The connectivity domain: Analyzing resting state fMRI data using feature-based data-driven and model-based methods

Abstract

Spontaneous fluctuations of resting state functional MRI (rsfMRI) have been widely used to understand the macro-connectome of the human brain. However, these fluctuations are not synchronized among subjects, which leads to limitations and makes utilization of first-level model-based methods challenging. Considering this limitation of rsfMRI data in the time domain, we propose to transfer the spatiotemporal information of the rsfMRI data to another domain, the connectivity domain, in which each value represents the same effect across subjects. Using a set of seed networks and a connectivity index to calculate the functional connectivity for each seed network, we transform data into the connectivity domain by generating connectivity weights for each subject. Comparison of the two domains using a data-driven method suggests several advantages in analyzing data using data-driven methods in the connectivity domain over the time domain. We also demonstrate the feasibility of applying model-based methods in the connectivity domain, which offers a new pathway for the use of first-level model-based methods on rsfMRI data. The connectivity domain, furthermore, demonstrates a unique opportunity to perform first-level feature-based data-driven and model-based analyses. The connectivity domain can be constructed from any technique that identifies sets of features that are similar across subjects and can greatly help researchers in the study of macro-connectome brain function by enabling us to perform a wide range of model-based and data-driven approaches on rsfMRI data, decreasing susceptibility of analysis techniques to parameters that are not related to brain connectivity information, and evaluating both static and dynamic functional connectivity of the brain from a new perspective.

Keywords: Connectivity domain; Feature-based analysis; General linear model (GLM); Independent component analysis (ICA); Model-based method; Resting state functional MRI (rsfMRI).

Fig. 1

Schematic of analytical approaches which can be applied on rsfMRI data. The connectivity domain, similar to the time domain, allows us to perform a wide range of data-driven methods. The connectivity domain also supports implementing model-based methods such as first-level generalized linear model (GLM) on rsfMRI data (blue box). While feature-based approaches have been performed as second-level analyses, the connectivity domain provides us the opportunity to perform feature-based techniques at both first and second levels.

Fig. 2

Analogy for the time and connectivity domains using X = AS equation.

Fig. 3

Schematic of the analysis pipeline. Data was preprocessed, and either kept in the time domain or transformed into the connectivity domain, which involved calculating connectivity weights using seed networks. Similar data-driven approaches were applied in both domains and compared between the two domains. Feasibility of applying model-based methods was evaluated in the connectivity domain.

Fig. 4

Functional connectivity weights calculation. Overlay of 145 ROIs on (a) coronal, (b) sagittal, and (c) axial views of MNI atlas. (d) Color code map of 145 ROIs. (e) Functional connectivity weights of ROIs 50 (right insular cortex), 75 (right subcallosal cortex), 83 (posterior division of parahippocampal gyrus), 101 (left caudate), and 102 (left putamen), respectively; the ROIs are annotated on Fig. 4.a and c. For this study Harvard-Oxford cortical and subcortical structural atlases were used.

Fig. 5

Spatial maps identified for both domains at both time points presented as thresholded t-statistic map. The upper portion of the figure reveals nine consistently-identified brain networks found in both domains including the default mode network (DMN) (a), left parietal–frontal (working memory) network (b), right parietal–frontal (working memory) network (c), auditory network (d), frontal default mode network (e), motor network (f), primary visual network (g), secondary visual network (h), and subcallosal network (i). An attention network (j) seems consistent between two domains; however, it was not appropriately extracted in the second session for the time domain (j²). The lower portion shows the spatial maps which were identified in one domain but not the other one, or one time point but not the other.

Fig. 6

Spatial similarity between consistently-identified independent components (Fig. 5 a to i) in the time (red) and connectivity (blue) domains using 30 and 45 as the number of principle components at the individual level. For each individual, the spatial similarity between spatial maps of each component obtained using 30 and 45 principles was measured. The spatial similarities between independent components obtained from the TC-BR analysis using 30 and 45 principle components is significantly higher in the connectivity domain as compared to the time domain in several spatial maps, identified by *. The low spatial similarity in network d is due to high variability in network d across individuals when change the number of principle components. Fig. S1 shows the network d obtained using different numbers of principle components (30 and 45).

Fig. 7

Comparison between time domain and connectivity domain analysis in extracting IC maps of individuals' brain when different group data were used. (a) Flowchart of computation of subject-level spatial similarity between the same data analyzed with different group data and the same analytical approach (i.e. concatenated ICA followed by the back-projection), which was performed in both time and connectivity domains. i (= 1, 2, …, 17) is the 1^st session data of one individual from WSU. (b) Results of the spatial similarity at the subject level in both the time and connectivity domains. “*” indicates the average of similarity between 17 pairs of a certain IC map obtained using two different sets of group data. “**” indicates the standard deviation of similarity between 17 pairs of a certain IC map obtained using two different sets of group data. † indicates that the corresponding independent component has not been identified in the time domain when different group data was used.

Fig. 8

Demonstration of advantages of connectivity domain over time domain. For all results presented for both the time and connectivity domain in this figure, the two methods compared are different first-level analytical techniques that produce spatial maps for each individual subject. The spatial map for each component was averaged across subjects for each method and the spatial similarity between these average maps is reported here as a percentage. (a) Demonstrates the superiority of the connectivity domain for performing TC-BR analyses by comparing the spatial similarity of spatial maps (IC maps) generated with Infomax and FastICA in the time and connectivity domains. (b) Demonstrates the compatibility of model-based methods, such as GLM, with the connectivity domain by assessing the spatial similarity of output maps generated in the connectivity domain using TC-BR and GLM (design matrix 1: design matrix computed from session 1 data), which show good agreement. (c) Demonstrates the consistency of a design matrix over time by assessing the spatial similarity between GLM output maps generated using the 1^st session data and the design matrix from the same session as compared to the design matrix generated from the other session (design matrix 2: design matrix computed from session 2 data).

Fig. 9

Individual default mode network (DMN) maps for five randomly-selected subjects obtained using different analyses. The color code is the same as Fig. 8. Example of individual maps of other RSNs can be found at Figs. S3 to S5. This demonstrating the reproducibility of GLM analysis at the individual level using design matrices from different sessions.

Fig. 10

The resting state networks (RSNs) identified using a general linear model (GLM) method from the independent dataset. The design matrix that was used for this analysis is from the first session of the WSU dataset (a different group of subjects). Spatial maps were calculated for each individual separately and then averaged. This demonstrates the reproducibility of GLM analysis at the individual level using design matrices from a different group's data.