An Approach to Automatically Label and Order Brain Activity/Component Maps

Abstract

Background: Functional magnetic resonance imaging (fMRI) is a brain imaging technique that provides detailed insights into brain function and its disruption in various brain disorders. The data-driven analysis of fMRI brain activity maps involves several postprocessing steps, the first of which is identifying whether the estimated brain network maps capture signals of interest, for example, intrinsic connectivity networks (ICNs), or artifacts. This is followed by linking the ICNs to standardized anatomical and functional parcellations. Optionally, as in the study of functional network connectivity (FNC), rearranging the connectivity graph is also necessary to facilitate interpretation. Methods: Here we develop a novel and efficient method (Autolabeler) for implementing and integrating all of these processes in a fully automated manner. The Autolabeler method is pretrained on a cross-validated elastic-net regularized general linear model from the noisecloud toolbox to separate neuroscientifically meaningful ICNs from artifacts. It is capable of automatically labeling activity maps with labels from several well-known anatomical and functional parcellations. Subsequently, this method also maximizes the modularity within functional domains to generate a more systematically structured FNC matrix for post hoc network analyses. Results: Results show that our pretrained model achieves 86% accuracy at classifying ICNs from artifacts in an independent validation data set. The automatic anatomical and functional labels also have a high degree of similarity with manual labels selected by human raters. Discussion: At a time of ever-increasing rates of generating brain imaging data and analyzing brain activity, the proposed Autolabeler method is intended to automate such analyses for faster and more reproducible research. Impact statement Our proposed method is capable of implementing and integrating some of the crucial tasks in functional magnetic resonance imaging (fMRI) studies. It is the first to incorporate such tasks without the need for expert intervention. We develop an open-source toolbox for the proposed method that can function as stand-alone software and additionally provides seamless integration with the widely used group independent component analysis for fMRI toolbox (GIFT). This integration can aid investigators to conduct fMRI studies in an end-to-end automated manner.

Keywords: anatomical atlas; brain imaging; fMRI; functional network connectivity; functional parcellation.

FIG. 1.

Flowchart of analysis using the proposed approach. SM stands for spatial map and TC for time course. Four sets of inputs are used, two of them are group ICA results from the GIFT toolbox, and two in NIfTI format. The integration of noisecloud and BCTs is shown (Rubinov and Sporns, ; Sochat et al., 2014). A pretrained model for the noisecloud toolbox, a contribution of this work, as well as the outputs from the proposed Autolabeler toolbox are indicated in dark boxes. BCT, Brain Connectivity Toolbox; GIFT, group independent component analysis for fMRI toolbox; ICA, independent component analysis; NIfTI, Neuroimaging Informatics Technology Initiative. Color images are available online.

FIG. 2.

(A) Mosaic view of the Yeo et al. (2011) atlas with Buckner et al. (2011) cerebellum parcellation of the brain (Buckner et al., ; Yeo et al., 2011). (B) ICNs estimated from the FBIRN data set, grouped into functional domains based on the Yeo et al. (2011) atlas. See Supplementary Table S2 and prior work for ICN labels and peak coordinates (Damaraju et al., 2014). FBIRN, Function Biomedical Informatics Research Network; ICN, intrinsic connectivity network. Color images are available online.

FIG. 3.

The proposed Autolabeler toolbox separates resting-state ICNs from noise and groups the ICNs into functional domains with high modularity based on FNC. (A) Unordered versus (B) separated into ICN and noise and (C) reordered FNC matrices for the FBIRN data set. (D–F) Display the same for the COBRE data set. COBRE, Centers of Biomedical Research Excellence; FNC, functional network connectivity. Color images are available online.

FIG. 4.

Examples of predictions generated by the pretrained model of the proposed method in FBIRN data set. The labels indicate the output determined by the pretrained noisecloud model as well as the highest correlated anatomical and functional ROI labels for each IC. By default, the three highest correlated ROIs with each IC can be found in Autolabeler output. IC, independent component; ROI, region of interest. Color images are available online.