Multimodel Order Independent Component Analysis: A Data-Driven Method for Evaluating Brain Functional Network Connectivity Within and Between Multiple Spatial Scales - PubMed (original) (raw)

. 2022 Sep;12(7):617-628.

doi: 10.1089/brain.2021.0079. Epub 2021 Nov 22.

Armin Iraji 1, Zening Fu 1, Peter Kochunov 2, Aysenil Belger 3, Judith Ford 4 5, Sara McEwen 6, Daniel H Mathalon 4 5, Bryon A Mueller 7, Godfrey Pearlson 8, Steven G Potkin 9, Adrian Preda 9, Jessica Turner 1 10, Theo van Erp 11, Jing Sui 1 12 13, Vince D Calhoun 1 10

Affiliations

Multimodel Order Independent Component Analysis: A Data-Driven Method for Evaluating Brain Functional Network Connectivity Within and Between Multiple Spatial Scales

Xing Meng et al. Brain Connect. 2022 Sep.

Abstract

Background: While functional connectivity is widely studied, there has been little work studying functional connectivity at different spatial scales. Likewise, the relationship of functional connectivity between spatial scales is unknown. Methods: We proposed an independent component analysis (ICA)-based approach to capture information at multiple-model orders (component numbers), and to evaluate functional network connectivity (FNC) both within and between model orders. We evaluated the approach by studying group differences in the context of a study of resting-state functional magnetic resonance imaging (rsfMRI) data collected from schizophrenia (SZ) individuals and healthy controls (HC). The predictive ability of FNC at multiple spatial scales was assessed using support vector machine-based classification. Results: In addition to consistent predictive patterns at both multiple-model orders and single-model orders, unique predictive information was seen at multiple-model orders and in the interaction between model orders. We observed that the FNC between model orders 25 and 50 maintained the highest predictive information between HC and SZ. Results highlighted the predictive ability of the somatomotor and visual domains both within and between model orders compared with other functional domains. Also, subcortical-somatomotor, temporal-somatomotor, and temporal-subcortical FNCs had relatively high weights in predicting SZ. Conclusions: In sum, multimodel order ICA provides a more comprehensive way to study FNC, produces meaningful and interesting results, which are applicable to future studies. We shared the spatial templates from this work at different model orders to provide a reference for the community, which can be leveraged in regression-based or fully automated (spatially constrained) ICA approaches. Impact statement Multimodel order independent component analysis (ICA) provides a comprehensive way to study brain functional network connectivity within and between multiple spatial scales, highlighting findings that would have been ignored in single-model order analysis. This work expands upon and adds to the relatively new literature on resting functional magnetic resonance imaging-based classification and prediction. Results highlighted the differentiating power of specific intrinsic connectivity networks on classifying brain disorders of schizophrenia patients and healthy participants, at different spatial scales. The spatial templates from this work provide a reference for the community, which can be leveraged in regression-based or fully automated ICA approaches.

Keywords: functional network connectivity; independent component analysis; intrinsic connectivity networks; machine learning; multiple spatial scales; resting fMRI.

PubMed Disclaimer

Conflict of interest statement

No competing financial interests exist.

Figures

FIG. 1.

FIG. 1.

Pipeline of the classification model. The whole dataset was split into a training set (80%) and a testing set (20%). Feature selection was performed on the training set (50% were selected randomly every time). To select the most predictive features, we repeated the feature selection process for ten rounds and retained those features with a high average weight (top 70%) among all the rounds. The final SVM model was built based on the selected FNC features. We ran the modeling process for a total number of 100 iterations to obtain a stable SVM model. FNC, functional network connectivity; SVM, support vector machine. Color images are available online.

FIG. 2.

FIG. 2.

ICN maps selected from model order of 25 (a), 50 (b), 75 (c), and 100 (d). A total number of 127 ICNs were determined between all model orders (15, 28, 36, and 48 from 25, 50, 75, and 100, respectively). All the ICNs were identified from each model order and included components with peak activations in gray matter as well as low-frequency time courses. ICN, intrinsic connectivity network. Color images are available online.

FIG. 3.

FIG. 3.

Identified ICNs across all model orders 25, 50, 75, and 100. ICNs were divided into groups (functional domains) based on their anatomical and functional properties, and include CR, CC, DM, SM, SB, TP, and VS. Each functional domain is displayed at the three most informative slices. CC, cognitive control; CR, cerebellum; DM, default mode; SB, subcortical; SM, somatomotor; TP, temporal; VS, visual. Color images are available online.

FIG. 4.

FIG. 4.

(a) (Left). Average FNC plots. We calculated the mean FNC (_z_-fisher score) based on the aggregated FNC matrix of all individuals. (a) (Left). Block plot of mean FNC matrix between model orders. The ICNs in this FNC matrix were sorted by domains first, and within each domain, ICNs were sorted by model orders (from 25 to 100). The dotted lines in each domain divide different model orders (b) (Right). Finger plot of mean FNC matrix between model orders. ICNs within each model order were sorted in the order of CR, CC, DM, SM, SB, TP, and VS. As we can see in the plot, DM from model order 25 shows strong anticorrelations with SM and TP of model order 100; similar anticorrelations were also observed between model orders 75 and 100, which did not show up in the other sections of the matrix. Color images are available online.

