuroscience and Neurobiology
subject: Social and Behavioral Sciences
subject: Systems Neuroscience
subject: Psychology
subject: FOS: Psychology

description: Hypothesis: The goal of this proposed study is to use congruent and incongruent task blocks within an fMRI scan with the data processing method of edge time series to examine cortical network connectivity patterns. The hypothesized outcome of this study is marked changes in modularity within the cortex i.e. high modularity in congruent task blocks due to the less strenuous nature of the task and low modularity (integration) during incongruent task blocks due to this commanding more intellectual resources. Based on the model by Shine (2021), participation of basal ganglia nuclei should associate with cortical areas being in a more integrated state (i.e., low modularity) whereas participation of cerebellar regions should associate with cortical areas being in a more segregated state (i.e., high modularity). METHODS Participants: A total of 242 typical subjects (female=119, mean age=40 ± 6 years, min age=30, max age=51 years) will be included in this study, based on fMRI task and resting state data generated by the Pittsburgh Imaging Project (PIP). Selected scans for inclusion in the dataset - which entail three fMRI tasks - all have low average motion (mean framewise displacement (FD) &lt; 0.35mm). Scan Protocol: MRI data was collected on a 3 Tesla Trio TIM whole-body scanner (Siemens, Erlangen, Germany), using a 12 channel head coil. For registration purposes a 1mm isotropic T1-weighted MPRAGE structural scan were acquired for each subject with the following specifications: repetition time (TR)=2100ms, echo time (TE)=3.29ms, field of view (FOV)=256 x 208mm, inversion time=1100ms, flip angle=8O (Rasero et al., 2021). A 3mm isotropic functional blood oxygen level dependent (BOLD) scan was acquired with a T2*-weighted gradient echo-planar sequence using the following specifications: repetition time (TR)=2000ms, echo time (TE)=28ms, field of view (FOV)=205 x 205mm, flip angle=90O (Rasero et al., 2021). Task Acquisition: Variants of Multi-Source Interference Task (MSIT) and Stroop tasks will be used to assess neural response to congruent (no conflict) and incongruent (conflicting) information (Sheu, Jennings &amp; Gianaros, 2012). Task blocks of congruence and incongruence were interleaved for a duration of 52-60secs followed by a 10-17sec delay where participants were instructed to fixate on a crosshair. During the MSIT block participants were tasked with pressing one of three buttons to indicate a highlighted number. In the congruent phase participants should press the button corresponding to the location of the highlighted number. While in the incongruent phase participants should suppress the urge to press the button associated with the location of the highlighted number, but instead press the button of the actual number value (Bush et al., 2003). For the congruent phase of the Stroop task participants were given a prompt of a colour word visually shown with the associated colour and the goal was to select the colour the word specifies and was shown in. During the incongruent phase of this task participants were shown a prompt colour word that is in a different colour from that of its spelling, and participants then needed to select the colour the word is presented in instead of the colour the word specifies (Stroop, 1935). As a control condition for difference in individual responses and to mediate an adaptive stress response on par with the performance of the subject, both the Stroop and MSIT incongruent task blocks included variable intertrial intervals. The interval lengths were determined by the accuracy rating of the participant, where correct responses resulted in shortened rest periods to amplify the stressor conditions of the task (Gianaros et al., 2017). Examine edge-centric functional connectivity patterns for integrative and segregative states coupled with task performance. Data Analysis: Edge time series will be calculated based on recent work from Faskowitz et al (2020), which will construct active networks along the timescale during congruent and incongruent phases, producing matrices at every time point. This will be obtained by using for example seeded regions i and j, where initially the time series data is standardized to a z-score using equation 1. The pairwise dot product of all segmented brain regions is then calculated using equation 2 to derive the edge time series. $$z_i = \frac{x_i - \mu_i}{\sigma_i}$$ $$Let \; c_{ij} = [z_i(1) \cdot z_j(1),\dots,z_i(T) \cdot z_j(T)] \;$$ The participation coefficient - an estimate for integration within a network - will then be calculated using equation 3, to determine whether there is a higher incidence of integrated cortical activation patterns during incongruent blocks compared to congruent. Where KisT is the strength of i’s edge weights compared to the edge weights of those in region s at time T and Kis is the sum of i’s edge weights at time T and nM is the total number of communities (Shine et al., 2016a). $$B_{iT} = 1 - \sum^{nM}_{s=1} \left( \frac{K_{isT}} {K_{iT}} \right)^2$$ We will use the Louvain algorithm for maximizing the modularity function. The outcome of this will be used to supplement the participation coefficient results, as it returns a scalar value, Q, signifying modularity and segregation among cortical networks. More specifically, the modularity index will be used to determine whether there is a high rate of segregation during congruent time blocks with a converse effect observed during incongruent blocks. Prior to determining the modularity index, Q, the modularity matrix B is determined; as noted in equation 4, where A is the observed matrix of pairwise correlated cortical activity and P is the expected matrix, otherwise termed the null model which is used as standardization for the observed matrix A (Zamani Esfahlani et al., 2021). $$B = A - P$$ There are a number of methods for defining the expected matrix P, as detailed by Zamani Esfahlani et al. (2021), for this study we will be using the averaged resting state matrix as the expected matrix P. Intuitively, communities detected using this null model correspond to groups of brain regions (or edges) whose connectivity during task blocks exceeds what is expected at rest. In other words, the communities reflect supra-rest patterns of nodal and edge functional connectivity. At which point the calculation of the modularity matrix B can be defined by equation 5, where the observed A would be the functional task matrix and the expected P would be the averaged resting state matrix. $$B = FC_{task} - FC_{rest}$$ The modularity index Q will then be calculated using equation 6, where 𝜎 ∈ {1, … , K} and details which K community region i or j is segmented into a priori. 𝛿(𝜎i, 𝜎j) is the Kronecker function which returns 1 when 𝜎i = 𝜎j and 0 otherwise, indicating whether two regions i and j are in the same community. The Q value will then be optimized by using modularity maximization with the goal of segmenting communities of brain areas based on modular cortical activity a posteriori. Modularity maximization randomly assigns nodes into communities with the goal of determining the maximum Q. Once a single node search has been exhausted, groups of nodes are randomly assigned to determine the optimal Q until the algorithm has converged (Zamani Esfahlani et al., 2021). $$Q = \sum_{ij} B_{ij}\delta\left( \sigma_i,\sigma_j \right)$$ Determine whether network patterns are modulated by basal ganglia and cerebellum. Data Analysis: The modularity index and participation coefficient from the previous section will be used to determine the level of involvement of the subcortical structures basal ganglia and cerebellum. Figure 1. Simulation of anticipated research outcomes - cortical and subcortical activation patterns showing varying levels of network integration. Fig1. shows the fMRI task paradigm with (top) congruent and incongruent trial blocks. Middle shows participation coefficient values of the cerebrum, indicating increased global integration during incongruent trial blocks. Bottom indicates participation coefficient values for the basal ganglia and cerebellum with high participation for the basal ganglia during incongruent blocks and high cerebral global integration. Vice versa is seen in the cerebellum where there is low anticipated participation during incongruent blocks but high involvement during congruent blocks paired with low cerebral integration. This expected pattern from the basal ganglia and cerebellum would indicate the basal ganglia drives integration in the brain, while the cerebellum influences a segregated state.