A method for independent component graph analysis of resting‐state fMRI

Abstract

IntroductionIndependent component analysis (ICA) has been extensively used for reducing task‐free BOLD fMRI recordings into spatial maps and their associated time‐courses. The spatially identified independent components can be considered as intrinsic connectivity networks (ICNs) of non‐contiguous regions. To date, the spatial patterns of the networks have been analyzed with techniques developed for volumetric data.ObjectiveHere, we detail a graph building technique that allows these ICNs to be analyzed with graph theory.MethodsFirst, ICA was performed at the single‐subject level in 15 healthy volunteers using a 3T MRI scanner. The identification of nine networks was performed by a multiple‐template matching procedure and a subsequent component classification based on the network “neuronal” properties. Second, for each of the identified networks, the nodes were defined as 1,015 anatomically parcellated regions. Third, between‐node functional connectivity was established by building edge weights for each networks. Group‐level graph analysis was finally performed for each network and compared to the classical network.ResultsNetwork graph comparison between the classically constructed network and the nine networks showed significant differences in the auditory and visual medial networks with regard to the average degree and the number of edges, while the visual lateral network showed a significant difference in the small‐worldness.ConclusionsThis novel approach permits us to take advantage of the well‐recognized power of ICA in BOLD signal decomposition and, at the same time, to make use of well‐established graph measures to evaluate connectivity differences. Moreover, by providing a graph for each separate network, it can offer the possibility to extract graph measures in a specific way for each network. This increased specificity could be relevant for studying pathological brain activity or altered states of consciousness as induced by anesthesia or sleep, where specific networks are known to be altered in different strength.

Comparing brain graphs in which nodes are regions of interest or independent components: A simulation study

“…An ideal node definition for building an fMRI brain graph should define functionally homogeneous nodes, represent functional heterogeneity across nodes, and account for spatial relationships (Fornito et al, 2013). The method of defining a brain node varies considerably in the literature (Ribeiro de Paula et al, 2017; Stanley et al, 2013). In fMRI studies, nodes are often defined as spatial regions of interest (ROIs) in which anatomical approaches utilize atlases (e.g.…”

Manyeti̇k Rezonans Görüntüleme Temelli̇ Fonksi̇yonel Bağlantisallik Yöntemleri̇

“…BBA, çoklu değişkenli dinlenim durumu fMRG verilerini bileşenlere ayrıştırmak için de kullanılmaktadır (29). BOLD sinyalini bağımsız bileşenlerine ayrıştırmak için her bir vokselin BOLD sinyali ayrı bir değişken olarak analiz edilmez, bunun yerine tüm voksellerden gelen veri birlikte ele alınır.…”

“…Grup düzeyinde BBA için tüm katılımcıların ön işlemden geçirilmiş (hareket artefaktlarından arındırılmış ve filtre uygulanmış) dinlenim durumu BOLD verisi girdi olarak tanımlanır. Grup düzeyindeki bileşenlerin çıkartılması için tüm katılımcıların verisi birleştirilir; bu nedenle katılımcıların fMRG verileri standart bir mekâna taşınır (29,32). Sonrasında ise tüm katılımcıların birleşiminden oluşan veri kümesi BBA tarafından grup bileşenlerine ayrıştırılır.…”

“…Bileşen adedini arttırmak çeşitli temel ağların alt sistemlere bölünmesine neden olurken bileşen adedinin düşük olması ise çeşitli ağların ayrıştırılamamasına neden olabilir. Bu yüzden doğru bileşen adedinin belirlenmesi bu yöntem için oldukça önemlidir (29,30). fMRG verilerinin standart mekânda işlenmesi özellikle grup analizi yaparken araştırmacılara büyük kolaylıklar sağlamaktadır.…”

A Survey of Graph Based Complex Brain Network Analysis Using Functional and Diffusional MRI

“…The development of graph-theory based complex network analysis provides an important mathematical framework to characterize the global and regional topology in brain connectivity networks (Ribeiro de Paula et al, 2017). Using graph-theory based complex network analysis and network based statistic approach, Xu et al (2016) examined the topology and connectivity in resting-state functional brain networks of adults with BPD versus healthy controls.…”