FIG. 5.

FIG. 5.

(a) (Left). Block plot of GLM contrast of difference mean FNC matrix (HC–SZ). To test the group difference between HC and SZ, we fit a GLM with age, gender, data acquisition site, and meanFD as covariates. This figure shows the difference between HC and SZ in FNC. Blue areas indicate increased FNC in SZ compared with HC, and red areas indicate decreased FNC in SZ compared with HC. The statistical results of _p_-values of the GLM were corrected for multiple comparisons using a 5% FDR (b) (Right). Finger plot of GLM contrast of difference mean FNC matrix (HC–SZ). (a, b) Show the intensity [−sign(T) × log10(FDR)] matrixes of both block and finger plots for the mean FNC of the GLM model, where T is the _t_-statistic values of the GLM. The lower triangles show covariate pairs that were significantly different (FDR <0.05). FD, framewise displacement; FDR, false discovery rate; GLM, generalized linear model; HC, healthy control. Color images are available online.

FIG. 6.

FIG. 6.

(a) (Left). SVM block plot of FNC feature average weights of 100 iterations. The hotspots indicate FNC features with strong weights in predicting group differences of HC and SZ. Statistically, the relevance level of a relevant feature is expected to be >0 and that of an irrelevant one is expected to be 0 (or negative). The cerebellum, somatomotor, subcortical, and temporal domains almost always contribute to the classification. Generally, strong predictive abilities of FNC features in the somatomotor and visual domains were seen at all model orders. Visual was predictive only within the region between somatomotor and visual, but only seen at high model order (100) (b) (Right). SVM finger plot of FNC feature average weights of 100 iterations. Color images are available online.

FIG. 7.

FIG. 7.

The intensity [−sign(T) × log10(FDR)] of average feature weights between model orders. We computed a two-sample _t_-test between the averaged feature weights of every two model orders, to identify significant differences between them. The negative intensity value in the lower triangle of the figure indicates the average feature weights of model orders in the row (x) were smaller than the ones in the column (y), and the positive intensity value in the lower triangle indicates the model orders' average feature weights in the row were larger than the ones in the column. The upper triangle shows significant differences (p < 0.05, FDR corrected) between each pair of model orders, whose direction is opposite to the lower triangle. Color images are available online.

FIG. 8.

FIG. 8.

(a) (Left). Average feature weight between domains. Each domain contains four model orders, from 25, 50, 75 to 100, and all the features within each model order were averaged across 100 iterations (b) (Right). Maximum feature weight within each domain. Each domain column contains four averaged features of different model orders, from 25, 50, 75 to 100. The maximum feature weight within each domain was selected based on the averaged feature weight across 100 iterations. It shows that higher average feature weights are mainly seen in somatomotor, subcortical, temporal, and visual. Color images are available online.

FIG. 9.

FIG. 9.

The intensity [−sign(T) × log10(FDR)] of average feature weights between domains. The negative intensity values in the lower triangle indicate smaller average feature weights of the ICN in the row (x) compared with the ones in the column (y), and the positive intensity values in the lower triangle indicate larger average feature weights of ICN in the row compared with the column. The upper triangle shows the significant differences (p < 0.05, FDR corrected) between each pair of domains, whose direction is opposite to the lower triangle. Color images are available online.

FIG. 10.

FIG. 10.

The intensity [−sign(T) × log10(FDR)] of SVM accuracies between model orders. The negative intensity value in the lower triangle of the figure indicates the average feature weights of model orders in the row (x) were smaller than the ones in the column (y), and the positive intensity value in the lower triangle indicates the model orders' average feature weights in the row were larger than the ones in the column. The upper triangle shows the significant differences (p < 0.05, FDR corrected) between each pair of model orders, whose direction is opposite to the lower triangle. The top circle indicates that higher individual model orders always have higher accuracies compared with lower individual model orders. The middle circle shows that model orders of 50–100 outperform lower between model orders. The bottom circle indicates that higher between model orders outperform individual model orders. Color images are available online.

References

    1. Abou-Elseoud A, Starck T, Remes J, et al. 2010. The effect of model order selection in group PICA. Hum Brain Mapp 31:1207–1216. -PMC -PubMed
    1. Allen EA, Erhardt EB, Damaraju E, et al. 2011. A baseline for the multivariate comparison of resting-state networks. Front Syst Neurosci 5:1–23. -PMC -PubMed
    1. Anderson A, Cohen MS. 2013. Decreased small-world functional network connectivity and clustering across resting state networks in Schizophrenia: an fMRI classification tutorial. Front Hum Neurosci 7:1–18. -PMC -PubMed
    1. Beckmann CF, DeLuca M, Devlin JT, et al. 2005. Investigations into resting-state connectivity using independent component analysis. Phil Trans R Soc B Biol Sci 360:1001–1013. -PMC -PubMed
    1. Breiman L. 1996. Bagging predictors. Mach Learn 24:123–140.

Publication types

MeSH terms

Grants and funding

LinkOut - more resources