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.

The spatial chronnectome reveals a dynamic interplay between functional segregation and integration

“…Their spatial maps have significant overlap with gray matter, their peak activations fall within gray matter, and low spatial overlap with known ventricular, motion, and susceptibility artifact components (Allen et al, ). Furthermore, the selected components have high spatial similarity with one of the established ICNs (Allen et al, ; Beckmann, DeLuca, Devlin, & Smith, ; Damoiseaux et al, ; Fox, Corbetta, Snyder, Vincent, & Raichle, ; Iraji et al, ; Smith et al, ; Yeo et al, ; Zuo et al, ). The identified brain networks are the auditory, cerebellar, default mode, (dorsal) attention, left and right frontoparietal, somatomotor, language, salience, subcortical, primary visual, and secondary visual networks (Figure ).…”

“…Spatial ICA (sICA) was applied to the fMRI data to obtain brain networks (Calhoun & Adali, ; Calhoun, Adali, Pearlson, & Pekar, ). ICA was performed using the GIFT software package (http://mialab.mrn.org/software/gift/) in the following steps similar to our previous work (Iraji et al, ): (a) Subject‐level principal component analysis (PCA) was applied and the 30 principal components accounting for the maximum variance in each individual dataset were retained. (b) All subject‐level principal components were concatenated together across the time dimension, and group‐level spatial PCA was applied on 9,270 (30 × Subject) concatenated components.…”

“…2.4 | Identifying large-scale brain networks: Spatial ICA Spatial ICA (sICA) was applied to the fMRI data to obtain brain networks (Calhoun & Adali, 2012;Calhoun, Adali, Pearlson, & Pekar, 2001a). ICA was performed using the GIFT software package (http:// mialab.mrn.org/software/gift/) in the following steps similar to our previous work (Iraji et al, 2016) First, spatial independent component analysis (sICA) is applied to obtain brain networks and their associated times courses. ICA consists of several steps including data reduction (PCA), group-level sICA, and subject-level sICA.…”

Spatial dynamics within and between brain functional domains: A hierarchical approach to study time‐varying brain function

“…The nine FDs were defined based on the prior knowledge from previous studies (Allen et al, 2014; Allen et al, 2011; Damaraju et al, 2014; Iraji et al, 2016) and large-scale brain networks obtained from low-order ICA. The nine FDs are attention (Allen et al, 2011; Damoiseaux et al, 2006; Lee et al, 2013), auditory (Allen et al, 2014; Allen et al, 2011; Damoiseaux et al, 2006), default mode (Allen et al, 2011; Iraji et al, 2016; Zuo et al, 2010), frontal default mode (Iraji et al, 2016; Zuo et al, 2010), frontoparietal (Allen et al, 2011; Iraji et al, 2016; Lee et al, 2013; Zuo et al, 2010), language (Lee et al, 2013; Tie et al, 2014), somatomotor (Allen et al, 2011; Damoiseaux et al, 2006; Iraji et al, 2016), subcortical (Allen et al, 2014; Allen et al, 2011), and visual (Allen et al, 2011; Damoiseaux et al, 2006; Iraji et al, 2016; Zuo et al, 2010). hICN selection and FD labeling were performed using the anatomical and presumed functional properties of hICNs, and their relationships with large-scale brain networks obtained from low-order ICA.…”

BrainSync: An Orthogonal Transformation for Synchronization of fMRI Data Across Subjects

“…Since rfMRI data reflect spontaneous brain activity, it is not possible to directly compare signals across subjects [9]. Instead, comparisons make use of connectivity features, typically computed from pairwise correlations of the rfMRI time series between a point of interest and other locations in the brain [6].…”